essay on qualitative and quantitative research

Essay on Qualitative vs. Quantitative Research

Both qualitative and quantitative researches are valued in the research world and are often used together under a single project. This is despite the fact that they have significant differences in terms of their theoretical, epistemological, and methodological formations. Qualitative research is usually in form of words while quantitative research takes the numerical approach. This paper discusses the similarities, differences, advantages, and disadvantages of qualitative and quantitative research and provides a personal stand.

Similarities

Both qualitative and quantitative research approaches begin with a problem on which scholars seek to find answers. Without a research problem or question, there would be no reason for carrying out the study. Once a problem is formulated, researchers at their own discretion and depending on the nature of the question choose the appropriate type of research to employ. Just like in qualitative research, data obtained from quantitative analysis need to be analyzed (Miles & Huberman, 1994). This step is crucial for helping researchers to gain a deeper understanding of the issue under investigation. The findings of any research enjoy confirmability after undergoing a thorough examination and auditing process (Miles & Huberman, 1994).

Both types of research approaches require a concise plan before they are carried out. Once researchers formulate the study question, they must come up with a plan for investigating the matter (Yilmaz, 2013). Such plans include deciding the appropriate research technique to implement, estimating budgets, and deciding on the study areas. Failure to plan before embarking on the research project may compromise the research findings. In addition, both qualitative and quantitative research are dependent on each other and can be used for a single research project (Miles & Huberman, 1994). Quantitative data helps the qualitative research in finding a representative study sample and obtaining the background data. In the same way, qualitative research provides the quantitative side with the conceptual development and instrumentation (Miles & Huberman, 1994).

Differences

Qualitative research seeks to explain why things are the way they seem to be. It provides well-grounded descriptions and explanations of processes in identifiable local contexts (Miles & Huberman, 1994). Researchers use qualitative research to dig deeper into the problem and develop a relevant hypothesis for potential quantitative research. On the other hand, Quantitative research uses numerical data to state and quantify the problem (Yilmaz, 2013). Researchers in quantitative research use measurable data in formulating facts and uncovering the research pattern.

Quantitative research approach involves a larger number of participants for the purpose of gathering as much information as possible to summarize characteristics across large groups. This makes it a very expensive research approach. On the contrary, qualitative research approach describes a phenomenon in a more comprehensive manner. A relatively small number of participants take part in this type of research. This makes the overall process cheaper and time friendly.

Data collection methods differ significantly in the two research approaches. In quantitative research, scholars use surveys, questionnaires, and systematic measurements that involve numbers (Yilmaz, 2013). Moreover, they report their findings in impersonal third person prose by using numbers. This is different from the qualitative approach where only the participants’ observation and deep document analysis is necessary for conclusions to be drawn. Findings are disseminated in the first person’s narrative with sufficient quotations from the participants.

Advantages and Disadvantages of Qualitative Research

Qualitative data is based on human observations. Respondent’s observations connect the researcher to the most basic human experiences (Rahman, 2016). It gives a detailed production of participants’ opinions and feelings and helps in efficient interpretation of their actions (Miles & Huberman, 1994). Moreover, this research approach is interdisciplinary and entails a wide range of research techniques and epistemological viewpoints. Data collection methods in qualitative approach are both detailed and subjective (Rahman, 2016). Direct observations, unstructured interviews, and participant observation are the most common techniques employed in this type of research. Researchers have the opportunity to mingle directly with the respondents and obtain first-hand information.

On the negative side, the smaller population sample used in qualitative research raises credibility concerns (Rahman, 2016). The views of a small group of respondents may not necessarily reflect those of the entire population. Moreover, conducting this type of research on certain aspects such as the performance of students may be more challenging. In such instances, researchers prefer to use the quantitative approach instead (Rahman, 2016). Data analysis and interpretation in qualitative research is a more complex process. It is long, has elusive data, and has very stringent requirements for analysis (Rahman, 2016). In addition, developing a research question in this approach is a challenging task as the refining question mostly becomes continuous throughout the research process.

Advantages and Disadvantages of Quantitative Research

The findings of a quantitative research can be generalized to a whole population as it involves larger samples that are randomly selected by researchers (Rahman, 2016). Moreover, the methods used allows for use of statistical software in test taking (Rahman, 2016). This makes the approach time effective and efficient for tackling complex research questions. Quantitative research allows for objectivity and accuracy of the study results. This approach is well designed to provide essential information that supports generalization of a phenomenon under study. It involves few variables and many cases that guarantee the validity and credibility of the study results.

This research approach, however, has some limitations. There is a limited direct connection between the researcher and respondents. Scholars who adopt this approach measure variables at specific moments in time and disregards the past experiences of the respondents (Rahman, 2016). As a result, deep information is often ignored and only the overall picture of the variables is represented. The quantitative approach uses standard questions set and administered by researchers (Rahman, 2016). This might lead to structural bias by respondents and false representation. In some instances, data may only reflect the views of the sample under study instead of revealing the real situation. Moreover, preset questions and answers limit the freedom of expression by the respondents.

Preferred Method

I would prefer quantitative research method over the qualitative approach. Data management in this technique is much familiar and more accessible to researchers’ contexts (Miles & Huberman, 1994). It is a more scientific process that involves the collection, analysis, and interpretation of large amounts of data. Researchers have more control of the manner in which data is collected. Unlike qualitative data that requires descriptions, quantitative approach majors on numerical data (Yilmaz, 2013). With this type of data, I can use the various available software for classification and analyzes. Moreover, researchers are more flexible and free to interact with respondents. This gives an opportunity for obtaining first-hand information and learning more about other behavioral aspects of the population under study.

As highlighted above, qualitative and quantitative techniques are the two research approaches. Both seek to dig deeper into a particular problem, analyze the responses of a selected sample and make viable conclusions. However, qualitative research is much concerned with the description of peoples’ opinions, motivations, and reasons for a particular phenomenon. On the other hand, Quantitative research uses numerical data to state and explain research findings. Use of numerical data allows for objectivity and accuracy of the research results. However structural biases are common in this approach. Data collection and sampling in qualitative research is more detailed and subjective. Considering the different advantages and disadvantages of the two research approaches, I would go for the quantitative over qualitative research.

Miles, M., & Huberman, A. (1994).  Qualitative data analysis  (2nd Ed.). Beverly Hills: Sage.

Rahman, M. (2016). The Advantages and Disadvantages of Using Qualitative and Quantitative Approaches and Methods in Language “Testing and Assessment” Research: A Literature Review.  Journal of Education and Learning , 6(1), 102.

Yilmaz, K. (2013). Comparison of Quantitative and Qualitative Research Traditions: epistemological, theoretical, and methodological differences.  European Journal of Education , 48(2), 311-325.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Quantitative vs. Qualitative Research in Psychology

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Reference management. Clean and simple.

Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

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Qualitative and Quantitative Research — Explore the differences

In the research arena, there are two main approaches that researchers can take —  qualitative and quantitative research. Understanding the fundamentals of these two methods is crucial for conducting effective research and obtaining accurate results.

This article provides insights into the differences between qualitative and quantitative research and we also discuss how to develop research questions for qualitative and quantitative studies, and how to gather and analyze data using these research approaches. Furthermore, we will examine how to interpret findings from qualitative and quantitative research, as well as identify ethical considerations.

By the end of this comprehensive article, readers will be equipped with the knowledge and tools to apply qualitative and quantitative research to advance knowledge in their respective fields.

What is Qualitative and Quantitative Research?

Qualitative research aims to understand complex phenomena by exploring the subjective experiences and perspectives of individuals. It focuses on gathering in-depth data through techniques such as interviews, observations, and open-ended surveys. This approach allows researchers to delve into the intricacies of the topic, uncovering unique insights that may not be captured through quantitative methods alone.

For example, imagine a study on the impact of social media on mental health. Qualitative research would involve conducting interviews with individuals who have experienced negative effects from excessive social media use. Through these interviews, researchers can gain a deep understanding of the participants' experiences, emotions, and thoughts. They can explore the nuances of how social media affects different aspects of mental health, such as self-esteem, body image, and social comparison.

Conversely, quantitative research involves collecting numerical data and analyzing it using statistical methods to identify patterns, trends, and relationships. This approach allows researchers to generalize their findings to a larger population and calculate statistically significant results. It relies on structured surveys, experiments, and other data collection methods that provide standardized data for analysis.

Continuing with the example of social media and mental health, quantitative research would involve administering surveys to a large sample of individuals. The surveys would include questions that measure various aspects of mental health, such as anxiety, depression, and life satisfaction. By collecting numerical data from a large and diverse sample, researchers can identify trends and relationships between social media use and mental health outcomes.

Both qualitative and quantitative research have their strengths and weaknesses. Qualitative research allows for a deep understanding of the topic, providing rich insights and capturing the context of the participants' experiences. It allows researchers to uncover unique perspectives and shed light on subjective experiences.

On the other hand, quantitative research entails a structured and systematic approach to data collection and analysis, allowing for comparisons and generalizations across different groups and contexts.

However, it is crucial to emphasize that qualitative and quantitative research are not mutually exclusive. They frequently serve as a complement to one another within the realm of research studies. Researchers may use qualitative methods to explore a topic in-depth and generate hypotheses, which can then be tested using quantitative methods. This combination of approaches, known as mixed methods research, allows for a more comprehensive understanding of complex phenomena.

Advantages and Disadvantages of Each Research Method

Qualitative research offers the advantage of generating detailed and nuanced data. It allows researchers to explore complex issues and gain a deeper understanding of participants' thoughts, emotions, and behaviors. However, qualitative research can be time-consuming, and data analysis may be subjective.

In contrast, quantitative research provides objective and quantifiable data, making it easier to draw conclusions and establish causation. It enables researchers to collect data from large samples, increasing the generalizability of findings. Nevertheless, quantitative research may overlook important contextual information and fail to capture the complexities of human experiences. Additionally, it requires a solid understanding of statistical techniques for accurate analysis.

When to Use Qualitative or Quantitative Research?

The choice between qualitative and quantitative research depends on the research questions and objectives. Qualitative research is appropriate when exploring new or complex phenomena, seeking in-depth insights, or generating hypotheses for further investigation. It is particularly useful in social sciences and humanities. On the other hand, quantitative research is suitable when aiming to establish causal relationships, generalize findings to a larger population, or measure phenomena systematically and objectively. It is commonly employed in sciences such as psychology, economics, and medicine.

By considering the nature of the research question, the available resources, and the desired outcomes, researchers can make an informed decision on the appropriate research approach.

How to develop research Questions for Qualitative and Quantitative Studies?

A well-defined research question is essential for conducting meaningful research. In qualitative studies, research questions are exploratory and aim to understand the experiences, perceptions, and meanings of participants. These questions should be open-ended and allow for in-depth exploration of the phenomenon under investigation.

In quantitative research, research questions are often formulated to test hypotheses or examine relationships between variables. These questions should be clear, specific, and measurable to guide data collection and analysis.

Regardless of the research approach, it is crucial to develop research questions that align with the research objectives, is feasible to investigate and contribute to existing knowledge in the field.

Gathering and Analyzing Data

Qualitative research involves collecting data through various techniques, such as interviews, focus groups, and observations. Researchers must establish rapport with participants to encourage open and honest responses. The data collected is then analyzed using methods like thematic analysis and constant comparison to identify patterns, themes, and categories. In quantitative research, data is collected using surveys, experiments, or other structured methods. Researchers aim to obtain a representative sample and ensure the reliability and validity of the data. Statistical analysis techniques, such as descriptive statistics, correlation, and regression, are then applied to conclude.

Regardless of the research approach, it is essential to document the data collection and analysis process thoroughly to ensure transparency and reproducibility.

Interpreting Findings

Interpreting findings from qualitative research involves carefully analyzing the patterns, themes, and categories identified during data analysis. Researchers aim to understand the overarching meaning of the data and draw conclusions based on the participants' experiences and perspectives. The findings are often supported by direct quotes or examples from the data. In quantitative research, findings are interpreted by analyzing statistical results and examining the significance of relationships or differences. Researchers must carefully consider the limitations of the study and the generalizability of the findings. The results are often presented using tables, charts, and graphs for clarity.

Irrespective of the research approach, it is crucial to avoid generalizing beyond the scope of the data and to consider alternative interpretations.

Identifying Ethical Considerations in Qualitative and Quantitative Research

Both qualitative and quantitative research must adhere to ethical guidelines to protect the rights and well-being of participants. Researchers should obtain informed consent, ensure confidentiality, and prevent harm. In qualitative research, building trust and maintaining participant anonymity is crucial. In quantitative research, privacy and data protection are paramount.

Additionally, researchers must consider the potential biases, power dynamics, and conflicts of interest that may influence the research process and findings. Being aware of these ethical considerations helps ensure the integrity and reliability of the research.

How to Write a Research Report Based on Qualitative or Quantitative Data

When writing a research report, it is essential to structure it clearly and concisely. In qualitative research, the report typically includes an introduction, literature review, methodology, findings, discussion, and conclusion. The findings section focuses on the themes and patterns identified during analysis and is supported by quotes or examples from the data.

In quantitative research, the report generally consists of an introduction, literature review, methodology, results, discussion, and conclusion. The results section presents the statistical analysis and findings in a clear and organized manner, often using tables, charts, and graphs.

The report should be written in a scholarly tone, provide sufficient details, and communicate the research findings and implications.

Assessing Reliability and Validity of Qualitative and Quantitative Results

Reliability and validity are crucial considerations in research. In qualitative research, researchers can enhance reliability by using multiple researchers to analyze the data and compare their interpretations. Validity can be strengthened by employing rigorous data collection methods, establishing trustworthiness, and including participant validation.

In quantitative research, reliability can be assessed through test-retest reliability or inter-rater reliability. Validity can be evaluated by examining internal validity, external validity, and construct validity. Additionally, researchers should carefully consider potential confounding variables and ensure proper control measures are in place.

By assessing reliability and validity, researchers can enhance the credibility and trustworthiness of their research findings.

Qualitative and quantitative research are distinct yet complementary approaches to conducting research. Understanding when to use each method, developing appropriate research questions, gathering and analyzing data, interpreting findings, and addressing ethical considerations are all critical aspects of conducting valuable research. By embracing these methodologies and applying them appropriately, researchers can contribute to the advancement of knowledge and make meaningful contributions to their respective fields.

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Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

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Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

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When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research?

What is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

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• Methodology
• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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When it comes to writing a research paper , there are two main approaches: qualitative and quantitative.

Both qualitative and quantitative research have their advantages and uses. Understanding the differences between them is essential to choosing the right research method for your study. 

This blog explore these types of research methods and the differences between them. You’ll also get some real-life examples to help you determine which approach is best for your project..

Read on to learn about qualitative vs quantitative research.

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Qualitative vs Quantitative Research Comparison: What’s the Difference?

The two research methods differ in the way they collect data, analyze it, and reach conclusions.

• Qualitative data collection involves methods such as interviews, focus groups, and observations. The data collected is typically non-numerical and is analyzed through techniques such as content analysis, thematic analysis, and narrative analysis.

The goal of qualitative research is to gain an in-depth understanding of a particular phenomenon. Often through exploring the experiences and perspectives of participants.

• Meanwhile, quantitative data collection involves collecting numerical data through methods such as surveys and experiments. The measurable data is analyzed through statistical techniques to identify patterns and relationships between variables.

The goal of quantitative research is to measure and quantify a particular phenomenon. Often with the aim of making predictions or generalizations about a larger population.

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Here is a table that clarifies the major differences between qualitative and quantitative research.

Just getting started with research? Head to our types of research blog to get a complete overview.

Qualitative vs. Quantitative Research Questions

Your research questions reflect the direction and methodology of your research project.  

The research questions asked in quantitative and qualitative studies are different. Let’s take a look at how research questions for qualitative and quantitative research are formulated.

Qualitative Research Questions

Qualitative research questions often focus on “how” or “why” something occurs, rather than measuring or quantifying it.

Examples of Qualitative Research Questions

• How do students experience online learning during the pandemic?
• What are the factors that influence employee motivation in a small business?

Quantitative Research Questions

Quantitative research questions often focus on what, how much, or how many of something. These questions have clear variables and seek to uncover any relationships between these variables.

Examples of Quantitative Research Questions 

• How does the age of a driver affect the likelihood of being involved in a car accident?
• What is the relationship between job satisfaction and employee turnover in the banking industry?

Here is a short video explaining the key differences between the two methods:

Qualitative vs. Quantitative Research

Let’s explore each type of research methodology in-depth. Knowing the unique qualities of each one will help you better understand their differences.

Qualitative Research - Characteristics, Examples, Usage, Strengths and Limitations 

Qualitative research is a research method that aims to understand the subjective experiences of individuals or groups in their natural settings. 

Qualitative research typically involves gathering data through methods like interviews, focus groups, and observation. It analyzes data through coding, interpreting, and categorizing it to identify themes and patterns.

Characteristics of Qualitative Research

Here are the features of qualitative research that differentiates it from quantitative research:

• Exploration of complex phenomena: Qualitative research is used to explore complex phenomena, such as emotions, beliefs, and experiences that cannot be easily quantified.
• In-depth analysis: Qualitative research involves in-depth analysis of a few participants rather than a large sample size.
• Contextualization: Qualitative research emphasizes understanding the social context in which the phenomena occurs.

Examples of Qualitative Research Methods

There are different methods within qualitative research suitable for different research goals. Here are a few examples:

• Interviews: Interviews involve open-ended questions that allow participants to share their experiences and perspectives.
• Focus groups: Focus groups are small group discussions that allow researchers to explore participants' opinions and attitudes towards a particular topic.
• Observation: Observation involves direct observation of participants in their natural setting, allowing researchers to gain insights into behavior and experiences.

Strengths and Limitations of Qualitative Research

There are several advantages that come with qualitative research, including:

• Depth of understanding: Qualitative research provides rich and detailed information about complex phenomena that cannot be captured through quantitative methods.
• Flexibility: Qualitative research allows researchers to adapt their methods to the needs of the participants. It can lead to more natural and accurate data.
• Insights into context: Qualitative research provides insights into the social context in which the phenomena occurs. This is useful in understanding the larger social and cultural implications of the phenomena.

However, there are also some limitations to qualitative research. For instance, 

• Lack of Generalizability: Qualitative research uses a small sample size, which limits the application of the findings and how much they can be generalized.
• Subjectivity: Qualitative research involves subjective interpretation of data, which can introduce bias into the analysis.
• Time-consuming: Qualitative research is often more time-consuming than quantitative research due to the detailed analysis required.

When to Use Qualitative Research

Qualitative research is best suited for exploring subjective and social phenomena. It is more suitable for understanding the perspectives and experiences of individuals or groups. 

It is also useful in situations where little is known about the topic or the researcher wants to generate hypotheses for further investigation. 

In addition, qualitative research is also used where quantitative data is not available. Or, when it is necessary to complement quantitative data with more in-depth qualitative analysis.

Quantitative Research 

Quantitative research is a research method that aims to measure and quantify phenomena through numerical data and statistical analysis.

It typically involves gathering data through methods like surveys and experiments. Then, analyzing the data using statistical methods to identify patterns and relationships between variables.

Characteristics of Quantitative Research 

Quantitative research is defined by the following characteristics:

• Objective Measurement: Quantitative research aims to measure and quantify phenomena objectively and standardized.
• Large Sample Size: Quantitative research typically involves gathering data from large sample sizes to ensure statistical significance.
• Statistical Analysis: Quantitative research involves statistical analysis to identify patterns and relationships between variables.

Examples of Quantitative Research Methods 

Here are some of the most commonly used quantitative research methods:

• Surveys: Surveys involve asking participants a set of standardized questions and analyzing the responses to identify patterns and relationships.
• Experiments: Experiments involve manipulating one or more variables and measuring the effect on another variable to identify cause-and-effect relationships.
• Content analysis: Content analysis involves analyzing written or visual media to identify patterns and relationships between variables.

Strengths and Limitations of Quantitative Research

Quantitative research methods often lead to very valuable findings. Here are some more benefits of using quantitative research.

• Objectivity: Quantitative research provides objective measurements that can be analyzed statistically, which reduces the potential for bias.
• Verifiability: Quantitative research involves gathering data from a large sample size. This increases the statistical power and the verifiability of the study.
• Generalizability: Quantitative research aims to provide generalizable findings that can be applied to larger populations.

Even though it has several advantages over the qualitative method, it also has several limitations. These include:

• Limited Understanding of Context: Quantitative research only provides numerical data, but it does not provide insights into the context in which the phenomena occur.
• Limited Depth of Understanding: Quantitative research only reveals statistical relationships between variables. It does not explain the phenomena and does not provide a deep understanding.
• Potential for Bias: The data collection and analysis processes could be subtly affected by personal and institutional biases. This can lead to biased results.

When to Use Quantitative Research

Quantitative research is best suited for testing a hypothesis. It should be used in situations where a cause-and-effect relationship between variables needs to be identified. 

Additionally, it is helpful when the researcher wants to generalize the findings to a larger population. 

Qualitative Research and Quantitative Research Examples

Here are some examples of qualitative and quantitative research to help you get an even better understanding.  

qualitative vs. quantitative research sociology

qualitative vs. quantitative research in nursing

qualitative vs. quantitative research in health care

qualitative research method example

quantitative research method example

To sum up, 

Choosing the right form of research - qualitative or quantitative - depends on the research problem and the goals of the research project. 

Qualitative research explores experiences, perspectives, and meanings related to a particular phenomenon. On the other hand, quantitative research aims to measure and quantify, often to make predictions or generalizations. 

By getting help from an AI essay generato r, you can select an appropriate approach to achieve your research objectives. 

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Quantitative and Qualitative Research Methods: Similarities and Differences Compare & Contrast Essay

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Introduction

The aim of this paper is to analyze and to compare quantitative and qualitative research methods. The analysis will begin with the definition and description of the two methods. This will be followed by a discussion on the various aspects of the two research methods.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics, data collection methods, data analysis methods, and the validity issues associated with them, as well as, their strengths and weaknesses.

Definition and Description

Qualitative research method is a technique of “studying phenomena by collecting and analyzing data in non-numeric form”. It focuses on exploring the topic of the study by finding as much detail as possible. The characteristics of qualitative research include the following.

First, it focuses on studying the behavior of individuals in their natural settings. Thus, it does not use artificial experiments. This helps researchers to avoid interfering with the participants’ normal way of life.

Second, qualitative research focuses on meanings, perspectives, and understandings. It aims at finding out the meanings that the subjects of the study “attach to their behavior, how they interpret situations, and what their perspectives are on particular issues”.

Concisely, it is concerned with the processes that explain why and how things happen.

Quantitative research is “explaining phenomena by collecting numerical data that are analyzed using mathematical techniques such as statistics”.

It normally uses experiments to answer research questions. Control is an important aspect of the experiments because it enables the researcher to find unambiguous answers to research questions.

Quantitative research also uses operational definitions. Concisely, the terms used in a quantitative study must be defined according to the operations employed to measure them in order to avoid confusion in meaning or communication.

Moreover, the results of quantitative research are considered to be reliable only if they are replicable. This means that the same results must be produced if the research is repeated using the same techniques.

Hypothesis testing is also an integral part of quantitative research. Concisely, hypotheses enable the researcher to concentrate on a specific aspect of a problem, and to identify the methods for solving it.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics

Quantitative and qualitative studies are similar in the following ways. To begin with, qualitative research is normally used to generate theory. Similarly, quantitative studies can be used to explore new areas, thereby creating a new theory.

Even though qualitative research focuses on generating theory, it can also be used to test hypotheses and existing theories. In this regard, it is similar to quantitative studies that mainly focus on testing theories and hypotheses.

Both qualitative and quantitative studies use numeric and non-numeric data. For instance, the use of statements such as less than normally involves the use of quantitative data in qualitative studies.

Similarly, quantitative studies can use questionnaires with open-ended questions to collect qualitative data.

Despite these similarities, quantitative and qualitative studies differ in the following ways. To begin with, the purpose of qualitative research is to facilitate understanding of fundamental meanings, reasons, and motives.

It also aims at providing valuable insights concerning a problem through determination of common trends in thought and generation of ideas.

On the other hand, the purpose of quantitative research is to quantify data and to use the results obtained from a sample to make generalizations on a particular population.

The sample used in qualitative research is often small and non-representative of the population. On the contrary, quantitative research uses large samples that represent the population. In this regard, it uses random sampling techniques to select a representative sample.

Qualitative research uses unstructured or semi-structured data collection techniques such as focus group discussions, whereas quantitative research uses structured techniques such as questionnaires.

Moreover, qualitative research uses non-statistical data analysis techniques, whereas quantitative uses statistical methods to analyze data. Finally, the results of qualitative research are normally exploratory and inconclusive, whereas the results of quantitative research are usually conclusive.

The similarities and differences between quantitative and qualitative research methods can be seen in their data collection methods

The main data collection methods in qualitative research include observations, interviews, content review, and questionnaires. The researcher can use participant or systematic observation to collect data.

In participant observation, the researcher engages actively in the activities of the subjects of the study. Researchers prefer this technique because it enables them to avoid disturbing the natural settings of the study.

In systematic observation, schedules are used to observe the behaviors of the participants at regular intervals. This technique enhances objectivity and reduces bias during data collection.

Most qualitative studies use unstructured interviews in which the interviewer uses general ideas to guide the interview and prompts to solicit more information.

Content review involves reading official documents such as diaries, journals, and minutes of meetings in order to obtain data. The importance of this technique is that it enables the researcher to reconstruct events and to describe social relationships.

Questionnaires are often used when the sample size is too large to be reached through face-to-face interviews. However, its use is discouraged in qualitative research because it normally influences the way participants respond, rather than allowing them to act naturally during data collection.

Quantitative research mainly uses surveys for data collection. This involves the use of questionnaires and interviews with closed-ended questions to enable the researcher to obtain data that can be analyzed with the aid of statistical techniques.

The questionnaires can be mailed or they can be administered directly to the respondents.

Observations are also used to collect data in quantitative studies. For example, the researcher can count the number of customers queuing at a point of sale in a retail shop.

Finally, quantitative researchers use management information systems to collect data. This involves reviewing documents such as financial reports to obtain quantitative data.

The similarities and differences between quantitative and qualitative research methods can be seen in their data analysis methods

Qualitative researchers often start the analysis process during the data collection and preparation stage in order to discover emerging themes and patterns. This involves continuous examination of data in order to identify important points, contradictions, inconsistencies, and common themes.

After this preliminary analysis, qualitative data is usually organized through systematic categorization and concept formation. This involves summarizing data under major categories that appear in the data set.

Data can also be summarized through tabulation in order to reveal its underlying features. The summaries usually provide descriptions that are used to generate theories. Concisely, the data is used to develop theories that explain the causes of the participants’ behavior.

Theories are also developed through comparative analysis. This involves comparing observations “across a range of situations over a period of time among different participants through a variety of techniques”.

Continuous comparisons provide clues on why participants behave in a particular manner, thereby facilitating theory formulation.

Quantitative analysis begins with the identification of the level of measurement that is appropriate for the collected data. After identifying the measurement level, data is usually summarized under different categories in tables by calculating frequencies and percentage distributions.

A frequency distribution indicates the number of observations or scores in each category of data, whereas a percentage distribution indicates the proportion of the subjects of the study who are represented in each category.

Descriptive statistics help the researcher to describe quantitative data. It involves calculating the mean and median, as well as, minimum and maximum values. Other analytical tools include correlation, regression, and analysis of variance.

Correlation analysis reveals the direction and strength of the relationship associated with two variables. Analysis of variance tests the statistical significance of the independent variables. Regression analysis helps the researcher to determine whether the independent variables are predictors of the dependent variables.

The similarities and differences between quantitative and qualitative research methods can be seen in their validity issues

Validity refers to the “degree to which the evidence proves that the interpretations of the data are correct and appropriate”. Validity is achieved if the measurement instrument is reliable. Replicability is the most important aspect of reliability in quantitative research.

This is because the results of quantitative research can only be approved if they are replicable. In quantitative research, validity is established through experiment review, data triangulation, and participant feedback, as well as, regression and statistical analyses.

In qualitative research, validity depends on unobtrusive measures, respondent validation, and triangulation. The validity of the results is likely to improve if the researcher is unobtrusive. This is because the presence of the researcher will not influence the responses of the participants.

Respondent validation involves obtaining feedback from the respondents concerning the accuracy of the data in order to ensure reliability. Triangulation involves collecting data using different methods at different periods from different people in order to ensure reliability.

The similarities and differences between quantitative and qualitative research methods can be seen in their strengths and weaknesses

The strengths of qualitative research include the following. First, it enables the researcher to pay attention to detail, as well as, to understand meanings and complexities of phenomena.

Second, it enables respondents to convey their views, feelings, and experiences without the influence of the researcher.

Third, qualitative research involves contextualization of behavior within situations and time. This improves the researcher’s understanding, thereby enhancing the reliability of the conclusions made from the findings.

Finally, the findings of qualitative research are generalizable through the theory developed in the study.

Qualitative research has the following weaknesses. Participant observation can lead to interpretation of phenomena based only on particular situations, while ignoring external factors that may influence the behavior of participants.

This is likely to undermine the validity of the research. Additionally, conducting a qualitative research is usually difficult due to the amount of time and resources required to negotiate access, to build trust, and to collect data from the respondents.

Finally, qualitative research is associated with high levels of subjectivity and bias.

Quantitative research has the following strengths. First, it has high levels of precision, which is achieved through reliable measures.

Second, it uses controlled experiments, which enable the researcher to determine cause and effect relationships.

Third, the use of advanced statistical techniques such as regression analysis facilitates accurate and sophisticated analysis of data.

Despite these strengths, quantitative research is criticized because it ignores the fact that individuals are able to interpret their experiences, as well as, to develop their own meanings.

Furthermore, control of variables often leads to trivial findings, which may not explain the phenomena that are being studied. Finally, quantitative research cannot be used to study phenomena that are not quantifiable.

The aim of this paper was to analyze quantitative and qualitative research methods by comparing and contrasting them. The main difference between qualitative and quantitative research is that the former uses non-numeric data, whereas the later mainly uses numeric data.

The main similarity between them is that they can be used to test existing theories and hypothesis. Qualitative and quantitative research methods have strengths and weaknesses. The results obtained through these methods can be improved if the researcher addresses their weaknesses.

Gravetter, F., & Forzano, L.-A. (2011). Research methods for the behavioral sciences. New York, NY: McGraw-Hill.

Kothari, C. (2009). Research methodology: Methods adn techniques. London, England: Sage.

McNeill, P., & Chapman, S. (2005). Research methods. London, England: Palgrave.

Rosenthal, R., & Rosnow, R. (2007). Essentials of behavioral research: Methods and data analysis. Upper River Saddle, NJ: Prentice Hall.

Stangor, C. (2010). Research methods for the behavioral sciences. New York, NY: John Wiley and Sons.

Wallnau, L., & Gravetter, F. (2009). Statistics for the behavioral sciences. London, England: Macmillan.

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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'Qualitative' and 'quantitative' methods and approaches across subject fields: implications for research values, assumptions, and practices

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• Published: 30 September 2023
• Volume 58 , pages 2357–2387, ( 2024 )

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There is considerable literature showing the complexity, connectivity and blurring of 'qualitative' and 'quantitative' methods in research. Yet these concepts are often represented in a binary way as independent dichotomous categories. This is evident in many key textbooks which are used in research methods courses to guide students and newer researchers in their research training. This paper analyses such textbook representations of 'qualitative' and 'quantitative' in 25 key resources published in English (supported by an outline survey of 23 textbooks written in German, Spanish and French). We then compare these with the perceptions, gathered through semi-structured interviews, of university researchers (n = 31) who work in a wide range of arts and science disciplines. The analysis of what the textbooks say compared to what the participants report they do in their practice shows some common features, as might be assumed, but there are significant contrasts and contradictions. The differences tend to align with some other recent literature to underline the complexity and connectivity associated with the terms. We suggest ways in which future research methods courses and newer researchers could question and positively deconstruct such binary representations in order to free up directions for research in practice, so that investigations can use both quantitative or qualitative approaches in more nuanced practices that are appropriate to the specific field and given context of investigations.

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1 Introduction: qualitative and quantitative methods, presentations, and practices

Teaching in research methods courses for undergraduates, postgraduates and newer researchers is commonly supported or guided through textbooks with explanations of 'qualitative' and 'quantitative' methods and cases of how these methods are employed. Student dissertations and theses commonly include methodology chapters closely aligned with these textbook representations. Unexceptionally, dissertations and theses we supervise and examine internationally have methodology chapters and frequently these consider rationales and methods associated with positivist or interpretivist paradigms. Within such positivist or interpretivist frameworks, research approaches are amplified with elaborations of the rationale, the methods, and reasons for their choice over likely alternatives. In an apparent convention, related data are assigned as quantitative or qualitative in nature, with associated labelling as ‘numerical’ or ‘textual'. The different types of data yield different values and interpretive directions, and are clustered conceptually with particular research traditions, approaches, and fields or disciplines. Frequently, these clusters are oriented around 'quantitative' and 'qualitative' conceptualizations.

This paper seeks to show how ‘qualitative’ and ‘quantitative’, whether stereotyped or more nuanced, as binary divisions as presented in textbooks and published resources describing research methods may not always accord with the perceptions and day-to-day practices of university researchers. Such common binary representations of quantitative and qualitative and their associated concepts may hide complexities, some of which are outlined below. Any binary divide between ‘qualitative’ and ‘quantitative’ needs caution to show complexity and awareness of disparities with some researchers’ practices.

To date, as far as the present authors are aware, no study has first identified a range of binary representations of ‘quantitative’ and ‘qualitative’ methods and approaches in a literature review study of the many research methods textbooks and sources which guide students and then, secondly, undertaken an interview study with a range of established participant researchers in widely divergent fields to seek their understandings of ‘quantitative’ and ‘qualitative’ in their own fields. The findings related here complement and extend the complexities and convergences of understanding the concepts in different disciplines. Arguably, this paper demonstrates how students and novice researchers should not be constrained in their studies by any binary representations of ‘quantitative’ and ‘qualitative’ the terms. They should feel free to use either (or neither) or both in strategic combinations, as appropriate to their fields.

1.1 Presentations

Characteristically, presentations in research methods textbooks distinguish postivist and interpretivist approaches or paradigms (e.g. Guba and Lincoln 1994 ; Howe 1988 ; Denzin and Lincoln 2011 ) or ‘two cultures’ (Goertz and Mahoney 2012 ) with associated debates or ‘wars’ (e.g. Creswell 1995 ; Morse 1991 ). Quantitative data are shown as ‘numbers’ gathered through experiments (Moore 2006 ) or mathematical models (Denzin and Lincoln 1998 ), whereas qualitative data are usually words or texts (Punch 2005 ; Goertz and Mahoney 2012 ), characteristically gathered through interviews or life stories (Denzin and Lincoln 2011 ). Regarding analysis, some sources claim that establishing objective causal relationships is key in quantitative analysis (e.g. Goertz and Mahoney 2012 ) whereas qualitative analysis uses more discursive and interpretative procedures.

Thus, much literature presents research in terms of two generally distinct methods—quantitative and qualitative—which many students are taught in research methods courses. The binary divide may seem to be legitimated in the titles of many academic journals. This division prevails as designated strands of separated research methods in courses which apparently handle both (cf. Onwuegbuzie and Leech 2005 ). Consequently, students may follow this seemingly stereotyped binary view or feel uncomfortable to deviate from it. Arguably, PhD candidates need to demonstrate understanding of such concepts and procedures in a viva—or risk failure (cf. Trafford and Leshem 2002 ). The Cambridge Dictionary defines ‘quality’ as “how good or bad something is”; while ‘quantity' is “the amount or number of something, especially that can be measured” (Cambridge 2022 ). But definitions of ‘Qualitative' can be elusive, since “a precise definition of qualitative research, and specifically… its distinctive feature of being “qualitative”, the literature is meager” (Aspers and Corte 2019 , p.139). Some observe a “paradox… that researchers act as if they know what it is, but they cannot formulate a definition” and that “there is no consensus about specific qualitative methods nor… data” (Aspers and Corte 2019 , p40). In general, ‘qualitative research’ is an iterative process to discover more about a phenomenon (ibid.). Elsewhere, 'qualitative’ is defined negatively: "It is research that does not use numbers” (Seale 1999b , p.119). But this oversimplifies and hides possible disciplinary variation. For example, when investigating criminal action, numeric information (quantity) always follows an interpretation (De Gregorio 2014 ), and consequently this is a quantity of a quality (cf. Uher 2022 ).

Indeed, many authorities note the presence of elements of one in the other. For example, in analysis specifically, that what are considered to be quantitative elements such as statistics are used in qualitative analysis (Miles and Huberman 1994 ). More generically, that “a qualitative dimension is present in quantitative work as well” (Aspers and Corte 2019 , p.139). In ‘mixed methods’ research (cf. Tashakkori et al. 1998 ; Johnson et al. 2007 ; Teddlie and Tashakkori 2011 ) many researchers ‘mix’ the two approaches (Seale 1999a ; Mason 2006 ; Dawson 2019 ), either using multiple methods concurrently, or doing so sequentially. Mixed method research logically depends on prior understandings of quantitative and qualitative concepts but this is not always obvious (e.g. De Gregorio 2014 ); for instance Heyvaert et al. ( 2013 ) define mixed methods as combining quantitative and qualitative items, but these key terms are left undefined. Some commentators characterize such mixing as a skin, not a sweater to be changed every day (Marsh and Furlong 2002 , cited in Grix 2004 ). In some disciplines, these terms are often blurred, interchanged or conjoined. In sociology, for instance, “any quality can be quantified. Any quantity is a quality of a social context, quantity versus quality is therefore not a separation” (Hanson 2008 , p.102) and characterizing quantitative as ‘objective’ and qualitative as ‘subjective’ is held to be false when seeking triangulation (Hanson 2008 ). Additionally, approaches to measuring and generating quantitative numerical information can differ in social sciences compared to physics (Uher 2022 ). Indeed, quantity may consist of ‘a multitude’ of divisible aspects and a ‘magnitude’ for indivisible aspects (Uher 2022 ). Notably, “the terms ‘measurement’ and ‘quantification’ have different meanings and are therefore prone to jingle-jangle fallacies” (Uher 2022 ) where individuals use the same words to denote different understandings (cf. Bakhtin 1986 ). Comparatively, the words ‘unit’ and ‘scale’ are multitudinous in different sciences, and the key principles of numerical traceability and data generation traceability arguably need to be applied more to social sciences and psychology (Uher 2022 ). The interdependence of the terms means any quantity is grounded in a quality of something, even if the inverse does not always apply (Uher 2022 ).

1.2 Practices

The present paper compares representations found in research methods textbooks with the reported practices of established researchers given in semi-structured interviews. The differences revealed between what the literature review of methods texts showed and what the interview study showed both underlines and extends this complexity, with implications for how such methodologies are approached and taught. The interview study data (analysed below) show that many participant researchers in disciplines commonly located within an ostensibly ‘positivist’ scientific tradition (e.g. chemistry) are, in fact, using qualitative methods as scientific procedures (contra Tashakkori et al 1998 ; Guba and Lincoln 1994 ; Howe 1988 ; Lincoln and Guba 1985 ; Teddlie and Tashakkori 2011 ; Creswell 1995 ; Morse 1991 ). These interview study data also show that many participant researchers use what they describe as qualitative approaches to provide initial measurements (geotechnics; chemistry) of phenomena before later using quantitative procedures to measure the quantity of a quality (cf. Uher 2022 ). Some participant researchers also say they use quantitative procedures to reveal data for which they subsequently use qualitative approaches to interpret and understand (biology; dendrology) through their creative imaginations or experience (contra e.g. Hammersley, 2013 ). Participant researchers in ostensibly ‘positivist’ areas describe themselves as doubting ‘facts’ measured by machines programmed by humans (thus showing they feel researchers are not outside the world looking in (contra. e.g. Punch 2005 )) or doubting the certainty of quantitative data over time (contra e.g. Punch 2005 ). Critically, the interview study data show that these participant researchers often engage in debate over what a ‘number’ is and the extent to which ‘numbers’ can be considered ‘quantitative’. For example the data show how a mathematician considers that many individuals do not know what they mean by the word ‘quantitative’, and an engineer interprets any numbers involving human judgements as ‘qualitative’. Further, both a chemist and a geotechnician routinely define and use ‘qualitative’ methods and analysis to arrive at numerical values (contra. Davies and Hughes 2014 ; Denzin and Lincoln 2011 ).

Such data refute many textbook and key source representations of quantitative and qualitative as being binary and separately ringfenced entities as shown in the literature review study below (contra e.g. Punch 2005 ; Goertz and Mahoney 2012 ). Nevertheless, they resonate with much recent and current literature in the field (e.g. Uher 2022 ; De Gregorio 2014 ). They also arguably extend the complexities of the terms and approaches. In some disciplines, these participant researchers only do a particular type of research and never need anything other than clear ‘quantitative’ definitions (Mathematics), and some only ever conduct research involving text and never numbers (Literature). Moreover, some participant researchers consider certain aspects lie outside the ‘qualitative’ or ‘quantitative’ (the theoretical in German Literature), or do research which they maintain does not contain ‘knowledge’ (Fine-Art Sculpture), while others outline how they feel they do foundational conceptual research which they believe comes at a stage before any quantity or quality can be assessed (Philosophy). Indeed, of the 31 participant researchers we spoke to, nine of them considered the terms ‘quantitative’ and ‘qualitative’ to be of little relevance for their subject.

1.3 Outline of the two studies

This paper reports and discusses findings from a constructivist grounded approach interview study that interviewed experienced participant researchers (N = 31) in various disciplines (see Table 1 below) about their understandings of ‘qualitative’ and ‘quantitative’ in their subject areas. Findings from this interview study were compared with findings from a research methods literature review study that revealed many disparities with received and often binary presentations of the concepts in much key literature that informs student research methods courses. In this section we outline the review criteria, the method of analysis, and our findings. The findings are grouped according to how the sources reviewed consider ‘quantitative’ and ‘qualitative’ approaches the aspects of positivism and constructivism; the nature of research questions; research methods; analysis; issues of reliability, validity and generalizability; and the value and worth of the different approaches. Following this. We outline the approach, method, and procedure adopted for the interviews with research participants; sampling and saturation; and analysis; beside details of the participant researchers. Subsequently, Theme 2 focuses on contrasts of the interview data with ‘binary’ textbook and key source representations. Theme 3 focuses on what the interview data show about participant researcher perceptions of the value of ‘quantitative’ and ‘qualitative’ methods and approaches. This section outlines where, how, and sometimes why, participant researchers considered ‘quantitative’ and ‘qualitative’ methods approaches to be (or to not be) useful to them. These interview study findings show a surprising range of understandings, usage, and often perceived irrelevance of the terms. In the Discussion section, these findings form the focus of comparison with the literature as well as a consideration of possible implications for approaching and teaching research methods. In the conclusion we summarise the implications for research methods courses, for researchers in different disciplines and interdisciplinary contexts and discuss limitations and suggest future research. Besides adding to the debate on how ‘quantitative’ and ‘qualitative’ are conceptualized and how they are related, the paper appeals to those delivering research methods courses and to novice researchers to consider the concepts as highly complex and overlapping, to loosen constraints, and elaborate nuances of the commonplace binary representations of the terms.

2 Literature review study: some key textbooks and sources for teaching Research Methods.

2.1 review criteria.

To identify how concepts are presented in key materials we undertook a literature review study by consulting research methods course reading lists, library search engines, physically available shelves in institutional libraries, and Google Scholar. We wanted to encompass textbooks and some key texts which are recommended to UG, PG Masters and PhD students., for example, ‘textbooks’ like ‘Doing Your Research Project: A Guide for first-time researchers’ (Bell and Waters 2014 ) and ‘Introduction to Research Methods: A Practical Guide for Anyone Undertaking a Research project (5th Edition)’ (Dawson 2019 ). Such sources were frequently mentioned on reading lists and are freely available in many institutional libraries. We consulted seminal thinkers who have published widely on research methods, such as Denzin and Lincoln, or Cresswell, but we also considered texts which are likely less known such as ‘A tale of two cultures’ (Goertz and Mahoney 2012 ) and key articles such as ‘Five misunderstandings about case-study research’ (Flyvbjerg 2006 ). Students can freely find such sources, and are easily directed to them by supervisors. Although a more comprehensively robust search is possible, we nevertheless followed procedures and standard criteria for literature reviews (Atkinson et al. 2015 ).

3 Method of analysis

We assembled a total of 25 sources to look for a number of key tenets. We examined the sources for occurrence of the following: whether quantitative was described as positivist and qualitative was described as constructivist; whether quantitative was said to be science-based and qualitative was more reflective and non-science based; whether the research questions were presented as predetermined in quantitative methods and initially less focused in qualitative methods; whether quantitative methods were structured and qualitative methods were discussed as less structured; whether quantitative analysis focused on cause-effect type relationships and qualitative analysis was more exploratory; whether reliability, validity and generalizability were achieved through large numbers in quantitative research and through in-depth study in qualitative research; whether for particular subjects such as the sciences quantitative approaches were perceived to be of value (and qualitative was implied to have less value) and whether the converse was the case for other subjects such as history and anthropology; and whether mixed methods were considered possible or not possible. The 25 sources are detailed in Appendix 1 . As a confirmatory but less detailed exercise, and also detailed in Appendix 1 , we checked a further 23 research methods textbooks in German, Spanish and French, authored in those languages (rather than translations from English).

3.1 Findings

Overall, related to what quantitative and qualitative approaches, methods and analysis are, we found many key, often binary representations in this literature review. We outline these here below.

3.2 Positivism and constructivism

Firstly, 20 of the sources we reviewed stated that quantitative is considered positivist, and qualitative constructivist (e.g. Tashakkori et al 1998 ; Guba and Lincoln 1994 ; Howe 1988 ; Lincoln and Guba 1985 ; Teddlie and Tashakkori 2011 ; Creswell 1995 ; Morse 1991 ). Even if not everyone doing quantitative research (e.g. in sociology) consider themselves positivists (Marsh 1979 ), it is generally held quantitative research is positivist. Here, 12 of the sources noted that quantitative is considered ‘scientific’, situating observers outside the world looking in, e.g. through gathering numerical data (Punch 2005 ; Davis and Hughes 2014 ) whereas qualitative “locates the observer in the world” (Denzin and Lincoln 2011 , p.3). Quantitative researchers “collect facts and study the relationship of one set of facts to another”, whereas qualitative researchers “doubt whether social ‘facts’ exist and question whether a ‘scientific’ approach can be used when dealing with human beings” (Bell and Waters 2014 , p. 9).

3.3 The nature of research questions

Secondly, regarding research questions, “qualitative research… typically has… questions and methods… more general at the start, and… more focused as the study progresses” (Punch 2005 , p.28). In contrast, quantitative research uses “numerical data and typically… structured and predetermined research questions, conceptual frameworks and designs” (Punch 2005 , p.28). Of the sources we reviewed, 16 made such assertions. This understanding relates to type, and nature, of data, which is in turn anchored to particular worldviews. Punch ( 2005 , p 3–4) writes of how “in teaching about research, I find it useful to approach the qualitative-quantitative distinction primarily through…. the nature of the data. Later, the distinction can be broadened to include …. ways of conceptualising the reality being studied, and methods.” Here, the nature of data influences approach: numbers are for quantitative, and not-numbers (commonly words) for qualitative. Similarly, for Miles et al. ( 2018 ) “the nature of qualitative data” is “primarily on data in the form of words, that is, language in the form of extended text” (Miles et al. 2018 , no page). These understandings in turn relate to methods used.

Commonly, specific types of methods are said to be related to the type of approach adopted, and 18 of the sources we reviewed presented quantitative methods as being structured, and qualitative methods as less structured. For example, Davies and Hughes ( 2014 , p.23) claim “there are two principal options open to you: 1… quantitative research methods, using the traditions of science. 2… qualitative research, employing a more reflective or exploratory approach.” Here, quantitative methods are “questionnaires or structured interviews” whereas qualitative methods are “such as interviews or focus groups” (Dawson 2019 , no page given). Quantitative methods are more scientific, involve controlling a set of variables, and may involve experiments, something which, “qualitative researchers are agreed in their opposition to this definition of scientific research, or at least its application to social inquiry” (Hammersley 2013 , p. ix). As Punch notes ( 2005 , p.208), “the experiment was seen as the basis for establishing cause-effect relationships between variables, and its outcome (and control) variables had to be measured.”

4.1 Analysis

Such understandings often relate to analysis, and 16 of the sources we reviewed presented quantitative analysis as being statistical and number related, and qualitative analysis as being text based. With quantitative methods, “the data is subjected to statistical analysis, using techniques… likely to produce quantified, and, if possible, generalizable conclusions” (Bell and Waters 2014 , p.281). With qualitative research, however, this “calls for advanced skills in data management and text-driven creativity during the analysis and write-up” (Davies and Hughes 2014 ). Again, the data’s nature is key, and whilst qualitative analysis may condense data, it does not seek numbers. Indeed, “by data condensation, we do not necessarily mean quantification”, however, “occasionally, it may be helpful to convert the data into magnitudes… but this is not always necessary” (Miles et al. 2018 , npg). Qualitative analysis may involve stages such as assigning codes, subsequently sorting and sifting them, isolating patterns, then gradually refining any assertions made and comparing them to other literature (Miles et al. 2018 ). This could involve condensing, displaying, then drawing conclusions from the data (Miles et al. 2018 ). In this respect, some sources consider qualitative and quantitative analysis broadly similar in overall goals, yet different because quantitative analyses use “well-defined, familiar methods; are guided by canons; and are usually more sequential than iterative or cyclical” (Miles et al. 2018 , npg). In contrast, “qualitative researchers are… more fluid and… humanistic” in meaning making (Miles et al. 2018 , npg). Here, both approaches seek causation and may attempt to reveal ‘cause and effect’ but qualitative approaches often seek multiple and interacting influences, and effects and are less rigid (Miles et al. 2018 ). In quantitative inquiry search for causation relates to “causal mechanisms (i.e. how did X cause Y)” whereas in “the human sciences, this distinction relates to causal effects (i.e. whether X causes Y)” (Teddlie and Tashakkori 2011 , p.286). Similarly, that “scientific research in any area… seeks to trace out cause-effect relationships” (Punch 2005 , p.78). In contrast, qualitative research seeks interpretative understandings of human behaviour, “not ‘caused’ in any mechanical way, but… continually constructed and reconstructed” (Punch 2005 , p.126).

4.2 Issues of reliability, validity and generalizability

Regarding reliability, validity and generalizability, 19 of the sources we reviewed presented ideas along the lines that quantitative research is understood to seek large numbers, so quantitative researchers, “use techniques… likely to produce quantified and, if possible, generalizable conclusions (Bell and Waters 2014 , p.9). This means quantitative “research researches many more people” (Dawson 2019 , npg). Given quantitative researchers aim, “to discover answers to questions through the application of scientific procedures” it is anticipated these procedures will “increase the likelihood that the information… will be reliable and unbiased” (Davies and Hughes 2014 , p.9). Conversely, qualitative researchers are considered “more concerned to understand individuals’ perceptions of the world” (Bell and Waters 2014 , p.281) and consequently aim for in-depth data with smaller numbers, “as it is attitudes, behaviour and experiences that are important” (Dawson 2019 , npg). Consequently, generalizability of data is not key, as qualitative research has its “emphasis on a specific case, a focused and bounded phenomenon embedded in its context” (Miles et al. 2018 , npg). Yet, such research is considered generalizable in theoretical insight if not actual data (Flyvbjerg 2006 ).

4.3 The value and worth of the different approaches

Regarding ‘value’ and ‘worth’, many see this related with appropriacy to the question being researched. Thus, if questions involve more quantitative approaches, then these are of value, and if more qualitative, then these are of value, and 6 of the sources we reviewed presented these views (e.g. Bell and Waters 2014 ; Punch 2005 ; Dawson 2019 ). This resonates with disciplinary orientations where choices between given approaches are valued more in specific disciplines. History and Anthropology are seen more qualitative, whereas Economics and Epidemiology may be more quantitative (Kumar 1996 ). Qualitative approaches are valuable to study human behaviour and reveal in-depth pictures of peoples’ lived experience (e.g. Denzin and Lincoln 2011 ; Miles et al. 2018 ). Many consider there to be no real inherent superiority for one approach over another, and “asking whether quantitative or qualitative research is superior to the other is not a useful question” (Goertz and Mahoney 2012 , p.2).

Nevertheless, some give higher pragmatic value to quantitative research for studying individuals and people; neoliberal governments consistently value quantitative over qualitative research (Barone 2007 ; Bloch 2004 ; St Pierre 2004 ). Concomitantly, data produced by qualitative research is criticised by quantitative proponents “because of their problematic generalizability” (Bloor and Wood 2006 , p.179). However, other studies find quantitative researchers see qualitative methods and approaches positively (Pilcher and Cortazzi 2016 ). Some even question the qualitative/quantitative divide, and suggest “a more subtle and realistic set of distinctions that capture variation in research practice better” (Hammersley 2013 , p.99).

The above literature review study of key texts is hardly exhaustive, but shows a general outline of the binary divisions and categorizations that exist in many sources students and newer researchers encounter. Thus, despite the complex and blurred picture as outlined in the introduction above, many key texts students consult and that inform research methods courses often present a binary understanding that quantitative is positivist, focused on determining cause and effect, numerical or magnitude focused, uses experiments, and is grounded in an understanding the world can be observed from the outside in. Conversely, qualitative tends to be constructivist, focused on determining why events occur, is word or textual based (even if these elements are measured by their magnitude in a number or numerical format) and grounded in understanding the researcher is part of the world. The sciences and areas such as economics are said to tend towards the quantitative, and areas such as history and anthropology towards the qualitative.

We also note that in our literature review study we focused on English language textbooks, but we also looked at outline details, descriptions, and contents lists of texts in the languages of German, Spanish and French. We find that these broadly confirm the perception of a division between quantitative and qualitative research, and we detail a number of these in Appendix 1 . These examples are all research methods handbooks and student guides intended for under and post-graduates in social sciences and humanities; many are inter-disciplinary but some are more specifically books devoted to psychology, health care, education, politics, and management. Among the textbooks and handbooks examined in other languages, more recent books pay attention to online research and uses of the internet, social media and sometimes to big data and software for data analysis.

In these sources in languages other than English we find massive predominance of two (quantitative/qualitative) or three approaches (mixed). These are invariably introduced and examined with related theories, examples and cases in exactly that order: quantitative; qualitative; mixed. Here there is perhaps the unexamined implication that this is a historical order of research method development and also of acceptability of use (depending on research purposes). Notably, Molina Marin (2020) is oriented to Latin America and makes the point that most European writing about research methods is in English or German, while there are far fewer publications in Spanish and few with Latin American contextual relevance, which may limit epistemological perspectives. This point is evident in French and Spanish publications (much less the case in German) where bibliographic details seem dominated by English language publications (or translations from them). We now turn to outline our interview study.

5 Interview study

5.1 approach and choice of method.

We approached our interview study from a constructivist standpoint of exploring and investigating different subject specialists’ understandings of quantitative and qualitative. Critically, we were guided by the key constructivist tenet that knowledge is not independent of subjects seeking it (Olssen 1996 ), nor of subjects using it. Extending from this we considered interviews more appropriate than narratives or focus groups. Given the exploratory nature of our study, we considered interviews most suited as we wanted to have a free dialogue (cf. Bakhtin 1981 ) regarding how the terms are understood in their subject contexts as opposed to their neutral dictionary definitions (Bakhtin 1986 ), and not to focus on a specific point with many individuals. Specifically, we used ‘semi’-structured interviews. ‘Semi’ can mean both ‘half in quantity or value’ but also ‘to some extent: partly: incompletely’ (e.g. Merriam Webster 2022 ). Our interviews, following our constructionist and exploratory approach, aligned with the latter definition (see Appendix 2 for the Interview study schedule). This loose ‘semi’ structure was deliberately designed to (and did) lead to interviews directed by the participants, who themselves often specifically asked what was meant by the questions. This created a highly technical dialogue (Buber, 1947) focused on the subject.

5.2 Sampling and saturation

Our sampling combined purposive and snowball sampling (Sharma 2017 ; Levitt et al. 2018 ). Initially, participants were purposively identified by subject given the project sought to understand different subject perspectives of ‘qualitative’ and ‘quantitative.’ Later, a combined purposive and snowball sampling technique was used whereby participants interviewed were asked if they knew others teaching particular subjects. Regarding priorities for participant eligibility, this was done according to subject, although generally participants also had extensive experience (see Table 1 ). For most, English was their first language, where it was not, participants were proficient in English. The language of interview choice was English as it was most familiar to both participants and interviewer (Cortazzi et al. 2011 ).

Regarding saturation, some argue saturation occurs within 12 interviews (Guest et al. 2006 ), others within 17 (Francis et al. 2010 ). Arguably, however, saturation cannot be determined in advance of analysis and is “inescapably situated and subjective” (Braun and Clarke 2021 , p.201). This critical role of subjectivity and context guided how we approached saturation, whereby it was “operationalized in a way consistent with the research question(s) and the theoretical position and analytic framework adopted” (Saunders et al. 2018 , p.1893). We recognise that more could always be found but are satisfied that 31 participants provided sufficient data for our investigation. Indeed, our original intention was to recruit 20 participants, feeling this would provide sufficient saturation (Francis et al. 2010 ; Guest et al. 2006 ) but when we reached 20, and as we had already started analysis (cf. Braun and Clarke 2021 ) as we ourselves transcribed the interviews (Bird 2005 ) we wanted to explore understandings of ‘qualitative’ and ‘quantitative’ with other subject fields. As Table 1 shows, ‘English Literature’, ‘Philosophy, and ‘Sculpture’ were only explored after interview 20. These additional subject fields added significantly (see below) to our data.

5.3 Analysis and participant researcher details

Our analysis followed Braun and Clarke’s ( 2006 ) thematic analysis. Given the study’s exploratory constructionist nature, we combined ‘top down’ deductive type analysis for anticipated themes, and ‘bottom up’ inductive type analysis for any unexpected themes. The latter was similar to a constructivist grounded theory analysis (Charmaz 2010 ) whereby the transcripts were explored through close repeated reading for themes to emerge from the bottom up. We deliberately did not use any CAQDAS software such as NVivo as we wanted to manually read the scripts in one lengthy word document. We recognise that such software could allow us to do this but we were familiar with the approach we used and have found it effective for a number of years. We thus continued to use it here as well. We counted instances of themes through cross-checking after reading transcripts and discussing them, thereby heightening reliability and validity (Golafshani 2003 ). All interviews were undertaken with informed consent and participants were assured all representation was anonymous (Christians 2011 ). The study was approved by relevant ethics committees. Table 1 above shows the subject area, years of experience, and first language of the participant researchers. We also bracket after each subject area whether we consider it to be ‘Science’ or ‘Arts’ or whether we consider them as ‘Arts/Science’ or ‘Science/Arts’. This is of course subjective and in many ways not possible to do, but we were guided in how we categorised these subjects by doing so according to how we feel the methodology sources form the literature review study would categorize them.

5.4 Presentation of the interview study data compared with data from the literature review study

We present our interview study data in the three broad areas that emerged through analysis. Our approach to thematic analysis was to deductively code the interview transcripts manually under the three broad areas of: where data aligns with textbook and key source ‘binary’ representations; where the data contrasts with such representations; and where the data relates to interviewee perceptions of the value of ‘qualitative’ and ‘quantitative’. The latter relates to whether participant researchers expressed views that suggested they considered each approach to be useful, valuable, or not. We also read through the transcripts inductively with a view to being open to emerging and unanticipated themes. For each data citation, we note the subject field to show the range of subject areas. We later discuss these data in terms of their implications for research values, assumptions and practices and for their use when teaching about different methods. We provide illustrative citations and numbers of participant researchers who commented in relation to the key points below, but first provide an overview in Table 2 .

5.4.1 Theme 1: Alignments with ‘binary’ textbook and key source representations

The data often aligned with textbook representations. Seven participant researchers explicitly said, or alluded to the representation that ‘quantitative’ is positivist and seeks objectivity whereas ‘qualitative’ is more constructivist and subjective. For example: “the main distinction… is that qualitative is associated with subjectivity and quantitative being objective.” This was because “traditionally quantitative methods they’ve been associated with the positivist scientific model of research whereas qualitative methods are rooted in the constructivist and interpretivist model” (Psychology). Similarly, “quantitative methods… I see that as more… logical to a scientific mode of generating knowledge so… largely depends on numbers to establish causal relations… qualitative, I want to more broadly summarize that as anything other than numbers” (Communication Studies). One Statistics researcher had “always associated quantitative research more with statistics and numbers… you measure something… I think qualitative… you make a statement… without saying to what extent so… so you run fast but it’s not clear how fast you actually run…. that doesn’t tell you much because it doesn’t tell you how fast.” One mathematics participant researcher said mathematics was “ super quantitative… more beyond quantitative in the sense that not only is there a measurement of size in everything but everything is defined in… really careful terms… in how that quantity kind of interacts with other quantities that are defined so in that sense it’s kind of beyond quantitative.” Further, this applied at pre-data and data integration stages. Conversely, ‘qualitative’ “would be more a kind of verbalistic form of reasoning or… logic.”

Another representation four participant researchers noted was that ‘quantitative ‘ has structured predetermined questions whereas ‘qualitative’ has initially general questions that became more focused as research proceeded. For example, in Tourism, “with qualitative research I would go with open ended questions whereas with quantitative research I would go with closed questions.” This was because ‘qualitative’ was more exploratory: “quantitative methods… I would use when the parameters… are well understood, qualitative research is when I’m dealing with topics where I’m not entirely sure about… the answers.” As one Psychology participant researcher commented: “the main assumption in quantitative… is one single answer… whereas qualitative approaches embrace… multiplicity.”

Nineteen participant researchers considered ‘quantitative’ numbers whereas ‘qualitative’ was anything except numbers. For example, “quantitative research… you’re generating numbers and the analysis is involving numbers… qualitative is… usually… text-based looking for something else… not condensing it down to numbers” (Psychology). Similarly, ‘quantitative’ was “largely… numeric… the arrangement of larger scale patterns” whereas, “in design field, the idea of qualitative…is about the measure… people put against something… not [a] numerical measure” (Design). One participant researcher elaborated about Biology and Ecology, noting that “quantitative it’s a number it’s an amount of something… associated with a numerical dimension… whereas… qualitative data and… observations… in biology…. you’re looking at electron micrographs… you may want to describe those things… purely in… QUALitative terms… and you can do the same in… Ecology” (Human Computer Interaction). One participant researcher also commented on the magnitude of ‘quantitative’ data often involving more than numbers, or having a complex involvement with numbers: “I was thinking… quantitative… just involves numbers…. but it’s not… if… NVivo… counts the occurrence of a word… it’s done in a very structured way…. to the point that you can even… then do statistical analysis” (Logistics).

Regarding mixed methods, data aligned with the textbook representations that there are two distinct ‘camps’ but also that these could be crossed. Six participants felt opposing camps and paradigms existed. For example, in Nursing, that “it does feel quite divided in Nursing I think you’re either a qualitative or a quantitative researcher there’s two different schools… yeah some people in our school would be very anti-qualitative.” Similarly, in Music one participant researcher felt “it is very split and you’ll find… some people position themselves in one or the other of those camps and are reluctant to consider the other side. In Psychology, “yes… they’re quite… territorial and passionately defensive about the rightness of their own approaches so there’s this… narrative that these two paradigms… of positivistic and interpretivist type… cannot be crossed… you need to belong to one camp.” Also, in Communication Studies, “I do think they are kind of mutually exclusive although I accept… they can be combined… but I don’t think they, they fundamentally… speak to each other.” One Linguistics participant researcher felt some Linguists were highly qualitative and never used numbers, but “then you have… the corpus analysts who quantify everything and always under the headline ‘Corpus linguistics finally gets to the point… where we get rid of researcher bias; it objectifies the analysis’ because you have big numbers and you have statistical values and therefore… it’s led by the data not by the researcher.” This participant researcher found such striving for objectivity a “very strange thing” as any choice was based on previously argued ideas, which themselves could not be objective: “because all the decisions that you need to put into which software am I using, which algorithm am I using, which text do I put in…. this is all driven by ideas.”

Nevertheless, three participant researchers felt the approaches not diametrically opposed. For example, the same Psychology participant researcher cited immediately above felt people’s views could change: “some people although highly defensive over time… may soften their view as mixed method approaches become more prominent.” Comparatively flexibly, a Historian commented “I don’t feel very concerned by the division between qualitative and quantitative; I think they’re just two that are separate sometimes complementary approaches to study history.” In Translation and Interpreting, one participant researcher said methods could be quantitative, but have qualitative analysis, saying one project had: “an excellent use of quantitative tools… followed by not a qualitative method but qualitative analysis of what that implied.” Thus, much of the data did align with the binary representations of the key textbooks reviewed above and also the representation that approaches could be combined.

5.4.2 Theme 2: Contrasts with ‘binary’ textbook and key source representations

One recurrent contrast with common textbook representations was where both qualitative and quantitative were used in some sciences; nine participant researchers felt this. For example, in Geotechnics, when ascertaining soil behaviour: “the first check, the Qualitative check is to look whether those [the traditional and new paths of soil direction] bear resemblance, [be] coz if that doesn’t have that shape how can I expect there to be a quantitative comparison or… fit.” Both qualitative and quantitative approaches combined helped “rule out coincidence” and using both represented “a check which moves through qualitative… to quantitative.” Quantitative was a “capital Q for want of a better expression” and consisted of ‘bigger numbers’, which constituted “the quantitative or calculated strength.” However, this ‘capital Q’ quantitative data aimed to quantify a qualitatively measured numerically estimated phenomenon. So both were numerical. Nevertheless, over the long-term, even the quantitative became less certain because: “when you introduce that time element… you create… circumstances in which you need to be careful with the way you define the strength… different people have come up with different values… so the quantitative match has to be done with an element of uncertainty.”

Similarly, in Chemistry, both qualitative and quantitative methods and analysis were used, where “ the qualitative is the first one, and after you have the other ones [I—Right to kind of verify] if… if you need that.” Both were used because, “we need to know what is there and how much of each component is there… and a knowledge of what is there is a qualitative one, how much of each one is a quantitative one.” Moreover, “they are analysed sometimes by the same technique ” which could be quantitative or qualitative: “[I—and chromatography, again… would that be qualitative or quantitative or both?] Both, both… the quantitative is the area of the peak, the qualitative is the position in which this characteristic appears.” Here, both were key, and depending on the research goal: “we… use them according to what we need… sometimes it’s enough to detect [qualitative] … other times you need to know how much [quantitative] ”.

For Biology also, both were key: “quantitative is the facts and… qualitative is the theory you’re trying to make fit to the facts you can’t do it the other way around… the quantitative data… just doesn’t tell you anything without the qualitative imagination of what does it mean?” Inversely, in an area commonly understood as quantitative, Statistics, the qualitative was an initial, hypothetical stage requiring later quantitative testing. For example: “very often the hypothesis is a qualitative hypothesis” and then, “you would test it by putting in all sorts of data and then the test result would give you a p-value… and the p-value of course is quantitative because that’s a number.”

In Engineering, both helped research sound frequencies: “we need to measure the spectrum of the different frequencies… created… all those things were quantifiable, but then we need to get participants to listen and tell us… which one do you prefer?… this is a qualitative answer.” Mathematical Biology also used both: qualitative for change in nature of a state, and quantitative for the magnitude of that change. Here: “quantitative changes the numerical value of the steady state but it doesn’t change its stability… but qualitative change is when you… change the parameters and you either change its stability or you change whether it exists or not… and that point over which you cross to change it from being stable to unstable is called a bifurcation point… that’s where I use quantitative and qualitative the most in my research.”

The idea of ‘quantitative’ involving large data sets was expressed; however, the ‘qualitative’ could help represent these. In Computing Mathematics one participant researcher commented that: “quantitative… I do almost 90% of the time…. calculating metrics… and using significance testing to determine whether the numbers mean anything.” Yet, this participant researcher also used qualitative representations for simplified visual representation of large number sets: “I think for me QUALitative work is almost always about visualizing things in a way that tries to illustrate the trends… so I’m not actually calculating numbers but I’m just saying if I somehow present it in in this way.” Concomitantly, ‘quantitative’ could be smaller scale. For example, in Architecture: “my expectation is it wouldn’t be valid until you have a certain quantity of response but that said [I] have had students use… quantitative analysis on a small sample.” Similarly, in History: “you could have a quantitative study of a small data set or a small… number of statistics I really think it’s determined by the questions… you’re asking.”

Interestingly, two participant researchers questioned their colleagues’ understandings of ‘quantitative’ and of ‘numbers’. For example, one Mathematician considered some researchers did not know what ‘quantitative’ meant, because “when they say quantitative… I think what they mean is the same as qualitative except it’s got numbers in it somewhere.” For example, “I’m talking to a guy who does research in pain and, so I do know now what he means by quantitative research, and what he means is that he doesn’t know what he means [both laugh] and he wants me to define what it means… I think he means he wants some form of modelling with data and… he’s not quite sure how to go about doing that.” For this Mathematician, engineers would, “Mean that purposefully when they talk about quantitative modelling” whereas, “generically you know when politicians [consider these things] quantitative just means there’s a number in it somewhere.”

Three participant researchers felt that when ‘quantitative’ involved human elements or decisions, subjectivity was inevitable. One Logistics participant researcher felt someone doing materials research was “Doing these highly quantitative analyses still there is a degree of subjectivity because… this involves human assessment… they’re using different photometric equipment… taking photos… what is the angle.” Another researcher in Sciences similarly noted, “I don’t know why people believe in machines so much because they’re built by humans and there’s so many errors.” An Engineer commented: “To me, just the involvement of humans… gives it a qualitative element no matter what.” For this researcher, with people’s ‘quantitative’ reaction times and memory recall, “I would call that again qualitative you know… yes we did quantify the reaction time… the correct number of answers, but… it’s a person… I could get somebody else now doing it and not get exactly the same answer, so that uncertainty of human participants to me make it a qualitative approach.” For this participant researcher, anything involving human participants was ‘qualitative’: “I would say anything that is measurable, but by measurable I mean physically measurable… or predictable through numbers is quantitative [and] anything that involves a judgment, therefore human participants… is qualitative.”

‘Qualitative’ was often highly subject-specific. For example, in Film Studies and Media—English, ‘qualitative’ was: “about… the qualities of particular texts…. I’ve read a lot about silence as a texture and a technique in cinema… so silence is a quality, and also what are the qualities of that silence.” One Sciences researcher felt ‘qualitative’ involved experience applied to interpreting data: “Qualitative I would define as using your own experience to see if the data makes sense… and… something that… cannot be measured so far by machine… like the shape of a tree.” One Historian also highlighted the importance of subject-sub-branches, saying, “I’d situate myself in history but I guess you’d probably get a different response depending on… whether that historian saw themselves as a cultural historian or as a social and economic historian or… an intellectual historian.”

A fluidity regarding ‘quantitative’ and ‘qualitative’ was characterized. One Human Computer Interaction participant researcher commented, “I think sometimes people can use both terms quite loosely without really sort of thinking about [them] .” Comparatively, one Psychology participant researcher commented that “even within the Qual[itative] people they disagree about how to do things [laughs] … so you have people talking about doing IPA [Interpretative Phenomenological Analysis] and they’re doing… and presenting it in completely different ways.” Another Psychologist felt using ‘quantitative’ and ‘qualitative’ as an ‘either/or’ binary division erroneously suggested all questions were answerable, whereas: “no method… can… answer this question… and this is something… many people I don’t think are getting is that those different methodologies come with huge limitations… and as a researcher you need… to appreciate… how far your work can go.” One Communication Studies participant researcher even perceived the terms were becoming less used in all disciplines, and that, “we’re certainly in a phase where even these labels now are becoming so arbitrary almost… that they’re not, not carrying a lot of meaning.” However, the terms were considered very context dependent: “I think I’d be very hesitant about… pigeonholing any particular method I’d want to look very closely at the specific context in which that particular method or methodology is being used.” Further, some concepts were considered challenging to align with textbook representations. One German Literature participant researcher, reflecting on how the ‘theoretical’ worked, concluded, “… the theoretical… I’m not sure whether… that is actually within the terms quantitative or qualitative or whether that’s a term… on a different level altogether .” Indeed, many participant researchers (nine in total across many subject areas e.g. Design, Film and Media, Philosophy, Mathematical Biology) confirmed they were fully aware of the commonplace representations, but felt they did not apply to their own research, only using them to communicate with particular audiences (see below).

5.4.3 Theme 3: Perceptions on the value of ‘Quantitative’ and ‘Qualitative’ methods and approaches

As the data above show, many participant researchers valued both ‘quantitative’ and ‘qualitative’, including many scientists (in Geotechnics; Biology, Chemistry, Engineering). Many considered the specific research question key. For example: “I certainly don’t think quantitative bad, qualitative good: it’s horses for courses, yeah” (Tourism). Participant researchers in History and Music Education felt similarly; the latter commenting how “I do feel it’s about using the right tools which is why I wouldn’t want to… enter into this kind of vitriolic negative mud-slinging thing that does happen within the fields because I think people… get too entrenched in one or the other and forget about the fact that these are just various ways to approach inquiry.” Similarly, one Psychologist observed, “I’m always slightly irritated [laughs] when I hear people you know say ‘Oh I’m only doing… qualitative research’ or ‘I’m only doing quantitative research’… I think it’s the research question that should drive the methodological choices.” This participant researcher had “seen good quality in both quantitative and qualitative research.”

Five participant researchers considered quantitative approaches to be of little value if they were applied inappropriately. For example, a Translation and Interpreting participant researcher felt quantitative data-generating eye tracking technology was useful “for marketing,… product placement,… [or] surgeons.” However, for Translation and Interpreting, “I don’t think… it is a method that would yield results… you could find better in a more nuanced manner through other methods, interviews or focus groups, or even ethnographic observation.” One Chemist questioned the value of quantitative methods when the sample was too small. For example, when students were asked about their feedback on classes, and one student in 16 evaluated the classes badly, “4% it was one person [laughs] in 16, one person, but I received that evaluation and I think this is not correct… because sometimes…. I think that one person probably he or she didn’t like me… well, it’s life, so I think these aspects… may happen also but it’s with the precision of the system… the capacity of the system to detect and to measure.” Meaningfulness was held to be key: “When we do the analysis the sample has meaning” . Similarly, a Theoretical Physicist felt quantitative approaches unsuited to education: “in the context of education… we all produce data all the time… we grade students… we assess creativity… people will say… ‘you measure somebody's IQ using this made-up test and you get this kind of statis[tic]..’ and then you realize that all of those things are just bogus… or at least… doesn't measure anything of any real serious significance.” Comparatively, one participant researcher in Design felt ‘quantitative’ had a danger to “lead to stereotypes”; for example, when modern search engines use quantitative data to direct people to particular choices, “There’s potential there to constrain kind of broader behaviours and thinking… and therefore it can become a programmer in its own right.” One Mathematical Biologist commented how statistics can be misused, and how a popular Maths book related “How statistics are a light shone on a particular story from a particular angle to paint a picture that people want you to see but… it’s almost never the whole picture, it’s a half-truth, if you like, at best.”

Seven participant researchers considered that their disciplines valued quantitative over qualitative. This could be non-judgmental, and perhaps inherent in major areas of a discipline, as in Theoretical Physics, where precision is crucial, although this was said not to be ‘disparaging’: “theoretical physics… or physics in general… we… tend to think of ourselves as being very, very quantitative and very precise, and we think of qualitative, I guess… as being a bit vague, right?… which is not disparaging, because sometimes… we have to be a bit vague… and we're working things out.” In Psychology, however, despite “a call to advocate for more qualitative methods”, there, “definitely… is a bias toward quantitative… measures in psychology; all the high impact factor journals advocate for quantitative measures.” In Nursing, quantitative was also deemed paramount, with “the randomized control trial seen as being… you know the apex and… some researchers in our school would absolutely say it’s the only reliable thing… would be very anti-qualitative.”

Yet, four participant researchers were positively oriented towards anything qualitative. For example, one Tourism researcher felt that, “in an uncertain world, such as the one we’re living in today, qualitative research is the way forward.” Also, an Architect highlighted that in one of their studies, “I think the most important finding of my questionnaires was in the subjective comments.” One Music education participant researcher personally favoured qualitative approaches but regretted how their field was biased toward quantitative data, saying they had been informed: “ ‘what journals really care about is that p-value…’ and I remember… thinking… that’s a whole area of humanity… you’re failing to acknowledge.”

Nevertheless, side-stepping this debate, nine researchers considered the terms of little value, and simply irrelevant for their own research. One Film and Media—English participant researcher commented: “I have to say… these are terms I’m obviously familiar with, but… not terms… I… tend to really use in my own research… to describe what I do … mainly because everything that I do is qualitative.” As an English Literature participant researcher noted in email correspondence: “they are not terms we use in literary research, probably because most of what we do is interpretation of texts and substantiating arguments through examples. I have really only encountered these terms in the context of teaching and have never used them myself.” In the interview, this participant researcher commented that “I can imagine… they would be terms… quite common in the sciences and mathematics, but not Social Sciences and Arts.” A German Literature participant researcher felt similarly, commenting that in “German Literature… the term quantitative hadn’t even entered my vocabulary all the way through the PhD [laughs] … because… you could argue the methods in literary research are always qualitative.”

Complementing such perspectives, in Theoretical Physics ‘qualitative’ and ‘quantitative’ was: “not something that ever comes up… I don’t think I read a paper ever that will say we do qualitative research in any way, but I never… or hardly ever handle any data… I just have a bunch of principles that are sort of either taken to be true or are… a model… we’re exploring.” In Mathematics, ‘quantitative’ was simply never used as all mathematics research was quantitative: “I never use the word in the company of my colleagues, never, it’s a non-vocabulary word, for the simple reason that when everything is so well defined why do you need a generic term when you’ve got very specific reference points in the language that you’re using?”.

One Philosopher felt the terms did not fit conceptual analysis in philosophy, given that the object of consideration was uncertain: “I guess… I thought it didn’t fit conceptual analysis… you need to know what you’re dealing with in order to then do the quantitative or qualitative whereas in philosophy it feels like… you don’t quite know what you’re dealing with you’re trying to work out… what are rights?… What is knowledge? What is love?… and then look at its qualities.” For this researcher, Philosophy was tentatively pre-quantitative or pre-qualitative, because philosophy “feels like it’s before then.” The terms were not considered valuable for Philosophy or for the humanities generally: “in philosophy we wouldn’t use the term qualitative or quantitative research… you just use the tools… you need… to develop your argument and so you don’t see the distinction… I would say in the humanities that’s relatively similar.” Further, a Fine Art—Sculpture participant researcher said: “they’re not words I would use… partly because… I’m engaged with… through… research and… teaching… what I’d call practice research… and… my background’s in fine art, predominantly in making sculpture and that doesn’t contain knowledge.” Here, the participant researcher related how they may consider a student’s work hideous but if the student had learned a lot through creating the work, they should be rewarded. This participant researcher spoke of a famous sound artist, concluding, “if you asked him about qualitative and quantitative… it just wouldn’t come into his thing at all…. He doesn’t need to say well there were a thousand visitors plus you know it’s just ‘bang’… he wouldn’t think about those things… not as an artist.”

Six participant researchers said they only ever used the terms for particular audiences. For example, for ‘quantitative’ in Film and Media: “the only time is when it’s been related to public engagement that we’ve ever sort of produced anything that is more along quantitative lines,” and that “it was not complex data we were giving them.” In Fine-Art Sculpture, too, the terms were solely used with a funder, for example, to measure attendance at an exhibition for impact, but “that’s not the type of research that I’m involved with necessarily.” One Logistics participant researcher commented that “it really depends on the audience how you define qualitative or quantitative.” For this researcher, if communicating with “statisticians econometricians or a bunch of people who are number crunchers” then “they will be very precise on what quantitative is and what qualitative is” and would only recognise mathematical techniques as quantitative. Indeed, “they wouldn’t even recognize Excel as quantitative because it’s not that hard.” In contrast, for social scientists, Excel would be quantitative, as would “anything to do with numbers… I suppose you know a questionnaire where you have to analyse responses would be probably classed as quantitative.”

Conversely, a Mathematical Biology participant researcher commented they had been doing far more public outreach work, “using quantitative data so numbers… even with things that might often be treated in a qualitative way… so stuff which… is often treated I think qualitatively we try to quantify… I think partly because it’s easier to make those comparisons when you quantify something.” One researcher in Communication Studies said they advised a student that “it depends on your research objectives; if you are focusing on individual experiences… I think naturally that’s going towards qualitative, but if you’re … doing this research oriented to a leader of … [a] big number of people… for informing policy… then you need some sort of insights that can be standardized… so it’s a choice.”

Another Communication participant researcher felt political shifts in the 1990s and 2000s meant that a ‘third way’ now dominated with a move towards hybridity and a breakdown in ‘qualitative’ and ‘quantitative’ with everything now tied to neoliberalism. Therefore, since “the late 90s and early noughties I’ve seen this kind of hybridity in research methods almost as being in parallel with the third way there seems to be… no longer opposition between left and right everything… just happens to buy into neoliberalism so likewise… with research methods… there’s a breakdown of qual and quant.” Comparatively, a Historian felt underpinning power structures informed approaches, commenting that “the problem is not the terminology it’s the way in which power is working in the society in which we live in that’s the root problem it seems to me and what’s valued and what’s not.” A Philosopher felt numbers appealed to management even when qualitative data were more suitable: “I think management partly… are always more willing to listen to numbers… finding the right number can persuade people of things that actually… you think really a better persuasion would do something more qualitative and in context.” One Fine Art participant researcher felt ‘quantitative’ and ‘qualitative’ only became important when they focused on processes related to the Research Excellence Framework but not for their research as such: “I guess we are using qualitative and quantitative things in the sense of moving ourselves through the process as academics but that’s not what I’d call research.”

6 Discussion: implications for teaching research methods

Research Methods teaching for undergraduate, postgraduate and newer researchers is commonly guided by textbook and seminal text understandings of what constitutes ‘qualitative’ and ‘quantitative’. Often, the two are treated in parallel, or interlinked, and used in combination or sequentially in research. But the relations between these are complex. The above analysis of the interview study with established participant researchers underlines and often extends this complexity, with implications for how such methodologies are approached and taught. Many of these participant researchers in disciplines commonly located within an ostensibly ‘positivist’ scientific tradition are, in fact, using qualitative methods as scientific procedures. They do so to provide initial measurements of phenomena before later using quantitative procedures to measure the quantity of a quality. They also use quantitative procedures to reveal data for which they subsequently use qualitative approaches to interpret and understand through their creative imaginations or experience. Participant researchers in ostensibly positivist disciplines describe themselves as doubting ‘facts’ measured by machines programmed by humans or doubting the certainty of quantitative data over time. Critically, these participant researchers engage in debate over what a ‘number’ is and the extent to which ‘numbers’ can be considered ‘quantitative’. One mathematician spoke of how many individuals do not know what they mean by the word ‘quantitative’, and an engineer interpreted any numbers involving human judgements as ‘qualitative’. Both a chemist and a geotechnician routinely defined and use ‘qualitative’ methods and analysis to arrive at numerical values.

Although this analysis of participant researchers’ reported practices refutes many textbook and key research methods source representations of quantitative and qualitative as being binary and separately ringfenced entities (contra e.g. Punch 2005 ; Goertz and Mahoney 2012 ), they resonate with much recent and current literature in the field (e.g. Uher 2022 ; De Gregorio 2014 ). In some disciplines, participant researchers only do a particular type of research and never need anything other than clear ‘quantitative’ definitions (Mathematics); others only ever conduct research involving text and never numbers (Literature). Further, other participant researchers considered how certain aspects lie outside the ‘qualitative’ or ‘quantitative’ (the ‘theoretical’ in German Literature), or they did research which they maintain does not contain ‘knowledge’ (Fine-Art Sculpture), while others do foundational ‘conceptual’ research which they claim comes at a stage before any quantity or quality can be assessed (Philosophy). Nine researchers considered the terms of little relevance at all to their subject areas.

This leads to subsequent questions. Firstly, do the apparently emerging tensions and contradictions between commonplace textbook and key source presentations and on-the-ground participant researcher practices matter? Secondly, what kind of discourse might reframe the more conventional one?

Regarding whether tensions and contradictions matter: in one practical way, perhaps not, since participant researchers in all these areas continue to be productive in their current research practices. Nevertheless, the foundations of the binary quantitative and qualitative divide are discourse expressions common to research methods courses. These expressions frame how the two terms are understood as the guide for novices to do research. This guiding discourse is evident in specifically designated chapters in research handbooks, in session titles in university research methods modules, and in entries for explanations of research terms within glossaries. The literature review study detailed above illustrates this. ‘Quantitative’ means numbers, ‘qualitative’ means words. ‘Quantitative’ connotes positivist, objective, scientific; ‘qualitative’ implies constructivist, subjective, non-science-based. Arguably, any acceptance of the commonplace research method understanding gives an apparent solidity which can sometimes be a false basis that masks the complexities or inadequacies involved. Such masking can, in turn, allow certain agencies or individuals to claim their policies and practices are based on ‘objective’ numerical data when they are merely framing something as ‘quantitative’ when, as a cited Mathematician participant researcher observed above, it is simply something with a number in it somewhere. Conventionally, limitations are mentioned in research studies, but often they seem ritualized remarks which refer to insufficient numbers, or restricted types of participants, or a constrained focus on a particular area. Rarely do research studies (let alone handbooks and guides for postgraduates) question a taken-for-granted understanding, such as whether the very idea of using numbers with human participants may mean the number is not objective. Ironically, it is the field of Qualitative Inquiry itself in which occasionally some of these issues are mentioned. Concurrently, while the quantitative is promoted as ‘scientific’ and ‘objective evidence’, we find some scientists researching in sciences often question the terms, or consciously set them aside in their practices.

Concerning what could replace the commonplace terms and reframe the research discourse environment: arguably, any discussion of ‘quantitative’/‘qualitative’ should be preceded by key questions of how they are understood by researchers. Hammersley ( 2013 ) has suggested the value of a more nuanced approach. As the Communication Studies participant researcher here commented, the two terms seem to be breaking down somewhat. Nevertheless, alongside the data and arguments here, we see some value in considering things as being ‘quantitative’ or ‘qualitative’, and other value in viewing them as separate. The terms can still be simply outlined, not just as methodological listings of characteristics, but as a critical point, Outlines of methods should include insider practitioner views—illustrations of how they are used and understood by practising researchers in different disciplines (as in Table 2 above). This simple suggestion has benefits. When outlining approaches as qualitative or quantitative, we suggest space is devoted to how this is understood in disciplines, together with the opportunity to question the issues raised by these understandings. This would help to position the understandings of qualitative and quantitative within specific disciplinary contexts, especially in inter-disciplinary fields and, implicitly, it encourages reflection on the objectivity and subjectivity evoked by the terms. Such discussion can be included in research methods texts and in research methods courses, dissertations and frameworks for viva examinations (Cortazzi and Jin 2021 ). Here, rather than start with outlining what the terms mean by using concrete definitions such as ‘Quantitative means X’ the terms should be outlined using subject contextualised phrases such as ‘In the field of X quantitative is understood to mean Y’. In this way, quantitative and qualitative methods and approaches can be seen, understood and contextualised within their subject areas, rather than prescriptively outlined in a generic or common form. Furthermore, if the field is one that has no use for such terms, this can also be stated, to prevent any unnecessary need for their use. Discourse around the terms can be extended if they are seen in line with much current literature and the data above that shows their complexities and overlaps, and goes beyond the binary choices and representations of many textbooks.

7 Conclusion

This paper has presented and discussed data from an interview study with experienced participant researchers (n = 31) regarding their perceptions of ‘qualitative’ and ‘quantitative’ in their research areas. This interview study data was compared with findings from a literature review study of common textbooks and research methods publications (n = 25) that showed often binary and reified representations of the terms and related concepts. The interview study data show many participant researcher understandings do in some ways align with the binary and commonplace representations of ‘qualitative ‘and ‘quantitative’ as shown to be presented in many research methods textbooks and sources from the literature review study. However, the interview study data more often illustrate how such representations are somewhat inaccurate regarding how research is undertaken in the different areas researched by the participant researchers. Rather, they corroborate much of the current literature that shows the blurring and complexity of the terms. Often, they extend this complexity. Sometimes they bypass complexity when these terms are considered irrelevant to their research fields by many researcher participants. For some researchers, the terms are simply valueless. We propose that future research methods courses could present and discuss the data above, perhaps using something akin to Table 2 as a starting point, so that students and novice researchers are able to loosen or break free of the chains of any stereotypical representations of such terms or use them reflectively with awareness of disciplinary specific usage. This could help them to advance their research, recognizing complex caveats related to the boundaries of what they do, what methods they use, and how to conduct research using both quantitative and qualitative approaches, as interpreted and used in their own fields. In multi- or inter-disciplinary research, such reflective awareness seems essential. Future research could also study the impact of the use of the data here in research methods courses so that such courses encompass both qualitative and quantitative methods (cf. Onwuegbuzie and Leech 2005 ) yet also question and contextualise such terms in specific subject areas order to free research from any constraints created by binary representations of the terms.

Whilst we interviewed 31 participant researchers to approach what seems a reasonable level of saturation, clearly future research could add to what we have found here by speaking to a wider range and larger number of researchers. The 25 research methods sources in English (supplemented by 23 sources in German, Spanish and French) examined here can clearly be expanded for a wider analysis of ‘quantitative’ and ‘qualitative’ in other languages for a more comprehensive European perspective. This strategy might ascertain likely asymmetries between the numerous English language texts (and their translations) and relatively smaller numbers of texts written by national or local experts in other languages. As a world-wide consideration, given the relative paucity of published research guidance in many languages, this point is especially significant related to fitting research methods to local contexts and cultures without imposition. Translating and discussing the terms ‘qualitative’ and ‘quantitative’, in and beyond European languages, will need care to avoid binary stereotyped or formulaic expression and to maintain some of the insight, resonances and complexities shown here.

Aspers, P., Corte, U.: What is qualitative in qualitative research. Qual. Sociol. 42 (2), 139–160 (2019)

Article   Google Scholar  

Atkinson, K.M., Koenka, A.C., Sanchez, C.E., Moshontz, H., Cooper, H.: Reporting standards for literature searches and report inclusion criteria: making research syntheses more transparent and easy to replicate. Res. Synth. Methods 6 (1), 87–95 (2015)

Autran, D., Bassel, G.W., Chae, E., Ezer, D., Ferjani, A., Fleck, C., Wolf, S.: What is quantitative plant biology? Quant. Plant Biol. (2021). https://doi.org/10.1017/qpb.2021.8

Bakhtin, M.: The dialogic imagination. University of Texas Press, Austin (1981)

Google Scholar  

Bakhtin, M. M. Speech genres and other late essays. In: Trans. Vern W. McGee; Ed. Caryl Emerson and Michael Holquist. University of Texas Press, Austin, (1986)

Barone, T.: A return to the gold standard? Questioning the future of narrative construction as educational research. Qual. Inq. 13 (4), 454–470 (2007)

Bell, J., Waters, S.: Doing your research project: a guide for first-time researchers (6 th edit.). McGraw-Hill Education, London, (2014)

Bird, C.M.: How I stopped dreading and learned to love transcription. Qual. Inq. 11 (2), 226–248 (2005)

Bloch, M.: A discourse that disciplines, governs, and regulates: The national research c report on scientific research in education. Qual. Inq. 10 (1), 96–110 (2004)

Bloor, M., Wood, F.: Keywords in qualitative methods: A vocabulary of research concepts. Sage, London (2006)

Book   Google Scholar  

Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

Braun, V., Clarke, V.: To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales. Qualit. Res. Sport Exer. Health 13 (2), 201–216 (2021)

Cambridge: Cambridge Dictionary. English Dictionary. Available at: https://dictionary.cambridge.org/dictionary/english/ Last Accessed January 2023. (2022)

Chan, E.S., Okumus, F., Chan, W.: What hinders hotels’ adoption of environmental technologies: a quantitative study. Int. J. Hosp. Manag. 84 , 102324 (2020)

Charmaz, K.: Grounded theory. Objectivist and constructivist methods. In W. Luttrell (Ed.), Qualitative educational research: Readings in reflexive methodology and transformative practice (pp. 183–207). Routledge, New York. (2010)

Christians, C.G.: Ethics and politics in qualitative research. In: Denzin, N.K., Lincoln, Y.S. (eds.) The Sage handbook of qualitative research, pp. 61–80. Sage, Thousand Oaks, CA (2011)

Cortazzi, M., Pilcher, N., Jin, L.: Language choices and ‘blind shadows’: investigating interviews with Chinese participants. Qual. Res. 11 (5), 505–535 (2011)

Cortazzi, M., Jin, L.: The doctoral viva: questions for, with and to candidates (or supervisors). Int. J. Educat. Lit. Stud. 9 (4), 2–15 (2021)

Creswell, J.W.: Research design: Qualitative & quantitative approaches. Sage, Thousand Oaks, CA (1995)

Davies, M.B., Hughes, N.: Doing a successful research project: Using qualitative or quantitative methods. Macmillan International Higher Education, London (2014)

Dawson, C.: Introduction to Research Methods: A Practical Guide for Anyone Undertaking a Research Project, 5th edn. Robinson, London (2019)

De Gregorio, E.: Bridging “quality” and “quantity” in the study of criminal action. Qual. Quant. 48 (1), 197–215 (2014)

Denzin, N.K., Lincoln, Y.S. (eds.): The landscape of qualitative research: Theories and issues. Sage, Thousand Oaks (1998)

Denzin, N.K., Lincoln, Y.S. (eds.): The SAGE handbook of qualitative research (4th edit). Sage, Thousand Oaks (2011)

Flyvbjerg, B.: Five misunderstandings about case-study research. Qual. Inq. 12 (2), 219–245 (2006)

Francis, J.J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V., Eccles, M.P., Grimshaw, J.M.: What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol. Health 25 (10), 1229–1245 (2010)

Goertz, G., Mahoney, J.: A tale of two cultures. Princeton University Press, New Jersey (2012)

Golafshani, N.: Understanding reliability and validity in qualitative research the qualitative report, vol. 8 no. 4 597–607. (2003). http://www.nova.edu/ssss/QR/QR8-4/golafshani.pd

Grix, J.: The undations of research. Palgrave Macmillan, New York (2004)

Guba, E.G, Lincoln, Y.S: Competing paradigms in qualitative research. In: Denzin, N.K. Lincoln, Y.S. (eds.) Handbook of qualitative research, pp. 105–117. Sage, Thousand Oaks, 1994

Guest, G., Bunce, A., Johnson, L.: How many interviews are enough? An experiment with data saturation and variability. Field Methods 18 (1), 59–82 (2006)

Hammersley, M.: What is qualitative research? Bloomsbury Academic, London (2013)

Hanson, B.: Wither qualitative/quantitative? Grounds for methodological convergence. Qual. Quant. 42 , 97–111 (2008)

Heyvaert, M., Maes, B., Onghena, P.: Mixed methods research synthesis: definition, framework, and potential. Qual. Quant. 47 (2), 659–676 (2013)

Howe, K.R.: Against the quantitative-qualitative incompatibility thesis or dogmas die hard. Educ. Res. 17 (8), 10–16 (1988)

Johnson, R.B., Onwuegbuzie, A.J., Turner, L.A.: Toward a definition of mixed methods research. J. Mixed Methods Res. 1 (2), 112–133 (2007)

Kumar, R.: Research methodologies: a step-by-step guide for beginners. Sage, London (1996)

Levitt, H.M., Bamberg, M., Creswell, J.W., Frost, D.M., Josselson, R., Suarez-Orozco, C.: Journal article reporting standards for qualitative research in psychology: The APA publications and communications board task force report. Am. Psychol. 73 (1), 26–46 (2018)

Lincoln, Y.S., Guba, E.G.: Naturalistic inquiry. Sage, Thousand Oaks (1985)

Marsh, C.: Problems with surveys: method or epistemology? Sociology 13 (2), 293–305 (1979)

Marsh, D., Furlong, P.: A skin, not a sweater: ontology and epistemology in political science. Theory Methods Polit. Sci. 2 (1), 17–41 (2002)

Mason, J.: Mixing methods in a qualitatively driven way. Qual. Res. 6 (1), 9–25 (2006)

Miles, M. B., Huberman, A. M., Saldaña, J.: Qualitative data analysis: A methods sourcebook (4th edit.). Sage, Los Angeles, (2018)

Miles, M.B., Huberman, A.M.: Qualitative data analysis: An expanded sourcebook. Sage, Thousand Oaks (1994)

Moore, N.: How to do research: a practical guide to designing and managing research projects, 3rd edn. Facet, London (2006)

Morse, J.M.: Approaches to qualitative-quantitative methodological triangulation. Nurs. Res. 40 (2), 120–123 (1991)

Olssen, M.: Radical constructivism and its failings: anti-realism and individualism. Br. J. Educ. Stud. 44 (3), 275–295 (1996)

Onwuegbuzie, A.J., Leech, N.L.: Taking the “Q” out of research: teaching research methodology courses without the divide between quantitative and qualitative paradigms. Qual. Quant. 39 (3), 267–295 (2005)

Pilcher, N., Cortazzi, M.: Dialogues: QUANT researchers on QUAL methods. Qual. Report 21 (3), 450–473 (2016)

Punch, K.: Introduction to social research quantitative and qualitative approaches. Sage, Thousand Oaks (2005)

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Jinks, C.: Saturation in qualitative research: exploring its conceptualization and operationalization. Qualit. Quant. 52 (4), 1893–1907 (2018)

Seale, C.: Quality in qualitative research. Qual. Inq. 5 , 465–478 (1999a)

Seale, C.: The Quality of Qualitative Research. Sage, London (1999b)

Sharma, G.: Pros and cons of different sampling techniques. Int. J. Appl. Res. 3 (7), 749–752 (2017)

St Pierre, E.A.: Refusing alternatives: a science of contestation. Qual. Inq. 10 (1), 130–139 (2004)

Tashakkori, A., Teddlie, C., Teddlie, C.B.: Mixed methodology: Combining qualitative and quantitative approaches. Sage, Thousand Oaks (1998)

Teddlie, C., Tashakkori, A. Mixed methods research. Contemporary Issues in an emerging Field. In Denzin, N.K., Lincoln, Y.S. (eds.) The Sage handbook of qualitative research (4 th edit.), pp. 285–300. Sage, Thousand Oaks, (2011)

Trafford, V., Leshem, S.: Starting at the end to undertake doctoral research: predictable questions as stepping stones. High. Educ. Rev. 35 (1), 31–49 (2002)

Uher, J.: Functions of units, scales and quantitative data: fundamental differences in numerical traceability between sciences. Qual. Quant. 56 (4), 2519–2548 (2022)

Merriam Webster: Definition of ‘semi’. (2022). Available at https://www.merriam-webster.com/dictionary/semi

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Appendix 1: Literature review study

The table below contains details of the binary representations and possibilities in the two columns on the left and in the right it contains the numbers of the key sources that conveyed or adhered to these binary representations. The details of these sources and their respective numbers are listed below.

Table: Textbook and key source binary representations

Bell, J., & Waters, S. (2014). Doing your research Project: A Guide for first-time researchers. McGraw-Hill Education (UK). 6 th edn

Bloor, M., & Wood, F. (2006). Keywords in qualitative methods: A vocabulary of research concepts. London, UK: Sage Publications.

Bryman, A. (2008). Social research methods. Oxford, UK: Oxford University Press. [with caveats for many but still using the divide as ‘useful’]

Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 and 16: A guide for social scientists. London, UK: Routledge.

Ceglowski, D., Bacigalupa, C., & Peck, E. (2011). Aced out: Censorship of qualitative research in the age of "scientifically based research." Qualitative Inquiry, 17(8), 679–686.

Daly, K. J. (2007). Qualitative Methods for Family Studies and Human Development. London, UK: Sage.

Davies, M. B., & Hughes, N. (2014).  Doing a successful research project: Using qualitative or quantitative methods . Bloomsbury Publishing.

Dawson, C. (2019).  Introduction to Research Methods 5th Edition: A Practical Guide for Anyone Undertaking a Research Project . Robinson.

Denzin, N. K., & Lincoln, Y. S. (Eds.). (1998). The landscape of qualitative research: Theories and issues. Thousand Oaks, CA: Sage Publications. [with caveat that original qual was positivist in root but not now]

Denzin and Lincoln (2011) Introduction: The Discipline and Practice of Qualitative Research. In Denzin, N. K., & Lincoln, Y. S. (2011). The Sage handbook of qualitative research . Thousand Oaks, Calif: Sage. Pp1-20

Goertz, G., & Mahoney, J. (2012).  A tale of two cultures . Princeton University Press.

Grix, J. (2004). The foundations of research. New York, NY: Palgrave Macmillan.

Hammersley, M. (2007). The issue of quality in qualitative research. International Journal of Research & Method in Education, 30(3), 287–305.

Hammersley, M. (2013). What is qualitative research? London, UK: Bloomsbury Academic. [caveat that some qual do use causal analysis – and if you mix you abandon key assumptions associated with qualitative work]

Harman, W. W. (1996). The shortcomings of western science. Qualitative Inquiry, 2(1), 30–38.

Howe, K. R. (2011). Mixed methods, mixed causes? Qualitative Inquiry, 17(2), 166–171.

Mason, J. (2006). Mixing methods in a qualitatively driven way. Qualitative Research, 6(1), 9–25.

Miles, M. B., Huberman, A. M., & Saldaña, J. (2018).  Qualitative data analysis: A methods sourcebook . Sage publications.

Punch, K. (2005). Introduction to Social Research Quantitative and Qualitative Approaches. Sage.

Sandelowski, M. (1997). "To be of use": Enhancing the utility of qualitative research. Nursing Outlook, 45(3), 125–132 [caveat – does rebut many of the ideas but nevertheless outlines them as how the two are seen – e.g. of generalizability]

Seale, C. (1999). Quality in qualitative research. Qualitative Inquiry, 5, 465–478.

Silverman, D. (2016). Introducing qualitative research.  Qualitative research ,  3 (3), 14–25.

Tashakkori, A., Teddlie, C., & Teddlie, C. B. (1998).  Mixed methodology: Combining qualitative and quantitative approaches  (Vol. 46). sage. [with the caveat that they talk about the differences as existing even though say they are not that wide]

Teddlie, C., & Tashakkori, A. (2011). Mixed methods research. Contemporary Issues in an emerging Field. in The Sage handbook of qualitative research ,  4 , 285–300.

Torrance, H. (2008). Building confidence in qualitative research: Engaging the demands of policy. Qualitative Inquiry, 14(4), 507–527.

1.1 Sources in languages other than English, and brief notes regarding their focus and content

Whilst not part of the literature review study, we also consulted the outline details, abstracts and contents lists of a number of sources in languages other than English. We put brief notes about after each source. Each source, unless specifically noted, adhered to similar binary treatment of quantitative and qualitative methods and approaches as the English language sources outlined above.

1.1.1 German

Blandz, M. (2021) Forschungsmethoden und Statistik für die Soziale Arbeit : Grundlage und Anwendingen. 2 nd . edit. Stuttgart: Kohlhammer Verlag. – this is a multidisciplinary source that focuses mostly on quantitative and mixed methods. It follows the suggestion that a qualitative study can be a preliminary study for the main quantitative study.

Caspari, D; Klippel, F; Legutke, M. & Schram, K. (2022) Forschungsmethoden: in der Fremdsprachendidaktik; Ein Handbuch. Tübingen: Narr Franke Altempo Verlag. [Focused on foreign language teaching, details quantitative, then qualitative and then mixed; all separately]

Dōring, N. (2023) Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. 6. th edit. Berlin: Springer. [Focused on the Social Sciences and humanities; as with the previous source it has separate chapters on quantitative and qualitative and a section on mixed, and contains some critical commentary]

Frankenberger, N. (Ed.) (2022) Grundlagen der Politikwissenschaft : Forschungsmethoden und Forschendes Lernen. Stuttgart: Kohlhammer Verlag. [Political science focused and based around distinctions between quantitative and qualitative approaches, each of which is elaborated with different methods; there is no obvious section on mixed methods]

Hussy, W; Schiener, M; Echterhoff, G. (2013) Forschungsmethoden in Psychologie und Sozialwissenschaften für Bachelor. Berlin: Springer. [This book is focused on psychology and social sciences for undergraduates. It has separate parts to focus on quantitative and on qualitative and then a chapter on mixed, identifying mixed methods as an emerging trend]

Niederberger, M. & Finne, E. (Eds.) (2021) Forschungsmethoden in der Gesundsheitsfōrderung und Prävention. Berlin: Springer. [Focused on Health and wellbeing; develops the roles of quantitative, qualitative and mixed (in combinations) in multidisciplinary, interdisciplinary and transdisciplinary research. Notes much research is exclusively quantitative and that social sciences are more qualitative or mixed. Makes the argument that the quantitative versus qualitative divide was surpassed by ‘post-positivist’ versus ‘combined’ thinking and that integrated approaches are now widely accepted]

1.1.2 Spanish

Campos-Arenas, A. (2014) Métodos mixtos de investigación. Bogota: Magisterio Editorial. [Social science focused; devoted to mixed or combined approaches in Latin American contexts]

Hernandez-Sampieri, R. & Mendoza Torres, C. P. (2018) Metodología de investigación: Las rutas cuantitativa , cualitativa y mixta. Mexico: McGrw-Hill. [Social science focused with an introduction and conclusion focused on ‘three routes to research’ that are exceptionally and thoroughly elaborated; quantitative given 8 chapters; qualitative 3 and mixed just one]

Léon-García, O. G. & Carda-Celay, I. M. (2020) Méthodos de investigación en psicología y educación: Las tradiciones cuantitativas y qualitativas. 5. th edit. Barcelona : McGraw-Hill, España. [Psychology and education focused; based on relatively clearly cut distinctinos between ‘the two traditions’ of quantitative and qualitative]

Molina Marin, G. (Ed.) (2020) Integración de métodos de investigación : Estrategias metodológicas u experiencias en salud pública. Bogotá: Universidad de Antioquia. [Public health focused; gives most attention to multi-method combinations and asks questions about the epistemological integrity of integrating different approaches]

Ortega-Sanchez, D, (Ed.) (2023) ¿Como investigar en didáctica de las ciencias sociales? Fundamentos metodológicos , técnicas e instrumentos de investigación. Barcelona: Octaedro. [Education, research, pedagogy of teaching social sciences; focused on quantitative, qualitative and mixed methods in Spanish contexts]

Páramo-Reales, D. (2020) Métodos de investigación caulitativa : Fundamentos y aplicaciones . Bogota: Editorial Unimagdalena. [Social sciences: basic applications of qualitative approaches in Latin America]

Ponce, O. A. (2014) Investigación de métodos mixtos en educación, 2. nd edit. San Jaun: Publicaciones Puertoriqueñas. [Education and Pedagogy; Puerto Rican context and entirely about mixed methods]

Vasilachis de Giradino, I. (Ed.) (2009) Estrategias de investigación cauitativa. Barcelona: Editorial Gedisa. [Social sciences; much detail on research design; focus exclusively on qualitative methods in Spanish contexts]

1.1.3 French

Bouchard, S. & Cyr, C. (Eds.) (2005) Reserche psycosocial pour harmoniser reserche st pratique. Quebec: Prese de la Université de Quebec. [Focused on psychology and sociology. Despite its title about ‘harmonizing’ research it is mainly focused on quantitative approaches, with a small section on qualitative and nothing on mixed approaches]

Corbière, M. & Lamviere, N. (2021) Méthodes quantitatives , qualitatives et mixtes , dans la reserche en sciences humaines et de la santé. 2. nd edit. Quebec : PU Quebec. [Focused on Humanities and health care; highlights the division between quantitative, qualitative and mixed methods]

Devin, G. (Ed.) (2016) Méthodes de recherche en relations internationals. Paris: Sciences Po. [Focused on politics and international relations; mostly wholly focused on quantitative; only a little on qualitative]

Gavard-Perret, M.L; Gotteland, D; Haon, C. & Jolibert, A. (2018) Methodologie de la recherche en sciences de gestion : Réussir son mémoire ou sa these. Paris: Pearson. [Business and management focused and geared towards thesis research; notes clear distinctions between quantitative and qualitative approaches with nothing on mixed]

Komu, S. C. S. (2020) Le receuil des méthodes en sciences sociales : Mèthodo;ogies en reserche. Manitoba: Sciences Script. [Social sciences focused; mostly quantitative methods with some attention to focus groups and participatory research]

Lepillier, O; Fournier, T; Bricas, N. & Figuié, M. (2011 ) Méthodes d’investigation de l’alimentation et des mangeurs. Versailles: Editions Quae. [Focused on nutrition, health studies and diet; details quantitative and qualitative methods and has very little on mixed]

Millette, M; Millerand, F; Myles, D. & Latako-Toth, T. (2021) Méthodes de reserches en contexte humanique , une orientation qualiificative. Montreal: PU Montreal. [Humanities focused; outlines quantitative and qualitative methods and, unusually, attends to ‘qualitative investigations in numerical contexts’ in Canada]

Moscarda, J. (2018) Faire parler les donées: Méthodologies quantitatives et qualitatives. Caen: Editions EMS. [Has a multidisciplinary focus on ‘let the data talk’; deals with quantitative methods and then qualitative methods and also mixed]

Vallerand, R. J. (2000) Méthodes de recherche en psychologie. Quebec: Gaetan Morin. [Focused on psychology; emphasis on quantitative research; brief section on qualitative; Canadian contexts]

Appendix 2: Interview study schedule

2.1 understandings of ‘qualitative’ and ‘quantitative’.

This research project is exploratory and intends to delve into understandings of the specific terms ‘quantitative’ and ‘qualitative’ as they are perceived, used, and interpreted by researchers in very different fields. Such research is intended to shed light on the fields of quantitative and qualitative research. The idea for the research arises from a previous project where the researcher interviewed quantitative focused researchers and saw the use of qualitative and quantitative being used and interpreted very differently to how the terms are presented and understood in the research methods literature. It is expected that exploring these understandings further will add to the field by shedding light on the subtleties of how they are used and also in turn help researchers make informed decisions about the optimum approaches and methods to use in their own research.

2.2 Interview questions

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Pilcher, N., Cortazzi, M. 'Qualitative' and 'quantitative' methods and approaches across subject fields: implications for research values, assumptions, and practices. Qual Quant 58 , 2357–2387 (2024). https://doi.org/10.1007/s11135-023-01734-4

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Introduction, compare and contrast: qualitative and quantitative research, works cited.

• Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
• Denzin, N. K., & Lincoln, Y. S. (Eds.). (2018). The SAGE handbook of qualitative research (5th ed.). Sage Publications.
• Neuman, W. L. (2014). Social research methods: Qualitative and quantitative approaches (7th ed.). Pearson.
• Palys, T., & Atchison, C. (2018). Qualitative research in the digital era: Obstacles and opportunities. International Journal of Qualitative Methods, 17(1), 1-11. https://doi.org/10.1177/1609406918813901
• Maxwell, J. A. (2013). Qualitative research design: An interactive approach (3rd ed.). Sage Publications.
• Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
• Onwuegbuzie, A. J., & Leech, N. L. (2007). A call for qualitative power analyses. Quality & Quantity, 41(1), 105-121. https://doi.org/10.1007/s11135-006-9018-6
• Babbie, E. (2016). The practice of social research (14th ed.). Cengage Learning.
• Tashakkori, A., & Teddlie, C. (Eds.). (2019). SAGE handbook of mixed methods in social & behavioral research (3rd ed.). Sage Publications.
• Morgan, D. L. (2013). Integrating qualitative and quantitative methods: A pragmatic approach. Sage Publications.

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Conducting and Writing Quantitative and Qualitative Research

Edward barroga.

1 Department of Medical Education, Showa University School of Medicine, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

Atsuko Furuta

Makiko arima, shizuma tsuchiya, chikako kawahara, yusuke takamiya.

Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Graphical Abstract

INTRODUCTION

Novel research studies are conceptualized by scientific researchers first by asking excellent research questions and developing hypotheses, then answering these questions by testing their hypotheses in ethical research. 1 , 2 , 3 Before they conduct novel research studies, scientific researchers must possess considerable knowledge of both quantitative and qualitative research. 2

In quantitative research, researchers describe existing theories, generate and test a hypothesis in novel research, and re-evaluate existing theories deductively based on their experimental results. 1 , 4 , 5 In qualitative research, scientific researchers raise and answer research questions by performing a novel study, then propose new theories by clarifying their results inductively. 1 , 6

RATIONALE OF THIS ARTICLE

When researchers have a limited knowledge of both research types and how to conduct them, this can result in substandard investigation. Researchers must be familiar with both types of research and skilled to conduct their investigations within the frames of their chosen type of research. Thus, meticulous care is needed when planning quantitative and qualitative research studies to avoid unethical research and poor outcomes.

Understanding the methodological and writing assumptions 7 , 8 underpinning quantitative and qualitative research, especially by non-Anglophone researchers, is essential for their successful conduct. Scientific researchers, especially in the academe, face pressure to publish in international journals 9 where English is the language of scientific communication. 10 , 11 In particular, non-Anglophone researchers face challenges related to linguistic, stylistic, and discourse differences. 11 , 12 Knowing the assumptions of the different types of research will help clarify research questions and methodologies, easing the challenge and help.

SEARCH FOR RELEVANT ARTICLES

To identify articles relevant to this topic, we adhered to the search strategy recommended by Gasparyan et al. 7 We searched through PubMed, Scopus, Directory of Open Access Journals, and Google Scholar databases using the following keywords: quantitative research, qualitative research, mixed-method research, deductive reasoning, inductive reasoning, study design, descriptive research, correlational research, experimental research, causal-comparative research, quasi-experimental research, historical research, ethnographic research, meta-analysis, narrative research, grounded theory, phenomenology, case study, and field research.

AIMS OF THIS ARTICLE

This article aims to provide a comparative appraisal of qualitative and quantitative research for scientific researchers. At present, there is still a need to define the scope of qualitative research, especially its essential elements. 13 Consensus on the critical appraisal tools to assess the methodological quality of qualitative research remains lacking. 14 Framing and testing research questions can be challenging in qualitative research. 2 In the healthcare system, it is essential that research questions address increasingly complex situations. Therefore, research has to be driven by the kinds of questions asked and the corresponding methodologies to answer these questions. 15 The mixed-method approach also needs to be clarified as this would appear to arise from different philosophical underpinnings. 16

This article also aims to discuss how particular types of research should be conducted and how they should be written in adherence to international standards. In the US, Europe, and other countries, responsible research and innovation was conceptualized and promoted with six key action points: engagement, gender equality, science education, open access, ethics and governance. 17 , 18 International ethics standards in research 19 as well as academic integrity during doctoral trainings are now integral to the research process. 20

POTENTIAL BENEFITS FROM THIS ARTICLE

This article would be beneficial for researchers in further enhancing their understanding of the theoretical, methodological, and writing aspects of qualitative and quantitative research, and their combination.

Moreover, this article reviews the basic features of both research types and overviews the rationale for their conduct. It imparts information on the most common forms of quantitative and qualitative research, and how they are carried out. These aspects would be helpful for selecting the optimal methodology to use for research based on the researcher’s objectives and topic.

This article also provides information on the strengths and weaknesses of quantitative and qualitative research. Such information would help researchers appreciate the roles and applications of both research types and how to gain from each or their combination. As different research questions require different types of research and analyses, this article is anticipated to assist researchers better recognize the questions answered by quantitative and qualitative research.

Finally, this article would help researchers to have a balanced perspective of qualitative and quantitative research without considering one as superior to the other.

TYPES OF RESEARCH

Research can be classified into two general types, quantitative and qualitative. 21 Both types of research entail writing a research question and developing a hypothesis. 22 Quantitative research involves a deductive approach to prove or disprove the hypothesis that was developed, whereas qualitative research involves an inductive approach to create a hypothesis. 23 , 24 , 25 , 26

In quantitative research, the hypothesis is stated before testing. In qualitative research, the hypothesis is developed through inductive reasoning based on the data collected. 27 , 28 For types of data and their analysis, qualitative research usually includes data in the form of words instead of numbers more commonly used in quantitative research. 29

Quantitative research usually includes descriptive, correlational, causal-comparative / quasi-experimental, and experimental research. 21 On the other hand, qualitative research usually encompasses historical, ethnographic, meta-analysis, narrative, grounded theory, phenomenology, case study, and field research. 23 , 25 , 28 , 30 A summary of the features, writing approach, and examples of published articles for each type of qualitative and quantitative research is shown in Table 1 . 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43

QUANTITATIVE RESEARCH

Deductive approach.

The deductive approach is used to prove or disprove the hypothesis in quantitative research. 21 , 25 Using this approach, researchers 1) make observations about an unclear or new phenomenon, 2) investigate the current theory surrounding the phenomenon, and 3) hypothesize an explanation for the observations. Afterwards, researchers will 4) predict outcomes based on the hypotheses, 5) formulate a plan to test the prediction, and 6) collect and process the data (or revise the hypothesis if the original hypothesis was false). Finally, researchers will then 7) verify the results, 8) make the final conclusions, and 9) present and disseminate their findings ( Fig. 1A ).

Types of quantitative research

The common types of quantitative research include (a) descriptive, (b) correlational, c) experimental research, and (d) causal-comparative/quasi-experimental. 21

Descriptive research is conducted and written by describing the status of an identified variable to provide systematic information about a phenomenon. A hypothesis is developed and tested after data collection, analysis, and synthesis. This type of research attempts to factually present comparisons and interpretations of findings based on analyses of the characteristics, progression, or relationships of a certain phenomenon by manipulating the employed variables or controlling the involved conditions. 44 Here, the researcher examines, observes, and describes a situation, sample, or variable as it occurs without investigator interference. 31 , 45 To be meaningful, the systematic collection of information requires careful selection of study units by precise measurement of individual variables 21 often expressed as ranges, means, frequencies, and/or percentages. 31 , 45 Descriptive statistical analysis using ANOVA, Student’s t -test, or the Pearson coefficient method has been used to analyze descriptive research data. 46

Correlational research is performed by determining and interpreting the extent of a relationship between two or more variables using statistical data. This involves recognizing data trends and patterns without necessarily proving their causes. The researcher studies only the data, relationships, and distributions of variables in a natural setting, but does not manipulate them. 21 , 45 Afterwards, the researcher establishes reliability and validity, provides converging evidence, describes relationship, and makes predictions. 47

Experimental research is usually referred to as true experimentation. The researcher establishes the cause-effect relationship among a group of variables making up a study using the scientific method or process. This type of research attempts to identify the causal relationships between variables through experiments by arbitrarily controlling the conditions or manipulating the variables used. 44 The scientific manuscript would include an explanation of how the independent variable was manipulated to determine its effects on the dependent variables. The write-up would also describe the random assignments of subjects to experimental treatments. 21

Causal-comparative/quasi-experimental research closely resembles true experimentation but is conducted by establishing the cause-effect relationships among variables. It may also be conducted to establish the cause or consequences of differences that already exist between, or among groups of individuals. 48 This type of research compares outcomes between the intervention groups in which participants are not randomized to their respective interventions because of ethics- or feasibility-related reasons. 49 As in true experiments, the researcher identifies and measures the effects of the independent variable on the dependent variable. However, unlike true experiments, the researchers do not manipulate the independent variable.

In quasi-experimental research, naturally formed or pre-existing groups that are not randomly assigned are used, particularly when an ethical, randomized controlled trial is not feasible or logical. 50 The researcher identifies control groups as those which have been exposed to the treatment variable, and then compares these with the unexposed groups. The causes are determined and described after data analysis, after which conclusions are made. The known and unknown variables that could still affect the outcome are also included. 7

QUALITATIVE RESEARCH

Inductive approach.

Qualitative research involves an inductive approach to develop a hypothesis. 21 , 25 Using this approach, researchers answer research questions and develop new theories, but they do not test hypotheses or previous theories. The researcher seldom examines the effectiveness of an intervention, but rather explores the perceptions, actions, and feelings of participants using interviews, content analysis, observations, or focus groups. 25 , 45 , 51

Distinctive features of qualitative research

Qualitative research seeks to elucidate about the lives of people, including their lived experiences, behaviors, attitudes, beliefs, personality characteristics, emotions, and feelings. 27 , 30 It also explores societal, organizational, and cultural issues. 30 This type of research provides a good story mimicking an adventure which results in a “thick” description that puts readers in the research setting. 52

The qualitative research questions are open-ended, evolving, and non-directional. 26 The research design is usually flexible and iterative, commonly employing purposive sampling. The sample size depends on theoretical saturation, and data is collected using in-depth interviews, focus groups, and observations. 27

In various instances, excellent qualitative research may offer insights that quantitative research cannot. Moreover, qualitative research approaches can describe the ‘lived experience’ perspectives of patients, practitioners, and the public. 53 Interestingly, recent developments have looked into the use of technology in shaping qualitative research protocol development, data collection, and analysis phases. 54

Qualitative research employs various techniques, including conversational and discourse analysis, biographies, interviews, case-studies, oral history, surveys, documentary and archival research, audiovisual analysis, and participant observations. 26

Conducting qualitative research

To conduct qualitative research, investigators 1) identify a general research question, 2) choose the main methods, sites, and subjects, and 3) determine methods of data documentation access to subjects. Researchers also 4) decide on the various aspects for collecting data (e.g., questions, behaviors to observe, issues to look for in documents, how much (number of questions, interviews, or observations), 5) clarify researchers’ roles, and 6) evaluate the study’s ethical implications in terms of confidentiality and sensitivity. Afterwards, researchers 7) collect data until saturation, 8) interpret data by identifying concepts and theories, and 9) revise the research question if necessary and form hypotheses. In the final stages of the research, investigators 10) collect and verify data to address revisions, 11) complete the conceptual and theoretical framework to finalize their findings, and 12) present and disseminate findings ( Fig. 1B ).

Types of qualitative research

The different types of qualitative research include (a) historical research, (b) ethnographic research, (c) meta-analysis, (d) narrative research, (e) grounded theory, (f) phenomenology, (g) case study, and (h) field research. 23 , 25 , 28 , 30

Historical research is conducted by describing past events, problems, issues, and facts. The researcher gathers data from written or oral descriptions of past events and attempts to recreate the past without interpreting the events and their influence on the present. 6 Data is collected using documents, interviews, and surveys. 55 The researcher analyzes these data by describing the development of events and writes the research based on historical reports. 2

Ethnographic research is performed by observing everyday life details as they naturally unfold. 2 It can also be conducted by developing in-depth analytical descriptions of current systems, processes, and phenomena or by understanding the shared beliefs and practices of a particular group or culture. 21 The researcher collects extensive narrative non-numerical data based on many variables over an extended period, in a natural setting within a specific context. To do this, the researcher uses interviews, observations, and active participation. These data are analyzed by describing and interpreting them and developing themes. A detailed report of the interpreted data is then provided. 2 The researcher immerses himself/herself into the study population and describes the actions, behaviors, and events from the perspective of someone involved in the population. 23 As examples of its application, ethnographic research has helped to understand a cultural model of family and community nursing during the coronavirus disease 2019 outbreak. 56 It has also been used to observe the organization of people’s environment in relation to cardiovascular disease management in order to clarify people’s real expectations during follow-up consultations, possibly contributing to the development of innovative solutions in care practices. 57

Meta-analysis is carried out by accumulating experimental and correlational results across independent studies using a statistical method. 21 The report is written by specifying the topic and meta-analysis type. In the write-up, reporting guidelines are followed, which include description of inclusion criteria and key variables, explanation of the systematic search of databases, and details of data extraction. Meta-analysis offers in-depth data gathering and analysis to achieve deeper inner reflection and phenomenon examination. 58

Narrative research is performed by collecting stories for constructing a narrative about an individual’s experiences and the meanings attributed to them by the individual. 9 It aims to hear the voice of individuals through their account or experiences. 17 The researcher usually conducts interviews and analyzes data by storytelling, content review, and theme development. The report is written as an in-depth narration of events or situations focused on the participants. 2 , 59 Narrative research weaves together sequential events from one or two individuals to create a “thick” description of a cohesive story or narrative. 23 It facilitates understanding of individuals’ lives based on their own actions and interpretations. 60

Grounded theory is conducted by engaging in an inductive ground-up or bottom-up strategy of generating a theory from data. 24 The researcher incorporates deductive reasoning when using constant comparisons. Patterns are detected in observations and then a working hypothesis is created which directs the progression of inquiry. The researcher collects data using interviews and questionnaires. These data are analyzed by coding the data, categorizing themes, and describing implications. The research is written as a theory and theoretical models. 2 In the write-up, the researcher describes the data analysis procedure (i.e., theoretical coding used) for developing hypotheses based on what the participants say. 61 As an example, a qualitative approach has been used to understand the process of skill development of a nurse preceptor in clinical teaching. 62 A researcher can also develop a theory using the grounded theory approach to explain the phenomena of interest by observing a population. 23

Phenomenology is carried out by attempting to understand the subjects’ perspectives. This approach is pertinent in social work research where empathy and perspective are keys to success. 21 Phenomenology studies an individual’s lived experience in the world. 63 The researcher collects data by interviews, observations, and surveys. 16 These data are analyzed by describing experiences, examining meanings, and developing themes. The researcher writes the report by contextualizing and reporting the subjects’ experience. This research approach describes and explains an event or phenomenon from the perspective of those who have experienced it. 23 Phenomenology understands the participants’ experiences as conditioned by their worldviews. 52 It is suitable for a deeper understanding of non-measurable aspects related to the meanings and senses attributed by individuals’ lived experiences. 60

Case study is conducted by collecting data through interviews, observations, document content examination, and physical inspections. The researcher analyzes the data through a detailed identification of themes and the development of narratives. The report is written as an in-depth study of possible lessons learned from the case. 2

Field research is performed using a group of methodologies for undertaking qualitative inquiries. The researcher goes directly to the social phenomenon being studied and observes it extensively. In the write-up, the researcher describes the phenomenon under the natural environment over time with no implantation of controls or experimental conditions. 45

DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Scientific researchers must be aware of the differences between quantitative and qualitative research in terms of their working mechanisms to better understand their specific applications. This knowledge will be of significant benefit to researchers, especially during the planning process, to ensure that the appropriate type of research is undertaken to fulfill the research aims.

In terms of quantitative research data evaluation, four well-established criteria are used: internal validity, external validity, reliability, and objectivity. 23 The respective correlating concepts in qualitative research data evaluation are credibility, transferability, dependability, and confirmability. 30 Regarding write-up, quantitative research papers are usually shorter than their qualitative counterparts, which allows the latter to pursue a deeper understanding and thus producing the so-called “thick” description. 29

Interestingly, a major characteristic of qualitative research is that the research process is reversible and the research methods can be modified. This is in contrast to quantitative research in which hypothesis setting and testing take place unidirectionally. This means that in qualitative research, the research topic and question may change during literature analysis, and that the theoretical and analytical methods could be altered during data collection. 44

Quantitative research focuses on natural, quantitative, and objective phenomena, whereas qualitative research focuses on social, qualitative, and subjective phenomena. 26 Quantitative research answers the questions “what?” and “when?,” whereas qualitative research answers the questions “why?,” “how?,” and “how come?.” 64

Perhaps the most important distinction between quantitative and qualitative research lies in the nature of the data being investigated and analyzed. Quantitative research focuses on statistical, numerical, and quantitative aspects of phenomena, and employ the same data collection and analysis, whereas qualitative research focuses on the humanistic, descriptive, and qualitative aspects of phenomena. 26 , 28

Structured versus unstructured processes

The aims and types of inquiries determine the difference between quantitative and qualitative research. In quantitative research, statistical data and a structured process are usually employed by the researcher. Quantitative research usually suggests quantities (i.e., numbers). 65 On the other hand, researchers typically use opinions, reasons, verbal statements, and an unstructured process in qualitative research. 63 Qualitative research is more related to quality or kind. 65

In quantitative research, the researcher employs a structured process for collecting quantifiable data. Often, a close-ended questionnaire is used wherein the response categories for each question are designed in which values can be assigned and analyzed quantitatively using a common scale. 66 Quantitative research data is processed consecutively from data management, then data analysis, and finally to data interpretation. Data should be free from errors and missing values. In data management, variables are defined and coded. In data analysis, statistics (e.g., descriptive, inferential) as well as central tendency (i.e., mean, median, mode), spread (standard deviation), and parameter estimation (confidence intervals) measures are used. 67

In qualitative research, the researcher uses an unstructured process for collecting data. These non-statistical data may be in the form of statements, stories, or long explanations. Various responses according to respondents may not be easily quantified using a common scale. 66

Composing a qualitative research paper resembles writing a quantitative research paper. Both papers consist of a title, an abstract, an introduction, objectives, methods, findings, and discussion. However, a qualitative research paper is less regimented than a quantitative research paper. 27

Quantitative research as a deductive hypothesis-testing design

Quantitative research can be considered as a hypothesis-testing design as it involves quantification, statistics, and explanations. It flows from theory to data (i.e., deductive), focuses on objective data, and applies theories to address problems. 45 , 68 It collects numerical or statistical data; answers questions such as how many, how often, how much; uses questionnaires, structured interview schedules, or surveys 55 as data collection tools; analyzes quantitative data in terms of percentages, frequencies, statistical comparisons, graphs, and tables showing statistical values; and reports the final findings in the form of statistical information. 66 It uses variable-based models from individual cases and findings are stated in quantified sentences derived by deductive reasoning. 24

In quantitative research, a phenomenon is investigated in terms of the relationship between an independent variable and a dependent variable which are numerically measurable. The research objective is to statistically test whether the hypothesized relationship is true. 68 Here, the researcher studies what others have performed, examines current theories of the phenomenon being investigated, and then tests hypotheses that emerge from those theories. 4

Quantitative hypothesis-testing research has certain limitations. These limitations include (a) problems with selection of meaningful independent and dependent variables, (b) the inability to reflect subjective experiences as variables since variables are usually defined numerically, and (c) the need to state a hypothesis before the investigation starts. 61

Qualitative research as an inductive hypothesis-generating design

Qualitative research can be considered as a hypothesis-generating design since it involves understanding and descriptions in terms of context. It flows from data to theory (i.e., inductive), focuses on observation, and examines what happens in specific situations with the aim of developing new theories based on the situation. 45 , 68 This type of research (a) collects qualitative data (e.g., ideas, statements, reasons, characteristics, qualities), (b) answers questions such as what, why, and how, (c) uses interviews, observations, or focused-group discussions as data collection tools, (d) analyzes data by discovering patterns of changes, causal relationships, or themes in the data; and (e) reports the final findings as descriptive information. 61 Qualitative research favors case-based models from individual characteristics, and findings are stated using context-dependent existential sentences that are justifiable by inductive reasoning. 24

In qualitative research, texts and interviews are analyzed and interpreted to discover meaningful patterns characteristic of a particular phenomenon. 61 Here, the researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences. 4

Qualitative hypothesis-generating research involves collecting interview data from study participants regarding a phenomenon of interest, and then using what they say to develop hypotheses. It involves the process of questioning more than obtaining measurements; it generates hypotheses using theoretical coding. 61 When using large interview teams, the key to promoting high-level qualitative research and cohesion in large team methods and successful research outcomes is the balance between autonomy and collaboration. 69

Qualitative data may also include observed behavior, participant observation, media accounts, and cultural artifacts. 61 Focus group interviews are usually conducted, audiotaped or videotaped, and transcribed. Afterwards, the transcript is analyzed by several researchers.

Qualitative research also involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions. Such approach uncovers meaningful patterns that describe a particular phenomenon. 2 Thus, qualitative research requires skills in grasping and contextualizing data, as well as communicating data analysis and results in a scientific manner. The reflective process of the inquiry underscores the strengths of a qualitative research approach. 2

Combination of quantitative and qualitative research

When both quantitative and qualitative research methods are used in the same research, mixed-method research is applied. 25 This combination provides a complete view of the research problem and achieves triangulation to corroborate findings, complementarity to clarify results, expansion to extend the study’s breadth, and explanation to elucidate unexpected results. 29

Moreover, quantitative and qualitative findings are integrated to address the weakness of both research methods 29 , 66 and to have a more comprehensive understanding of the phenomenon spectrum. 66

For data analysis in mixed-method research, real non-quantitized qualitative data and quantitative data must both be analyzed. 70 The data obtained from quantitative analysis can be further expanded and deepened by qualitative analysis. 23

In terms of assessment criteria, Hammersley 71 opined that qualitative and quantitative findings should be judged using the same standards of validity and value-relevance. Both approaches can be mutually supportive. 52

Quantitative and qualitative research must be carefully studied and conducted by scientific researchers to avoid unethical research and inadequate outcomes. Quantitative research involves a deductive process wherein a research question is answered with a hypothesis that describes the relationship between independent and dependent variables, and the testing of the hypothesis. This investigation can be aptly termed as hypothesis-testing research involving the analysis of hypothesis-driven experimental studies resulting in a test of significance. Qualitative research involves an inductive process wherein a research question is explored to generate a hypothesis, which then leads to the development of a theory. This investigation can be aptly termed as hypothesis-generating research. When the whole spectrum of inductive and deductive research approaches is combined using both quantitative and qualitative research methodologies, mixed-method research is applied, and this can facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Data curation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Formal analysis: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C.
• Investigation: Barroga E, Matanguihan GJ, Takamiya Y, Izumi M.
• Methodology: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Project administration: Barroga E, Matanguihan GJ.
• Resources: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Supervision: Barroga E.
• Validation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Visualization: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.

• Open access
• Published: 26 August 2024

Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research

• Elham Nasiri   ORCID: orcid.org/0000-0002-0644-1646 1 &
• Laleh Khojasteh   ORCID: orcid.org/0000-0002-6393-2759 1  

BMC Medical Education volume  24 , Article number:  925 ( 2024 ) Cite this article

Metrics details

This study investigates the effectiveness of panel discussions, a specific interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, in English for Specific Purposes (ESP) courses for international medical students. This approach aims to simulate professional conference discussions, preparing students for future academic and clinical environments where such skills are crucial. While traditional group presentations foster critical thinking and communication, a gap exists in understanding how medical students perceive the complexities of preparing for and participating in panel discussions within an ESP setting. This qualitative study investigates the perceived advantages and disadvantages of these discussions from the perspectives of both panelists (medical students) and the audience (peers). Additionally, the study explores potential improvements based on insights from ESP instructors. Utilizing a two-phase design involving reflection papers and focus group discussions, data were collected from 46 medical students and three ESP instructors. Thematic analysis revealed that panel discussions offer unique benefits compared to traditional presentations, including enhanced engagement and more dynamic skill development for both panelists and the audience. Panelists reported gains in personal and professional development, including honing critical thinking, communication, and presentation skills. The audience perceived these discussions as engaging learning experiences that fostered critical analysis and information synthesis. However, challenges such as academic workload and concerns about discussion quality were also identified. The study concludes that panel discussions, when implemented effectively, can be a valuable tool for enhancing critical thinking, communication skills, and subject matter knowledge in ESP courses for medical students. These skills are transferable and can benefit students in various academic and professional settings, including future participation in medical conferences. This research provides valuable insights for ESP instructors seeking to integrate panel discussions into their curriculum, ultimately improving student learning outcomes and preparing them for future success in professional communication.

Peer Review reports

Introduction

In the field of medical education, the acquisition and application of effective communication skills are crucial for medical students in today’s global healthcare environment [ 1 ]. This necessitates not only strong English language proficiency but also the ability to present complex medical information clearly and concisely to diverse audiences.

Language courses, especially English for Specific Purposes (ESP) courses for medical students, are highly relevant in today’s globalized healthcare environment [ 2 ]. In non-English speaking countries like Iran, these courses are particularly important as they go beyond mere language instruction to include the development of critical thinking, cultural competence, and professional communication skills [ 3 ]. Proficiency in English is crucial for accessing up-to-date research, participating in international conferences, and communicating with patients and colleagues from diverse backgrounds [ 4 ]. Additionally, ESP courses help medical students understand and use medical terminologies accurately, which is essential for reading technical articles, listening to audio presentations, and giving spoken presentations [ 5 ]. In countries where English is not the primary language, ESP courses ensure that medical professionals can stay current with global advancements and collaborate effectively on an international scale [ 6 ]. Furthermore, these courses support students who may seek to practice medicine abroad, enhancing their career opportunities and professional growth [ 7 ].

Moreover, ESP courses enable medical professionals to communicate effectively with international patients, which is crucial in multicultural societies and for medical tourism, ensuring that patient care is not compromised due to language barriers [ 8 ]. Many medical textbooks, journals, and online resources are available primarily in English, and ESP courses equip medical students with the necessary language skills to access and comprehend these resources, ensuring they are well-informed about the latest medical research and practices [ 9 ].

Additionally, many medical professionals from non-English speaking countries aim to take international certification exams, such as the USMLE or PLAB, which are conducted in English, and ESP courses prepare students for these exams by familiarizing them with the medical terminology and language used in these assessments [ 10 ]. ESP courses also contribute to the professional development of medical students by improving their ability to write research papers, case reports, and other academic documents in English, which is essential for publishing in international journals and contributing to global medical knowledge [ 11 ]. In the increasingly interdisciplinary field of healthcare, collaboration with professionals from other countries is common, and ESP courses facilitate effective communication and collaboration with international colleagues, fostering innovation and the exchange of ideas [ 12 ].

With the rise of telemedicine and online medical consultations, proficiency in English is essential for non-English speaking medical professionals to provide remote healthcare services to international patients, and ESP courses prepare students for these modern medical practices [ 13 ].

Finally, ESP courses often include training on cultural competence, which is crucial for understanding and respecting the cultural backgrounds of patients and colleagues, leading to more empathetic and effective patient care and professional interactions [ 14 ]. Many ESP programs for medical students incorporate group presentations as a vital component of their curriculum, recognizing the positive impact on developing these essential skills [ 15 ].

Group projects in language courses, particularly in ESP for medical students, are highly relevant for several reasons. They provide a collaborative environment that mimics real-world professional settings, where healthcare professionals often work in multidisciplinary teams [ 16 ]. These group activities foster not only language skills but also crucial soft skills such as teamwork, leadership, and interpersonal communication, which are essential in medical practice [ 17 ].

The benefits of group projects over individual projects in language learning are significant. Hartono, Mujiyanto [ 18 ] found that group presentation tasks in ESP courses led to higher self-efficacy development compared to individual tasks. Group projects encourage peer learning, where students can learn from each other’s strengths and compensate for individual weaknesses [ 19 ]. They also provide a supportive environment that can reduce anxiety and increase willingness to communicate in the target language [ 20 ]. However, it is important to note that group projects also come with challenges, such as social loafing and unequal contribution, which need to be managed effectively [ 21 ].

Traditional lecture-based teaching methods, while valuable for knowledge acquisition, may not effectively prepare medical students for the interactive and collaborative nature of real-world healthcare settings [ 22 ]. Panel discussions (hereafter PDs), an interactive teaching technique where a group of students leads a pre-planned, topic-focused discussion with audience participation, are particularly relevant in this context. They simulate professional conference discussions and interdisciplinary team meetings, preparing students for future academic and clinical environments where such skills are crucial [ 23 ].

PDs, also known as moderated discussions or moderated panels, are a specific type of interactive format where a group of experts or stakeholders engage in a facilitated conversation on a particular topic or issue [ 22 ]. In this format, a moderator guides the discussion, encourages active participation from all panelists, and fosters a collaborative environment that promotes constructive dialogue and critical thinking [ 24 ]. The goal is to encourage audience engagement and participation, which can be achieved through various strategies such as asking open-ended questions, encouraging counterpoints and counterarguments, and providing opportunities for audience members to pose questions or share their own experiences [ 25 ]. These discussions can take place in-person or online, and can be designed to accommodate diverse audiences and settings [ 26 ].

In this study, PD is considered a speaking activity where medical students are assigned specific roles to play during the simulation, such as a physician, quality improvement specialist, policymaker, or patient advocate. By taking on these roles, students can gain a better understanding of the diverse perspectives and considerations that come into play in real-world healthcare discussions [ 23 ]. Simulating PDs within ESP courses can be a powerful tool for enhancing medical students’ learning outcomes in multiple areas. This approach improves language proficiency, academic skills, and critical thinking abilities, while also enabling students to communicate effectively with diverse stakeholders in the medical field [ 27 , 28 ].

Theoretical framework

The panel discussions in our study are grounded in the concept of authentic assessment (outlined by Villarroel, Bloxham [ 29 ]), which involves designing tasks that mirror real-life situations and problems. In the context of medical education, this approach is particularly relevant as it prepares students for the complex, multidisciplinary nature of healthcare communication. Realism can be achieved through two means: providing a realistic context that describes and delivers a frame for the problem to be solved and creating tasks that are similar to those faced in real and/or professional life [ 30 ]. In our study, the PDs provide a realistic context by simulating scenarios where medical students are required to discuss and present complex medical topics in a professional setting, mirroring the types of interactions they will encounter in their future careers.

The task of participating in PDs also involves cognitive challenge, as students are required to think critically about complex medical topics, analyze information, and communicate their findings effectively. This type of task aims to generate processes of problem-solving, application of knowledge, and decision-making that correspond to the development of cognitive and metacognitive skills [ 23 ]. For medical students, these skills are crucial in developing clinical reasoning and effective patient communication. The PDs encourage students to go beyond the textual reproduction of fragmented and low-order content and move towards understanding, establishing relationships between new ideas and previous knowledge, linking theoretical concepts with everyday experience, deriving conclusions from the analysis of data, and examining both the logic of the arguments present in the theory and its practical scope [ 24 , 25 , 27 ].

Furthermore, the evaluative judgment aspect of our study is critical in helping students develop criteria and standards about what a good performance means in medical communication. This involves students judging their own performance and regulating their own learning [ 31 ]. In the context of panel discussions, students reflect on their own work, compare it with desired standards, and seek feedback from peers and instructors. By doing so, students can develop a sense of what constitutes good performance in medical communication and what areas need improvement [ 32 ]. Boud, Lawson and Thompson [ 33 ] argue that students need to build a precise judgment about the quality of their work and calibrate these judgments in the light of evidence. This skill is particularly important for future medical professionals who will need to continually assess and improve their communication skills throughout their careers.

The theoretical framework presented above highlights the importance of authentic learning experiences in medical education. By drawing on the benefits of group work and panel discussions, university instructor-researchers aimed to provide medical students with a unique opportunity to engage with complex cases and develop their communication and collaboration skills. As noted by Suryanarayana [ 34 ], authentic learning experiences can lead to deeper learning and improved retention. Considering the advantages of group work in promoting collaborative problem-solving and language development, the instructor-researchers designed a panel discussion task that simulates real-world scenarios, where students can work together to analyze complex cases, share knowledge, and present their findings to a simulated audience.

While previous studies have highlighted the benefits of interactive learning experiences and critical thinking skills in medical education, a research gap remains in understanding how medical students perceive the relevance of PDs in ESP courses. This study aims to address this gap by investigating medical students’ perceptions of PD tasks in ESP courses and how these perceptions relate to their language proficiency, critical thinking skills, and ability to communicate effectively with diverse stakeholders in the medical field. This understanding can inform best practices in medical education, contributing to the development of more effective communication skills for future healthcare professionals worldwide [ 23 ]. The research questions guiding this study are:

What are the perceived advantages of PDs from the perspectives of panelists and the audience?

What are the perceived disadvantages of PDs from the perspectives of panelists and the audience?

How can PDs be improved for panelists and the audience based on the insights of ESP instructors?

Methodology

Aim and design.

For this study, a two-phase qualitative design was employed to gain an understanding of the advantages and disadvantages of PDs from the perspectives of both student panelists and the audience (Phase 1) and to acquire an in-depth understanding of the suggested strategies provided by experts to enhance PPs for future students (Phase 2).

Participants and context of the study

This study was conducted in two phases (Fig.  1 ) at Shiraz University of Medical Sciences (SUMS), Shiraz, Iran.

Participants of the study in two phases

In the first phase, the student participants were 46 non-native speakers of English and international students who studied medicine at SUMS. Their demographic characteristics can be seen in Table  1 .

These students were purposefully selected because they were the only SUMS international students who had taken the ESP (English for Specific Purposes) course. The number of international students attending SUMS is indeed limited. Each year, a different batch of international students joins the university. They progress through a sequence of English courses, starting with General English 1 and 2, followed by the ESP course, and concluding with academic writing. At the time of data collection, the students included in the study were the only international students enrolled in the ESP course. This mandatory 3-unit course is designed to enhance their language and communication skills specifically tailored to their profession. As a part of the Medicine major curriculum, this course aims to improve their English language proficiency in areas relevant to medicine, such as understanding medical terminology, comprehending original medicine texts, discussing clinical cases, and communicating with patients, colleagues, and other healthcare professionals.

Throughout the course, students engage in various interactive activities, such as group discussions, role-playing exercises, and case studies, to develop their practical communication skills. In this course, medical students receive four marks out of 20 for their oral presentations, while the remaining marks are allocated to their written midterm and final exams. From the beginning of the course, they are briefed about PDs, and they are shown two YouTube-downloaded videos about PDs at medical conferences, a popular format for discussing and sharing knowledge, research findings, and expert opinions on various medical topics.

For the second phase of the study, a specific group of participants was purposefully selected. This group consisted of three faculty members from SUMS English department who had extensive experience attending numerous conferences at national and international levels, particularly in the medical field, as well as working as translators and interpreters in medical congresses. Over the course of ten years, they also gained considerable experience in PDs. They were invited to discuss strategies helpful for medical students with PDs.

Panel discussion activity design and implementation

When preparing for a PD session, medical students received comprehensive guidance on understanding the roles and responsibilities of each panel member. This guidance was aimed at ensuring that each participant was well-prepared and understood their specific role in the discussion.

Moderators should play a crucial role in steering the conversation. They are responsible for ensuring that all panelists have an opportunity to contribute and that the audience is engaged effectively. Specific tasks include preparing opening remarks, introducing panelists, and crafting transition questions to facilitate smooth topic transitions. The moderators should also manage the time to ensure balanced participation and encourage active audience involvement.

Panelists are expected to be subject matter experts who bring valuable insights and opinions to the discussion. They are advised to conduct thorough research on the topic and prepare concise talking points. Panelists are encouraged to draw from their medical knowledge and relevant experiences, share evidence-based information, and engage with other panelists’ points through active listening and thoughtful responses.

The audience plays an active role in the PDs. They are encouraged to participate by asking questions, sharing relevant experiences, and contributing to the dialogue. To facilitate this, students are advised to take notes during the discussion and think of questions or comments they can contribute during the Q&A segment.

For this special course, medical students were advised to choose topics either from their ESP textbook or consider current medical trends, emerging research, and pressing issues in their field. Examples included breast cancer, COVID-19, and controversies in gene therapy. The selection process involved brainstorming sessions and consultation with the course instructor to ensure relevance and appropriateness.

To accommodate the PD sessions within the course structure, students were allowed to start their PD sessions voluntarily from the second week. However, to maintain a balance between peer-led discussions and regular course content, only one PD was held weekly. This approach enabled the ESP lecturer to deliver comprehensive content while also allowing students to engage in these interactive sessions.

A basic time structure was suggested for each PD (Fig.  2 ):

Time allocation for panel discussion stages in minutes

To ensure the smooth running of the course and maintain momentum, students were informed that they could cancel their PD session only once. In such cases, they were required to notify the lecturer and other students via the class Telegram channel to facilitate rescheduling and minimize disruptions. This provision was essential in promoting a sense of community among students and maintaining the course’s continuity.

Research tools and data collection

The study utilized various tools to gather and analyze data from participants and experts, ensuring a comprehensive understanding of the research topic.

Reflection papers

In Phase 1 of the study, 46 medical students detailed their perceptions of the advantages and disadvantages of panel discussions from dual perspectives: as panelists (presenters) and as audience members (peers).

Participants were given clear instructions and a 45-minute time frame to complete the reflection task. With approximately 80% of the international language students being native English speakers and the rest fluent in English, the researchers deemed this time allocation reasonable. The questions and instructions were straightforward, facilitating quick comprehension. It was estimated that native English speakers would need about 30 min to complete the task, while non-native speakers might require an extra 15 min for clarity and expression. This time frame aimed to allow students to respond thoughtfully without feeling rushed. Additionally, students could request more time if needed.

Focus group discussion

In phase 2 of the study, a focus group discussion was conducted with three expert participants. The purpose of the focus group was to gather insights from expert participants, specifically ESP (English for Specific Purposes) instructors, on how presentation dynamics can be improved for both panelists and the audience.

According to Colton and Covert [ 35 ], focus groups are useful for obtaining detailed input from experts. The appropriate size of a focus group is determined by the study’s scope and available resources [ 36 ]. Morgan [ 37 ] suggests that small focus groups are suitable for complex topics where specialist participants might feel frustrated if not allowed to express themselves fully.

The choice of a focus group over individual interviews was based on several factors. First, the exploratory nature of the study made focus groups ideal for interactive discussions, generating new ideas and in-depth insights [ 36 ]. Second, while focus groups usually involve larger groups, they can effectively accommodate a limited number of experts with extensive knowledge [ 37 ]. Third, the focus group format fostered a more open environment for idea exchange, allowing participants to engage dynamically [ 36 ]. Lastly, conducting a focus group was more time- and resource-efficient than scheduling three separate interviews [ 36 ].

Data analysis

The first phase of the study involved a thorough examination of the data related to the research inquiries using thematic analysis. This method was chosen for its effectiveness in uncovering latent patterns from a bottom-up perspective, facilitating a comprehensive understanding of complex educational phenomena [ 38 ]. The researchers first familiarized themselves with the data by repeatedly reviewing the reflection papers written by the medical students. Next, an initial round of coding was independently conducted to identify significant data segments and generate preliminary codes that reflected the students’ perceptions of the advantages and disadvantages of presentation dynamics PDs from both the presenter and audience viewpoints [ 38 ].

The analysis of the reflection papers began with the two researchers coding a subset of five papers independently, adhering to a structured qualitative coding protocol [ 39 ]. They convened afterward to compare their initial codes and address any discrepancies. Through discussion, they reached an agreement on the codes, which were then analyzed, organized into categories and themes, and the frequency of each code was recorded [ 38 ].

After coding the initial five papers, the researchers continued to code the remaining 41 reflection paper transcripts in batches of ten, meeting after each batch to review their coding, resolve any inconsistencies, and refine the coding framework as needed. This iterative process, characterized by independent coding, joint reviews, and consensus-building, helped the researchers establish a robust and reliable coding approach consistently applied to the complete dataset [ 40 ]. Once all 46 reflection paper transcripts were coded, the researchers conducted a final review and discussion to ensure accurate analysis. They extracted relevant excerpts corresponding to the identified themes and sub-themes from the transcripts to provide detailed explanations and support for their findings [ 38 ]. This multi-step approach of separate initial coding, collaborative review, and frequency analysis enhanced the credibility and transparency of the qualitative data analysis.

To ensure the trustworthiness of the data collected in this study, the researchers adhered to the Guba and Lincoln standards of scientific accuracy in qualitative research, which encompass credibility, confirmability, dependability, and transferability [ 41 ] (Table  2 ).

The analysis of the focus group data obtained from experts followed the same rigorous procedure applied to the student participants’ data. Thematic analysis was employed to examine the experts’ perspectives, maintaining consistency in the analytical approach across both phases of the study. The researchers familiarized themselves with the focus group transcript, conducted independent preliminary coding, and then collaboratively refined the codes. These codes were subsequently organized into categories and themes, with the frequency of each code recorded. The researchers engaged in thorough discussions to ensure agreement on the final themes and sub-themes. Relevant excerpts from the focus group transcript were extracted to provide rich, detailed explanations of each theme, thereby ensuring a comprehensive and accurate analysis of the experts’ insights.

1. What are the advantages of PDs from the perspective of panelists and the audience?

The analysis of the advantages of PDs from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  2 and 3 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  3 ), the overarching theme was “Personal and Professional Development.” The most frequently reported advantage was knowledge sharing (93.5%), followed closely by increased confidence (91.3%) and the importance of interaction in presentations (91.3%).

Notably, all categories within this theme had at least one code mentioned by over 80% of participants, indicating a broad range of perceived benefits. The category of “Effective teamwork and communication” was particularly prominent, with collaboration (89.1%) and knowledge sharing (93.5%) being among the most frequently cited advantages. This suggests that PDs are perceived as valuable tools for fostering interpersonal skills and collective learning. In the “Language mastery” category, increased confidence (91.3%) and better retention of key concepts (87.0%) were highlighted, indicating that PDs are seen as effective for both language and content learning.

The audience perspective (Table  4 ), encapsulated under the theme “Enriching Learning Experience,” showed similarly high frequencies across all categories.

The most frequently mentioned advantage was exposure to diverse speakers (93.5%), closely followed by the range of topics covered (91.3%) and increased audience interest (91.3%). The “Broadening perspectives” category was particularly rich, with all codes mentioned by over 70% of participants. This suggests that audience members perceive PDs as valuable opportunities for expanding their knowledge and viewpoints. In the “Language practice” category, the opportunity to practice language skills (89.1%) was the most frequently cited advantage, indicating that even as audience members, students perceive significant language learning benefits.

Comparing the two perspectives reveals several interesting patterns:

High overall engagement: Both panelists and audience members reported high frequencies across all categories, suggesting that PDs are perceived as beneficial regardless of the role played.

Language benefits: While panelists emphasized increased confidence (91.3%) and better retention of concepts (87.0%), audience members highlighted opportunities for language practice (89.1%). This indicates that PDs offer complementary language learning benefits for both roles.

Interactive learning: The importance of interaction was highly rated by panelists (91.3%), while increased audience interest was similarly valued by the audience (91.3%). This suggests that PDs are perceived as an engaging, interactive learning method from both perspectives.

Professional development: Panelists uniquely emphasized professional growth aspects such as experiential learning (84.8%) and real-world application (80.4%). These were not directly mirrored in the audience perspective, suggesting that active participation in PDs may offer additional professional development benefits.

Broadening horizons: Both groups highly valued the diversity aspect of PDs. Panelists appreciated diversity and open-mindedness (80.4%), while audience members valued diverse speakers (93.5%) and a range of topics (91.3%).

2. What are the disadvantages of PDs from the perspective of panelists and the audience?

The analysis of the disadvantages of panel discussions (PDs) from the perspectives of both panelists and audience members revealed several key themes and categories. Tables  4 and 5 present the frequency and percentage of responses for each code within these categories.

From the panelists’ perspective (Table  5 ), the theme “Drawbacks of PDs” was divided into two main categories: “Academic Workload Challenges” and “Coordination Challenges.” The most frequently reported disadvantage was long preparation (87.0%), followed by significant practice needed (82.6%) and the time-consuming nature of PDs (80.4%). These findings suggest that the primary concern for panelists is the additional workload that PDs impose on their already demanding academic schedules. The “Coordination Challenges” category, while less prominent than workload issues, still presented significant concerns. Diverse panel skills (78.3%) and finding suitable panelists (73.9%) were the most frequently cited issues in this category, indicating that team dynamics and composition are notable challenges for panelists.

The audience perspective (Table  6 ), encapsulated under the theme “Drawbacks of PDs,” was divided into two main categories: “Time-related Issues” and “Interaction and Engagement Issues.” In the “Time-related Issues” category, the most frequently mentioned disadvantage was the inefficient use of time (65.2%), followed by the perception of PDs as too long and boring (60.9%). Notably, 56.5% of respondents found PDs stressful due to overwhelming workload from other studies, and 52.2% considered them not very useful during exam time. The “Interaction and Engagement Issues” category revealed more diverse concerns. The most frequently mentioned disadvantage was the repetitive format (82.6%), followed by limited engagement with the audience (78.3%) and the perception of PDs as boring (73.9%). The audience also noted issues related to the panelists’ preparation and coordination, such as “Not practiced and natural” (67.4%) and “Coordination and Interaction Issues” (71.7%), suggesting that the challenges faced by panelists directly impact the audience’s experience.

Workload concerns: Both panelists and audience members highlighted time-related issues. For panelists, this manifested as long preparation times (87.0%) and difficulty balancing with other studies (76.1%). For the audience, it appeared as perceptions of inefficient use of time (65.2%) and stress due to overwhelming workload from other studies (56.5%).

Engagement issues: While panelists focused on preparation and coordination challenges, the audience emphasized the quality of the discussion and engagement. This suggests a potential mismatch between the efforts of panelists and the expectations of the audience.

Boredom and repetition: The audience frequently mentioned boredom (73.9%) and repetitive format (82.6%) as issues, which weren’t directly mirrored in the panelists’ responses. This indicates that while panelists may be focused on content preparation, the audience is more concerned with the delivery and variety of the presentation format.

Coordination challenges: Both groups noted coordination issues, but from different perspectives. Panelists struggled with team dynamics and finding suitable co-presenters, while the audience observed these challenges manifesting as unnatural or unpracticed presentations.

Academic pressure: Both groups acknowledged the strain PDs put on their academic lives, with panelists viewing it as a burden (65.2%) and the audience finding it less useful during exam times (52.2%).

3. How can PDs be improved for panelists and the audience from the experts’ point of view?

The presentation of data for this research question differs from the previous two due to the unique nature of the information gathered. Unlike the quantifiable student responses in earlier questions, this data stems from expert opinions and a reflection discussion session, focusing on qualitative recommendations for improvement rather than frequency of responses (Braun & Clarke, 2006). The complexity and interconnectedness of expert suggestions, coupled with the integration of supporting literature, necessitate a more narrative approach (Creswell & Poth, 2018). This format allows for a richer exploration of the context behind each recommendation and its potential implications (Patton, 2015). Furthermore, the exploratory nature of this question, aimed at generating ideas for improvement rather than measuring prevalence of opinions, is better served by a detailed, descriptive presentation (Merriam & Tisdell, 2016). This approach enables a more nuanced understanding of how PDs can be enhanced, aligning closely with the “how” nature of the research question and providing valuable insights for potential implementation (Yin, 2018).

The experts provided several suggestions to address the challenges faced by students in panel discussions (PDs) and improve the experience for both panelists and the audience. Their recommendations focused on six key areas: time management and workload, preparation and skill development, engagement and interactivity, technological integration, collaboration and communication, and institutional support.

To address the issue of time management and heavy workload, one expert suggested teaching students to “ break down the task to tackle the time-consuming nature of panel discussions and balance it with other studies .” This approach aims to help students manage the extensive preparation time required for PDs without compromising their other academic responsibilities. Another expert emphasized “ enhancing medical students’ abilities to prioritize tasks , allocate resources efficiently , and optimize their workflow to achieve their goals effectively .” These skills were seen as crucial not only for PD preparation but also for overall academic success and future professional practice.

Recognizing the challenges of long preparation times and the perception of PDs being burdensome, an expert proposed “ the implementation of interactive training sessions for panelists .” These sessions were suggested to enhance coordination skills and improve the ability of group presenters to engage with the audience effectively. The expert emphasized that such training could help students view PDs as valuable learning experiences rather than additional burdens, potentially increasing their motivation and engagement in the process.

To combat issues of limited engagement and perceived boredom, experts recommended increasing engagement opportunities for the audience through interactive elements like audience participation and group discussions. They suggested that this could transform PDs from passive listening experiences to active learning opportunities. One expert suggested “ optimizing time management and restructuring the format of panel discussions ” to address inefficiency during sessions. This restructuring could involve shorter presentation segments interspersed with interactive elements to maintain audience attention and engagement.

An innovative solution proposed by one expert was “ using ChatGPT to prepare for PDs by streamlining scenario presentation preparation and role allocation. ” The experts collectively discussed the potential of AI to assist medical students in reducing their workload and saving time in preparing scenario presentations and allocating roles in panel discussions. They noted that AI could help generate initial content drafts, suggest role distributions based on individual strengths, and even provide practice questions for panelists, significantly reducing preparation time while maintaining quality.

Two experts emphasized the importance of enhancing collaboration and communication among panelists to address issues related to diverse panel skills and coordination challenges. They suggested establishing clear communication channels and guidelines to improve coordination and ensure a cohesive presentation. This could involve creating structured team roles, setting clear expectations for each panelist, and implementing regular check-ins during the preparation process to ensure all team members are aligned and progressing.

All experts were in agreement that improving PDs would not be possible “ if nothing is done by the university administration to reduce the ESP class size for international students .” They believed that large class sizes in ESP or EFL classes could negatively influence group oral presentations, hindering language development and leading to uneven participation. The experts suggested that smaller class sizes would allow for more individualized attention, increased speaking opportunities for each student, and more effective feedback mechanisms, all of which are crucial for developing strong presentation skills in a second language.

Research question 1: what are the advantages of PDs from the perspective of panelists and the audience?

The results of this study reveal significant advantages of PDs for both panelists and audience members in the context of medical education. These findings align with and expand upon previous research in the field of educational presentations and language learning.

Personal and professional development for panelists

The high frequency of reported benefits in the “Personal and Professional Development” theme for panelists aligns with several previous studies. The emphasis on language mastery, particularly increased confidence (91.3%) and better retention of key concepts (87.0%), supports the findings of Hartono, Mujiyanto [ 42 ], Gedamu and Gezahegn [ 15 ], Li [ 43 ], who all highlighted the importance of language practice in English oral presentations. However, our results show a more comprehensive range of benefits, including professional growth aspects like experiential learning (84.8%) and real-world application (80.4%), which were not as prominently featured in these earlier studies.

Interestingly, our findings partially contrast with Chou [ 44 ] study, which found that while group oral presentations had the greatest influence on improving students’ speaking ability, individual presentations led to more frequent use of metacognitive, retrieval, and rehearsal strategies. Our results suggest that PDs, despite being group activities, still provide significant benefits in these areas, possibly due to the collaborative nature of preparation and the individual responsibility each panelist bears. The high frequency of knowledge sharing (93.5%) and collaboration (89.1%) in our study supports Harris, Jones and Huffman [ 45 ] emphasis on the importance of group dynamics and varied perspectives in educational settings. However, our study provides more quantitative evidence for these benefits in the specific context of PDs.

Enriching learning experience for the audience

The audience perspective in our study reveals a rich learning experience, with high frequencies across all categories. This aligns with Agustina [ 46 ] findings in business English classes, where presentations led to improvements in all four language skills. However, our study extends these findings by demonstrating that even passive participation as an audience member can lead to significant perceived benefits in language practice (89.1%) and broadening perspectives (93.5% for diverse speakers). The high value placed on diverse speakers (93.5%) and range of topics (91.3%) by the audience supports the notion of PDs as a tool for expanding knowledge and viewpoints. This aligns with the concept of situated learning experiences leading to deeper understanding in EFL classes, as suggested by Li [ 43 ] and others [ 18 , 31 ]. However, our study provides more specific evidence for how this occurs in the context of PDs.

Interactive learning and engagement

Both panelists and audience members in our study highly valued the interactive aspects of PDs, with the importance of interaction rated at 91.3% by panelists and increased audience interest at 91.3% by the audience. This strong emphasis on interactivity aligns with Azizi and Farid Khafaga [ 19 ] study on the benefits of dynamic assessment and dialogic learning contexts. However, our study provides more detailed insights into how this interactivity is perceived and valued by both presenters and audience members in PDs.

Professional growth and real-world application

The emphasis on professional growth through PDs, particularly for panelists, supports Li’s [ 43 ] assertion about the power of oral presentations as situated learning experiences. Our findings provide more specific evidence for how PDs contribute to professional development, with high frequencies reported for experiential learning (84.8%) and real-world application (80.4%). This suggests that PDs may be particularly effective in bridging the gap between academic learning and professional practice in medical education.

Research question 2: what are the disadvantages of pds from the perspective of panelists and the audience?

Academic workload challenges for panelists.

The high frequency of reported challenges in the “Academic Workload Challenges” category for panelists aligns with several previous studies in medical education [ 47 , 48 , 49 ]. The emphasis on long preparation (87.0%), significant practice needed (82.6%), and the time-consuming nature of PDs (80.4%) supports the findings of Johnson et al. [ 24 ], who noted that while learners appreciate debate-style journal clubs in health professional education, they require additional time commitment. This is further corroborated by Nowak, Speed and Vuk [ 50 ], who found that intensive learning activities in medical education, while beneficial, can be time-consuming for students.

Perceived value of pds relative to time investment

While a significant portion of the audience (65.2%) perceived PDs as an inefficient use of time, the high frequency of engagement-related concerns (82.6% for repetitive format, 78.3% for limited engagement) suggests that the perceived lack of value may be more closely tied to the quality of the experience rather than just the time investment. This aligns with Dyhrberg O’Neill [ 27 ] findings on debate-based oral exams, where students perceived value despite the time-intensive nature of the activity. However, our results indicate a more pronounced concern about the return on time investment in PDs. This discrepancy might be addressed through innovative approaches to PD design and implementation, such as those proposed by Almazyad et al. [ 22 ], who suggested using AI tools to enhance expert panel discussions and potentially improve efficiency.

Coordination challenges for panelists

The challenges related to coordination in medical education, such as diverse panel skills (78.3%) and finding suitable panelists (73.9%), align with previous research on teamwork in higher education [ 21 ]. Our findings support the concept of the free-rider effect discussed by Hall and Buzwell [ 21 ], who explored reasons for non-contribution in group projects beyond social loafing. This is further elaborated by Mehmood, Memon and Ali [ 51 ], who proposed that individuals may not contribute their fair share due to various factors including poor communication skills or language barriers, which is particularly relevant in medical education where clear communication is crucial [ 52 ]. Comparing our results to other collaborative learning contexts in medical education, Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] measured teamwork competence development in a multidisciplinary project-based learning environment. They found that while teamwork skills improved over time, initial coordination challenges were significant. This aligns with our findings on the difficulties of coordinating diverse panel skills and opinions in medical education settings.

Our results also resonate with Chou’s [ 44 ] study comparing group and individual oral presentations, which found that group presenters often had a limited understanding of the overall content. This is supported by Wilson, Ho and Brookes [ 54 ], who examined student perceptions of teamwork in undergraduate science degrees, highlighting the challenges and benefits of collaborative work, which are equally applicable in medical education [ 52 ].

Quality of discussions and perception for the audience

The audience perspective in our study reveals significant concerns about the quality and engagement of PDs in medical education. The high frequency of issues such as repetitive format (82.6%) and limited engagement with the audience (78.3%) aligns with Parmar and Bickmore [ 55 ] findings on the importance of addressing individual audience members and gathering feedback. This is further supported by Nurakhir et al. [ 25 ], who explored students’ views on classroom debates as a strategy to enhance critical thinking and oral communication skills in nursing education, which shares similarities with medical education. Comparing our results to other interactive learning methods in medical education, Jones et al. [ 26 ] reviewed the use of journal clubs and book clubs in pharmacy education. They found that while these methods enhanced engagement, they also faced challenges in maintaining student interest over time, similar to the boredom issues reported in our study of PDs in medical education. The perception of PDs as boring (73.9%) and not very useful during exam time (52.2%) supports previous research on the stress and pressure experienced by medical students [ 48 , 49 ]. Grieve et al. [ 20 ] specifically examined student fears of oral presentations and public speaking in higher education, which provides context for the anxiety and disengagement observed in our study of medical education. Interestingly, Bhuvaneshwari et al. [ 23 ] found positive impacts of panel discussions in educating medical students on specific modules. This contrasts with our findings and suggests that the effectiveness of PDs in medical education may vary depending on the specific context and implementation.

Comparative analysis and future directions

Our study provides a unique comparative analysis of the challenges faced by both panelists and audience members in medical education. The alignment of concerns around workload and time management between the two groups suggests that these are overarching issues in the implementation of PDs in medical curricula. This is consistent with the findings of Pasandín et al. [ 56 ], who examined cooperative oral presentations in higher education and their impact on both technical and soft skills, which are crucial in medical education [ 52 ]. The mismatch between panelist efforts and audience expectations revealed in our study is a novel finding that warrants further investigation in medical education. This disparity could be related to the self-efficacy beliefs of presenters, as explored by Gedamu and Gezahegn [ 15 ] in their study of TEFL trainees’ attitudes towards academic oral presentations, which may have parallels in medical education. Looking forward, innovative approaches could address some of the challenges identified in medical education. Almazyad et al. [ 22 ] proposed using AI tools like ChatGPT to enhance expert panel discussions in pediatric palliative care, which could potentially address some of the preparation and engagement issues identified in our study of medical education. Additionally, Ragupathi and Lee [ 57 ] discussed the role of rubrics in higher education, which could provide clearer expectations and feedback for both panelists and audience members in PDs within medical education.

Research question 3: how can PDs be improved for panelists and the audience from the experts’ point of view?

The expert suggestions for improving PDs address several key challenges identified in previous research on academic presentations and student workload management. These recommendations align with current trends in educational technology and pedagogical approaches, while also considering the unique needs of medical students.

The emphasis on time management and workload reduction strategies echoes findings from previous studies on medical student stress and academic performance. Nowak, Speed and Vuk [ 50 ] found that medical students often struggle with the fast-paced nature of their courses, which can lead to reduced motivation and superficial learning approaches. The experts’ suggestions for task breakdown and prioritization align with Rabbi and Islam [ 58 ] recommendations for reducing workload stress through effective assignment prioritization. Additionally, Popa et al. [ 59 ] highlight the importance of acceptance and planning in stress management for medical students, supporting the experts’ focus on these areas.

The proposed implementation of interactive training sessions for panelists addresses the need for enhanced presentation skills in professional contexts, a concern highlighted by several researchers [ 17 , 60 ]. This aligns with Grieve et al. [ 20 ] findings on student fears of oral presentations and public speaking in higher education, emphasizing the need for targeted training. The focus on interactive elements and audience engagement also reflects current trends in active learning pedagogies, as demonstrated by Pasandín et al. [ 56 ] in their study on cooperative oral presentations in engineering education.

The innovative suggestion to use AI tools like ChatGPT for PD preparation represents a novel approach to leveraging technology in education. This aligns with recent research on the potential of AI in scientific research, such as the study by Almazyad et al. [ 22 ], which highlighted the benefits of AI in supporting various educational tasks. However, it is important to consider potential ethical implications and ensure that AI use complements rather than replaces critical thinking and creativity.

The experts’ emphasis on enhancing collaboration and communication among panelists addresses issues identified in previous research on teamwork in higher education. Rodríguez-Sedano, Conde and Fernández-Llamas [ 53 ] noted the importance of measuring teamwork competence development in project-based learning environments. The suggested strategies for improving coordination align with best practices in collaborative learning, as demonstrated by Romero-Yesa et al. [ 61 ] in their qualitative assessment of challenge-based learning and teamwork in electronics programs.

The unanimous agreement on the need to reduce ESP class sizes for international students reflects ongoing concerns about the impact of large classes on language learning and student engagement. This aligns with research by Li [ 3 ] on issues in developing EFL learners’ oral English communication skills. Bosco et al. [ 62 ] further highlight the challenges of teaching and learning ESP in mixed classes, supporting the experts’ recommendation for smaller class sizes. Qiao, Xu and bin Ahmad [ 63 ] also emphasize the implementation challenges for ESP formative assessment in large classes, further justifying the need for reduced class sizes.

These expert recommendations provide a comprehensive approach to improving PDs, addressing not only the immediate challenges of preparation and delivery but also broader issues of student engagement, workload management, and institutional support. By implementing these suggestions, universities could potentially transform PDs from perceived burdens into valuable learning experiences that enhance both academic and professional skills. This aligns with Kho and Ting [ 64 ] systematic review on overcoming oral presentation anxiety among tertiary ESL/EFL students, which emphasizes the importance of addressing both challenges and strategies in improving presentation skills.

This study has shed light on the complex challenges associated with PDs in medical education, revealing a nuanced interplay between the experiences of panelists and audience members. The findings underscore the need for a holistic approach to implementing PDs that addresses both the academic workload concerns and the quality of engagement.

Our findings both support and extend previous research on the challenges of oral presentations and group work in medical education settings. The high frequencies of perceived challenges across multiple categories for both panelists and audience members suggest that while PDs may offer benefits, they also present significant obstacles that need to be addressed in medical education. These results highlight the need for careful consideration in the implementation of PDs in medical education, with particular attention to workload management, coordination strategies, and audience engagement techniques. Future research could focus on developing and testing interventions to mitigate these challenges while preserving the potential benefits of PDs in medical education.

Moving forward, medical educators should consider innovative approaches to mitigate these challenges. This may include:

Integrating time management and stress coping strategies into the PD preparation process [ 59 ].

Exploring the use of AI tools to streamline preparation and enhance engagement [ 22 ].

Developing clear rubrics and expectations for both panelists and audience members [ 57 ].

Incorporating interactive elements to maintain audience interest and participation [ 25 ].

Limitations and future research

One limitation of this study is that it focused on a specific population of medical students, which may limit the generalizability of the findings to other student populations. Additionally, the study relied on self-report data from panelists and audience members, which may introduce bias and affect the validity of the results. Future research could explore the effectiveness of PDs in different educational contexts and student populations to provide a more comprehensive understanding of the benefits and challenges of panel discussions.

Future research should focus on evaluating the effectiveness of these interventions and exploring how PDs can be tailored to the unique demands of medical education. By addressing the identified challenges, PDs have the potential to become a more valuable and engaging component of medical curricula, fostering both academic and professional development. Ultimately, the goal should be to transform PDs from perceived burdens into opportunities for meaningful learning and skill development, aligning with the evolving needs of medical education in the 21st century.

Future research could also examine the long-term impact of PDs on panelists’ language skills, teamwork, and communication abilities. Additionally, exploring the effectiveness of different training methods and tools, such as AI technology, in improving coordination skills and reducing workload stress for panelists could provide valuable insights for educators and administrators. Further research could also investigate the role of class size and audience engagement in enhancing the overall effectiveness of PDs in higher education settings. By addressing these gaps in the literature, future research can contribute to the ongoing development and improvement of PDs as a valuable learning tool for students in higher education.

However, it is important to note that implementing these changes may require significant institutional resources and a shift in pedagogical approaches. Future research could focus on piloting these recommendations and evaluating their effectiveness in improving student outcomes and experiences with PDs.

Data availability

We confirm that the data supporting the findings are available within this article. Raw data supporting this study’s findings are available from the corresponding author, upon request.

Abbreviations

Artificial Intelligence

English as a Foreign Language

English for Specific Purposes

Panel Discussion

Shiraz University of Medical Sciences

Harden RM, Laidlaw JM. Essential skills for a medical teacher: an introduction to teaching and learning in medicine. Elsevier Health Sciences; 2020.

Ibrahim Mohamed O, Al Jadaan DO. English for Specific purposes (Esp) Needs Analysis for Health Sciences students: a cross-sectional study at a University in the UAE. English for Specific purposes (Esp) Needs Analysis for Health Sciences Students: A Cross-Sectional Study at a University in the UAE.

Li Y, Heron M. English for general academic purposes or English for specific purposes? Language learning needs of medical students at a Chinese university. Theory Pract Lang Stud. 2021;11(6):621–31.

Article   Google Scholar  

Chan SMH, Mamat NH, Nadarajah VD. Mind your language: the importance of English language skills in an International Medical Programme (IMP). BMC Med Educ. 2022;22(1):405.

Cortez Faustino BS, Ticas de Córdova CK, de la Hernández DI. Teaching English for specific purposes: contents and methodologies that could be implemented in the English for Medical purposes (EMP) course for the doctor of Medicine Major at the University of El Salvador. Universidad de El Salvador; 2022.

BENYAMINA E-Z BOUKAHLAH. Enhancing Specialty Language learning through content-based instruction: students of Paramedical Institute of Tiaret as a case study. Université IBN KHALDOUN-Tiaret; 2023.

Prikazchikov M. Medical English course for russian-speaking dentists: a needs analysis study. Iowa State University; 2024.

Kim C, Lee SY, Park S-H. Is Korea Ready to be a key player in the Medical Tourism Industry? An English Education Perspective. Iran J Public Health. 2020;49(2):267–73.

Google Scholar  

Syakur A, Zainuddin H, Hasan MA. Needs analysis English for specific purposes (esp) for vocational pharmacy students. Budapest International Research and Critics in Linguistics and Education (BirLE). Journal. 2020;3(2):724–33.

Chan S, Taylor L. Comparing writing proficiency assessments used in professional medical registration: a methodology to inform policy and practice. Assess Writ. 2020;46:100493.

Hyland K, Jiang FK. Delivering relevance: the emergence of ESP as a discipline. Engl Specif Purp. 2021;64:13–25.

Maftuna B. The role of English in ESP. Am J Adv Sci Res. 2024;1(2):1–5.

LEON LI, HUMANIZING THE FOREIGN LANGUAGE. COURSE: NEW TEACHING METHODS FOR MEDICAL STUDENTS. Language, Culture and Change. 2022:243.

Dahm MR, Yates L. Rapport, empathy and professional identity: Some challenges for international medical graduates speaking English as a second or foreign language. Multilingual Healthcare: A Global View on Communicative Challenges. 2020:209 – 34.

Gedamu AD, Gezahegn TH. TEFL trainees’ attitude to and self-efficacy beliefs of academic oral presentation. Cogent Educ. 2023;10(1):2163087.

Saliu B, Hajrullai H. Best practices in the English for specific purpose classes at the language center. Procedia-Social Behav Sci. 2016;232:745–9.

Clokie TL, Fourie E. Graduate employability and communication competence: are undergraduates taught relevant skills? Bus Prof Communication Q. 2016;79(4):442–63.

Hartono H, Mujiyanto J, Fitriati SW, Sakhiyya Z, Lotfie MM, Maharani MM. English Presentation Self-Efficacy Development of Indonesian ESP students: the effects of Individual versus Group Presentation tasks. Int J Lang Educ. 2023;7(3):361–76.

Azizi Z, Farid Khafaga A. Scaffolding via Group-dynamic Assessment to positively affect motivation, learning anxiety, and willingness to Communicate: a Case Study of High School Students. J Psycholinguist Res. 2023;52(3):831–51.

Grieve R, Woodley J, Hunt SE, McKay A. Student fears of oral presentations and public speaking in higher education: a qualitative survey. J Furth High Educ. 2021;45(9):1281–93.

Hall D, Buzwell S. The problem of free-riding in group projects: looking beyond social loafing as reason for non-contribution. Act Learn High Educ. 2013;14(1):37–49.

Almazyad M, Aljofan F, Abouammoh NA, Muaygil R, Malki KH, Aljamaan F, et al. Enhancing Expert Panel discussions in Pediatric Palliative Care: innovative scenario development and summarization with ChatGPT-4. Cureus. 2023;15(4):e38249.

Bhuvaneshwari S, Rashmi R, Deepika K, Anirudh VM, Vijayamathy A, Rekha S, Kathiravan R. Impact of panel discussion in educating AETCOM First Module among Undergraduate Medical Students. Latin Am J Pharmacy: Life Sci J. 2023;42(6):407–12.

Johnson BR, Logan LD, Darley A, Stone RH, Smith SE, Osae SP, et al. A scoping review for Debate-Style Journal Clubs in Health Professional Education. Am J Pharm Educ. 2023;87(6):100064.

Nurakhir A, Palupi FN, Langeveld C, Nurmalia D. Students’ views of classroom debates as a strategy to enhance critical thinking and oral communication skills. 2020.

Jones EP, Nelson NR, Thorpe CT, Rodgers PT, Carlson RB. Use of journal clubs and book clubs in pharmacy education: a scoping review. Currents Pharm Teach Learn. 2022;14(1):110–9.

Dyhrberg O’Neill L. Assessment of student debates in support of active learning? Students’ perceptions of a debate-based oral final exam. Act Learn High Educ. 2024.

Dyment JE, O’Connell TS. Assessing the quality of reflection in student journals: a review of the research. Teach High Educ. 2011;16(1):81–97.

Villarroel V, Bloxham S, Bruna D, Bruna C, Herrera-Seda C. Authentic assessment: creating a blueprint for course design. Assess Evaluation High Educ. 2018;43(5):840–54.

Schultz M, Young K, Gunning K, Harvey T. Defining and measuring authentic assessment: a case study in the context of tertiary science. Assess Evaluation High Educ. 2022;47(1):77–94.

Sundrarajun C, Kiely R. The oral presentation as a context for learning and assessment. Innov Lang Learn Teach. 2010;4(2):101–17.

Wyatt-Smith C, Adie L. The development of students’ evaluative expertise: enabling conditions for integrating criteria into pedagogic practice. J Curriculum Stud. 2021;53(4):399–419.

Boud D, Lawson R, Thompson DG. The calibration of student judgement through self-assessment: disruptive effects of assessment patterns. High Educ Res Dev. 2015;34(1):45–59.

A. S. Enhancing Meaningful Learning experiences through Comprehension and Retention by students. Twentyfirst Century Publications Patiala. 2023;49.

Colton D, Covert RW. Designing and constructing instruments for social research and evaluation. Wiley; 2007.

Krueger RA, Casey MA. Focus group interviewing. Handbook of practical program evaluation. 2015:506 – 34.

Morgan DL. Handbook of interview research: Context and method. Oaks, CA, USA: Sage Publications Thousand; 2002.

Braun V, Clarke V. Conceptual and design thinking for thematic analysis. Qualitative Psychol. 2022;9(1):3.

Elliott V. Thinking about the coding process in qualitative data analysis. Qualitative Rep. 2018;23(11).

Syed M, Nelson SC. Guidelines for establishing reliability when coding narrative data. Emerg Adulthood. 2015;3(6):375–87.

Lincoln Y. Naturalistic inquiry: Sage; 1985.

Hartono H, Mujiyanto J, Fitriati SW, Sakhiyya Z, Lotfie MM, Maharani MM. English presentation self-efficacy development of Indonesian ESP students: the effects of Individual versus Group Presentation tasks. Int J Lang Educ. 2023;7(3).

Li X. Teaching English oral presentations as a situated task in an EFL classroom: a quasi-experimental study of the effect of video-assisted self-reflection. Revista Signos. 2018;51(98):359–81.

Chou M-h. The influence of learner strategies on oral presentations: a comparison between group and individual performance. Engl Specif Purp. 2011;30(4):272–85.

Harris A, Jones M, Huffman J. Teachers leading educational reform. The power of. 2017.

Agustina L. Stimulating students to speak up through presentation in business English class. J Appl Stud Lang. 2019;3(1):21–8.

Babal JC, Abraham O, Webber S, Watterson T, Moua P, Chen J. Student pharmacist perspectives on factors that influence wellbeing during pharmacy school. Am J Pharm Educ. 2020;84(9):ajpe7831.

Moir F, Yielder J, Sanson J, Chen Y. Depression in medical students: current insights. Adv Med Educ Pract. 2018;323:33.

Pavlinac Dodig I, Lusic Kalcina L, Demirovic S, Pecotic R, Valic M, Dogas Z. Sleep and lifestyle habits of medical and non-medical students during the COVID-19 lockdown. Behav Sci. 2023;13(5):407.

Nowak G, Speed O, Vuk J. Microlearning activities improve student comprehension of difficult concepts and performance in a biochemistry course. Currents Pharm Teach Learn. 2023;15(1):69–78.

Mehmood K, Memon S, Ali F. Language barriers to Effective Communication in speaking English: a phenomenological study of Pakistan International cricketers. Pakistan Lang Humanit Rev. 2024;8(1):107–14.

Buelow JR, Downs D, Jorgensen K, Karges JR, Nelson D. Building interdisciplinary teamwork among allied health students through live clinical case simulations. J Allied Health. 2008;37(2):e109–23.

Rodríguez-Sedano FJ, Conde M, Fernández-Llamas C, editors. Measuring teamwork competence development in a multidisciplinary project based learning environment. Learning and Collaboration Technologies Design, Development and Technological Innovation: 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15–20, 2018, Proceedings, Part I 5; 2018: Springer.

Wilson L, Ho S, Brookes RH. Student perceptions of teamwork within assessment tasks in undergraduate science degrees. Assess Evaluation High Educ. 2018;43(5):786–99.

Parmar D, Bickmore T. Making it personal: addressing individual audience members in oral presentations using augmented reality. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020;4(2):1–22.

Pasandín AMR, Pérez IP, Iglesias PO, Díaz JJG. Cooperative oral presentations in higher education to enhance technical and soft skills in engineering students. Int J Continuing Eng Educ Life Long Learn. 2023;33(6):592–607.

Ragupathi K, Lee A. Beyond fairness and consistency in grading: The role of rubrics in higher education. Diversity and inclusion in global higher education: Lessons from across Asia. 2020:73–95.

Rabbi MF, Islam MS. The effect of academic stress and Mental anxiety among the students of Khulna University. Edukasiana: Jurnal Inovasi Pendidikan. 2024;3(3):280–99.

Popa CO, Schenk A, Rus A, Szasz S, Suciu N, Szabo DA, Cojocaru C. The role of acceptance and planning in stress management for medical students. Acta Marisiensis-Seria Med. 2020;66(3):101–5.

Christianson M, Payne S. Helping students develop skills for better presentations: Using the 20x20 format for presentation training. 語学研究. 2012;26:1–15.

Romero-Yesa S, Fonseca D, Aláez M, Amo-Filva D. Qualitative assessment of a challenge-based learning and teamwork applied in electronics program. Heliyon. 2023;9(12).

Bosco TJ, Gabriel B, Florence M, Gilbert N. Towards effective teaching and learning ESP in mixed classes: students’ interest, challenges and remedies. Int J Engl Literature Social Sci. 2020;5(2):506–16.

Qiao L, Xu Y, bin Ahmad N, An Analysis Of Implementation Challenges For English, For Specific Purposes (Esp) Formative Assessment Via Blended Learning Mode At Chinese Vocational Polytechnics. Journal Of Digital Education, Communication, And Arts (DECA). 2023;6(02):64–76.

Kho MG-W, Ting S-H. Overcoming oral presentation anxiety: a systematic review of Tertiary ESL/EFL Students’ challenges and strategies. Qeios. 2023.

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Nasiri, E., Khojasteh, L. Evaluating panel discussions in ESP classes: an exploration of international medical students’ and ESP instructors’ perspectives through qualitative research. BMC Med Educ 24 , 925 (2024). https://doi.org/10.1186/s12909-024-05911-3

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Paramedics’ experiences and observations: work-related emotions and well-being resources during the initial months of the COVID-19 pandemic—a qualitative study

• Henna Myrskykari 1 , 2 &
• Hilla Nordquist 3  

BMC Emergency Medicine volume  24 , Article number:  152 ( 2024 ) Cite this article

Metrics details

As first responders, paramedics are an extremely important part of the care chain. COVID-19 significantly impacted their working circumstances. We examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic.

This qualitative study utilized reflective essay material written by experienced, advanced-level Finnish paramedics ( n  = 30). The essays used in this study were written during the fall of 2020 and reflected the period when Finland had declared a state of emergency (on 17.3.2020) and the Emergency Powers Act was implemented. The data was analyzed using an inductive thematic analysis.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: (1) New concerns arose that were constantly present; (2) Surviving without proper guidance; and (3) Rapidly approaching breaking point. Three themes were formed from work-related factors that were identified as resources for the well-being of the EMS personnel. These were: (1) A high level of organizational efficiency was achieved; (2) Adaptable EMS operations; and (3) Encouraging atmosphere.

Conclusions

Crisis management practices should be more attentive to personnel needs, ensuring that managerial and psychological support is readily available in crisis situations. Preparedness that ensures effective organizational adaptation also supports personnel well-being during sudden changes in working circumstances.

Peer Review reports

At the onset of the COVID-19 pandemic, healthcare personnel across the globe faced unprecedented challenges. As initial responders in emergency healthcare, paramedics were quickly placed at the front lines of the pandemic, dealing with a range of emergencies in unpredictable conditions [ 1 ]. The pandemic greatly changed the everyday nature of work [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Those working on the front line were suddenly forced to adjust to personal protective equipment (PPE) requirements [ 9 , 10 ] and rapidly changing instructions that caused significant adjustments to their job description [ 11 , 12 ]. For instance, it has been reported that during the initial stages of the COVID-19 pandemic, Emergency Medical Services (EMS) personnel, including paramedics working in prehospital emergency care, experienced a significant increase in stress [ 10 , 13 ] due to several reasons, such as the lack of protection and support, increased demands, lack of personnel, fear of exposure to COVID-19 during missions, concerns of spreading the virus to family members, and frustration over quickly changing work policies [ 11 , 14 , 15 ].

With the unprecedented challenges posed by the COVID-19 pandemic, some research has been directed toward identifying available resources that help in coping with such situations. For example, Sangal et al. [ 15 ] underscored the association between effective communication and reduced work stress and burnout, and emphasized the critical need for two-way communication, consistent messaging, and the strategic consolidation of information prior to its dissemination. In parallel, Dickson et al. [ 16 ] highlight the pivotal role of leadership strategies in fostering a healthful work environment. These strategies include being relationally engaging, visibly present, open, and caring for oneself and others, while embodying core values such as compassion, empathy, courage, and authenticity. Moreover, Awais et al. [ 14 ] identify essential measures to reduce mental distress and support EMS personnel’s overall well-being in pandemic conditions, such as by providing accessible mental health and peer support, ensuring a transparent information flow, and the implementation of clear, best-practice protocols and guidelines. As a lesson learned from COVID-19, Kihlström et al. (2022) add that crisis communication, flexible working conditions, compensation, and allowing for mistakes should be part of crisis management. They also emphasize the importance of psychological support for employees. [ 12 ]

Overall, the COVID-19 pandemic had a multifaceted impact on EMS personnel, highlighting the necessity for comprehensive support and resilience strategies to safeguard their well-being [ 11 , 17 , 18 ] alongside organizational functions [ 12 , 19 ]. For example, in Finland, it has been noted in the aftermath of COVID-19 that the availability and well-being of healthcare workers are key vulnerabilities of the resilience of the Finnish health system [ 12 ]. Effective preparedness planning and organizational resilience benefit from learning from past events and gaining a deeper understanding of observations across different organizational levels [ 12 , 19 , 20 ]. For these reasons, it is important to study how the personnel experienced the changing working circumstances and to recognize the resources, even unexpected ones, that supported their well-being during the initial phase of the COVID-19 pandemic [ 12 , 19 ].

The aim of this study was to examine the emotions experienced and the resources identified as supportive of work well-being during the initial months of the COVID-19 pandemic, from the perspective of the paramedics. Our research questions were: According to the experiences and observations of paramedics, (1) what kinds of emotions did the EMS personnel experience in the new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic? In this study, emotions are understood as complex responses involving psychological, physiological, and behavioral components, triggered by significant events or situations [ 21 ]. Resources are understood as physical, psychological, social, or organizational aspects of the work that help achieve work goals, reduce demands and associated costs [ 22 ].

Materials and methods

This qualitative study utilized reflective essay material written in the fall of 2020 by experienced, advanced-level paramedics who worked in the Finnish EMS during the early phase of the pandemic, when Finland had declared (March 17, 2020 onward) a state of emergency and implemented the Emergency Powers Act. This allowed for new rules and guidelines from the government to ensure the security of healthcare resources. Some work rules for healthcare personnel changed, and non-urgent services were limited.

Data collection procedures

This study is part of a broader, non-project-based research initiative investigating the work well-being of paramedics from various perspectives, and the data was collected for research purposes from this standpoint. The data collection for this study was conducted at the South-Eastern Finland University of Applied Sciences as part of the Current Issues in EMS Management course. The course participants were experienced, advanced-level Finnish paramedics who were students of the master’s degree program in Development and Management of Emergency Medical Services. A similar data collection method has been utilized in other qualitative studies [for example, 23 , 24 ].

The South-Eastern Finland University of Applied Sciences granted research permission for the data collection on August 20, 2020. The learning platform “Learn” (an adapted version of Moodle [ 25 ]) was used to gather the data. A research notice, privacy statement, and essay writing instructions were published on the platform on August 21, 2020. The paramedics were asked to write about their own experiences and observations regarding how the state of emergency impacted the work well-being of EMS personnel. They were instructed not to use references but only their own reflections. Three guiding questions were asked: “What kind of workloads did EMS personnel experience during the state of emergency?” “How has this workload differed from normal conditions?” and “What effects did this workload have on the well-being of the EMS personnel?”. The assignment did not refer solely to paramedics because the EMS field community may also include individuals with other titles (such as EMS field supervisors or firefighters performing prehospital emergency care); hence the term “EMS personnel” was used.

The essay was part of the mandatory course assignments, but submitting it for research purposes was voluntary. The paramedics were informed that their participation in the study would not affect their course evaluations. They had the freedom to decline, remove parts of, or withdraw the essay before analysis. None of the paramedics exercised these options. They were also informed that the last author removes any identifying details (such as names, places, and organizational descriptions that could reveal their workplace) before sharing the data with other, at the time unnamed, researchers. The last author (female) is a senior researcher specializing in EMS and work well-being topics, a principal lecturer of the respective course, and the head of the respective master’s program, and familiar to all of them through their studies. The paramedics were aware that the essays were graded by the last author on a pass/fail scale as part of the course assessment. However, comprehensive and well-reasoned reflections positively influenced the course grade. The evaluation was not part of this study. The paramedics had the opportunity to ask further questions about the study directly from the last author during and after the essay writing process and the course.

The paramedics wrote the essays between August 23, 2020, and November 30, 2020. Thirty-two paramedics (out of 39) returned their essays using the Learn platform during this timeframe. Thus, seven of the course completions were delayed, and the essays written later were no longer appropriate to include in the data due to the time elapsed since the initial months of the COVID-19 pandemic.

All 32 gave their informed consent for their essays to be included in the study. Essays written by paramedics who had not actively participated in EMS field work during exceptional circumstances were excluded from the material ( n  = 2), because they wrote the essay from a different perspective, as they could not reflect on their own experiences and observations. Thus, a total of 30 essays were included in the study. The total material was 106 pages long and comprised 32,621 words in Finnish.

Study participants

Thirty advanced-level paramedics from Finland participated in this study. They all had a bachelor’s degree in emergency care or nursing with additional emergency care specialization. At the time of the study, they were pursuing their master’s studies. Thirteen of them were women, and seventeen were men. The average age of the participants was 33.5 years among women and 35.9 years among men. Women had an average of 8.7 years of work experience, and men had 8.8 years. All the participating paramedics worked in EMS in different areas across Finland (except northern Finland) during their studies and the early phase of the pandemic.

Data analysis

The data was analyzed with a thematic analysis following the process detailed by Braun & Clarke [ 26 ]. First, the two researchers thoroughly familiarized themselves with the data, and the refined aim and research questions of the study were formulated inductively in collaboration based on the content of the data (see [ 26 ], page 84). After this, a thorough coding process was mainly carried out by the first author (female), who holds a master’s degree, is an advanced-level paramedic who worked in EMS during the pandemic, and at the time of the analysis was pursuing her doctoral studies in a different subject area related to EMS. Generating the initial codes involved making notes of interesting features of anything that stood out or seemed relevant to the research question systematically across the entire dataset. During this process, the original paragraphs and sentences were copied from the essay material into a table in Microsoft Word, with each research question in separate documents and each paragraph or sentence in its own row. The content of these data extracts was then coded in the adjacent column, carefully preserving the original content but in a more concise form. Then, the content was analyzed, and codes were combined to identify themes. After that, the authors reviewed the themes together by moving back and forth between the original material, the data in the Word documents, and the potential themes. During this process, the authors worked closely and refined the themes, allowing them to be separated and combined into new themes. For example, emotions depicting frustration and a shift to indifference formed their own theme in this kind of process. Finally, the themes were defined into main, major and minor themes and named. In the results, the main themes form the core in response to the research questions and include the most descriptions from the data. The major themes are significant but not as central as the main themes. Major themes provide additional depth and context to the results. One minor theme was formed as the analysis process progressed, and it provided valuable insights and details that deepened the response to the research question. All the coded data was utilized in the formed themes. The full content of the themes is reported in the Results section.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: New concerns arose that were constantly present (main theme); Surviving without proper guidance (major theme); and Rapidly approaching breaking point (major theme) (Fig.  1 ). Work-related factors identified as resources for the well-being of EMS personnel formed three themes: A high level of organizational efficiency was achieved (main theme); Adaptable EMS operations (major theme); and Encouraging atmosphere (minor theme) (Fig.  2 ).

Emotions experienced by the EMS personnel in their new working circumstances

Main theme: New concerns arose that were constantly present

The main theme included several kinds of new concerns. In the beginning, the uncertainty about the virus raised concerns about work safety and the means to prevent the spread of the disease. The initial lack of training and routines led to uncertainty. In addition, the decrease in the number of EMS missions raised fears of units being reduced and unilateral decisions by the management to change the EMS personnel’s work responsibilities. The future was also a source of uncertainty in the early stages. For example, the transition to exceptional circumstances, concerns about management and the supervisors’ familiarity with national guidelines and lack of information related to sickness absence procedures, leave, personal career progression, and even the progress of vaccine development, all contributed to this feeling of uncertainty. The initial uncertainty was described as the most challenging phase, but the uncertainty was also described as long-lasting.

Being on the front line with an unknown, potentially dangerous, and easily transmissible virus caused daily concerns about the personnel’s own health, especially when some patients hid their symptoms. The thought of working without proper PPE was frightening. On the other hand, waiting for a patient’s test result was stressful, as it often resulted in many colleagues being quarantined. A constant concern for the health of loved ones and the fear of contracting the virus and unknowingly bringing it home or transmitting it to colleagues led the EMS personnel to change their behavior by limiting contact.

Being part of a high-risk group , I often wondered , in the case of coronavirus , who would protect me and other paramedics from human vanity and selfishness [of those refusing to follow the public health guidelines]? (Participant 25)

The EMS personnel felt a weight of responsibility to act correctly, especially from the perspective of keeping their skills up to date. The proper selection of PPE and aseptic procedures were significant sources of concern, as making mistakes was feared to lead to quarantine and increase their colleagues’ workloads. At the same time, concerns about the adequacy of PPE weighed on the personnel, and they felt pressure on this matter to avoid wastage of PPEs. The variability in the quality of PPE also caused concerns.

Concerns about acting correctly were also tied to ethical considerations and feelings of inadequacy when the personnel were unable to explain to patients why COVID-19 caused restrictions on healthcare services. The presence of students also provoked such ethical concerns. Recognizing patients’ symptoms correctly also felt distressing due to the immense responsibility. This concern was also closely tied to fear and even made some question their career choices. The EMS personnel were also worried about adequate treatment for the patients and sometimes felt that the patients were left alone at home to cope. A reduction in patient numbers in the early stages of the pandemic raised concerns about whether acutely ill individuals were seeking help. At the same time, the time taken to put on PPE stressed the personnel because it increased delays in providing care. In the early phase of the pandemic, the EMS personnel were stressed that patients were not protected from them.

I’m vexed in the workplace. I felt it was immediately necessary to protect patients from us paramedics as well. It wasn’t specifically called for , mostly it felt like everyone had a strong need to protect themselves. (Participant 30)

All these concerns caused a particularly heavy psychological burden on some personnel. They described feeling more fatigued and irritable than usual. They had to familiarize themselves with new guidelines even during their free time, which was exhausting. The situation felt unjust, and there was a looming fear of the entire healthcare system collapsing. COVID-19 was omnipresent. Even at the base station of the EMS services, movement was restricted and social distancing was mandated. Such segregation, even within the professional community, added to the strain and reduced opportunities for peer support. The EMS personnel felt isolated, and thoughts about changing professions increased.

It was inevitable that the segregation of the work community would affect the community spirit , and a less able work community has a significant impact on the individual level. (Participant 8)

Major theme: Surviving without proper guidance

At the onset of the pandemic, the job description of the EMS personnel underwent changes, and employers could suddenly relocate them to other work. There was not always adequate support for familiarizing oneself with the new roles, leading to a feeling of loss of control. The management was described as commanding and restricting the personnel’s actions. As opportunities to influence one’s work diminished, the sense of job satisfaction and motivation decreased.

Some felt that leadership was inadequate and neglectful, especially when the leaders switched to remote work. The management did not take the situation seriously enough, leaving the EMS personnel feeling abandoned. The lack of consistent leadership and failure to listen to the personnel caused dissatisfaction and reduced occupational endurance. In addition, the reduced contact with colleagues and close ones reduced the amount of peer support. The existing models for psychological support were found to be inadequate.

Particularly in the early stages, guidelines were seen as ambiguous and deficient, causing frustration, irritation, and fear. The guidelines also changed constantly, even daily, and it was felt that the information did not flow properly from the management to the personnel. Changes in protection recommendations also led to skepticism about the correctness of the national guidance, and the lack of consistent guidelines perplexed the personnel. Internalizing the guidelines was not supported adequately, but the necessity to grasp new information was described as immense and cognitively demanding.

At times , it felt like the work was a kind of survival in a jungle of changing instructions , one mission at a time. (Participant 11)

Major theme: Rapidly approaching breaking point

Risking one’s own health at work caused contentious feelings while concurrently feeling angry that management could work remotely. The arrogant behavior of people toward COVID-19 left them frustrated, while the EMS personnel had to limit their contacts and lost their annual leave. There were fears about forced labor.

Incomplete and constantly changing guidelines caused irritation and indifference, as the same tasks had to be performed with different levels of PPE within a short time. Some guidelines were difficult to comply with in practice, which was vexing.

Using a protective mask was described as distressing, especially on long and demanding missions. Communication and operation became more difficult. Some described frustration with cleaning PPE meant for single use.

Ensuring the proper implementation of a work pair’s aseptic and equipment maintenance was burdensome, and explaining and repeating guidelines was exhausting. A feeling of indifference was emphasized toward the end of a long shift.

After the initial stage, many began to slip with the PPE guidelines and found the instructions excessive. COVID-19 information transmitted by the emergency center lost its meaning, and instructions were left unheeded, as there was no energy to believe that the patient would have COVID-19, especially if only a few disease cases had been reported in their area.

It was disheartening to hear personnel being labeled as selfish for demanding higher pay during exceptional circumstances. This lack of recognition eroded professionalism and increased thoughts of changing professions.

However , being a doormat and a human toilet , as well as a lack of appreciation , undermines my professionalism and the prolonged situation has led me to seriously consider a different job , where values other than dedication and constant flexibility carry weight. I have heard similar thoughts from other colleagues. None of us do this for money. (Participant 9)

Work-related factors identified as resources for the well-being of EMS personnel

Main theme: A high level of organizational efficiency was achieved

The main theme held several different efficient functions. In the early stages of the pandemic, some felt that the information flow was active. Organizations informed the EMS personnel about the disease, its spread, and its impact on the workplace and emergency care activities.

Some felt that managers were easily accessible during the pandemic, at least remotely. Some managers worked long days to be able to support their personnel.

The response to hate and uncertainty was that one of the supervisors was always present in the morning and evening meetings. Supervisors worked long hours so as to be accessible via remote access. (Participant 26)

The organizations took effective steps to control infections. Quick access to COVID-19 tests, clear guidelines for taking sick leave, and permission to take sick leave with a low threshold were seen as positive things. The consideration of personnel belonging to risk groups by moving them to other work tasks was also perceived as positive. In addition, efforts were made to prevent the emergence of infection chains by isolating EMS personnel in their own social facilities.

Established guidelines, especially on the correct use of protective measures, made it easier to work. Some mentioned that the guidelines were available in ambulances and on phones, allowing the protection guidelines to be checked before going on a mission.

The employers took into account the need for psychological support in a diverse manner. Some organizations provided psychological support such as peer debriefing activities, talking therapy with mental health professionals, actively inquiring about their personnel’s feelings, and training them as support workers. The pandemic situation also caused organizations to create their own standard operating models to decrease mental load.

Fortunately , the problem has now been addressed actively , as a peer-to-peer defusing model was built up at our workplace during the crisis , and group defusing has started , the purpose of which is to lighten the work-related mental load. (Participant 3)

Major theme: Adaptable EMS operations

There were several different resources that clarified mission activities. The amount of protective and cleaning equipment was ramped up, and the treatment equipment was quickly updated to meet the demands brought about by the pandemic and to enable safety distances for the EMS personnel. In addition, various guidelines were amended to reduce exposure. For example, personnel on the dedicated COVID-19 ambulances were separated to work without physical contact with others, and field supervisors joined the EMS missions less often than before. Moreover, people at the scene were contacted by phone in advance to ensure that there would be no exposure risk, which also allowed other occupational safety risks to be identified. New practices resulted from the pandemic, such as cleaning communication equipment during shift changes and regularly using PPE with infected patients. All of these were seen as positive resources for efficient work.

At the end of each shift , all keys , telephones , etc., were cleaned and handed over to the next shift. This practice was not previously established in our area , but this will become a permanent practice in the future and is perceived by everyone in our work community as a positive thing. (Participant 10)

Some stated that access to PPE was sufficient, especially in areas where the number of COVID-19 infections was low. PPE was upgraded to make it easier to wear. Further, organizations acquired a variety of cleaning equipment to speed up the disinfection of ambulances.

Organizations hired more employees to enable leave and the operation of dedicated COVID-19 ambulances. The overall number of ambulances was also increased. Non-urgent missions were handled through enhanced phone services, reducing the unnecessary exposure of EMS personnel to COVID-19.

Five extra holiday substitutes were hired for EMS so that the employer could guarantee the success of agreed leave , even if the Emergency Preparedness Act had given them opportunities to cancel or postpone it. (Participant 12)

Minor theme: Encouraging atmosphere

Peer support from colleagues, a positive, comfortable, pleasant work environment, and open discussion, as well as smooth cooperation with other healthcare employees were felt to be resources for work well-being by reducing the heavy workload experienced. Due to the pandemic, the appreciation of healthcare was felt to increase slightly, which was identified as a resource.

One factor affecting resilience in the healthcare sector is certainly that in exceptional circumstances , visibility and appreciation have somewhat increased. (Participant 23)

This study examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic. Each research question was answered with three themes.

Previous studies have shown that the pandemic increased the workload of paramedics, prompting changes in their operating models and the function of EMS to align with new pandemic-related requirements [ 9 , 27 ]. Initially, the paramedics in the current study described facing unclear and deficient guidelines and feeling obligated to follow instructions without adequate support to internalize them. Constantly changing instructions were linked to negative emotions in various ways. Moreover, the overwhelming flood of information was heavily connected to this, although the information flow was also perceived as a resource, especially when it was timely and well-structured. The study by Sangal et al. [ 15 ] has raised similar observations and points out the importance of paying special attention to the personnel working in the frontline, as in EMS, who might be more heavily impacted by too much information and anxiety about it. They also discovered that three factors are crucial for addressing the challenges of information overload and anxiety: consolidating information before distributing it, maintaining consistent communication, and ensuring communication is two-way. McAlearney et al. [ 11 ] found that first responders, including EMS personnel, reported frustration regarding COVID-19 information because of inconsistencies between sources, misinformation on social media, and the impact of politics. A Finnish study also recognized that health systems were not sufficiently prepared for the flood of information in the current media environment [ 12 ]. Based on these previous results and our findings, it can be concluded that proper implementation of crisis communication should be an integral part of organizations’ preparedness in the future, ensuring that communication effectively supports employee actions in real-life situations. Secondly, this topic highlights the need for precise guidelines and their implementation. With better preparedness, similar chaos could be avoided in the future [ 17 ].

Many other factors also caused changes in work. The EMS mission profile changed [ 3 , 4 , 5 , 6 ], where paramedics in this study saw concerns. To prevent infection risk, the number of pre-arrival calls increased [ 7 ], the duration of EMS missions increased [ 8 , 9 ], and the continuous use of PPE and enhanced hygiene standards imposed additional burdens [ 9 , 10 ]. In Finland, there was no preparedness for the levels of PPE usage required in the early stages of the pandemic [ 12 ]. In this study, paramedics described that working with potentially inadequate PPE caused fear and frustration, which was increased by a lack of training, causing them to feel a great deal of responsibility for acting aseptically and caring for patients correctly. Conversely, providing adequate PPE, information and training has been found to increase the willingness to work [ 28 ] and the sense of safety in working in a pandemic situation [ 29 ], meaning that the role of precise training, operating instructions and leadership in the use of PPE is emphasized [ 30 ].

The paramedics in this study described many additional new concerns in their work, affecting their lives comprehensively. It has been similarly described that the pandemic adversely affected the overall well-being of healthcare personnel [ 31 ]. The restrictions implemented also impacted their leisure time [ 32 ], and the virus caused concerns for their own and their families’ health [ 11 , 28 ]. In line with this, the pandemic increased stress, burnout [ 10 , 33 ], and anxiety among EMS personnel and other healthcare personnel working on the frontline [ 11 , 14 , 34 , 35 ]. These kinds of results underscore the need for adequate guidance and support, a lack of which paramedics reported experiencing in the current study.

Personnel play a crucial role in the efficient operation of an organization and comprise the main identified resource in this study. Previous studies and summaries have highlighted that EMS personnel did not receive sufficient support during the COVID-19 pandemic [ 11 , 14 , 17 , 18 ]. Research has also brought to light elements of adequate support related to the pandemic, such as a review by Dickson et al. [ 16 ] that presents six tentative theories for healthful leadership, all of which are intertwined with genuine encounter, preparedness, and information use. In this current study, the results showed numerous factors related to these contexts that were identified as resources, specifically underlined by elements of caring, effective operational change, knowledge-based actions, and present leadership, similarly described in a study by Eaton-Williams & Williams [ 18 ]. Moreover, the paramedics in our study highlighted the importance of encouragement and identified peer support from colleagues as a resource, which is in line with studies in the UK and Finland [ 12 , 23 , 37 ].

In the early stages of the pandemic, it was noted that the EMS personnel lacked adequate training to manage their mental health, and there was a significant shortage of psychosocial support measures [ 14 ], although easy access to support would have been significant [ 18 ]. In the current study, some paramedics felt that mental health support was inadequate and delayed, while others observed an increase in mental health support during the pandemic, seeing it as an incentive for organizations to develop standard operating models for mental support, for example. This awakening was identified as a resource. This is consistent, as providing psychological support to personnel has been highlighted as a core aspect of crisis management in a Finnish study assessing health system resilience related to COVID-19 [ 12 ]. In a comprehensive recommendation commentary, Isakov et al. [ 17 ] suggest developing a national strategy to improve resilience by addressing the mental health consequences of COVID-19 and other occupational stressors for EMS personnel. This concept, applicable beyond the US, supports the view that EMS organizations are becoming increasingly aware of the need to prepare for and invest in this area.

A fundamental factor likely underlying all the described emotions was that changes in the job descriptions of the EMS personnel due to the pandemic were significant and, in part, mandated from above. In this study, paramedics described feelings of concern and frustration related to these many changes and uncertainties. According to Zamoum and Gorpe (2018), efficient crisis management emphasizes the importance of respecting emotions, recognizing rights, and making appropriate decisions. Restoring trust is a significant challenge in a crisis situation, one that cannot be resolved without complete transparency and open communication [ 38 ]. This perspective is crucial to consider in planning for future preparedness. Overall, the perspective of employee rights and obligations in exceptional circumstances has been relatively under-researched, but in Australia, grounding research on this perspective has been conducted with paramedics using various approaches [ 39 , 40 , 41 ]. The researchers conclude that there is a lack of clarity about the concept of professional obligation, specifically regarding its boundaries, and the issue urgently needs to be addressed by developing clear guidelines that outline the obligation to respond, both in normal day-to-day operations and during exceptional circumstances [ 39 ].

Complex adaptive systems (CAS) theory recognizes that in a resilient organization, different levels adapt to changing environments [ 19 , 20 ]. Barasa et al. (2018) note that planned resilience and adaptive resilience are both important [ 19 ]. Kihlström et al. (2022) note that the health system’s resilience was strengthened by a certain expectation of crisis, and they also recognized further study needs on how effectively management is responding to weak signals [ 12 ]. This could be directly related to how personnel can prepare for future changes. The results of this study revealed many negative emotions related to sudden changes, but at the same time, effective organizational adaptation was identified as a resource for the well-being of EMS personnel. Dissecting different elements of system adaptation in a crisis has been recognized as a highly necessary area for further research [ 20 ]. Kihlström et al. (2022) emphasize the importance of ensuring a healthy workforce across the entire health system. These frameworks suggest numerous potential areas for future research, which would also enhance effective preparedness [ 12 ].

Limitations of the study

In this study, we utilized essay material written in the fall of 2020, in which experienced paramedics reflected on the early stages of the COVID-19 pandemic from a work-oriented perspective. The essays were approached inductively, meaning that they were not directly written to answer our research questions, but the aim and the research questions were shaped based on the content [ 26 ]. The essays included extensive descriptions that aligned well with the aim of this study. However, it is important to remember when interpreting the results that asking specifically about this topic, for instance, in an interview, might have yielded different descriptions. It can be assessed that the study achieved a tentative descriptive level, as the detailed examination of complex phenomena such as emotions and resources would require various methods and observations.

Although the essays were mostly profound, well-thought-out, and clearly written, their credibility [ 42 ] may be affected by the fact that several months had passed between the time the essays were written and the events described. Memories may have altered, potentially influencing the content of the writings. Diary-like material from the very onset of the pandemic might have yielded more precise data, and such a data collection method could be considered in future research on exceptional circumstances.

The credibility [ 42 ] could also have been enhanced if the paramedics who wrote the essays had commented on the results and provided additional perspectives on the material and analysis through a multi-phase data collection process. This was not deemed feasible in this study, mainly because there was a 2.5-year gap between data collection and the start of the analysis. However, this also strengthened the overall trustworthiness of the study, as it allowed the first author, who had worked in prehospital emergency care during the initial phase of the pandemic, to maintain a distance from the subject, and enabled a comparison of our own findings with previously published research that investigated the same period in different contexts. The comparison was made when writing the discussion, with the analysis itself being inductive and following the thematic analysis process described by Braun & Clarke [ 26 ].

When evaluating credibility [ 42 ], it should also be noted that the participants who wrote the essays, i.e., the data for the study, were experienced paramedics but also students and one of the researchers was their principal lecturer. This could potentially limit credibility if the students, for some reason, did not want to produce truthful content for their lecturer to read. However, this risk can be considered small because the essays’ topics did not concern the students’ academic progress, the essays’ content was quite consistent, and the results aligned with other studies. As a strength, it can be considered that the students shared their experiences without holding back, as the thoughts were not for workplace use, and they could trust the data privacy statement.

To enhance transferability [ 42 ], the context of the study was described in detail, highlighting the conditions prevailing in Finnish prehospital emergency care during the early stages of the pandemic. Moreover, including a diverse range of perspectives from paramedics working in different regions of Finland (except Northern Finland) contributes to the transferability of the study, indicating that the results may be applicable and relevant to a wider context beyond a single specific region.

Dependability [ 42 ] was reinforced by the close involvement of two researchers from different backgrounds in the analysis of the material, but a limitation is that no separate analyses were conducted. However, the original data was repeatedly revisited during the analysis, which strengthened the dependability. Moreover, the first author kept detailed notes throughout the analysis process, and the last author supervised the progress while also contributing to the analysis and reporting. The research process is also reported in detail.

This study highlighted numerous, mainly negative emotions experienced by EMS personnel during the initial months of the COVID-19 pandemic due to new working circumstances. At the same time, several work-related factors were identified as resources for their well-being. The findings suggest that crisis management practices should be more attentive to personnel needs, ensuring that personnel have the necessary support, both managerial and psychological, readily available in crisis situations. Effective organizational adaptation in a crisis situation also supports personnel well-being, emphasizing the importance of effective preparedness. Future research should particularly focus on considering personnel well-being as part of organizational adaptation during exceptional circumstances and utilize these findings to enhance preparedness.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the inclusion of sensitive information and the extent of the informed consent provided by the participants.

Abbreviations

Complex Adaptive Systems (theory)

Coronavirus Disease 2019

Emergency Medical Services

Personal Protective Equipment

United Kingdom

Drennan IR, Blanchard IE, Buick JE. Opportunity for change: is it time to redefine the role of paramedics in healthcare? CJEM. 2021;23(2):139–40. https://doi.org/10.1007/s43678-021-00105-y

Boechler L, Cameron C, Smith JC, Ford-Jones P, Suthers P. Impactful approaches to Leadership on the Front lines of the COVID-19 pandemic: lived experiences of Canadian paramedics. Healthc Q. 2021;24(3):42–7. https://doi.org/10.12927/hcq.2021.26620 .

Article   PubMed   Google Scholar  

Lerner EB, Newgard CD, Mann NC. Effect of the Coronavirus Disease 2019 (COVID-19) pandemic on the U.S. Emergency Medical Services System: a preliminary Report. Acad Emerg Med. 2020;27(8):693–9. https://doi.org/10.1111/acem.14051 .

Article   PubMed   PubMed Central   Google Scholar  

O’Connor AW, Hannah HA, Burnor EA, Fukutaki KG, Peterson T, Ballard DW, et al. Emergency Medical Service Utilization and response following COVID-19 emergency and stay-at-home policies: an interrupted time-series analysis. Cureus. 2021;21(11). https://doi.org/10.7759/cureus.19794 .

Azbel M, Heinänen M, Lääperi M, Kuisma M. Effects of the COVID-19 pandemic on trauma-related emergency medical service calls: a retrospective cohort study. BMC Emerg Med. 2021;9(1):102. https://doi.org/10.1186/s12873-021-00495-3 .

Article   CAS   Google Scholar  

Lane DJ, Blanchard IE, Buick JE, Shaw M, McRae AD. Changes in presentation, presenting severity and disposition among patients accessing emergency services during the first months of the COVID-19 pandemic in Calgary, Alberta: a descriptive study. CMAJ Open. 2021;9(2):592–601. https://doi.org/10.9778/cmajo.20200313 .

Article   Google Scholar  

Shukla V, Lau CSM, Towns M, Mayer J, Kalkbrenner K, Beuerlein S, et al. COVID-19 exposure among First Responders in Arizona. J Occup Environ Med. 2020;62(12):981–5. https://doi.org/10.1097/JOM.0000000000002027 .

Article   CAS   PubMed   Google Scholar  

Andrew E, Nehme Z, Stephenson M, Walker T, Smith K. The impact of the COVID-19 pandemic on demand for emergency ambulances in Victoria, Australia. Prehosp Emerg Care. 2021;16:1–7. https://doi.org/10.1080/10903127.2021.1944409 .

Eskol JR, Zegers FD, Wittrock D, Lassen AT, Mikkelsen S. Increased ambulance on-scene times but unaffected response times during the first wave of the COVID-19 pandemic in Southern Denmark. BMC Emerg Med. 2022;9(1):61. https://doi.org/10.1186/s12873-022-00623-7 .

Schumann H, Böckelmann I, Thielmann B. Relaxation and strain among emergency medical service personnel and emergency control center dispatchers during the first two waves of the SARS-CoV-2 pandemic. Med Pr. 2023;15(5):353–62. https://doi.org/10.13075/mp.5893.01401 .

McAlearney AS, Gaughan AA, MacEwan SR, Gregory ME, Rush LJ, Volney J, et al. Pandemic experience of first responders: fear, frustration, and stress. Int J Environ Res Public Health. 2022;13(8):4693. https://doi.org/10.3390/ijerph19084693 .

Kihlström L, Huhtakangas M, Karreinen S, Viita-Aho M, Keskimäki I, Tynkkynen LK. Local cooperation has been the cornerstone: facilitators and barriers to resilience in a decentralized health system during COVID-19 in Finland. J Health Organ Manag. 2022. https://doi.org/10.1108/JHOM-02-2022-0069 .

Hendrickson RC, Slevin RA, Hoerster KD, Chang BP, Sano E, McCall CA, et al. The impact of the COVID-19 pandemic on Mental Health, Occupational Functioning, and Professional Retention among Health Care workers and First Responders. J Gen Intern Med. 2022;37(2):397–408. https://doi.org/10.1007/s11606-021-07252-z .

Awais SB, Martins RS, Khan MS. Paramedics in pandemics: protecting the mental wellness of those behind enemy lines. Br J Psychiatry. 2021;218(2):75–6. https://doi.org/10.1192/bjp.2020.193 .

Sangal RB, Bray A, Reid E, Ulrich A, Liebhardt B, Venkatesh AK, et al. Leadership communication, stress, and burnout among frontline emergency department staff amid the COVID-19 pandemic: a mixed methods approach. Healthc (Amst). 2021;9(4):100577. https://doi.org/10.1016/j.hjdsi.2021.100577 .

Dickson CAW, Davies C, McCormack B, Westcott L, Merrell J, Mcilfatrick S, et al. UK nurses’ and midwives’ experiences of healthful leadership practices during the COVID-19 pandemic: a rapid realist review. J Nurs Manag. 2022;30(8):3942–57. https://doi.org/10.1111/jonm.13790 .

Isakov A, Carr M, Munjal KG, Kumar L, Gausche-Hill MEMS. Agenda 2050 meets the COVID-19 pandemic. Health Secur. 2022;20(S1):S97–106. https://doi.org/10.1089/hs.2021.0179 .

Eaton-Williams PJ, Williams J. See us as humans. Speak to us with respect. Listen to us. A qualitative study on UK ambulance staff requirements of leadership while working during the COVID-19 pandemic. BMJ Lead. 2023;7(2):102–7. https://doi.org/10.1136/leader-2022-000622 .

Barasa E, Mbau R, Gilson L. What is resilience and how can it be nurtured? A systematic review of empirical literature on Organizational Resilience. Int J Health Policy Manag. 2018;7(6):491–503. https://doi.org/10.15171/ijhpm.2018.06 .

Coetzee C, Van Niekerk D, Raju E. Disaster resilience and complex adaptive systems theory: finding common grounds for risk reduction. Disaster Prev Manage. 2016;25(2):196–211. https://doi.org/10.1108/DPM-07-2015-0153 .

American Psychological Association. Emotion. APA Dictionary of Psychology. https://dictionary.apa.org/emotion . Accessed on 26.6.2024.

Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB. The job demands-resources model of burnout. J Appl Psychol. 2001;86(3):499–512.

Ericsson CR, Lindström V, Rudman A, Nordquist H. Paramedics’ perceptions of job demands and resources in Finnish emergency medical services: a qualitative study. BMC Health Serv Res. 2022;22(1):1469. https://doi.org/10.1186/s12913-022-08856-9 .

Rinkinen T, Kinnula M, Nordquist H. Technological development roles and needs in pre-hospital emergency care from the advanced level paramedics’ perspective. Int Emerg Nurs. 2024;73:101406. https://doi.org/10.1016/j.ienj.2024.101406 .

Moodle Pty Ltd. 2020. https://moodle.org/ . Accessed on date 17.6.2024.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:2:77–101. https://doi.org/10.1191/1478088706qp063oa .

Prezant DJ, Lancet EA, Zeig-Owens R, Lai PH, Appel D, Webber MP, et al. System impacts of the COVID-19 pandemic on New York City’s emergency medical services. J Am Coll Emerg Physicians Open. 2020;9(6):1205–13. https://doi.org/10.1002/emp2.12301 .

Alwidyan MT, Oteir AO, Trainor J. Working during pandemic disasters: views and predictors of EMS providers. Disaster Med Public Health Prep. 2022;16(1):116–22. https://doi.org/10.1017/dmp.2020.131 .

Rebmann T, Charney RL, Loux TM, Turner JA, Abbyad YS, Silvestros M. Emergency Medical Services Personnel’s pandemic influenza training received and willingness to work during a future pandemic. Prehosp Emerg Care. 2020;24(5):601–9. https://doi.org/10.1080/10903127.2019.1701158 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

McCann-Pineo M, Li T, Barbara P, Levinsky B, Berkowitz J. Factors influencing Use of Personal Protective Equipment among Emergency Medical services Responders during the COVID-19 pandemic: a Retrospective Chart Review. West J Emerg Med. 2022;23(3):396–407. https://doi.org/10.5811/westjem.2022.2.55217 .

Vanhaecht K, Seys D, Bruyneel L, Cox B, Kaesemans G, Cloet M, et al. COVID-19 is having a destructive impact on health-care workers’ mental well-being. Int J Qual Health Care. 2021;20(1):mzaa158. https://doi.org/10.1093/intqhc/mzaa158 .

Zolnikov TR, Furio F. First responders and social distancing during the COVID-19 pandemic. J Hum Behav Soc Environ. 2021;31(1–4):244–53. https://doi.org/10.1080/10911359.2020.1811826 .

Spychała A, Piwowarska M, Piekut A. The COVID-19 pandemic as a stress factor in the work of a paramedic. Med Pr. 2023;8(1):9–17. https://doi.org/10.13075/mp.5893.01278 .

Roberts R, Wong A, Jenkins S, Neher A, Sutton C, O’Meara P, et al. Mental health and well-being impacts of COVID-19 on rural paramedics, police, community nurses and child protection workers. Aust J Rural Health. 2021;29(5):753–67. https://doi.org/10.1111/ajr.12804 .

Chang YT, Hu YJ. Burnout and Health issues among Prehospital Personnel in Taiwan Fire Departments during a Sudden Spike in Community COVID-19 cases: a cross-sectional study. Int J Environ Res Public Health. 2022;16(4):2257. https://doi.org/10.3390/ijerph19042257 .

Mausz J, Donnelly EA, Moll S, Harms S, McConnell M. Mental disorder symptoms and the relationship with resilience among paramedics in a single Canadian site. Int J Environ Res Public Health. 2022;17(8):4879. https://doi.org/10.3390/ijerph19084879 .

Phung VH, Sanderson K, Pritchard G, Bell F, Hird K, Wankhade P, et al. The experiences and perceptions of wellbeing provision among English ambulance services staff: a multi-method qualitative study. BMC Health Serv Res. 2022;15(1):1352. https://doi.org/10.1186/s12913-022-08729-1 .

Zamoum K, Gorpe TS. Crisis Management: a historical and conceptual Approach for a better understanding of today’s crises. Crisis Manage - Theory Pract InTech. 2018. https://doi.org/10.5772/intechopen.76198 .

Anderson C, Pooley JA, Mills B, Anderson E, Smith EC. Do paramedics have a Professional Obligation to work during a pandemic? A qualitative exploration of Community Member expectations. Disaster Med Public Health Prep. 2020;14(3):406–12. https://doi.org/10.1017/dmp.2020.212 .

Smith E, Burkle FM, Gebbie K, Ford D, Bensimon C. Acceptable limitations on Paramedic Duty to treat during disaster: a qualitative exploration. Prehosp Disaster Med. 2018;33(5):466–70. https://doi.org/10.1017/S1049023X18000857 .

Smith E, Burkle F, Gebbie K, Ford D, Bensimon C. A qualitative study of paramedic duty to treat during disaster response. Disaster Med Public Health Prep. 2019;13(2):191–6. https://doi.org/10.1017/dmp.2018.15 .

Cypress BS. Rigor or reliability and validity in qualitative research: perspectives, strategies, reconceptualization, and recommendations. Dimens Crit Care Nurs. 2017;36(4):253–63. https://doi.org/10.1097/DCC.0000000000000253 .

TENK. Guidelines for the responsible conduct of research and for handling allegations of misconduct in Finland [Internet]. Helsinki: Finnish National Board on Research Integrity TENK; 2023. https://tenk.fi/sites/default/files/2023-11/RI_Guidelines_2023.pdf . Accessed 13 Jan 2024.

TENK. Ethical review in human sciences [Internet]. Helsinki: Finnish National Board on Research Integrity TENK; 2020. https://tenk.fi/sites/default/files/2021-1/Ethical_review_in_human_sciences_2020.pdf . Accessed 13 Jan 2024.

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We want to sincerely thank all the paramedics who participated in this study.

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The study followed the good scientific practice defined by the Finnish National Board on Research Integrity TENK [ 43 ]. The study was conducted in accordance with the Helsinki Declaration and applicable national guidelines. Adhering to the Finnish National Board on Research Integrity (TENK) guidelines on ethical principles of research with human participants and ethical review in the human sciences in Finland, an ethical review statement from a human sciences ethics committee was not required for this type of study. The participants consisted of adult students engaged in regular employment. Their involvement in the research was grounded on informed consent. The study did not involve concerns regarding the participants’ physical integrity, nor were they subjected to exceptionally strong stimuli. The potential for causing mental harm was not beyond what is typically encountered in everyday life, and their participation did not pose any safety risks [ 44 ].

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Myrskykari, H., Nordquist, H. Paramedics’ experiences and observations: work-related emotions and well-being resources during the initial months of the COVID-19 pandemic—a qualitative study. BMC Emerg Med 24 , 152 (2024). https://doi.org/10.1186/s12873-024-01072-0

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Solid health care waste management practice in Ethiopia, a convergent mixed method study

• Yeshanew Ayele Tiruneh 1 ,
• L. M. Modiba 2 &
• S. M. Zuma 2  

BMC Health Services Research volume  24 , Article number:  985 ( 2024 ) Cite this article

Metrics details

Introduction

Healthcare waste is any waste generated by healthcare facilities that is considered potentially hazardous to health. Solid healthcare waste is categorized into infectious and non-infectious wastes. Infectious waste is material suspected of containing pathogens and potentially causing disease. Non-infectious waste includes wastes that have not been in contact with infectious agents, hazardous chemicals, or radioactive substances, similar to household waste, i.e. plastic, papers and leftover foods.

This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia. The setting was all health facilities found in Hossaena town.

A mixed-method study design was used. For the qualitative phase of this study, eight FGDs were conducted from 4 government health facilities, one FGD from each private health facility (which is 37 in number), and forty-five FGDs were conducted. Four FGDs were executed with cleaners; another four were only health care providers because using homogeneous groups promotes discussion. The remaining 37 FGDs in private health facilities were mixed from health professionals and cleaners because of the number of workers in the private facilities. For the quantitative phase, all health facilities and health facility workers who have direct contact with healthcare waste management practice participated in this study. Both qualitative and quantitative study participants were taken from the health facilities found in Hossaena town.

Seventeen (3.1%) health facility workers have hand washing facilities. Three hundred ninety-two (72.6%) of the participants agree on the availability of one or more personal protective equipment (PPE) in the facility ‘‘ the reason for the absence of some of the PPEs, like boots and goggles, and the shortage of disposable gloves owes to cost inflation from time to time and sometimes absent from the market’’ . The observational finding shows that colour-coded waste bins are available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. Two hundred seventy-one (50.2%) of the respondents were satisfied or agreed that satisfactory procedures are available in case of an accident. Only 220 (40.8%) respondents were vaccinated for the Hepatitis B virus.

Hand washing facilities, personal protective equipment and preventive vaccinations are not readily available for health workers. Solid waste segregation practices are poor and showed that solid waste management practices (SWMP) are below the acceptable level.

Peer Review reports

Healthcare waste (HCW) encompasses all types of waste generated while providing health-related services, spanning activities such as diagnosis, immunization, treatment, and research. It constitutes a diverse array of materials, each presenting potential hazards to health and the environment. Within the realm of HCW, one finds secretions and excretions from humans, cultures, and waste containing a stock of infectious agents. Discarded plastic materials contaminated with blood or other bodily fluids, pathological wastes, and discarded medical equipment are classified as healthcare waste. Sharps, including needles, scalpels, and other waste materials generated during any healthcare service provision, are also considered potentially hazardous to health [ 1 ].

Healthcare waste in solid form (HCW) is commonly divided into two primary groups: infectious and non-infectious. The existence of pathogens in concentrations identifies infectious waste or amounts significant enough to induce diseases in vulnerable hosts [ 1 ] If healthcare facility waste is free from any combination with infectious agents, nearly 85% is categorized as non-hazardous waste, exhibiting characteristics similar to conventional solid waste found in households [ 2 ]. World Health Organization (WHO) recommends that appropriate colour-coded waste receptacles be available in all medical and other waste-producing areas [ 3 ].

Solid waste produced in the course of healthcare activities carries a higher potential for infection and injury than any other type of waste. Improper disposal of sharps waste increases the risk of disease transmission among health facility workers and general populations [ 1 ]. Inadequate and inappropriate handling of healthcare waste may have serious public health consequences and a significant environmental impact. The World Health Organization (2014) guidelines also include the following guidance for hand washing and the use of alcohol-based hand rubs: Wash hands before starting work, before entering an operating theatre, before eating, after touching contaminated objects, after using a toilet, and in all cases where hands are visibly soiled [ 4 ].

Among the infectious waste category, sharps waste is the most hazardous waste because of its ability to puncture the skin and cause infection [ 3 ]. Accidents or occurrences, such as near misses, spills, container damage, improper waste segregation, and incidents involving sharps, must be reported promptly to the waste management officer or an assigned representative [ 5 ].

Africa is facing a growing waste management crisis. While the volumes of waste generated in Africa are relatively small compared to developed regions, the mismanagement of waste in Africa already impacts human and environmental health. Infectious waste management has always remained a neglected public health problem in developing countries, resulting in a high burden of environmental pollution affecting the general masses. In Ethiopia, there is no updated separate regulation specific to healthcare waste management in the country to enforce the proper management of solid HCW [ 6 ].

In Ethiopia, like other developing countries, healthcare waste segregation practice was not given attention and did not meet the minimum HCWM standards, and it is still not jumped from paper. Previous study reveals that healthcare waste generation rates are significantly higher than the World Health Organization threshold, which ranges from 29.5–53.12% [ 7 , 8 ]. In Meneilk II Hospital, the proportion of infectious waste was 53.73%, and in the southern and northern parts of Ethiopia, it was 34.3 and 53%, respectively. Generally, this figure shows a value 3 to 4 times greater than the threshold value recommended by the World Health Organization [ 7 ].

Except for sharp wastes, segregation practice was poor, and all solid wastes were collected without respecting the colour-coded waste disposal system [ 9 ]. The median waste generation rate was found to vary from 0.361- 0.669 kg/patient/day, comprising 58.69% non-hazardous and 41.31% hazardous wastes. The amount of waste generated increased as the number of patients flow increased. Public hospitals generated a high proportion of total healthcare waste (59.22%) in comparison with private hospitals (40.48) [ 10 ]. The primary SHCW treatment and disposal mechanism was incineration, open burning, burring into unprotected pits and open dumping on municipal dumping sites as well as in the hospital backyard. Carelessness, negligence of the health workers, patients and cleaners, and poor commitment of the facility leaders were among the major causes of poor HCWM practice in Ethiopia [ 9 ]. This study aimed to investigate solid healthcare waste management practices and develop guidelines to improve solid healthcare waste management practices in Ethiopia.

The setting for this study was all health facilities found in Hossaena town, which is situated 232 kms from the capital city of Ethiopia, Addis Ababa, and 165 kms from the regional municipality of Hawasa. The health facilities found in the town were one university hospital, one private surgical centre, three government health centres, 17 medium clinics, and 19 small clinics were available in the city and; health facility workers who have direct contact with generating and disposal of HCW and those who are responsible as a manager of health facilities found in Hossaena town are the study settings. All health facilities except drug stores and health facility workers who have direct contact with healthcare waste generation participated in this study.

A mixed-method study design was used. For the quantitative part of this study, all healthcare workers who have direct contact with healthcare waste management practice participated in this study, and one focus group discussion from each health facility was used. Both of the study participants were taken from the same population. All health facility workers who have a role in healthcare waste management practice were included in the quantitative part of this study. The qualitative data collection phase used open-ended interviews, focus group discussions, and visual material analysis like posters and written materials. All FGDs were conducted by the principal investigator, one moderator, and one note-taker, and it took 50 to 75 min. 4–6 participants participated in each FGD.

According to Elizabeth (2018: 5), cited by Creswell and Plano (2007: 147), the mixed method is one of the research designs with philosophical assumptions as well as methods of inquiry. As a method, it focuses on collecting, analyzing, and mixing both quantitative and qualitative data in a single study. As a methodology, it involves philosophical assumptions guiding the direction of the collection and analysis and combining qualitative and quantitative approaches in many phases of the research project. The central premise is that using qualitative and quantitative approaches together provides a better understanding of the research problems than either approach alone.

The critical assumption of the concurrent mixed methods approach in this study is that quantitative and qualitative data provide different types of information, often detailed views of participants’ solid waste management practice qualitatively and scores on instruments quantitatively, and together, they yield results that should be the same. In this approach, the researcher collected quantitative and qualitative data almost simultaneously and analyzed them separately to cross-validate or compare whether the findings were similar or different between the qualitative and quantitative information. Concurrent approaches to the data collection process are less time-consuming than other types of mixed methods studies because both data collection processes are conducted on time and at the same visit to the field [ 11 ].

Data collection

The data collection involves collecting both quantitative and qualitative data simultaneously. The quantitative phase of this study assessed three components. Health care waste segregation practice, the availability of waste segregation equipment for HCW segregation, temporary storage facilities, transportation for final disposal, and disposal facilities data were collected using a structured questionnaire and observation of HCW generation. Recycling or re-using practice, waste treatment, the availability of the HCWM committee, and training data were collected.

Qualitative data collection

The qualitative phase of the data collection for this study was employed by using focus group discussions and semi-structured interviews about SHCWMP. Two focus group discussions (FGD) from each health facility were conducted in the government health facilities, one at the administrative level and one at the technical worker level, and one FGD was conducted for all private health facilities because of the number of available health facility workers. Each focus group has 4–6 individuals.

In this study, the qualitative and the quantitative data provide different information, and it is suitable for this study to compare and contrast the findings of the two results to obtain the best understanding of this research problem.

Quantitative data collection

The quantitative data were entered into Epi data version 3.1 to minimize the data entry mistakes and exported to the statistical package for social science SPSS window version 27.0 for analysis. A numeric value was assigned to each response in a database, cleaning the data, recoding, establishing a codebook, and visually inspecting the trends to check whether the data were typically distributed.

Data analysis

Data were analyzed quantitatively by using relevant statistical tools, such as SPSS. Descriptive statistics and the Pearson correlation test were used for the bivariate associations and analysis of variance (ANOVA) to compare the HCW generation rate between private and government health facilities and between clinics, health centres and hospitals in the town. Normality tests were performed to determine whether the sample data were drawn from a normally distributed population.

The Shapiro–Wilk normality tests were used to calculate a test statistic based on the sample data and compare it to critical values. The Shapiro–Wilk test is a statistical test used to assess whether a given sample comes from a normally distributed population. The P value greater than the significance level of 0.05 fails to reject the null hypothesis. It concludes that there is not enough evidence to suggest that the data does not follow the normal distribution. Visual inspection of a histogram, Q-Q plot, and P-P plot (probability-probability plot) was assessed.

Bivariate (correlation) analysis assessed the relationships between independent and dependent variables. Then, multiple linear regression analysis was used to establish the simple correlation matrices between different variables for investigating the strength relationships of the study variables in the analysis. In most variables, percentages and means were used to report the findings with a 95% confidence interval. Open-ended responses and focused group findings were undertaken by quantifying and coding the data to provide a thematic narrative explanation.

Appropriate and scientific care was taken to maintain the data quality before, during, and after data collection by preparing the proper data collection tools, pretesting the data collection tools, providing training for data collectors, and proper data entry practice. Data were cleaned on a daily basis during data collection practice, during data entry, and before analysis of its completeness and consistency.

Data analysis in a concurrent design consists of three phases. First, analyze the quantitative database in terms of statistical results. Second, analyze the qualitative database by coding the data and collapsing the codes into broad themes. Third comes the mixed-method data analysis. This is the analysis that consists of integrating the two databases. This integration consists of merging the results from both the qualitative and the quantitative findings.

Descriptive analysis was conducted to describe and summarise the data obtained from the samples used for this study. Reliability statistics for constructs, means and modes of each item, frequencies and percentage distributions, chi-square test of association, and correlations (Spearman rho) were used to portray the respondents’ responses.

All patient care-providing health facilities were included in this study, and the generation rate of healthcare waste and composition assessed the practice of segregation, collection, transportation, and disposal system was observed quantitatively using adopted and adapted structured questionnaires. To ensure representativeness, various levels of health facilities like hospitals, health centres, medium clinics, small clinics and surgical centres were considered from the town. All levels of health facilities are diagnosing, providing first aid services and treating patients accordingly.

The hospital and surgical centre found in the town provide advanced surgical service, inpatient service and food for the patients that other health facilities do not. The HCW generation rate was proportional to the number of patients who visited the health facilities and the type of service provided. The highest number of patients who visited the health facilities was in NEMMCSH; the service provided was diverse, and the waste generation rate was higher than that of other health facilities. About 272, 18, 15, 17, and 20 average patients visited the health facilities daily in NEMMCSH: government health centres, medium clinics, small clinics, and surgical centres. Paper and cardboard (141.65 kg), leftover food (81.71 kg), and contaminated gloves (42.96 kg) are the leading HCWs generated per day.

A total of 556 individual respondents from sampled health facilities were interviewed to complete the questionnaire. The total number of filled questionnaires was 540 (97.1) from individuals representing these 41 health facilities.

The principal investigator observed the availability of handwashing facilities near SHCW generation sites. 17(3.1%) of health facility workers had hand washing facilities near the health care waste generation and disposal site. Furthermore,10 (3.87%), 2 (2.1%), 2 (2.53%), 2 (2.1%), 1 (6.6%) of health facility workers had the facility of hand washing near the health care waste generation site in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH), government health centres, medium clinics, small clinics, and surgical centre respectively. This finding was nearly the same as the study findings conducted in Myanmar; the availability of hand washing facilities near the solid health care waste generation was absent in all service areas [ 12 ]. The observational result was convergent with the response of facility workers’ response regarding the availabilities of hand washing facilities near to the solid health care waste generation sites.

The observational result was concurrent with the response of facility workers regarding the availability of hand-washing facilities near the solid health care waste generation sites.

The availability of personal protective equipment (PPE) was checked in this study. Three hundred ninety-two (72.6%) of the respondents agree on the facility’s availability of one or more personal protective equipment (PPE). The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The analysis further shows that the availability of masks for healthcare workers was above the mean in NEMMCSH and surgical centres.

Focus group participants indicated that health facilities did not volunteer to supply Personal protective equipment (PPEs) for the cleaning staff.

“We cannot purchase PPE by ourselves because of the salary paid for the cleaning staff.”

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained about by all (41) health facility owners.

“the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves are owing to cost inflation from time to time and sometimes absent from the market is the reason why we do not supply PPE to our workers.”

Using essential personal protective equipment (PPEs) based on the risk (if the risk is a splash of blood or body fluid, use a mask and goggles; if the risk is on foot, use appropriate shoes) is recommended by the World Health Organization [ 13 ]. The mean availability of gloves in health facilities was 343 (63.5% (95% CI: 59.3–67.4). Private health institutions are better at providing gloves for their workers, 67.1%, 72.8%, and 62.5% in medium clinics, small clinics, and surgical centres, respectively, which is above the mean.

Research participants agree that.

‘‘ there is a shortage of gloves to give service in Nigist Eleni Mohamed Memorial Comprehensive Specialized Hospital (NEMMCSH) and government health centres .’’

Masks are the most available personal protective equipment for health facility workers compared to others. 65.4%, 55.6%, and 38% of the staff are available with gloves, plastic aprons and boots, respectively.

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. Health facility workers were asked about the availability of different personal protective equipment, and 38% of the respondents agreed with the presence of boots in the facility. Still, the qualitative observational findings of this study show that all health facility workers have no shoes or footwear during solid health care waste management practice.

SHCW segregation practice was checked by observing the availability of SHCW collection bins in each patient care room. Only 4 (1.7%) of the room’s SHCW bins are collected segregated (non-infectious wastes segregated in black bins and infectious wastes segregated in yellow bins) based on the World Health Organization standard. Colour-coded waste bins, black for non-infectious and yellow for infectious wastes, were available in 23 (9.6%) rooms. 90% of the sharp containers were reusable, and 100% of the waste storage bins were plastic buckets that were easily cleanable. Only 6.7% of the waste bins were pedal operated and adequately covered, and the rest were fully opened, or a tiny hole was prepared on the container’s cover. All of the healthcare waste disposal bins in each health facility and at all service areas were away from the arm’s reach distance of the waste generation places, and this is contrary to World Health Organization SHCWM guidelines [ 13 ]. The observation result reveals that the reason for the above result was that medication trolleys were not used during medication or while healthcare providers provided any health services to patients.

Most medical wastes are incinerated. Burning solid and regulated medical waste generated by health care creates many problems. Medical waste incinerators emit toxic air pollutants and ash residues that are the primary source of environmental dioxins. Public concerns about incinerator emissions and the creation of federal regulations for medical waste incinerators are causing many healthcare facilities to rethink their choices in medical waste treatment. Health Care Without Harm [ 14 ], states that non-incineration treatment technologies are a growing and developing field. The U.S. National Academy of Science 2000 argued that the emission of pollutants during incineration is a potential risk to human health, and living or working near an incineration facility can have social, economic, and psychological effects [ 15 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. Incineration of healthcare waste can produce secondary waste and pollutants if the treatment facilities are not appropriately constructed, designed, and operated. It can be one of the significant sources of toxic substances, such as polychlorinated dibenzo-dioxins/dibenzofurans (PCDD/ PCDF), polyvinyl chloride (PVC), hexachlorobenzenes and polychlorinated biphenyls, and dioxins and furans that are known as hazardous pollutants. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ].

All government health facilities (4 in number) used incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres. This finding contradicts the study findings in the United States of America and Malaysia, in which 49–60% and 59–60 were incinerated, respectively, and the rest were treated using other technologies [ 15 , 16 ].

World Health Organization (2014:45) highlighted those critical elements of the appropriate operation of incinerators include effective waste reduction and waste segregation, placing incinerators away from populated areas, satisfactory engineered design, construction following appropriate dimensional plans, proper operation, periodic maintenance, and staff training and management are mandatory.

Solid waste collection times should be fixed and appropriate to the quantity of waste produced in each area of the health care facility. General waste should not be collected simultaneously or in the same trolley as infectious or hazardous wastes. The collection should be done daily for most wastes, with collection timed to match the pattern of waste generation during the day [ 13 ].

SHCW segregation practices were observed for 240 rooms in 41 health facilities that provide health services in the town. In government health centres, medium clinics, small clinics, and surgical centres, SHCW segregation practice was not based on the World Health Organization standard. All types of solid waste were collected in a single container near the generation area, and there were no colour-coded SHCW storage dust bins. Still, in NEMMCSH, in most of the service areas, colour-coded waste bins are available, and the segregation practice was not based on the standard. Only 3 (10%) of the dust bins collected the appropriate wastes according to the World Health Organization standard, and the rest were mixed with infectious and non-infectious SHCW.

Table 1 below shows health facility managers were asked about healthcare waste segregation practices, and 9 (22%) of the facility leaders responded that there is an appropriate solid healthcare waste segregation practice in their health facilities. Still, during observation, only 4 (1.7%) of the rooms in two (4.87%) of the facilities, SHCW bins collected the segregated wastes (non-infectious wastes segregated at the black bin and infectious wastes segregated at yellow bin) based on the world health organization standard. The findings of this study show there is a poor segregation practice, and all kinds of solid wastes are collected together.

In 40 (97.56%) health facilities, infectious wastes were collected daily from the waste generation areas to the final disposal points. During observation in one of the study health facilities, infectious wastes were not collected daily and left for days. Utility gloves, boots, and aprons are not available for cleaning staff to collect and transport solid healthcare wastes in all study health facilities. 29.26% of the facilities’ cleaning staff have a face mask, and 36.5% of the facilities remove waste bins from the service area when 3/4 full, and the rest were not removed or replaced with new ones. There is a separate container only in 2 health facilities for infectious and non-infectious waste segregation practice, and the rest were segregated and collected using single and non-colour coded containers.

At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site were transported manually by carrying the collection container and there is no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Only 2 out of 41 health facilities have temporary solid waste storage points at the facility. One of the temporary storage places was clean, and the other needed to be properly cleaned and unsightly. Two (100%) of the temporary storage areas are not fenced and have no restriction to an authorized person. Temporary storage areas are available only in two health facilities that are away from the service provision areas.

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. 95% of the facilities have no water supply for hand washing during and after solid healthcare waste generation, collection, and disposal.

The United States Agency estimated sharp injuries from medical wastes to health professionals and sanitary service personnel for toxic substances and disease registry. Most of the injuries are caused during the recapping of hypodermic needles before disposal into sharps containers [ 13 ]. Nearly half of the respondents, 245 (51.5%), are recapping needles after providing an injection to the patient. Recapping was more practised in NEMMCSH and surgical centres, which is 57.5% and 57.5%, respectively. In government health centres, medium clinics, and surgical centres, the recapping of used needles was practised below the mean, which is 47.9%, 48, and 43.8%, respectively. This finding was reasonable compared to the study findings of Doylo et al. [ 18 ] in western Ethiopia, where 91% of the health workers are recapping needles after injection [ 18 ]. The research finding shows that there is no significant association P-value of 0.82 between the training and recapping of needles after injection.

Focus group participants ’ response for appropriate SHCWMP regarding patients ’ and visitors ’ lack of knowledge on SHCW segregation practice

“The personal responsibilities of patients and visitors on solid HCW disposal should be explained to help appropriate safe waste management practice and maintain good hygiene .” “Providing waste management training and creating awareness are the two aspects of improving SHCW segregation practice.” “Training upgrades and creates awareness on hygiene for all workers.”

Sharp waste collection practices were observed in 240 rooms in the study health facilities, and 9.2% of the rooms used disposable sharp containers.

Sixty per cent (60%), 13.3%, 8.24%, and 15.71% of the sharps containers in NEMMCSH, government health centres, medium clinics, and small clinics, respectively, were using disposable sharps containers; sharps were disposed together with the sharps container, and surgical centre was using reusable sharp collection container. All disposable sharps containers in medium and small clinics used non-puncture-resistant or simple packaging carton boxes. 60% and 13.3% of the disposable sharps containers in NEMMCSH and the government health centre use purposefully manufactured disposable safety boxes.

Needle sticks injury reporting and occurrence

A total of 70 injuries were reported to the health facility manager in the last one year, and 44 of the injuries were reported by health professionals. The rest of the injuries were reported by supportive staff. These injuries were reported from 35 health facilities, and the remaining six health facilities did not report any cases of injury related to work; see Tables 2 and 3 below.

Accidents or incidents, including near misses, spillages, damaged containers, inappropriate segregation, and any incidents involving sharps, should be reported to the waste-management officer. Accidental contamination must be notified using a standard-format document. The cause of the accident or incident should be investigated by the waste-management officer (in case of waste) or another responsible officer, who should also take action to prevent a recurrence [ 13 ]. Two hundred seventy-one (50.2% (CI: 45.7–54.6) of the respondents agree that satisfactory procedures are available in case of an accident, while the remaining 269 (49.8%( CI: 45.4–54.3) of respondents do not agree on the availability of satisfactory procedures in case of an accident, see Table  4 below. The availability of satisfactory procedures in case of an accident is above the mean in medium clinics, which is 60.8%. 132(24.4%) of the staff are pricked by needle stick injury while providing health services. Nearly half of the respondents, 269 (49.8%), who have been exposed to needle stick injury do not get satisfactory procedures after being pricked by a needle, and those who have not been stung by a needle stick injury for the last year. 204 (37.8%) disagree with the presence of satisfactory procedures in the case of a needle stick injury. In NEMMCSH, 30.2% of the research participants were pricked by needle stick injury within one year of period, and 48.8% of those who were stung by needle stick injuries did not agree upon the presence of satisfactory procedures in case of needle stick injuries in the study hospital. 17.9% and 49.5%, 24.1% and 60.8%, 7.6% and 50% of the respondents are pricked by needle sticks, and they disagree on the availability of satisfactory procedures in case of accidents, respectively, in government health centres, medium clinics, small clinics, and surgical centre respectively.

One hundred seventy-seven (32.7% (CI:29.1–37) respondents were exposed to needle stick injury while working in the current health facilities. One hundred three (58.1%) and 26 (32.9%) needle stick injuries were reported from WUNEMMCSH and medium clinics, which is above the mean. One hundred thirty-two(24.7% (95%CI:20.7–28.1) of the respondents are exposed to needle stick injury within one year of the period. Seventy-eight(30.2%), 17 (17.9%), 19 (24.1%), 15 (16.3%), 3 (18.8%) of the staff are injured by needle sticks from NEMMCSH, government health centres, medium clinics, small clinics, and surgical centre staffs respectively within one year of service.

The mean availabilities of satisfactory procedures in case of accidents were 321 (59.4% (CI:55.4–63.7). Out of this, 13.7% of the staff is injured by needle sticks within one year before the survey. Except in NEMMCSH, the mean availabilities of satisfactory procedures were above the mean, which is 50%, 60%, 77.2%, 66.3%, and 81.3% in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively.

Table 5 below shows that Hepatitis B, COVID-19, and tetanus toxoid vaccinations are the responses of the research participants to an open-ended question on which vaccine they took. The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One hundred fifty-six (70.9%) of the respondents are vaccinated to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19.

Appropriate health care waste management practice was assessed by using 12 questions: availability of colour-coded waste bins, foot-operated dust bins, elbow or foot-operated hand washing basin, personal protective equipment, training, role and responsibility of the worker, the presence of satisfactory procedures in case of an accident, incinerator, vaccination, guideline, onsite treatment, and the availability of poster. The mean of appropriate healthcare waste management practice was 55.58%. The mean of solid health care waste management practice based on the level of health facilities was summed and divided into 12 variables to get each health facility’s level of waste management practice. 64.9%, 45.58%, 49%, 46.9%, and 51.8% are the mean appropriate health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively. In NEMMCSH, the practice of solid healthcare waste management shows above the mean, and the rest was below the mean of solid healthcare waste management practice.

Healthcare waste treatment and disposal practice

Solid waste treatment before disposal was not practised at all study health facilities. There is an incineration practice at all of the study health facilities, and the World Health Organization 2014 recommended three types of incineration practice for solid health care waste management: dual-chamber starved-air incinerators, multiple chamber incinerators, and rotary kilns incinerators. Single-chamber, drum, and brick incinerators do not meet the best available technique requirements of the Stockholm Convention guidelines [ 13 ]. The findings of this study show that none of the incinerators found in the study health facilities meet the minimum standards of solid healthcare waste incineration practice, and they need an air inlet to facilitate combustion. Eleven (26.82%) of the health facilities have an ash pit to dispose of burned SHCW; the majority, 30 (73.17%), dispose of the incinerated ash and burned needles in the municipal waste disposal site. In one out of 11 health facilities with an ash pit, one of the incinerators was built on the ash pit, and the incinerated ashes were disposed of in the ash pit directly. Pre-treatment of SHCW before disposal was not practised at all health facilities; see Table  6 below.

All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the solid wastes are incinerated in WUNEMMCS Hospital and government health centres, respectively. This finding was not similar to the other studies because other technologies like autoclave microwave and incineration were used for 59–60% of the waste [ 15 ]. Forty-one (100%) of the study facilities were using incinerators, and only 5 (12.19%) of the incinerators were constructed by using brick and more or less promising than others for incinerating the generated solid wastes without considering the emitting gases into the atmosphere and the residue chemicals and minerals in the ashes.

Research participants’ understanding of the environmental friendliness of health care waste management practice was assessed, and the result shows that more than half, 312(57%) of the research participants do not agree on the environmental friendliness of the waste disposal practices in the health facilities. The most disagreement regarding environmental friendliness was observed in NEMMCSH; 100 (38.8%) of the participants only agreed the practice was environmentally friendly of the service. Forty-four (46.3%), 37 (46.8%), 40 (43.5%), and 7 (43.8%) of the participants agree on the environmental friendliness of healthcare waste management practice in government health centres, medium clinics, small clinics, and surgical centres, respectively.

One hundred twenty-five (48.4%) and 39(42.4%) staff are trained in solid health care waste management practice in NEMMCSH and small clinic staff, respectively; this result shows above the mean. Twenty-seven (28.4%), 30 (38%), and 4 (25%) of the staff are trained in health care waste management practice in Government health centres, medium clinics, and surgical centres, respectively. The training has been significantly associated with needle stick injury, and the more trained staff are, the less exposed to needle stick injury. One hundred ninety-six (36.4%) of the participants answered yes to the question about the availability of trainers in the institution. 43.8% of the NEMMCSH staff agreed on the availability of trainers on solid health care waste management, which is above the mean, and 26.3%, 31.6%, 31.5%, and 25% for the government health centres, medium clinics, small clinics, and surgical centre respectively, which is below the mean.

Trained health professionals are more compliant with SHCWM standards, and the self-reported study findings of this study show that 41.7% (95%CI:37.7–46) of the research participants are trained in health care waste management practice. This finding was higher compared to the study findings of Sahiledengle in 2019 in the southeast of Ethiopia, shows 13.0% of healthcare workers received training related to HCWM in the past one year preceding the study period and significantly lower when compared to the study findings in Egypt which is 71% of the study participants were trained on SHCWM [ 8 , 19 , 20 ].

Three out of four government health facility leaders, 17 (45.94%) of private health facility leaders/owners of the clinic and 141 FGD participants complain about the absence of some PPEs like boots and aprons to protect themselves from infectious agents.

‘ ‘Masks, disposable gloves, and changing gowns are a critical shortage at all health facilities.’’

Cleaners in private health facilities are more exposed to infectious agents because of the absence of personal protective equipment. Except for the cleaning staff working in the private surgical centre, all cleaning staff 40 (97.56) of the health facilities complain about the absence of changing gowns and the fact that there are no boots in the facilities.

Cost inflation and the high cost of purchasing PPEs like gloves and boots are complained by all of (41) the health facility owners and the reason for the absence of some of the PPEs like boots, goggles, and shortage of disposable gloves. Sometimes, absence from the market is the reason why we do not supply PPE to our workers.

Thirty-four (82.92%) of the facility leaders are forwarded, and there is a high expense and even unavailability of some of the PPEs, which are the reasons for not providing PPEs for the workers.

‘‘Medical equipment and consumables importers and whole sellers are selective for importing health supplies, and because of a small number of importers in the country and specifically, in the locality, we can’t get materials used for health care waste management practice even disposable gloves. ’’

One of the facility leaders from a private clinic forwarded that before the advent of COVID-19 -19) personal protective equipment was more or less chip-and-get without difficulty. Still, after the advent of the first Japanese COVID-19 patient in Ethiopia, people outside the health facilities collect PPEs like gloves and masks and storing privately in their homes.

‘‘PPEs were getting expensive and unavailable in the market. Incinerator construction materials cost inflation, and the ownership of the facility building are other problems for private health facilities to construct standard incinerators.’’

For all of the focus group discussion participants except in NEMMCSH and two private health facilities, covered and foot-operated dust bins were absent or in a critical shortage compared to the needed ones.

‘‘ Waste bins are open and not colour-coded. The practice attracts flies and other insects. Empty waste bins are replaced without cleaning and disinfecting by using chlorine solution.’’ “HCW containers are not colour-coded, but we are trying to label infectious and non-infectious in Amharic languages.”

Another issue raised during focus group discussions is incineration is not the final disposal method. It needs additional disposal sites, lacks technology, is costly to construct a brick incinerator, lacks knowledge for health facility workers, shortage of man powers /cleaners, absence of environmental health professionals in health centres and all private clinics, and continues exposure to the staff for needle stick injury, foully smell, human scavengers, unsightly, fire hazard, and lack of water supply in the town are the major teams that FGD participants raise and forwarded the above issue as a problem to improve SHCWMP.

Focus group participants, during the discussion, raised issues that could be more comfortable managing SHCWs properly in their institution. Two of the 37 private health facilities are working in their own compound, and the remaining 35 are rented; because of this, they have difficulty constructing incinerators and ash removal pits and are not confident about investing in SHCWM systems. Staff negligence and involuntary abiding by the rules of the facilities were raised by four of the government health facilities, and it was difficult to punish those who violated the healthcare waste management rules because the health facility leaders were not giving appropriate attention to the problem.

Focus group participants forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows:

“PPE should be available in quality and quantity for all health facility workers who have direct contact with SHCW.” “Scientific-based waste management technologies should be availed for health facilities.” “Continuous induction HCW management training should be provided to the workers. Law enforcement should be strengthened.” “Communal HCW management sites should be availed, especially for private health facilities.” “HCWM committee should be strengthened.” “Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places.” “Leaders should be knowledgeable on the SHCWM system and supervise the practice continuously.” “Patient and client should be oriented daily about HCW segregation practice.” “Regulatory bodies should supervise the health facilities before commencing and periodically between services .”

The above are the themes that FGD participants discussed and forwarded for the future improvements of SHAWMP in the study areas.

Lack of water supply in the town

Other issues raised during FGDs were health facilities’ lack of water supply. World Health Organization (2014: 89) highlights that water supply for the appropriate waste management system should be mandatory at any time in all health service delivery points.

Thirty-nine (95.12%) of the health facilities complain about the absence of water supply to improve HCW management practices and infection prevention and control practices in the facilities.

“We get water once per week, and most of the time, the water is available at night, and if we are not fetching as scheduled, we can’t get water the whole week”.

In this research, only those who have direct contact have participated in this study, and 434 (80.4%) of the respondents agree they have roles and responsibilities for appropriate solid health care waste management practice. The rest, 19.6%, do not agree with their commitment to manage health care wastes properly, even though they are responsible. Health facility workers in NEMMCSH and medium clinics know their responsibilities better than others, and their results show above the mean. 84.5%, 74.5%, 81%, 73.9% and 75% in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively.

Establishing a policy and a legal framework, training personnel, and raising public awareness are essential elements of successful healthcare waste management. A policy can be viewed as a blueprint that drives decision-making at a political level and should mobilize government effort and resources to create the conditions to make changes in healthcare facilities. Three hundred and seventy-four (69.3%) of the respondents agree with the presence of any solid healthcare waste management policy in Ethiopia. The more knowledge above the mean (72.9%) on the presence of the policy is reported from NEMMCSH.

Self-reported level of knowledge on what to do in case of an accident revealed that 438 (81.1% CI: 77.6–84.3%) of the respondents knew what to do in case of an accident. Government health centre staff and medium clinic staff’s knowledge about what to do in case of an accident was above the mean (88.4% and 82.3%), respectively, and the rest were below the mean. The action performed after an occupational accident revealed that 56 (35.7%) of the respondents did nothing after any exposure to an accident. Out of 56 respondents who have done nothing after exposure, 47 (83.92%) of the respondents answered yes to their knowledge about what to do in case of an accident. Out of 157 respondents who have been exposed to occupational accidents, only 59 (37.6%) of the respondents performed the appropriate measures, 18 (11.5%), 9 (5.7%), 26 (16.6%), 6 (3.8%) of the respondents are taking prophylaxis, linked to the incident officer, consult the available doctors near to the department, and test the status of the patient (source of infection) respectively and the rest were not performing the scientific measures, that is only practising one of the following practices washing the affected part, squeezing the affected part to remove blood, cleaning the affected part with alcohol.

Health facility workers’ understanding of solid health care waste management practices was assessed by asking whether the current SHCWM practice needs improvement. Four hundred forty-nine (83.1%) health facility workers are unsatisfied with the current solid waste management practice at the different health facility levels, and they recommend changing it to a scientific one. 82.6%, 87.4%, 89.9%, 75%, and 81.3% of the respondents are uncomfortable or need to improve solid health care waste management practices in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres, respectively.

Lack of safety box, lack of colour-coded waste bins, lack of training, and no problems are the responses to the question problems encountered in managing SHCWMP. Two Hundred and Fifty (46.92%) and 232 (42.96%) of the respondents recommend the availability of safety boxes and training, respectively.

Four or 9.8% of the facilities have infection prevention and control (IPC) teams in the study health facilities. This finding differed from the study in Pakistan, where thirty per cent (30%) of the study hospitals had HCWM or infection control teams [ 21 ]. This study’s findings were similar to those conducted in Pakistan by Khan et al. [ 21 ], which confirmed that the teams were almost absent at the secondary and primary healthcare levels [ 20 ].

The availability of health care waste management policy report reveals that 69.3% (95% CI: 65.4–73) of the staff are aware of the presence of solid health care waste management policy in the institution. Availability of health care waste management policy was 188 (72.9%), 66 (69.5%), 53 (677.1%), 57 (62%), 10 (62.5%) in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centre respectively. Healthcare waste management policy availability was above the mean in NEMMCSH and government health centres; see Table  6 below.

Open-ended responses on the SHCWM practice of health facility workers were collected using the prepared interview guide, and the responses were analyzed using thematic analysis. All the answered questions were tallied on the paper and exported to Excel software for thematic analysis.

The study participants recommend.

“appropriate segregation practice at the point of generation” "health facility must avail all the necessary supplies that used for SHCWMP, punishment for those violating the rule of SHCWMP",

“waste management technologies should be included in solid waste management guidelines, and enforcement should be strengthened.”

The availability of written national or adopted/adapted SHCWM policies was observed at all study health facilities. Twenty eight (11.66%) of the rooms have either a poster or a written document of the national policy document. However, all staff working in the observed rooms have yet to see the inside content of the policy. The presence of the policy alone cannot bring change to SHCWMP. This finding shows that the presence of policy in the institution was reasonable compared to the study findings in Menelik II hospital in Addis Ababa, showing that HCWM regulations and any applicable facility-based policy and strategy were not found [ 22 ]. The findings of this study were less compared to the study findings in Pakistan; 41% of the health facilities had the policy document or internal rules for the HCWM [ 21 ].

Focus group participants have forwarded recommendations on which interventions can improve the management of SHCW, and recommendations are summarised as follows.

‘‘Supplies should be available in quality and quantity for all health facility workers with direct contact with SHCW. Scientific-based waste management technologies should be available for health facilities. Continues and induction health care waste management training should be provided to the workers. Law enforcement should be strengthened. Community healthcare waste management sites should be available, especially for private health facilities. HCWM committee should be strengthened. Non-infectious wastes should be collected communally and transported to the municipal SHCW disposal places. Leaders should be knowledgeable about the SHCWM system and supervise the practice continuously. Patients and clients should be oriented daily about health care waste segregation practices. Regulatory bodies should supervise the health facilities before commencing and periodically in between the service are the themes those FGD participants discussed and forward for the future improvements of SHCWMP in the study areas.’’

The availability of PPEs in different levels of health facilities shows 392 (72.6%), 212 (82.2%), 56 (58.9%), 52 (65.8%), 60 (65.2%), 12 (75%) health facility workers in NEMMCSH, government health centres, medium clinics, small clinics, and surgical centres respectively agree to the presence of personal protective equipment in their department. The availability of PPEs in this study was nearly two-fold when compared to the study findings in Myanmar, where 37.6% of the staff have PPEs [ 12 ].

The mean availability of masks, heavy-duty gloves, boots, and aprons was 71.1%, 65.4%, 38%, and 44.4% in the study health facilities. This finding shows masks are less available in the study health facilities compared to other studies. The availability of utility gloves, boots, and plastic aprons is good in this study compared to the study conducted by Banstola, D in Pokhara Sub-Metropolitan City [ 23 ].

The findings of this study show there is a poor segregation practice, and all kinds of solid wastes were collected together. This finding was similar to the study findings conducted in Addis Ababa, Ethiopia, by Debere et al. [ 24 ] and contrary to the study findings conducted in Nepal and India, which shows 50% and 65–75% of the surveyed health facilities were practising proper waste segregation systems at the point of generation without mixing general wastes with hazardous wastes respectively [ 9 , 17 ].

Ninety percent of private health facilities collect and transport SHCW generated in every service area and transport it to the disposal place by the collection container (no separate container to collect and transport the waste to the final disposal site). This finding was similar to the study findings of Debre Markos’s town [ 25 ]. At all of the facilities in the study area, SHCW was transported from the service areas to the disposal site manually by carrying the collection container, and there was no trolley for transportation. This finding was contrary to the study findings conducted in India, which show segregated waste from the generation site was being transported through the chute to the carts placed at various points on the hospital premises by skilled sanitary workers [ 17 ].

Observational findings revealed that pre-treatment of SHCW before disposal was not practised at all study health facilities. This study was contrary to the findings of Pullishery et al. [ 26 ], conducted in Mangalore, India, which depicted pre-treatment of the waste in 46% of the hospitals [ 26 ]. 95% of the facilities have no water supply for handwashing during and after solid healthcare waste generation, collection, and disposal. This finding was contrary to the study findings in Pakistan hospitals, which show all health facilities have an adequate water supply near the health care waste management sites [ 27 ].

Questionnaire data collection tools show that 129 (23.8%) of the staff needle stick injuries have occurred on health facility workers within one year of the period before the data collection. This finding was slightly smaller than the study findings of Deress et al. [ 25 ] in Debre Markos town, North East Ethiopia, where 30.9% of the workers had been exposed to needle stick injury one year prior to the study [ 25 ]. Reported and registered needle stick injuries in health facilities are less reported, and only 70 (54.2%) of the injuries are reported to the health facilities. This finding shows an underestimation of the risk and the problem, which was supported by the study conducted in Menilik II hospitals in Addis Ababa [ 22 ]. 50%, 33.4%, 48%, 52%, and 62.5% of needle stick injuries were not reported in NEMMCSH, Government health centres, medium clinics, small clinics, and surgical centres, respectively, to the health facility manager.

Nearly 1/3 (177 or 32.7%) of the staff are exposed to needle stick injuries. Needle stick injuries in health facilities are less reported, and only 73 (41.24%) of the injuries are reported to the health facilities within 12 months of the data collection. This finding is slightly higher than the study finding of Deress et al. [ 25 ] in Debere Markos, Ethiopia, in which 23.3% of the study participants had encountered needle stick/sharps injuries preceding 12 months of the data collection period [ 25 ].

Seventy-three injuries were reported to the health facility manager in the last one year, 44 of the injuries were reported by health professionals, and the rest were reported by supportive staff. These injuries were reported from 35(85.3%) health facilities; the remaining six have no report. These study findings were better than the findings of Khan et al. [ 21 ], in which one-third of the facilities had a reporting system for an incident, and almost the same percentage of the facilities had post-exposure procedures in both public and private sectors [ 21 ].

Within one year of the study period, 129 (23.88%) needle stick injuries occurred. However, needle stick injuries in health facilities are less reported, and only 70 (39.5%) of the injuries are reported to the health facilities. These findings were reasonable compared to the study findings of the southwest region of Cameroon, in which 50.9% (110/216) of all participants had at least one occupational exposure [ 28 , 29 ]. This result report shows a very high exposure to needle stick injury compared to the study findings in Brazil, which shows 6.1% of the research participants were injured [ 27 ].

The finding shows that 220 (40.8%) of the respondents were vaccinated to prevent themselves from health facility-acquired infection. One Hundred Fifty-six (70.9%) of the respondents are vaccinated in order to avoid themselves from Hep B infection. Fifty-nine (26%0.8) of the respondents were vaccinated to protect themselves from two diseases that are Hep B and COVID-19. This finding was nearly the same as the study findings of Deress et al. [ 7 ],in Ethiopia, 30.7% were vaccinated, and very low compared to the study findings of Qadir et al. [ 30 ] in Pakistan and Saha & Bhattacharjya India which is 66.67% and 66.17% respectively [ 25 , 30 , 31 ].

The incineration of solid healthcare waste technology has been accepted and adopted as an effective method in Ethiopia. These pollutants may have undesirable environmental impacts on human and animal health, such as liver failure and cancer [ 15 , 16 ]. All government health facilities use incineration to dispose of solid waste. 88.4% and 100% of the wastes are incinerated in WUNEMMCSH and government health centres, respectively. This finding contradicts the study findings in the United States of America and Malaysia, which are 49–60% and 59–60 are incinerated, respectively, and the rest are treated using other technologies [ 15 , 16 ].

All study health facilities used a brick or barrel type of incinerator. The incinerators found in the study health facilities need to meet the minimum standards of solid health care waste incineration practice. These findings were similar to the study findings of Nepal and Pakistan [ 32 ]. The health care waste treatment system in health facilities was found to be very unsystematic and unscientific, which cannot guarantee that there is no risk to the environment and public health, as well as safety for personnel involved in health care waste treatment. Most incinerators are not properly operated and maintained, resulting in poor performance.

All government health facilities use incineration to dispose of solid waste. All the generated sharp wastes are incinerated using brick or barrel incinerators, as shown in Fig.  1 above. This finding was consistent with the findings of Veilla and Samwel [ 33 ], who depicted that sharp waste generation is the same as sharps waste incinerated [ 33 ]. All brick incinerators were constructed without appropriate air inlets to facilitate combustion except in NEMMCSH, which is built at a 4-m height. These findings were similar to the findings of Tadese and Kumie at Addis Ababa [ 34 ].

Barrel and brick incinerators used in private clinic

Strengths and limitations

This is a mixed-method study; both qualitative and quantitative study design, data collection and analysis techniques were used to understand the problem better. The setting for this study was one town, which is found in the southern part of the country. It only represents some of the country’s health facilities, and it is difficult to generalize the findings to other hospitals and health centres. Another limitation of this study was that private drug stores and private pharmacies were not incorporated.

Conclusions

In the study, health facilities’ foot-operated solid waste dust bins are not available for healthcare workers and patients to dispose of the generated wastes. Health facility managers in government and private health institutions should pay more attention to the availability of colour-coded dust bins. Most containers are opened, and insects and rodents can access them anytime. Some of them are even closed (not foot-operated), leading to contamination of hands when trying to open them.

Healthcare waste management training is mandatory for appropriate healthcare waste disposal. Healthcare-associated exposure should be appropriately managed, and infection prevention and control training should be provided to all staff working in the health facilities.

Availability of data and materials

The authors declare that data for this work are available upon request to the first author.

Chartier, Y et al. Safe management of wastes from health-care activities. 2nd ed. WHO; 2014.

Tesfahun E, et al. Developing models for the prediction of hospital healthcare waste generation rate. Waste Manag Res. 2014;34(1):75–80.

Manzoor J, Sharma M. Impact of Biomedical Waste on Environment and Human Health. Environmental Claims Journal. 2019;31(4):311–34.

Article   Google Scholar  

Yves C, Jorge E, Ute P, Annette P, et al. Safe management of wastes from health-care activities. WHO 2nd ed. 2014.

OSHA. Occupational Safety and Health Administration, Guidelines for Healthcare Waste Management. 2023.

Godfrey L, Ahmed M, et al. Solid waste management in Africa: governance failure or development opportunity?. Intech open. 2019.

Deress T, Jemal M, Girma M, Adane K. Knowledge, attitude, and practice of waste handlers about medical waste management in Debre Markos town healthcare facilities, northwest Ethiopia. BMC Res Notes. 2019;12(1):146.

Article   PubMed   PubMed Central   Google Scholar  

Sahiledengle B. Self-reported healthcare waste segregation practice and its correlate among healthcare workers in hospitals of Southeast Ethiopia. BMC Health Serv Res. 2019;19(1):591.

Debalkie D, Kume A. Healthcare Waste Management: The Current Issue in Menellik II Referral Hospital, Ethiopia. Curr World Environ. 2017;12(1):42–52.

Debere MK, Gelaye KA, Alamdo AG, Trifa ZM. Assessment of the health care waste generation rates and its management system in hospitals of Addis Ababa, Ethiopia, 2011. BMC Public Health. 2013;13(28).

Creswell JW. Research design qualitative, quantitative, & mixed method approach. 4th ed. SAGE Publications, Inc.; 2014.

Win EM, Saw YM, Oo KL, Than TM, Cho SM, Kariya T, et al. Healthcare waste management at primary health centres in Mon State, Myanmar: the comparisons between hospital and non-hospital type primary health centres. Nagoya J Med Sci. 2019;81(1):81–91.

PubMed   PubMed Central   Google Scholar  

WHO. Safe management of wastes from health-care activities. 2nd ed. editor Chartier, Y et al. 2014. 

Richard B, Ben A, Kristian S. Health care without harm climate-smart health care series green paper number one. 2019.

Khadem Ghasemi M, Mohd YR. Advantages and Disadvantages of Healthcare Waste Treatment and Disposal Alternatives: Malaysian Scenario. Pol J Environ Stud. 2016;25(1):17–25.

Mohseni-Bandpei A, Majlesi M, Rafiee M, Nojavan S, Nowrouz P, Zolfagharpour H. Polycyclic aromatic hydrocarbons (PAHs) formation during the fast pyrolysis of hazardous health-care waste. Chemosphere. 2019;227:277–88.

Article   PubMed   CAS   Google Scholar  

Pandey A, Ahuja S, Madan M, Asthana AK. Bio-Medical Waste Managment in a Tertiary Care Hospital: An Overview. J Clin Diagn Res. 2016;10(11):DC01-DC3.

Doylo T, Alemayehu T, Baraki N. Knowledge and Practice of Health Workers about Healthcare Waste Management in Public Health Facilities in Eastern Ethiopia. J Community Health. 2019;44(2):284–91.

Article   PubMed   Google Scholar  

Hosny G, Samir S, Sharkawy R. An intervention significantly improve medical waste handling and management: A consequence of raising knowledge and practical skills of health care workers. Int J Health Sci.2018;12(4).

Khan EA, Sabeeh SM, Chaudhry MA, Yaqoob A, Kumar R. et al. Health care waste management in Pakistan: A situational analisis and way forward. Pak J Public Health. 2016;6(3).

Khan BA, Cheng L, Khan AA, Ahmed H. Healthcare waste management in Asian developing countries: A mini review. Waste Manag Res. 2019;37(9):863–75.

Debalkie D, Kumie A. Healthcare Waste Management: The Current Issue in Menellik II Referral Hospital. Ethiopia Current World Environment. 2017;12(1):42–52.

Banstola D, Banstola R, Nepal D, Baral P. Management of hospital solid wastes: A study in Pokhara sub metropolitan city. J Institute Med. 2017;31(1):68–74.

Debere MK, Gelaye KA, Alamdo AG, Trifa, ZM. Assessment of the HCW generation rates and its management system in hospitals of Addis Ababa, Ethiopia. BMC Public Health. 2014;13(28):1–9.

Deress T, Hassen F, Adane K, Tsegaye A. Assessment of Knowledge, Attitude, and Practice about Biomedical Waste Management and Associated Factors among the Healthcare Professionals at Debre Markos Town Healthcare Facilities. Northwest Ethiopia J Environ Public Health. 2018;2018:7672981.

PubMed   Google Scholar  

Pullishery F, Panchmal GS, Siddique S, Abraham A. Awareness, knowledge, and practices on bio-medical waste management among health care professionals in Mangalore- A cross sectional study. Integr Med. 2016;3(1):29–35.

Google Scholar  

Ream PS, Tipple AF, Salgado TA, Souza AC, Souza SM, Galdino-Junior H, et al. Hospital housekeepers: Victims of ineffective hospital waste management. Arch Environ Occup Health. 2016;71(5):273–80.

Ngwa CH, Ngoh EA, Cumber SN. Assessment of the knowledge, attitude and practice of health care workers in Fako division on post exposure prophylaxis to blood borne viruses: a hospital based cross-sectional study. Pan Afr Med J. 2018;31.

Health care waste managemnt in pakistan. a situation analysis and way forward. Pakistan Journal of Public Health. 2016;6(3):35–45.

Qadir DM, Murad DR, Faraz DN. Hospital Waste Management; Tertiary Care Hospitals. The Professional Medical Journal. 2016;23(07):802–6.

Saha A, Bhattacharjya H. Health-Care Waste Management in Public Sector of Tripura, North-East India: An Observational Study. Indian J Community Med. 2019;44(4):368–72.

Pullishery F, Panchmal G, Siddique S, Abraham A. Awareness, knowledge, and practices on bio-medical waste management among health care professionals in Mangalore- A cross sectional study. Integr Med. 2016;3(1):29–35.

Veilla EM, Samwel VM. Assessment of sharps waste management practices in a referral hospital. Afr J Environ Sci Technol. 2016;10(3):86–95.

Tadesse ML, Kumie A. Healthcare waste generation and management practice in government health centers of Addis Ababa. Ethiopia BMC Public Health. 2014;14:1221.

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Acknowledgements

The authors are grateful to the health facility leaders and ethical committees of the hospitals for their permission. The authors acknowledge the cooperation of the health facility workers who participated in this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Yeshanew Ayele Tiruneh

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Dr. Yeshanew Ayele Tiruneh is a researcher of this study; the principal investigator does all the proposal preparation, methodology, data collection, result and discussion, and manuscript writing. Professor LM Modiba and Dr. SM Zuma are supervisors for this study. They participated in the topic selection and modification to the final manuscript preparation by commenting on and correcting the study. Finally, the three authors read and approved the final version of the manuscript and agreed to submit the manuscript for publication.

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Tiruneh, Y.A., Modiba, L.M. & Zuma, S.M. Solid health care waste management practice in Ethiopia, a convergent mixed method study. BMC Health Serv Res 24 , 985 (2024). https://doi.org/10.1186/s12913-024-11444-8

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• Volume 14, Issue 8
• Validation of a quantitative instrument measuring critical success factors and acceptance of Casemix system implementation in the total hospital information system in Malaysia
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• Noor Khairiyah Mustafa 1 , 2 ,
• http://orcid.org/0000-0002-4741-5970 Roszita Ibrahim 1 ,
• Zainudin Awang 3 ,
• Azimatun Noor Aizuddin 1 , 4 ,
• Syed Mohamed Aljunid Syed Junid 5
• 1 Department of Public Health Medicine , Universiti Kebangsaan Malaysia Fakulti Perubatan , Cheras , Federal Territory of Kuala Lumpur , Malaysia
• 2 Ministry of Health Malaysia , Putrajaya , Malaysia
• 3 Faculty of Business Management , Universiti Sultan Zainal Abidin , Kuala Terengganu , Malaysia
• 4 International Casemix Centre (ITCC) , Hospital Universiti Kebangsaan Malaysia , Cheras , Kuala Lumpur , Malaysia
• 5 Department of Public Health and Community Medicine , International Medical University , Kuala Lumpur , Federal Territory of Kuala Lumpur , Malaysia
• Correspondence to Dr Roszita Ibrahim; roszita{at}ppukm.ukm.edu.my

Objectives This study aims to address the significant knowledge gap in the literature on the implementation of Casemix system in total hospital information systems (THIS). The research focuses on validating a quantitative instrument to assess medical doctors’ acceptance of the Casemix system in Ministry of Health (MOH) Malaysia facilities using THIS.

Designs A sequential explanatory mixed-methods study was conducted, starting with a cross-sectional quantitative phase using a self-administered online questionnaire that adapted previous instruments to the current setting based on Human, Organisation, Technology-Fit and Technology Acceptance Model frameworks, followed by a qualitative phase using in-depth interviews. However, this article explicitly emphasises the quantitative phase.

Setting The study was conducted in five MOH hospitals with THIS technology from five zones.

Participants Prior to the quantitative field study, rigorous procedures including content, criterion and face validation, translation, pilot testing and exploratory factor analysis (EFA) were undertaken, resulting in a refined questionnaire consisting of 41 items. Confirmatory factor analysis (CFA) was then performed on data collected from 343 respondents selected via stratified random sampling to validate the measurement model.

Results The study found satisfactory Kaiser-Meyer-Olkin model levels, significant Bartlett’s test of sphericity, satisfactory factor loadings (>0.6) and high internal reliability for each item. One item was eliminated during EFA, and organisational characteristics construct was refined into two components. The study confirms unidimensionality, construct validity, convergent validity, discriminant validity and composite reliability through CFA. After the instrument’s validity, reliability and normality have been established, the questionnaire is validated and deemed operational.

Conclusion By elucidating critical success factor and acceptance of Casemix, this research informs strategies for enhancing its implementation within the THIS environment. Moving forward, the validated instrument will serve as a valuable tool in future research endeavours aimed at evaluating the adoption of the Casemix system within THIS, addressing a notable gap in current literature.

• quality in health care
• public health
• health economics

Data availability statement

No data are available.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-082547

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STRENGTHS AND LIMITATIONS OF THIS STUDY

The rigorous validation process of the questionnaire, including initial validation, translation, pre-testing and exploratory factor analysis using pilot test data, followed by confirmatory factor analysis using field data, enhances the reliability and validity of the instrument used for data collection.

The use of statistical techniques such as the Kaiser-Meyer-Olkin (KMO) measure, Bartlett’s test of sphericity, factor loadings, Cronbach’s alpha and various validity tests (unidimensionality, construct validity, convergent validity, discriminant validity) ensures the robustness of the analysis.

While the large sample size enhances generalisability to some extent, the study was conducted in only five selected hospitals in Malaysia; thus, the findings may not be representative of all hospitals in the country or other healthcare systems.

This study does not include other professional roles, such as paramedics, medical record officers, information technology officers and finance officers because the knowledge and involvement of these roles in the Casemix system are not comparable to that of medical doctors.

The findings of the study may be specific to the healthcare context in Malaysia and may not be directly applicable to other countries or healthcare systems with different sociocultural, organisational or technological characteristics.

Introduction

The global healthcare landscape is witnessing profound evolution driven by an array of challenges, including the rise of non-communicable diseases, the resurgence of communicable diseases, demographic shifts and escalating healthcare costs. 1 Governments and healthcare authorities worldwide are under mounting pressure to navigate these complexities while optimising operational efficiency and ensuring equitable access to quality healthcare services. 1 Within this context, Malaysia has emerged as a proactive player, spearheading innovative strategies to streamline healthcare delivery and bolster system performance. The Ministry of Health (MOH) Malaysia’s proactive stance is exemplified by its robust efforts to standardise and enhance the quality of healthcare services through the implementation of clinical standards and pathways based on international best practices. 2 Notably, initiatives such as the hospital information system (HIS) and the Casemix system have been instrumental in revolutionising healthcare management practices and fostering a culture of continuous improvement. 3–6

Background of Hospital Information System (HIS)

The HIS stands as a cornerstone of technological innovation in healthcare management, offering a comprehensive platform for efficient data collection, storage and processing. 7 HIS responsibilities include managing shared information, enhancing medical record quality, overseeing healthcare quality and error reduction, promoting institutional transparency, analysing healthcare economics and reducing examination and treatment durations. 8–13 In Malaysia, the adoption of HIS, categorised into total hospital information system (THIS), intermediate hospital information system (IHIS) and basic hospital information system, has paved the way for seamless integration of patient data, administrative tasks, and financial transactions and appointment management into a single system within a hospital. 14–19 The pioneering implementation of a fully integrated paperless system as a THIS facility at Hospital Selayang underscores Malaysia’s commitment to embracing cutting-edge technology to enhance healthcare delivery. 20–22 Today, 19 out of 149 Malaysian hospitals have IT facilities. 23 24 Despite challenges during implementation, the overall advantage of using a comprehensive system is priceless. 22 25–29

Background of Casemix system

The Casemix system is a global system that categorises patient information and treatments based on their types and associated costs, aiming to identify patients with similar resource needs and treatment expenses. 30 31 It is widely used globally such as in the USA, Western Europe, Australia, Eastern Europe and Asia, playing a crucial role in hospital financing. 32 33 Originating from Australia, it optimises resource utilisation, improves cost transparency and enhances healthcare service efficiency. 34 35 However, its adoption in developing nations like Malaysia faces challenges due to technological constraints and resource limitations. 23 36 37 The Malaysian diagnosis-related group (MalaysianDRG) Casemix system categorises patients based on healthcare costs, improving efficiency and resource allocation. 38–40 This system enhances provider payment measurement, healthcare service quality, equity and efficiency, and assists policymakers in allocating cash for hospitals. 24 41 The information from the MalaysianDRG is integrated into the executive information system, providing access to system outputs such as DRG, severity of illness, average cost per disease and Casemix Index. 38–40

Integration of Casemix within HIS

The integration of Casemix within HIS frameworks represents a paradigm shift in healthcare management, offering a unified platform for data-driven decision-making, performance monitoring and quality improvement initiatives. 42 In the USA, there is a need to evaluate existing HIS against advanced hardware and software. 42 As hospitals face public opposition due to rising medical expenses, governments are under pressure to manage healthcare costs more effectively. 42 Casemix-based reimbursement policies aim to compensate medical expenses based on Casemix rather than the number of services provided. 42 By consolidating clinical, administrative and financial data within a single system, Casemix-based systems are multifaceted and require organisational restructuring and educational initiatives for successful implementation. 33 Strategies such as providing feedback to clinicians and integrating decentralised databases into HIS are crucial for ensuring data credibility and accuracy. 33 Transitioning from traditional medical record management to health information management requires careful planning and adjustments due to the lack of automation in the current HIS. 33

Theoretical and conceptual framework

Multiple frameworks are commonly used to evaluate technology systems’ acceptance and success attributes. There are noteworthy frameworks, such as the technology acceptance model (TAM), the DeLone and McLean Information Systems Success Model (ISSM), the HOT-Fit Evaluation Framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). The TAM is a widely used framework for assessing the acceptability and success of technology systems, particularly in HIS. 43–47 It suggests that user perceptions of ease of use, usefulness and intention to use significantly impact system usage. 43–47 The DeLone and McLean ISSM evaluates the effectiveness of information systems by examining relationships between system quality, information quality, user happiness, individual impact, organisational impact and overall system success. 48 49 The HOT-Fit Evaluation Framework, evolved from the ISSM, evaluates the congruence of persons, organisations and technology within an information system, considering technological variables, organisational factors and human factors. 12 50 The UTAUT enhances the TAM by incorporating additional elements such as social impact, enabling situations, and behavioural intentions. 43 51 52

By integrating these frameworks within the context of Casemix implementation within THIS, the investigators aim to assess critical success factors and address barriers to adoption and acceptance, facilitating seamless integration and maximising the potential of healthcare modernisation efforts. Hence, the investigators opted to integrate HOT-Fit and TAM frameworks as this study’s conceptual framework to achieve the research’s specific objectives, scope and contextual considerations (see figure 1 ). HOT-Fit offers a comprehensive framework for examining the alignment between human, organisational and technological factors, while TAM provides a focused lens on individual-level technology acceptance dynamics. 12 44–47 Based on the current study’s conceptual framework, the HOT-Fit framework focuses on technological constructs like system, information and service quality, while the TAM framework covers human dimensions like perceived ease of use, usefulness, intention to use and acceptance. The integration of these frameworks is crucial for achieving the study’s specific and general objectives. Thus, these two frameworks are suitable and deemed appropriate for this study. On the other hand, UTAUT does not appear suitable for the current investigation due to the broad scope and complexity of existing TAM with additional external variables and ISSM was also not selected due to its simplicity. 43 51 52

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Conceptual framework.

This current study aims to evaluate the critical success factors (CSFs) and doctors’ acceptance of Casemix implementation within the THIS environment to understand the issues MOH Malaysia facilities experience better, fill a research gap on Casemix implementation and help shape plans for modernising healthcare. A comprehensive tool, such as a questionnaire, was created to meet the study objectives. This paper aims to examine a multidimensional instrument that was created to meet the study objectives. Consequently, the exploratory factor analysis (EFA) is instrumental in uncovering underlying factors within observed variables to ensure precision and robustness, while confirmatory factor analysis (CFA) was needed to verify the measurement model’s linkages and confirm that the theoretical model was valid, reliable and suitable for data collection, thereby yielding valuable insights. 53–56 Given its merits, the current study used CFA to evaluate the measurement model’s validity. After validation processes, structural equation modelling (SEM) was employed to analyse how exogenous, mediating and endogenous constructs interrelate and determine parameters into a structural model to analyse direct, mediating and moderating effects on the study’s goals and hypotheses. While the technology evaluation frameworks offer crucial insights, it is essential to note that Casemix is designed to organise patient data and treatment costs rather than analyse the acceptability and success of technology systems. Moreover, meeting the study objectives for evaluating Casemix adoption in THIS can be done without a separate instrument for each system. It can assist healthcare organisations and policymakers in understanding CSFs facilitating the implementation and acceptance of the Casemix system, and guiding the development of targeted strategies for seamless implementation, enhancing patient care, work efficiency and resource allocation. Therefore, a reliable and valid quantitative instrument is required to achieve these goals.

Methodology

Study design and ethical approval, study design.

This study employed a sequential explanatory mixed-methods design. Nevertheless, the researchers in the present article solely highlight the exploration and development of items, as well as the reliability and validation of the quantitative study. The data collection for the quantitative pilot study was from 1–14 February 2023, the quantitative phase was from 1 April to 31 June 2023, the qualitative pilot study was on 15 September 2023 and the qualitative field study was from 17 October 2023 to 4 January 2024. This paper highlights on the development of instruments for quantitative phase procedures and findings of the validation of quantitative study only. The quantitative phase used a cross-sectional study design to gather data throughout a specified duration. 53 57 58

Ethical approval

This study has obtained ethical approval from:

The Medical Research Ethics Committee of the Faculty of Medicine, Universiti Kebangsaan Malaysia (JEP-2022–777), see ( online supplemental file 1 ), and

The Medical Research Ethics Committee of the Ministry of Health Malaysia (NMRR ID-22–02621-DKX), see ( online supplemental file 2 ).

Supplemental material

Study instrument.

This study used a self-administered questionnaire to collect data on the CSF and acceptance of Casemix in THIS environment. The instrument was developed in Malay and English for a better understanding of the respondents due to the geographical areas of the study where Malay is the national language of Malaysia. The questionnaire comprised 60 items divided into three sections, each with a limited number of constructs. Section 1a consists of 8 questions that collected demographic information such as age, gender, educational background and work experience in the MOH Malaysia and current hospital. Section 1b assessed the comprehension/knowledge level of the Casemix system using 10 items. Meanwhile, Section 2 represented the perceived Critical Success Factors of Casemix implementation in the THIS context, consisting of 37 items within six constructs: system quality (SY)—4 items, information quality (IQ)—5 items, service quality (SQ)—5 items, organisational factors (O)—9 items, perceived ease of use (PEOU)—5 items, perceived usefulness (PU)—4 items and intention to use (ITU)—5 items. Section 3 encompasses the outcome of the study which is the user acceptance (UA) construct, which contains 5 items.

The study incorporates and modifies existing scholarly works rooted in the Human Organisation Technology (HOT-Fit) and TAM frameworks for sections 2 and 3. The two sections, each evaluated using a 10-point interval Likert scale. The 10-point interval scale offers respondents a greater range of response possibilities that align with their precise evaluation of a question. 55 56 59 60 A score of 1 represents ‘strongly disagree’, while a score of 10 represents ‘strongly agree’. The constructs and components of the instrument were derived from previous research. 12 43 44 48 50 61–64 These items represented eight constructs: SY, IQ, SQ, ORG, PEOU, PU, ITU and User Acceptance.

The constructs described in sections 2 and 3 underwent initial validation, reliability testing and EFA, using pilot data. CFA was also performed using field data. Details regarding the development validation and reliability procedures of the instrument are provided in subsequent sections. Hence, to facilitate transparency and reproducibility, a blank copy of the measurement instrument developed and validated in this study has been included as a supplementary file (see online supplemental file 3 : Blank Copy of Quantitative Instrument).

Independent variables

A few constructs have been examined in this study as mentioned in Subsection 1.6, the conceptual framework comprising technology, organisation and human dimensions.

Technological factors

Constructs such as system quality (SY), information quality (IQ) and service quality (SY) constitute the technological factors. Addressing system quality issues is imperative for fostering user acceptance and realising system benefits. 43 Reliable and accurate systems with dependable functionality enhance user acceptance, while a user-friendly interface and seamless performance enhance user experience. Integration with existing systems promotes acceptability and interoperability. 43 44 Conversely, information quality, encompassing data security and privacy, is crucial in safeguarding patient data, bolstering user confidence and fostering system adoption. 65 Service quality encompasses the support and assistance provided during and after system implementation, with practical training, responsive helpdesk support, and ongoing maintenance contributing to user satisfaction and system success. 51 66 67 Hence, these three constructs encompassing technological dimensions were adapted from the HOT-Fit framework. 12 50

Organizational characteristics

Organisational dimensions, such as an organisational structure and environment, can limit or facilitate the acceptance or implementation of technical advancements. 68 The elements of organisational dimension were the most generally surveyed attributes in IT adoption in organisations. 69 Previous research has identified relative benefit, centralisation, formalisation, top management support and perceived cost as essential organisational elements influencing any organisation’s decision to embrace current information systems technologies. Management barriers are defined as a lack of efficient planning, a lack of trained people, and limits linked to training courses, according to Abdulrahman and Subramanian. 70 The management, technological, ethical-legal and financial barriers were all integrated into the organisational factor category in this study. Previous research has found that technology adoption rates are related to preparedness and impediments to readiness. 71 Along with several other studies, senior leaders play a critical role in using information systems at the organisational level. 72 Direct involvement of senior executives in IS operations demonstrates the importance of IS and ensures their support and involvement in the overall performance of IS efforts in the organisation. 73 Organisational environment and structure can influence user acceptance of information technology, underscoring the importance of organisational improvement initiatives to enhance user acceptance. 74–77 Hence, this primary construct encompassing organisational dimensions was adapted from the HOT-Fit framework. 12 50

Human factors

The TAM is a framework that consists of five fundamental elements: PEOU, PU, ITU, actual system use and external Variables. 78–81 PEOU is a subjective evaluation of a technology’s ease of use, influenced by usability, training and user assistance. 78–81 PU quantifies the level of usefulness attributed to technology, influenced by factors such as usefulness and compatibility with user needs and responsibilities. 78–81 Intention to Use (ITU), External factors, such as organisational regulations, access and availability, can also influence the interactions within the model. 78–81 External variables, such as individual variances, cultural influences and supportive environments, can either amplify or reduce the impact of perceived ease of use and usefulness on behavioural intention and actual use. 78–81 The TAM has been a crucial paradigm for understanding technology acceptance and has significantly impacted research in information systems and technology adoption. The HOT-Fit Evaluation technique, which focuses on system use and user satisfaction, is suitable for this study. 12 50 These two constructs are interconnected to PEOU and PU, delineated by the TAM framework. 78–81 For successful implementation of an information system, medical doctors perceive it as easy to use (PEOU) through adequate training, user-friendly interfaces and intuitive system design. 78–81 Healthcare providers should also perceive the system as useful (PU) to ensure successful implementation, highlighting its benefits such as improved efficiency, quality of care and cost control. 78–81

Dependent variable

The only dependent variable in this study is acceptance which is adapted from the TAM. 44 45 82 The study presents a pragmatic taxonomy of eight different implementation outcomes, including acceptability/acceptance, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration and sustainability. 64 Acceptability is a crucial aspect of implementation, referring to the acceptance of a specific intervention, practice, technology or service within a specific care setting. 64 It can be measured from the perspective of various stakeholders, such as administrators, payers, providers and consumers. 64 Ratings of acceptability are assumed to be dynamic and may differ during pre-implementation and throughout various stages of implementation. In similar literature, Proctor et al delineated examples of measuring provider and patient acceptability/acceptance including case managers’ acceptance of evidence-based procedures in a child welfare system and patients’ acceptance of alcohol screening in an emergency department. 64 The terms acceptability and acceptance are interchangeably used to describe implementation outcomes. Therefore, in this study, the researchers would like to explore the acceptance of the Casemix system in the MOH’s THIS facilities.

Patients and public involvement

Participants in this study were medical doctors and this study did not involve any patients or the public. Hence, there was no patient or public involvement in this study.

Initial validation processes

The initial validation procedures were conducted to establish the content, criteria and face validity/pre-test of the instrument for the field study.

Content validity

Content validity is significant when developing new measurement tools because it links abstract ideas with tangible and measurable indicators. 83 This involves two main steps: identifying the all-inclusive domain of the relevant content and developing items that correspond to this domain. 83 The Content Validity Index (CVI) is often used to measure this validity. 84–86 Recent studies have demonstrated the content validity of assessment tools using the CVI. 87–90 The best method for calculating the CVI, suggesting that the number of experts reviewing an instrument should range from 2 to 20. 84–86 91 92 Typically, the number of experts varies from 2 to 20 individuals. 93 For the current study, two experts from the Hospital Financing (Casemix Subunit) at MOH Malaysia were selected. This is coherent with the number of experts that are recommended by a few literature in online supplemental file 4A . 84 There are two types of CVI: I-CVI for individual items and S-CVI for overall scales. 84–86 91 92 S-CVI can be calculated by averaging the I-CVI scores (S-CVI/Ave) or by the proportion of items rated as relevant by all experts (S-CVI/UA). 84–86 91 92 Before calculating CVI, relevance ratings are converted to binary scores. The relevance rating was re-coded as 1 (scale of 3 or 4) or 0 (scale of 1 or 2), as indicated in online supplemental file 4B . Online supplemental file 4C reveals two experts’ item-scale relevance evaluations to exhibit CVI Index calculation. In this study, the experts validated the questionnaire contents, achieving perfect scores of 3 or 4 for all items, resulting in S-CVI/Ave and S-SCVI/UA scores of 1.00. In conclusion, a thorough methodological approach to content validation, based on current data and best practices, is essential to confirm the overall validity of an evaluation.

Criterion validity

Criterion validity denotes to the degree of correlation between a measure and other established measures for the same construct. 62 88 89 94 95 An academic statistics expert and an expert in questionnaire development and validation procedures reviewed criterion validity. This can be reviewed in online supplemental file 5 . Subsequently, a certified translator translated the instrument from English to Malay back-to-back precisely.

Face validity

A face validity assessment was undertaken to evaluate the questionnaire’s consistency of responses, clarity, comprehensibility, ambiguity and overall comments. Before commencing the pilot study and fieldwork, the researchers acknowledged and resolved the concerns that were previously mentioned. 62 90 96 Following the validation process, 11 respondents were purposefully selected for face validity also known as pre-testing to accomplish the prerequisite for face validation. Furthermore, they must meet exclusion criteria like those stipulated for participants in the field study. Subsequently, these respondents were excluded from participation in the quantitative field study. The study population will be described further in Subsection 2.6.2. The objective of this pre-test or face-validation process was to assess the consistency of responses, and clarity, ambiguity and overall design of the questionnaire. 97 This will be done through the evaluation from the online Google Form of the Questionnaire. Before conducting the pilot study and fieldwork, the researchers took into consideration the concerns that had been raised. 97 The face validity result has been uploaded as online supplemental file 6 .

Quantitative pilot test and EFA

The pilot study was conducted at a Federal Territory hospital in Malaysia, Hospital W. The pilot study population also possess similar characteristics to the participants/samples involved in the subsequent quantitative field study. Additionally, these respondents were excluded from participation in the quantitative field study. This study used a minimum of 100 samples to ensure valid results for the EFA. 97 98 Hence, since the current pilot study is using EFA, the minimal sample size of 100 is therefore supported by a few studies and books experienced in research and validation procedures. 54–56 97 99 Therefore, to account for a projected drop-out rate of 20%, the minimum sample size for this preliminary pilot study was determined to be 125 medical doctors. 100 The research was conducted without participant or public involvement in the design, conduct, reporting or dissemination strategies. The data collection method was also like the field study. It was employed using an online Google Form Questionnaire. Participants were asked to scan a Google Form link or QR code to access information sheets, consent forms and online questionnaires. Each participant was notified that their information would be kept private their anonymity would be retained solely for the study, and they could withdraw at any time.

The pilot study will use EFA to measure data from a collection of hidden concepts. EFA is a method that generates more accurate results when each shared component is represented by many measured variables, either exogenous or endogenous constructs. 54–56 97 98 101–103 The collected data will be used to identify and quantify the dimensionality of items that assess the construct. 53–56 59 60 104 EFA is essential to determine whether items in a construct produce distinct dimensions from those found in previous studies. 53–56 59 60 104 Factors’ dimensionality may change as they are transported from other domains to a new research topic, and fluctuations in the population’s cultural heritage, socioeconomic status and passage of time might affect dimensionality. The EFA methodology uses principal component analysis (PCA) to decrease the amount of data, but it fails to discern between common and unique changes efficiently. 97 98 PCA is indicated when there is no known theoretical framework or model, and it is used to create the first solutions in EFA. Four requirements of PCA included (1) components with eigenvalues more than one, (2) factor loadings greater than 0.60 for practical relevance, (3) no item cross-loadings greater than 0.50 and (4) each factor has at least three items to be retained. 97 98 The data’s eligibility for factor analysis was determined using the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) of >0.6 and Bartlett’s test of sphericity. 55 56 105–107 The effectiveness of Bartlett’s test for factor analysis hinges on the significant result, with a value near p<0.001 (p<0.05) indicating acceptability. 53–56 107 The scree plot also determined the best number of constructs to keep. 53–56

Quantitative field study

Study location.

The present study gathered data from five hospitals situated in various Malaysian zones—South, North, West, East and East Malaysia—that are outfitted with the Total Hospital Information System (THIS) and Casemix system. The study used cluster sampling to select study sites in Malaysia, dividing the country into five distinct clusters. Five hospitals that had successfully implemented Casemix for at least 3 years were chosen to represent different regions of Malaysia. Hospital N was selected for the northern region, Hospital E for the eastern region, Hospital S for the southern region and Hospital W for the central/western region. Hospital EM was chosen for East Malaysia. Cluster sampling is suitable when the research encompasses a vast geographical expanse.

Target population for the study

The target population for this study was medical doctors by profession working in hospitals under MOH in 2023. The study collectively obtained a sampling frame of 3580 medical doctors by profession, encompassing hospital directors, deputy directors (medical division), consultants/specialists, medical officers and house officers from the five selected hospitals. These doctors should fulfil the inclusion and exclusion criteria of this study as follows:

Inclusion criteria

Permanent/ contract of service medical doctors who were posted to current participating hospital.

Has working experience in the current participating hospital for at least 3 months.

Agree to participate in the study.

Exclusion criteria

Attachment medical doctors.

Refuse to participate in the study.

The study population of face validation/pre-test and pilot test has characteristics similar to those of the study population in the field study. The pre-test and pilot-test samples will also be excluded from samples in the field study. Participants were given surveys to complete at their own pace, without fear or pressure.

Sample size and sampling method

The target population was selected using proportionate stratified random sampling, dividing the total population into homogeneous groups. 16 108–110 Proportionate stratified random sampling is a probability sampling method that includes separating the entire population into similar groups (strata) to conduct the sampling process.

The authors are concerned about the sample size needed for CFA validation of the measurement model. However, current studies do not have a consensus on the appropriate sample size. For small indicators, a minimum sample size of 100–150 respondents is often needed, 111–113 whereas, precise analysis for CFA may require 250–500 respondents. 114 115 Some authors suggested the following suggestions for the sample size requirement: (a) a sample size to parameter ratio of 5 or 10, (b) ten cases per observation/indicator and (c) 100 cases/observations per group for multigroup modelling. 116–118 In conclusion, the researchers opted to employ five times the number of indicators in the questionnaire because the number of indicators for latent variables is large. 116 119 The final questionnaires contain 59 items, requiring a total sample size of 295. However, there is an additional 20% anticipated dropout rate. The sample size was estimated using the formula n=n/1-d (n=total samples, n=minimum required samples and d=drop out rates), yielding a minimum sample size of 369. 100 This is also corroborated by other research, which states that because the conceptual framework in this study consists of eight constructs, each with at least four items, the required sample size is 300, with an additional 20% expected drop-out rate, the calculated sample size was 375. 56 97 100 102 As a result, the researchers opted to distribute questionnaires to the 375 participants using proportionate stratified random sampling depending on their professional roles as suggested. 56 97 102 116

Data collection methods

The data collection method for the quantitative field study is similar to the techniques used in the quantitative pilot study. This data collection method was elaborated in Subsection 2.4.4. However, the link for the participant information sheet (PIS) and informed consent forms was included on the first page of the questionnaire which is https://bit.ly/3F8IF2e . The participant’s information sheet and informed consent forms are attached as online supplemental files 7 and 8 , respectively. Similarly to the quantitative pilot study, respondents may do so freely without losing their data if they withdraw from the survey midway. Participants were assured that their information would be kept confidential and that their anonymity would be strictly protected during the field study. Participants who wish to participate must first consent and complete all survey questions. They were also instructed to contact the lead investigator with any questions. The participants have up to 2 weeks to complete and submit the online questionnaire. All survey information was linked to a research identification number. For example, study identifications 001 to 375 on the subject data sheets will be used instead of the subject’s name. The appropriate senior management and Casemix System Coordinators (CSCs), the department’s Casemix Coordinator and Heads of Department will be contacted 3 days before the data gathering session concludes. All measures were taken to safeguard participants’ privacy and anonymity.

Data analysis using Confirmatory Factor Analysis (CFA)

Once the EFA technique has been completed, these constructs and emerging components of the revised conceptual framework were used in the field study. Hair et al and Awang et al described two distinct models in the field study: the measurement model used in the CFA technique and the structural model used to estimate paths using the SEM. 54–56 97 99 This study paradigm has the features of a confirmatory form of research, with a focus on behavioural components. This type of SEM is known as covariance based-SEM (CB-SEM) and exhibits theory testing or theory-driven research that integrates existing theories to replicate an established theory into a new domain, confirming a pre-specified relationship. 54–56 97 99

The SPSS Analysis of Moment Structures (AMOS) V.24.0 software was used in CFA to evaluate the unidimensionality, validity and reliability of the measurement model. 53 54 56 The instrument’s normality is also achieved using CFA. 53 54 56 There are two ways to validate measurement models: pooled and individual CFA. 54–56 120 121 Pooled-confirmatory factor analysis’ (Pooled-CFA) higher degree of freedom enables model identification even when some constructs have fewer than four components. 54–56 120 121 The missing data will be omitted/discarded from the analysis. To ensure unidimensionality, the permissible loading factor for each latent construct is calculated, and items that cannot fit into the measurement model due to low factor loading are excluded. 53 55 56 97 122–125 The cut-off value for acceptable factor loading varies depending on the research goal. However, this study used a threshold value of 0.5 to minimise item deletion. 53 55 56 97 121 122 126 Convergent validity is assessed by calculating the average variance explained (AVE) for each construct. 53 55 56 97 111 122 Meanwhile, composite reliability (CR) assesses how often a construct’s underlying variables are used in structural equation modeling. 53 55 56 97 122 A latent construct’s CR must be 0.6 to achieve composite reliability. 53 55 56 97 122

Several fitness indicators were reported among scholars. Some recommendations are to report fit indices as absolute fit (chi-squared goodness-of-fit (Χ 2 ) and standardised root mean square residual, or SRMR), parsimony-corrected fit (root mean square error of approximation, or RMSEA), Comparative Fit Index (CFI) and comparative fit (Tucker-Lewis Fit Index (TLI)). 54–56 99 123 124 126–129 They advised using at least one index from the three fitness categories: absolute fit, incremental fit and parsimonious fit. 54–56 123 124 126–129 A model fit was indicated using a set of cut-off values: RMSEA values from 0.05 to 1.00, CFI >0.90 and Chisq/df<5.00, which would imply a reasonable fit. 53–56 126 129–131

Findings for the pilot test through exploratory factor analysis

Out of the required minimum sample size of 125, a total of 106 participants took part in the quantitative pilot study, resulting in an 84.8% response rate. According to Hair et al and Awang et al , in order to conduct an EFA, at least 100 samples are needed. 54–56 97 However, considering a potential drop-out rate of 20%, the minimum required sample size for this pilot study is 125. Researchers performed an EFA to find the primary dimensions from a wide set of latent constructs represented by 42 items before conducting the CFA. EFA uses PCA as the extraction method to reduce data and create a hypothesis or model without pre-existing preconceptions about the variables’ quantity or nature. 54–56 97 132 The EFA deemed indicators above 0.60 significant, and indicators loading into the same component were combined to match the measurement model. 97 The measurement model (for CFA) and structural model (for path estimation) of SEM will use EFA results. 54–56 97 99 EFA was used to evaluate and appraise the items measuring the construct, while CFA was used to validate the measurement. 12 43 44 50 61 EFA and CFA used pilot and field study data, respectively. EFA is a method used to select factors for retention or removal, using PCA and varimax rotation. It is a popular orthogonal factor rotation approach that clarifies factor analysis. 53 55 56 97 122 The extraction technique reduces the organisational factors (O) from nine to eight items, with one item, ‘Organisational competency to provide the resources for the implementation of the Casemix system in THIS setting,’ not reaching the factor loading of 0.6, hence it was 55 97 see table 1 .

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Factor loading of EFA with PCA and varimax rotation

To prepare for the next stage, the researcher reorganises the objects into their respective components and begins data collection in the field study. The EFA results also reveal that the two components of the organisational characteristics (O) construct were later named organisational structure (STR) and organisational environment (ENV). 53 55 56 97 122 The instrument was used for 41 items in the field study and analysed with Cronbach’s alpha, ensuring its internal reliability for the field study, 53–56 97 133 see table 2 below.

The number of items for each construct before and after EFA and Cronbach’s alpha

Consolidating correlated variables was EFA’s primary goal. EFA established eight constructs from the pilot study data and according to the researcher’s conceptual framework (See figure 1 ). 53–55 The overall results of KMO and Bartlett’s sphericity test for all constructs, see table 3 . The KMO value was 0.859, which is larger than 0.6. The result of Bartlett’s test of sphericity shows that p value <0.001 yielded statistically significant findings, which is p value <0.05. 53 55 56 97 122 Therefore, it is appropriate to proceed with further study.

Results of the KMO and Bartlett’s test of sphericity

The amount of variance accounted for, referred to as total variance explained (TVE), 53–56 97 see table 1 ( online supplemental file 9 ). Each component had an eigenvalue larger than 1 and the TVE was 84.07%, exceeding 60%. 53–56 97 The researcher should contemplate incorporating more items to assess the structures as it indicates that the existing items are inadequate for accurately assessing the constructs if the TVE is less than 60%. However, this does not occur in the present study.

The EFA approach also includes the scree plot. The researcher can ascertain the number of components by observing the distinct slopes in the scree plot. 53–56 97 The scree plot exhibits nine distinct slopes, as shown in figure 1 ( online supplemental file 9 ). Hence, the EFA identifies a total of nine components.

Cronbach’s alpha would calculate measuring each item’s internal reliability. Internal reliability assesses how well the selected items measure the same construct. 53–56 97 133 All constructs topped 0.7 Cronbach’s Alpha. Hence, this instrument is reliable for use in field study.

Findings for the field study through the confirmatory factor analysis

The ultimate measurement tool for field study comprises 41 elements from the EFA procedure. To adequately address the intricacy of the quantitative instrument for the field study, the researchers determined that a minimum of 300 samples was necessary to implement CFA. 97 An additional 20% drop-out rate resulted in a minimum sample size of 375 individuals for the field study. Hence, out of this sample, only 343 participants answered, indicating a response rate of 91.5%. 100 No missing data was reported.

CFA validates factor loading and assessment in this study. The researcher tests a theory or model using CFA. Unlike EFA, CFA is a form of structural equation modelling that makes assumptions and expectations about the number of factors and which factor theories or models best suit prior theory. 53–56 97 EFA relied mainly on outer loading; however, factor loadings and fitness indices are now considered. Researchers must confirm that both folds meet standards. CFA also lets academics test financial literacy indicators and measurement models. Thus, a proper measuring model helps researchers interpret their data.

Validity, unidimensionality and reliability were necessary for all latent construct assessment models. 53 55 56 97 122 The latent construct measurement model needed convergent, construct and discriminant validity. 53 55 56 97 122 AVE assesses convergent validity, while measurement model fitness indicators determine construct validity. 54–56 On the other hand, composite reliability (CR) was used to calculate instrument reliability since it was better than Cronbach’s alpha. 54–56 133

Figure 2 shows that Pooled-CFA validated all latent constructs in the measurement model simultaneously. These constructs were aggregated using double-headed arrows to execute a Pooled-CFA. Pooled-CFA’s increased degree of freedom allows model identification even when some constructs have fewer than four components. 54–56 Pooled-CFA was employed in this investigation since only one construct has two components.

Result from Pooled-CFA procedure.

Uni-dimensionality

Unidimensionality is a set of variables that can be explained by one construct. 7–9 Unidimensionality is achieved when all construct-specific measuring items have acceptable factor loading. 54–56 Remove CFA components with low factor loadings from the measurement model until fit indices are met. 53–56 97 134 Table 4 summarises the build items with factor loadings >0.6. 54–56

Factor loading of all items, composite reliability (CR) and average variant extracted (AVE) and normality testing

Convergent validity

Convergent validity is a group of indicators that measures a construct. 54–56 97 135 It assesses the strength of correlations between items that are hypothesised to measure the same latent construct. 56 97 The average variance extracted (AVE) statistic can be used to verify the convergent validity of a construct. If the concept’s AVE is more than 0.5, it possesses convergent validity. 53 56 97 136 Table 4 shows that the AVE for all structures was more than 0.5. Organisational characteristics/factors (ORG) AVE shows the highest AVE, which was 0.857, and environment component, the lowest AVE, which is 0.699. The model is, therefore, convergently valid.

Construct validity

When all model fitness indices met the criteria, construct validity was attained. 55 56 97 Construct validity was established using absolute, incremental and parsimonious fit indices. 55 56 97 Some researchers recommend using one fitness index from each model fit category. 55 56 97 This study employed RMSEA, CFI and normed chi-square (x2)/df as its main indicators. According to table 5 , this instrument met all three fitness indices: (1) the RMSEA value was below the threshold of 0.08 (0.054), confirming the absolute fit index; (2) the instrument achieved the incremental fit index category by obtaining a CFI value above 0.90; and (3) the parsimonious fit index, measured using Chisq/df, yielded a value of 2.014, which is below the accepted value of 3.0. 55 56 97 This study proved the instrument’s construct validity.

Fitness index summary

Discriminant validity

The survey’s discriminant validity was tested to ensure no redundant constructs were found in the model. The model is discriminant when the square root of the average variance extracted (AVE) for each construct is greater than its correlation value with other constructs. 55 56 136 Table 6 summarises the discriminant validity index, which showed that all constructs met the threshold. 55 56 136 The diagonal values (bold font) in this table were greater than all other values in their row and column, suggesting discriminant validity for all constructs. 55 56 136

Discriminant Validity Index

Composite reliability

Estimating model reliability uses composite reliability (CR). 55 56 97 CR between 0.6 and 0.7 is acceptable. 55 56 97 Table 4 above shows that the instrument’s composite reliability exceeded 0.6 for all structures. The environment component had the lowest CR (0.903), while the information quality construct had the highest (0.954). Therefore, this instrument’s composite reliability is accomplished.

Normality assessment

Each item evaluating the construct’s distributional normality was assessed. All skewness values must be within the usual range. 56 97 Skewness between −1.5 and 1.5 is considered acceptable. All model components’ skewness values are between −1.5 and 1.5, indicating their normal distribution. 56 97 The instrument’s data distribution met the normality condition, as shown in table 4 .

This study focused on redeveloping and validating an instrument to gauge medical doctors’ intent to use and accept the Casemix system within the Total Hospital Information System (THIS) context. The EFA and CFA indicated that the instrument was well-designed and validated for assessing medical practitioners’ acceptance of the Casemix system in THIS setting. 55 56 97 The acceptance of the Casemix system among medical physicians in hospital information systems was found to be influenced by various factors including system and service quality, perceived ease of use, usefulness, relevance to clinical practice, training and good organisational support, impact on efficiency and productivity, and confidence in information quality involving data accuracy and security. Healthcare organisations must address these components to gain physician acceptance. 43 44 137 They can optimise Casemix system use, improving patient care and results. 137

Principal findings

Findings of exploratory factor analysis (efa).

The pilot test data was analysed using EFA, which helps researchers understand complex datasets and discover observed variable correlations. 55 56 97 EFA reduces variable dimensions by identifying common patterns, shaping fundamental factors that influence observable variables and grouping related variables. 122 126 138 It simplifies model design by computing factor loadings, which indicate the intensity and direction of factor-observable variable interactions. EFA also finds underlying components in a dataset, while CFA analyses and confirms an EFA-proposed factor structure. 55 56 97

All structures underwent KMO and Bartlett’s sphericity tests, with all structures having KMO values over 0.6. 55 56 105–107 The scree plot, part of EFA, was used to count components and found nine constructs on 42 items. 55 56 105–107 The study found that one construct should now have two parts, mainly due to demographic changes, particularly socioeconomic status and education. Component 1 explained 14.115% of construct variance, while component 9 explained 6.610%. All constructs had 84.07% total variance Explained (TVE), exceeding the minimum threshold of 60%. 55 56 60 112 129

The EFA discovered nine components, including O1-O9 for organisational factors. 43 45 50 139 41 of 42 items had factor loadings above 0.6, requiring item O1 to be eliminated. 53 55 56 97 122 Only organisational factors (O) had nine items reduced to eight following extraction. The remaining seven constructs had only one component and no additional components, resembling HOT-Fit and TAM framework organisational constructs.

The study stresses tool dependability and internal consistency, using markers such as Cronbach’s alpha (α), person reliability, person measure and valid responses. 133 140 A Cronbach’s alpha coefficient of 0.7 or above is acceptable in social science and other studies. 53 138 141 142 Internal reliability is measured by how well-selected items measure the same idea. 53–56 97 98 133 143 The researcher reordered questionnaire items for the field investigation, and CFA authenticated and confirmed all eight constructs on field data, which is elaborated further in the next Subsection 4.1.2.

Findings of Confirmatory Factor Analysis (CFA)

Once the pilot data was assessed and the EFA was commenced, the final questionnaire will be used in the quantitative field study. Eventually, another procedure will be conducted to validate the questionnaire, also known as CFA, based on the field study data. The CFA will validate the instrument’s convergent, construct and discriminant validity. Unidimensionality, composite reliability and normality evaluations are also needed to reveal whether the instrument’s items are valid. 53–56 97 Therefore, the findings of this study demonstrate that the quantitative instrument has been validated and proven reliable for assessing medical practitioners’ intention to use and accept the Casemix system within the context of THIS. Using EFA and CFA is imperative for ensuring the instrument’s validity, reliability and trustworthiness. 53–56 97

By using EFA, the organisational factors (O) emerged into two components. The organisational factors (O) construct was renamed as organisational characteristics (ORG) in the measurement model, and the newly emerged components were named organisational structure (STR) and organisational environment (ENV). Measurement models refer to the implicit or explicit models that relate the latent variable to its indicators. 55 56 97 The organisational characteristics (ORG) construct is assessed as a second-order construct due to the emerged components. When dealing with a complex framework, researchers can choose to do the CFA individually for each second-order construct, and then followed by Pooled-CFA, through item parcelling or straight away employ Pooled-CFA. 55 56 The use of Pooled-CFA is beneficial because of its improved efficiency, effectiveness and ability to address identification difficulties. 55 56 However, although there are many constructs in this study, this measurement model only includes one second-order construct, which is the (ORG) construct with two emerged components. The other seven constructs are made up exclusively of first-order constructs, each consisting of a maximum of five items. Therefore, a direct Pooled-CFA was employed. 55 56

This study uses CFA to validate factor loading and assessment in a theory or model. 53–56 97 CFA is a form of structural equation modelling that makes assumptions and expectations about the number of factors and which factor theories or models best suit prior theory. 53–56 97 According to Baharum et al in their few studies, they measured success factors in newly graduated nurses’ adaptation and validation procedures. 129 144 145 Likewise, for example, CFA also allows academics to test financial literacy indicators and measurement models, ensuring that a proper measuring model helps researchers interpret their data as elaborated in a few studies. 146–148

Validity, unidimensionality and reliability were necessary for all latent construct assessment models. 53 55 56 97 122 The latent construct measurement model needed convergent, construct and discriminant validity. 53 55 56 97 122 Convergent validity is assessed using the average variance extracted (AVE) statistic, while construct validity is determined by measurement model fitness indicators. 54–56 Composite reliability (CR) was used to calculate instrument reliability since it was better than Cronbach’s alpha. 54–56 133

Unidimensionality is a set of variables that can be explained by one construct. 7–9 Unidimensionality is achieved when all construct-specific measuring items have acceptable factor loading. 54–56 Convergent validity is a group of indicators that are considered to measure a construct. 54–56 97 135 Convergent validity is achieved when the concept’s AVE is more than 0.5, and the highest AVE for all structures was 0.857. 53 56 97 136 Normality assessment was conducted on each item evaluating the construct’s distributional normality, with skewness values within the usual range (–1.5 to 1.5). 56 97 The instrument’s data distribution met the normality condition.

Construct validity is attained when all model fitness indices meet the criteria, using absolute, incremental and parsimonious fit indices. 55 56 97 The instrument met all three fitness indices, confirming the absolute fit index with RMSEA=0.054 (aim<0.1), achieving the incremental fit index category by obtaining a CFI value above 0.90 and yielding a parsimonious fit index of 2.014 (aim<5.0). 55 56 97

Discriminant validity was tested to ensure no redundant constructs were found in the model. 55 56 136 The model obtained discriminant validity since each construct’s square root of average variance extracted (AVE) is bigger than its correlation value with other constructs. 55 56 136 The summary discriminant validity index showed all constructs met discriminant validity.

The instrument’s composite reliability exceeded 0.6 for all structures, with the environment component having the lowest CR (0.903) and the information quality construct having the highest (0.954). 55 56 136 Calculating model reliability with composite reliability (CR). 55 56 97 Acceptable CR is 0.6–0.7. 55 56 97 As shown in table 1 , the instrument’s composite reliability exceeded 0.6 for all constructs. The environment component (ENV) had the lowest CR (0.903), while information quality had the highest (0.954). Thus, this instrument’s composite reliability is achieved.

Therefore, all necessary procedures to determine validity, reliability and normalcy were conducted, and no items were excluded. As a result, the total number of items remained at 41. Construct, convergent, discriminant validities and composite reliability have all been attained. All things satisfied the criteria of normality.

Strengths and weaknesses of the study

There are various ways in which this study could benefit the medical community and policymakers. 149 150 The research assesses important success elements that affect physicians’ adoption of the Casemix system in hospitals that have a THIS. Policymakers and hospital administrators may find it easier to pinpoint the critical elements influencing the Casemix system’s effective deployment with the aid of the study’s findings. 151 To successfully implement clinical pathway/case management programmes, policymakers may find the study to help understand the significance of ongoing clinician support and acceptance, top management leadership and support, and a committed team of case managers, nurses and paramedical professionals. 151 152 Policymakers can potentially use the findings to impact admissions decisions, thereby increasing clinical practice openness. 152–154

Strengths and limitations exist in this research. One of the strengths of the study was that it employed a sequential explanatory mixed-method approach to investigate the CSFs and acceptance of the Casemix system among medical practitioners in THIS. 58 155 156 The findings revealed that there might be unnoticed CSFs in the quantitative phase, suggesting the need for a qualitative method to identify more CSFs, perceptions and challenges/barriers. Quantitative data support hypothesised associations, but qualitative data provide in-depth data to supplement quantitative conclusions. 157 The mixed-method approach is expected to improve research design and yield more valid results.

Additionally, another strength of this study is that it uses a strict methodological approach to instrument development and validation. It uses both EFA with pilot test data and CFA using field data, which makes the instrument used for data collection more reliable and valid. Many statistical tests were used to make sure the instrument worked well and the analysis was accurate. These included the KMO measure, Bartlett’s test of sphericity, systematic deletion of items based on factor loadings, Cronbach’s alpha and different validity tests such as unidimensionality, construct validity, convergent validity and discriminant validity. 55 56 105–107

Although the study had a large sample size, it was only conducted in five selected hospitals in Malaysia. Therefore, the findings may not accurately represent all THIS hospitals in the country or other healthcare systems. Other professional positions, including paramedics, medical record officers, information technology officers and finance officers, are not included in this study since their involvement and level of understanding in the Casemix system are not similar to that of medical practitioners, despite being relatively involved in the Casemix system. Hence, this may limit the generalisability of the findings could be a potential weakness of the study. The study’s findings are likely to be distinctive/unique to the healthcare setting in Malaysia and may or may not be directly transferable to other nations or healthcare systems that have distinct sociocultural, organisational or technological characteristics. While this study’s findings are rooted in Malaysia’s healthcare setting, where the Casemix system and THIS are prevalent, their applicability to other countries or healthcare systems with different sociocultural, organisational or technological characteristics should be carefully considered. Despite this, there are potential avenues through which the insights gained from this research could benefit other nations or healthcare systems. For example, the principles of efficiency and effectiveness in healthcare management highlighted in this study could be adapted and implemented in various settings. Additionally, the lessons learnt from the challenges faced in Malaysia’s healthcare system could serve as valuable guidance for other countries looking to improve their systems.

Strengths and weaknesses concerning other studies

Compared with previous studies, this research contributes to the field by providing a validated instrument tailored to assess the acceptance of the Casemix system within the THIS environment. Prior literature has examined various aspects of Casemix implementation in Malaysia as well as in other countries. However, no one has investigated Casemix in THIS or even in HIS. Thus, this study offers a comprehensive evaluation tool that addresses critical success factors influencing medical doctors’ acceptance, filling a significant research gap. Given the absence of prior research in this area, the newly created quantitative tool would be advantageous in achieving the study objectives and serve as a point of reference for future investigations.

However, previous literature by Beth Reid describes the importance of developing Casemix-based hospital information system management. 33 The Casemix-based hospital information system is a comprehensive approach to healthcare management that involves estimating costs per diagnosis-related group (DRG), building a Casemix-based system and addressing organisational design and education issues for successful implementation. 33 It is crucial to provide Casemix reports to hospital staff and clinicians to identify errors in data. Improving the quality of data is essential for both hospitals and universities. To ensure the credibility of the HIS, it must tap into decentralised databases to ensure common input data for each patient’s diseases and procedures. 33 Sharing data is beneficial for clinicians as it allows them to avoid investing time and effort in ensuring database accuracy to discover that the data used for Casemix activities, such as funding, is obtained from the medical record. 40 This approach is essential for ensuring the accuracy and efficiency of healthcare management. 33

Additionally, a study by Saizan showed that THIS hospital showed the lowest Casemix performance in terms of accuracy of the main diagnosis, the completeness of other diagnoses, and the coding of main and other diagnoses. 16 This article outlines two themes with three subthemes, each theme based on why the performance is the lowest. These two themes are the poor commitment of clinicians and obstacles in the work process. Furthermore, another study revealed that one THIS hospital in Malaysia had the lowest Casemix performance in terms of main diagnosis accuracy, other diagnosis completeness, and main diagnosis and other diagnostic coding accuracy. 16 This article presents two overarching themes, each consisting of three subthemes based on the qualitative, in-depth interview findings. These themes are centred around the underlying reasons behind the lowest Casemix performance. The two main themes identified are the lack of dedication among professionals and the challenges encountered in the workflow.

Meaning of the study: possible explanations and implications

The validated and reliable instrument developed in this study holds implications for clinicians, policymakers and healthcare organisations aiming to optimise Casemix system implementation within HIS. Identifying critical factors influencing acceptance, such as system, information and service quality, is imperative to meet study objectives. Organisational characteristics such as environment and structure, as well as human factors such as perceived ease of use and perceived usefulness, the findings offer actionable insights for enhancing system adoption, utilisation and success. Policymakers and hospital administrators can use these findings to streamline Casemix deployment strategies, improving patient care outcomes and operational efficiency within the THIS.

First, while the specific details of the findings may not directly translate to other contexts, the underlying principles and methodologies employed in this study can serve as a valuable template for researchers in different settings. By adapting and contextualising the research methods and instruments used in this study, researchers in other countries can conduct similar investigations tailored to their healthcare environments. 158 159

Second, the identification and evaluation of critical success factors for implementing healthcare information systems, such as the Casemix system, are universal challenges healthcare organisations face worldwide. 33 158 160 Because of this, the conceptual framework and analytical methods created in this study can help us understand what makes people accept and use these kinds of systems in different situations. Researchers and policymakers in other countries can leverage these insights to inform their strategies for implementing and optimising healthcare information systems.

Additionally, while the contexts and details of the Casemix system and THIS may vary across different countries, the broader goals of improving resource allocation, clinical decision-making and quality of care are shared objectives across healthcare systems globally. Therefore, the findings of this study, particularly regarding the factors influencing system acceptance and success, have the potential to resonate with stakeholders in other countries who are working towards similar goals. 151 161 162

Overall, while recognising the contextual specificity of the study’s findings, there is potential for the insights generated to contribute to the broader body of knowledge on healthcare information systems and inform practices in other countries or healthcare settings with distinct characteristics. Through collaboration and adaptation, the lessons learnt from this research can be extrapolated and applied to diverse healthcare contexts, ultimately contributing to advancing healthcare delivery worldwide. 33 158 160 By sharing best practices and lessons learnt, healthcare systems around the world can benefit from the findings of this study and improve their information systems. This collaborative approach can lead to more efficient and effective healthcare delivery on a global scale.

Unanswered questions and future research

The current study proposes employing this instrument in future research, broadening the target population to include more professional occupations and increasing the sample size for more robust results. The novelty of this research lies in its comprehensive analysis of the direct and indirect effects of these parameters on user acceptance of implementing Casemix within THIS environment. SEM was employed to investigate the proposed model. Apart from that, mediating effects have been examined in this study involving a few critical constructs, such as PEOU, PU and ITU, using similar analysis methods. Additionally, more information on moderating characteristics, including age, gender, professional positions, degree of education, years of experience in MOH Malaysia and current THIS hospital and Casemix system knowledge, could improve the instrument. These moderating effects were examined using SEM as well.

The innovation of this study is that it examines the CSFs that influence the acceptance of the Casemix system in the THIS environment, specifically in MOH hospitals in Malaysia. The immediate findings have clear significance for healthcare organisations and policymakers in Malaysia, and even globally. However, the more significant implications for readers in other countries are also relevant. First and foremost, recognising CSF in implementing the Casemix system provides valuable information that can be applied to healthcare systems, especially those equipped with THIS facility universally. Gaining insight into these aspects can provide valuable strategic decision-making guidance in other nations seeking to implement or improve similar systems within their healthcare infrastructure.

Furthermore, the study uses a methodological approach that involves the use of a mixed-methods approach. The quantitative phase, elaborated on in this article, employs a reliable quantitative instrument that validates exploratory and confirmatory factor analyses and reliability testing. Moreover, semi-structured, in-depth interviews were conducted with the Deputy Directors representing the top management and the CSCs of 5 participating hospitals. Hence, these mixed-methods studies provide a strong foundation for evaluating the adoption of the Casemix system within healthcare information systems. Readers from different countries might use and modify these approaches to conduct comparable investigations in their specific circumstances, enhancing the comprehension of healthcare informatics worldwide.

Moreover, the study highlights the significance of interdisciplinary collaboration among healthcare practitioners, technology specialists and policymakers in facilitating the practical application of the Casemix system as one of the clinical and costing modules essential in healthcare settings, especially in facilities equipped with HIS. This interdisciplinary approach to tackling issues in healthcare informatics is generally applicable and can be implemented in various countries and healthcare systems.

To summarise, this study’s immediate findings may address the CSF of the Casemix system implementation within THIS of the healthcare system in Malaysia. However, its broader significance lies in providing valuable insights, methodological frameworks and interdisciplinary approaches that can be applied globally to adopt the Casemix system within the realm of the HIS in other countries, and it is not only applicable locally in the Malaysian setting.

In summary, this research has comprehensively evaluated the fundamental principles outlined in the conceptual framework. Various methodological approaches, including content validity, criterion validity, translation, pre-testing for face validity, pilot testing using EFA and field study employing CFA, have been employed to assess the validity of the items. 12 43 44 50 61 The EFA analysis computed KMO, Bartlett’s test for sphericity and Cronbach’s alpha values, all meeting the criteria for sample adequacy, sphericity and internal reliability. 53–56 97 Additionally, the CFA analysis tested for unidimensionality, construct validity, convergent validity, discriminant validity, composite reliability and normality, further confirming the validity and reliability of the instrument used to evaluate critical success factors and the acceptance of the Casemix system within the THIS context. 53–56 97

Consequently, this validated instrument holds promise for future quantitative analyses, including covariance-based structural equation modeling (CB-SEM) or variance-based structural equation modeling (VB-SEM). In this study, CB-SEM, in conjunction with SPSS-AMOS V.24.0, was used to explore the direct, indirect, mediating and moderating effects among the constructs outlined in the conceptual framework. The findings from these quantitative analyses will be presented in forthcoming articles, providing further insights into the Casemix system’s applicability within the current healthcare landscape. Moreover, the instrument’s demonstrated statistical reliability and validity position is a valuable tool for future research endeavours concerning the Casemix system in the THIS context, addressing an existing research gap. With the establishment of the instrument’s normality, validity and reliability, it can now be considered operational and validated for use in subsequent studies. This research holds the potential to enhance our understanding of the critical success factors and acceptance of the Casemix system, thereby facilitating its improved implementation within the THIS setting. Moving forward, the instrument will be instrumental in conducting further research initiatives to assess the adoption and effectiveness of the Casemix system in THIS environment, addressing a current scarcity of literature.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Ethics approval

This study was approved by both the Medical Research Ethics Committee from the Ministry of Health and the Medical Research Ethics Committee from the Faculty of Medicine, Universiti Kebangsaan Malaysia with the reference numbers: NMRR ID-22-02621-DKX and JEP-2022-777 respectively. Informed consent was obtained from all participants through the Google form with a statement that all data would be confidential. All methods were carried out under the ethical standards of the institutional research committee and conducted according to the Declaration of Helsinki. All methods were performed based on the relevant guidelines and regulations. This study was not funded by any grants. The authors declare there were no conflicts of interest concerning this article.

Acknowledgments

In recognition of their involvement and contributions to this study, the authors would like to express their gratitude to the respondents. In addition, the authors would like to express their gratitude to all content and criterion validators of this study: Dr. Fawzi Zaidan and Dr. Nuratfina from the Hospital Financing (Casemix) Unit of the Ministry of Health Malaysia, and Prof. Dr. Zainudin Awang from Universiti Sultan Zainal Abidin. Their remarks and recommendations made a significant contribution to the advancement of this instrument.We express our appreciation to the Casemix System Coordinators, as well as the Hospital and the Deputy Directors from Hospitals W, E, S, N, and EM, for their great collaboration in distributing the questionnaire link and for actively engaging in this study.

Additionally, for their suggestions on improving this paper, the authors would like to express their gratitude to the reviewers. Finally, we also want to express our appreciation to Associate Professor Ts. Dr. Mohd Sharizal for proofreading this article.

• Braithwaite J ,
• Westbrook J ,
• Coiera E , et al
• Medical Development Division MOH
• van Boekholt TA ,
• Putera KAS ,
• Jihan Noris N
• Ministry of Health Malaysia
• Borzekowski DLG
• Vaganova E ,
• Ishchuk T ,
• Zemtsov A , et al
• Rahimly A , Universiti Teknologi Malaysia , et al
• Tachinardi U ,
• Gutierrez MA ,
• Moura L , et al
• Stergioulas LK
• Ismail NI ,
• Abdullah NH
• Merican I ,
• Abdul Hamid NB
• Nor MZM , et al
• Abdullah NH ,
• Shamsudin A , et al
• Alipour J ,
• Mehdipour Y ,
• Desi Hertin R ,
• Ismael Al-Sanjary O
• Sulaiman H ,
• Wickramasinghe N
• Ramayah T ,
• Shamsuddin A
• Mustaffa HR ,
• Aljunid SM ,
• Ahmed Z , et al
• Sapar A , et al
• Taufik Jamil A ,
• Fareed A , et al
• Nilashi M ,
• Ibrahim O , et al
• Mastura S ,
• Zafirah SA ,
• Puteh SEW , et al
• Kusumadewi S ,
• Roger France FH
• Gardner B , et al
• Hamzah Al-Junid SM ,
• Ali Jadoo SA ,
• Nur AM , et al
• Fedorowicz J
• Venkatesh V ,
• Morris MG ,
• Davis GB , et al
• Po-An Hsieh JJ ,
• Lotfnezhad Afshar H , et al
• DeLone WH ,
• Papazafeiropoulou A ,
• Paul RJ , et al
• Norman Huang NT
• Khechine H ,
• Pascot D , et al
• Zainudin NFS
• Afthanorhan A ,
• Lim SH , et al
• Creswell JW ,
• Siddiqui BA
• Siddiqui BA ,
• Awang Z , et al
• Erlirianto LM ,
• Herdiyanti A
• Parasuraman A ,
• Zeithaml VA ,
• Proctor EK ,
• Landsverk J ,
• Aarons G , et al
• Proctor E ,
• Silmere H ,
• Raghavan R , et al
• Strong DM ,
• Davoudizadeh R ,
• Hosseini Seno SA , Department of Management, Ferdowsi University of Mashhad, Mashhad, Iran , et al
• Johnson EC ,
• Hameed MA ,
• Counsell S ,
• Abdulrahman MD ,
• Subramanian N
• Zayyad MA ,
• Armstrong CP ,
• Sambamurthy V
• Dauwed M , et al
• Committee on Diagnostic Error in Health Care
• Board on Health Care Services
• Institute of Medicine
• Cortimiglia MN ,
• Oviedo-Trespalacios O
• Bagozzi RP ,
• Davies FD ,
• Venkatesh V
• Carmines E ,
• Hadie SNH ,
• Ismail ZIM , et al
• Yusoff MSB ,
• Lee YY , et al
• Baharuddin KA ,
• Mohamed SA , et al
• Mohamad Marzuki MF ,
• Yaacob NA ,
• Yusoff MSB , Department of Medical Education, School of Medical Sciences, Universiti Sains Malaysia, Malaysia
• Armstrong TS ,
• Eriksen L , et al
• Jung SY , et al
• Thong JYL ,
• Babin BJ , et al
• Howard MC ,
• MacCallum RC ,
• Widaman KF ,
• Zhang S , et al
• Alkhawaja MI ,
• Sobihah M ,
• Yahaya TAB ,
• Suandi T , et al
• Bartlett MS
• Marshall R , et al
• Aljunid SM , et al
• Bujang MA ,
• Soelar SA , et al
• Velicer WF ,
• Fitriana N ,
• Hutagalung FD ,
• Tinsley HEA ,
• Boomsma A ,
• Hoogland JJ
• Hoogland JJ ,
• Bentler PM ,
• Nunnally JC ,
• Bernstein IH
• Mohamad M , et al
• Shkeer AS ,
• Mohamad M ,
• Peterson J ,
• McGillis Hall L ,
• O’Brien-Pallas L , et al
• Mostapa MR , et al
• Rahlin NA ,
• Afthanorhan A , et al
• Abdul Halim B , et al
• Baharum H ,
• Hanim Ahmad J ,
• Mohamed Zin N
• Wan Afthanorhan WMA ,
• Bentler & Kano
• Cronbach LJ
• Asnawi AA ,
• Fornell C ,
• Mohamad MM ,
• Sulaiman NL ,
• Sern LC , et al
• Papazafeiropoulou A , et al
• Abdul Aziz A ,
• Amlus MH , et al
• Nur Izzati R
• Kamarul Azmi J
• Azma Rahlin N ,
• Zulkifli Abdul Rahim M , et al
• McKenna L , et al
• Zainol NR , et al
• Baistaman J ,
• Anthony M ,
• Law SH , et al
• Hovenga EJS ,
• Gleditsch KS
• Horrigan JB
• Erismann S ,
• Pesantes MA ,
• Beran D , et al
• Creswell JD
• Ridenour CS ,
• Cottrell K , et al
• Children W , et al
• Baharudin AS
• Todd PA , et al
• Jöreskog KG ,
• Tanaka JS ,
• McDonald RP ,

Contributors All authors, NKM, RI, ZA, ANA and SMASJ, have substantial contributions to the conception or design of the work; the acquisition, analysis or interpretation of data for the work; and drafting the work or reviewing it critically for important intellectual content. NKM carried out the pilot test and fieldwork, prepared the literature review, extensive search of articles, critical review of articles, performed the statistical analysis, interpretations, and technical parts, and designed the organization of this paper and original draft write-up. RI advised and supervised the overall write-up and conducted the final revisions of the article. ZA checked and validated the statistical analysis and interpretation of the results. ANA and SMASJ co-supervised the study, the manuscript preparation and the article revision. All authors have read and agreed to the final draft of the manuscript, hence, obtaining a final approval of the version to be published. Additionally, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. RI is responsible for the overall content as guarantor, since she is a corresponding author for this study. The guarantor accepts full responsibility for the finished work and/or the conduct of the study, has access to the data and controls the decision to publish.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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