qualitative and quantitative research methods and statistics

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.

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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.

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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.

Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

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Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”

iMotion, “Qualitative vs. Quantitative Research: What Is What?”

Scribbr, “Qualitative vs. Quantitative Research”

Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”

Typeform, “A Simple Guide to Qualitative and Quantitative Research”

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• 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.

Prevent plagiarism, run a free check.

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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Research methods are split broadly into quantitative and qualitative methods.

Which you choose will depend on your research questions, your underlying philosophy of research, and your preferences and skills.

Our pages Introduction to Research Methods and Designing Research set out some of the issues about the underlying philosophy.

This page provides an introduction to the broad principles of qualitative and quantitative research methods, and the advantages and disadvantages of each in particular situations.

Some definitions

Quantitative research is “ explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics). ”*

Qualitative research seeks to answer questions about why and how people behave in the way that they do. It provides in-depth information about human behaviour.

* Taken from: Aliaga and Gunderson ‘Interactive Statistics ‘3rd Edition (2005)

Quantitative Research

Quantitative research is perhaps the simpler to define and identify..

The data produced are always numerical, and they are analysed using mathematical and statistical methods. If there are no numbers involved, then it’s not quantitative research.

Some phenomena obviously lend themselves to quantitative analysis because they are already available as numbers. Examples include changes in achievement at various stages of education, or the increase in number of senior managers holding management degrees. However, even phenomena that are not obviously numerical in nature can be examined using quantitative methods.

Example: turning opinions into numbers

If you wish to carry out statistical analysis of the opinions of a group of people about a particular issue or element of their lives, you can ask them to express their relative agreement with statements and answer on a five- or seven-point scale, where 1 is strongly disagree, 2 is disagree, 3 is neutral, 4 is agree and 5 is strongly agree (the seven-point scale also has slightly agree/disagree).

Such scales are called Likert scales , and enable statements of opinion to be directly translated into numerical data.

The development of Likert scales and similar techniques mean that most phenomena can be studied using quantitative techniques.

This is particularly useful if you are in an environment where numbers are highly valued and numerical data is considered the ‘gold standard’.

However, it is important to note that quantitative methods are not necessarily the most suitable methods for investigation. They are unlikely to be very helpful when you want to understand the detailed reasons for particular behaviour in depth. It is also possible that assigning numbers to fairly abstract constructs such as personal opinions risks making them spuriously precise.

Sources of Quantitative Data

The most common sources of quantitative data include:

Surveys , whether conducted online, by phone or in person. These rely on the same questions being asked in the same way to a large number of people;

Observations , which may either involve counting the number of times that a particular phenomenon occurs, such as how often a particular word is used in interviews, or coding observational data to translate it into numbers; and

Secondary data , such as company accounts.

Our pages on Survey Design and Observational Research provide more information about these techniques.

Analysing Quantitative Data

There are a wide range of statistical techniques available to analyse quantitative data, from simple graphs to show the data through tests of correlations between two or more items, to statistical significance. Other techniques include cluster analysis, useful for identifying relationships between groups of subjects where there is no obvious hypothesis, and hypothesis testing, to identify whether there are genuine differences between groups.

Our page Statistical Analysis provides more information about some of the simpler statistical techniques.

Qualitative Research

Qualitative research is any which does not involve numbers or numerical data..

It often involves words or language, but may also use pictures or photographs and observations.

Almost any phenomenon can be examined in a qualitative way, and it is often the preferred method of investigation in the UK and the rest of Europe; US studies tend to use quantitative methods, although this distinction is by no means absolute.

Qualitative analysis results in rich data that gives an in-depth picture and it is particularly useful for exploring how and why things have happened.

However, there are some pitfalls to qualitative research, such as:

If respondents do not see a value for them in the research, they may provide inaccurate or false information. They may also say what they think the researcher wishes to hear. Qualitative researchers therefore need to take the time to build relationships with their research subjects and always be aware of this potential.

Although ethics are an issue for any type of research, there may be particular difficulties with qualitative research because the researcher may be party to confidential information. It is important always to bear in mind that you must do no harm to your research subjects.

It is generally harder for qualitative researchers to remain apart from their work. By the nature of their study, they are involved with people. It is therefore helpful to develop habits of reflecting on your part in the work and how this may affect the research. See our page on Reflective Practice for more.

Sources of Qualitative Data

Although qualitative data is much more general than quantitative, there are still a number of common techniques for gathering it. These include:

Interviews , which may be structured, semi-structured or unstructured;

Focus groups , which involve multiple participants discussing an issue;

‘Postcards’ , or small-scale written questionnaires that ask, for example, three or four focused questions of participants but allow them space to write in their own words;

Secondary data , including diaries, written accounts of past events, and company reports; and

Observations , which may be on site, or under ‘laboratory conditions’, for example, where participants are asked to role-play a situation to show what they might do.

Our pages on Interviews for Research , Focus Groups and Observational Research provide more information about these techniques.

Because qualitative data are drawn from a wide variety of sources, they can be radically different in scope.

There are, therefore, a wide variety of methods for analysing them, many of which involve structuring and coding the data into groups and themes. There are also a variety of computer packages to support qualitative data analysis. The best way to work out which ones are right for your research is to discuss it with academic colleagues and your supervisor.

Our page Analysing Qualitative Data provides more information about some of the most common methods.

It’s your research…

Finally, it is important to say that there is no right and wrong answer to which methods you choose.

Sometimes you may wish to use one single method, whether quantitative or qualitative, and sometimes you may want to use several, whether all one type or a mixture. It is your research and only you can decide which methods will suit both your research questions and your skills, even though you may wish to seek advice from others.

Continue to: Sampling and Sample Design Interviews for Research

See also: Writing a Research Proposal | Writing a Methodology Analysing Qualitative Data | Simple Statistical Analysis

Educational resources and simple solutions for your research journey

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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The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.

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• The differences between quantitative and qualitative research

The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.

On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.

• Qualitative vs. quantitative data collection

Focusing on different types of data means that the data collection methods vary. 

Quantitative data collection methods

As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.

Qualitative data collection methods

On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records. 

• Qualitative vs. quantitative outcomes

The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.

On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.

• How to analyze qualitative and quantitative data

Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.

Analyzing quantitative data

As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.

Analyzing qualitative data

Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation. 

Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.

• When to use qualitative vs. quantitative research

Choosing between the two approaches comes down to understanding what your goal is with the research.

Qualitative research approach

Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.

Quantitative research approach

On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions. 

Mixed methods approach

Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.

• Benefits and limitations

Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.

Qualitative studies: Pros and cons

This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.

On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.

It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well. 

Quantitative studies: Pros and cons

The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic. 

It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.

• How to balance qualitative and quantitative research

Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways. 

Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?

Open-ended vs. close-ended questions

One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.

An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:

1. How do you compensate for higher prices at the grocery store? (Select all that apply)

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

D. Use a food bank or other charity to fill the gaps

E. Make more food from scratch

2. How do rising prices affect your grocery shopping habits? (Write your answer)

We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs. 

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What Is Qualitative vs. Quantitative Study?

Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data.

In This Article:

What is qualitative research, what is quantitative research.

• How Do Qualitative and Quantitative Research Differ?

What’s the Difference Between a Qualitative and Quantitative Study?

Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university.

Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief.

Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair.

Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies.

Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews.

These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article.

Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure.

How Does Qualitative and Quantitative Research Differ?

How are the approaches of quantitative and qualitative research different?

In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated.

Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis.

Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research.

Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further.

Sources of Quantitative Research

Some example methods of quantitative research methods or sources include, but are not limited to, the following:

• Conducting polls, surveys, and experiments
• Compiling databases of records and information
• Observing the topic of the research, such as a specific reaction
• Performing a meta-analysis, which involves analyzing multiple prior studies in order to identify statistical trends or patterns
• Supplying online or paper questionnaires to participants

The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches.

Sources of Qualitative Research

Researchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples:

• Conducting ethnographic studies, which are studies that seek to explore different phenomena through a cultural or group-specific lens
• Conducting focus groups
• Examining various types of records, including but not limited to diary entries, personal letters, official documents, medical or hospital records, photographs, video or audio recordings, and even minutes from meetings
• Holding one-on-one interviews
• Obtaining personal accounts and recollections of events or experiences

Examples of Research Questions Best Suited for Qualitative vs. Quantitative Methods

Qualitative research questions:.

• How do patients experience the process of recovering from surgery?
• Why do some employees feel more motivated in remote work environments?
• What are the cultural influences on dietary habits among teenagers?

Quantitative Research Questions:

• What is the average recovery time for patients after surgery?
• How does remote work impact employee productivity levels?
• What percentage of teenagers adhere to recommended dietary guidelines?

These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis.

Mixed Methods Research

In addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.”

If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach.

When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers?

How to Analyze Qualitative Data

Below is a step-by-step overview of how to analyze qualitative data.

• Make sure all of your data is finished being compiled before you begin any analysis.
• Organize and connect your data for consistency using computer-assisted qualitative data analysis software (CAQDAS).
• Code your data, which can be partially automated using a feedback analytics platform.
• Start digging deep into analysis, potentially using augmented intelligence to get more accurate results.
• Report on your findings, ideally using engaging aids to help tell the story.

How to Analyze Quantitative Data

There are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats.

Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview.

• Connect measurement scales to study variables, which helps ensure that your data will be organized in the appropriate order before you proceed.
• Link data with descriptive statistics, such as mean, median, mode, or frequency.
• Determine what measurement scale you’ll use for your analysis.
• Organize the data into tables and conduct an analysis using methods like cross-tabulation or Total Unduplicated Reach and Frequency (TURF) analysis.

Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology.

Pros and Cons of Qualitative Research

Qualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision:

• Pro — More flex room for creativity and interpretation of results
• Pro — Greater freedom to utilize different research techniques as the study evolves
• Con — Potentially more vulnerable to bias due to their subjective nature
• Con — Sample sizes tend to be smaller and non-randomized

Pros and Cons of Quantitative Research

Quantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study.

• Pro — Large, random samples help ensure that the broader population is more realistically reflected
• Pro — Specific, precise results can be easily communicated using numbers
• Con — Data can suffer from a lack of context or personal detail around participant answers
• Con — Numerous participants are needed, driving up costs while posing logistical challenges

If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills.

With flexible course schedules, convenient online classes , scholarships and financial aid , and an inclusive military-friendly culture, higher education has never been more achievable or accessible. At National University, you’ll find opportunities to challenge and hone your research skills in more than 75 accredited graduate and undergraduate programs and fast-paced credential and certificate programs in healthcare, business, engineering, computer science, criminal justice, sociology, accounting, and more.

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Research methods--quantitative, qualitative, and more: qualitative research.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Qualitative Data

Qualitative data are data representing information and concepts that are not represented by numbers. They are often gathered from interviews and focus groups, personal diaries and lab notebooks, maps, photographs, and other printed materials or observations. Qualitative data are distinguished from  quantitative data , which focus primarily on data that can be represented with numbers. 

Qualitative data can be analyzed in multiple ways. One common method is data coding, which refers to the process of transforming the raw collected data into a set of meaningful categories that describe essential concepts of the data. Qualitative data and methods may be used more frequently in humanities or social science research and may be collected in descriptive studies.

(From the Data Glossary , National Center for Data Services, National Library of Medicine)

Methods Texts

Below are some methods texts recommended by qualitative workshop leaders from the UC Berkeley Library and the D-Lab: 

Workshops and Training

• Managing qualitative data 101 Tips on managing qualitative materials from your qualitative research librarian.
• D-Lab workshops Free online workshops on quant and qualitative skills, including coding and using qualitative analysis software.
• Institute for the Study of Societal Issues (ISSI) Training Ethnographic methods workshop from a campus institute.
• Qualitative Methods classes Filter to upcoming semesters and look for qualitative methods classes; the Graduate School of Education and School of Public Health offer extensive methods training.

Qualitative Data Analysis Software

Unfortunately, Berkeley does not yet have a sitewide license for any qualitative analysis software.

If you are a student, you can find affordable student licenses with a web search.

If you are a faculty member, instructor, lecturer, or visiting scholar without grant funding, unfortunately software is quite expensive.

You can find reviews of many qualitative software packages at this University of Surrey link:

• Choosing an Appropriate CAQDAS package .

You can also check out the websites of several major options below: 

• Taguette Taguette has fewer features than other qualitative analysis software, but is free and open-source.
• Atlas.ti Atlas.ti is a major qualitative analysis software, and has affordable licenses for students.
• MaxQDA MaxQDA is a major qualitative analysis software, with affordable student licenses. The D-Lab often teaches workshops on this software.
• NVIVO NVIVO is an established QDA software, with affordable student licenses.
• Dedoose Dedoose supports qualitaive and mixed methods research, using an online interface. Students pay $11 per month.

Resources for Qualitative Data Management

• Managing and Sharing Qualitative Data 101 This page from Berkeley's research data management website offers several things to consider.
• Tutorials on Ethnographic Data Management This curricula includes eight presentations and accompanying exercises for you to think through your qualitative data project--or coach others to do the same.
• Support Your Data: Evaluation Rubric Download the evaluation rubric on this page to assess where you are with qualitative data management, and consider areas to explore next.
• The Qualitative Data Repository (QDR) QDR is one of the top US-based repositories focused on the challenges of managing, storing, and sharing qualitative research materials.
• Research Data @ Berkeley Email Research Data for a consultation about how to set up your qualitative data management plan; they can help you locate other resources on campus.

Mixed Methods Research

Interpretations related to mixed (sometimes called merged) methods vary; be wary of jargon!  Gery Ryan, of the Kaiser Permanente School of Medicine, gives these definitions, while arguing that we should be thinking of the purposes of the research rather than the methodological labels:

Mixed methods research : “Combines elements of qualitative and quantitative research approaches (e. g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.”

Multimethod research : “Either solely combine multiple qualitative approaches or solely combine multiple quantitative approaches.”

Data triangulation : “Uses multiple sources of data or multiple approaches to analyzing data to enhance the credibility of a research study.”

(From " Mixed Methods Research Designs and Data Triangulation " by Gery Ryan, Kaiser Permanente School of Medicine)

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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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
• 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.

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

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Qualitative Research in Psychology

Research Methods in Psychology

January 2023

This twelve-hour course on qualitative research in psychology begins by exploring the historical and cross-disciplinary foundations of the field, emphasizing philosophical underpinnings such as postpositivism, constructivism, transformative, and pragmatism. The course highlights the iterative, naturalistic, and contextual facets of qualitative research, focusing on trustworthiness criteria like credibility, transferability, dependability, and confirmability. It also addresses common critiques from a quantitative perspective and the concept of reflexivity, stressing the importance of the researcher’s role and biases.

Phenomenology, narrative inquiry, and constructivist grounded theory are explored next. Phenomenology captures the essence of human experiences through in-depth interviews, revealing nuances like maternal identity development. Narrative inquiry uses storytelling to uncover life nuances, such as the challenges faced by first-generation college students. Constructivist grounded theory develops theories based on participants’ experiences, explaining social processes like identity development and resilience-building. Interviewing techniques tailored to each tradition are explored, emphasizing the researcher’s role as the core instrument of data collection.

The course then explores ethnographic inquiry and case studies. Ethnography involves direct engagement in the setting of interest, uncovering cultural phenomena through various genres like classical, mainstream, public, and postmodern. Case studies investigate phenomena bounded by time and place, focusing on individuals, interventions, organizations, or systems.

Finally, the course examines qualitative data analysis and coding techniques. The iterative nature of qualitative research is emphasized, where data collection and analysis occur simultaneously, allowing for constant comparison and refinement of codes.

Learning objectives

• Describe the philosophical and interpretive foundations of qualitative research.
• Differentiate qualitative claims, methods, and analyses from quantitative claims, methods, and analyses.
• Explore, identify, and evaluate core qualitative traditions (phenomenology, narrative inquiry, constructivist grounded theory, ethnographic inquiry, and case study).
• Distinguish common methods (e.g., interviewing, focus groups) and analytical techniques (qualitative data analysis) used within and across core qualitative traditions.
• Begin thinking about your own qualitative study of a topic in psychology.

This program does not offer CE credit.

More in this series

Principles of design and ethics for research in psychology

January 2023 On Demand Training

A concepts-focused introduction to basic descriptive and inferential statistics

'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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• Nick Pilcher   ORCID: orcid.org/0000-0002-5093-9345 1 &
• Martin Cortazzi 2  

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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.

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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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Qualitative VS Quantitative Definition – Research Methods and Data

When undertaking any type of research study, the data collected will fall into one of two categories: qualitative or quantitative. But what exactly is the difference between these two data types and research methodologies?

Put simply, quantitative data deals with numbers, objective facts and measurable statistics. For example, quantitative data provides specifics on values like website traffic metrics, sales figures, survey response rates, operational costs, etc.

Qualitative data , on the other hand, reveals deeper insights into people‘s subjective perspectives, experiences, beliefs and behaviors. Instead of numbers, qualitative findings are expressed through detailed observations, interviews, focus groups and more.

Now let‘s explore both types of research to understand how and when to apply these methodologies.

Qualitative Research: An In-Depth Perspective

The purpose of qualitative research is to comprehend human behaviors, opinions, motivations and tendencies through an in-depth exploratory approach. Qualitative studies generally seek to answer "why" and "how" questions to uncover deeper meaning and patterns.

Key Features of Qualitative Research

• Exploratory and open-ended data collection
• Subjective, experiential and perception-based findings
• Textual, audio and visual data representation
• Smaller purposeful sample sizes with participants studied in-depth
• Findings provide understanding and context around human behaviors

Some examples of popular qualitative methods include:

• In-depth interviews – Open discussions exploring perspectives
• Focus groups – Facilitated group discussions
• Ethnographic research – Observing behaviors in natural environments
• Content analysis – Studying documents, images, videos, etc.
• Open-ended surveys or questionnaires – Subjective questions

The benefit of these techniques is collecting elaborate and descriptive qualitative data based on personal experiences rather than just objective facts and figures. This reveals not just what research participants are doing but more importantly, why they think, feel and act in certain ways.

For example, an open-ended survey may find that 52% of respondents felt "happy" about using a particular smartphone brand. But in-depth interviews would help uncover exactly why they feel this way by collecting descriptive details on their user experience.

In essence, qualitative techniques like interviews and ethnographic studies add crucial context . This allows us to delve deeper into research problems to gain meaningful insights.

Quantitative Research: A Data-Driven Approach

Unlike qualitative methods, quantitative research relies primarily on the collection and analysis of objective, measurable numerical data. This structured empirical evidence is then manipulated using statistical, graphical and mathematical techniques to derive patterns, trends and conclusions.

Key Aspects of Quantitative Research

• Numerical, measurable and quantifiable data
• Objective facts and empirical evidence
• Statistical, mathematical or computational analysis
• Larger randomized sample sizes to generalize findings
• Research aims to prove, disprove or lend support to existing theories

Some examples of quantitative methods include:

• Closed-ended surveys with numeric rating scales
• Multiple choice/dichotomous questionnaires
• Counting behaviors, events or attributes as frequencies
• Scientific experiments generating stats and figures
• Economic and marketing modeling based on historical data

For instance, an online survey may find that 74% of respondents rate a particular laptop 4 or higher on a 5-point scale for quality. Or an experiment might determine that a revised checkout process increases e-commerce conversion rates by 14.5%.

The benefit of quantitative data is that it generates hard numbers and statistics that allow objective measurement and comparison between groups or changes over time. But the limitation is it lacks detailed insights into the subjective reasons and context behind the data.

Qualitative vs. Quantitative: A Comparison

Is Qualitative or Quantitative Research Better?

Qualitative and quantitative methodologies have differing strengths and limitations. Expert researchers argue both approaches play an invaluable role when combined effectively .

Qualitative research allows rich exploration of perceptions, motivations and ideas through open-ended inquiry. This generates impactful insights but typically with smaller sample sizes focused on depth over breadth.

Quantitative statistically analyzes empirical evidence to uncover patterns and test hypotheses. This lends generalizable support to relationships between variables but risks losing contextual qualitative detail.

In short, qualitative informs the human perspectives while quantitative informs the overarching trends. Together this approaches a problem from both a granular and big-picture level for robust conclusions.

Integrating Mixed Research Methods

Mixing qualitative and quantitative techniques leverages the strengths while minimizing the weaknesses of both approaches. This integration can happen sequentially in phases or concurrently in parallel strands:

Sequential Mixed Methods

• Initial exploratory qualitative data collection via interviews, ethnography etc.
• Develop hypotheses and theories based on qualitative findings
• Follow up with quantitative research to test hypotheses
• Interpret how quantitative results explain qualitative discoveries

Concurrent Mixed Methods

• Simultaneously collect both qualitative and quantitative data
• Merge findings to provide a comprehensive analysis
• Compare results between sources to cross-validate conclusions

This intermixing provides corroboration between subjective qualitative themes and hard quantitative figures to produce actionable insights.

Let‘s look at two examples of effective mixed methods research approaches.

Applied Examples of Mixed Methods

Hospital patient experience analysis.

A hospital administrator seeks to improve patient satisfaction rates.

Quantitative Data

• Statistical survey ratings for aspects like room cleanliness, wait times, staff courtesy etc.
• Rankings benchmarked over time and against other hospitals

Qualitative Data

• Patient interviews detailing frustrations, likes/dislikes and emotional journey
• Expert focus groups discussing challenges and brainstorming solutions

Combined Analysis

Statistical survey analysis coupled with patient interview narratives provides a robust perspective into precisely which issues most critically impact patient experience and what solutions may have the greatest impact.

Product Development Research

A technology company designs a new smartphone app prototype.

• App metric tracking showing feature usage frequencies, conversions, churn rates
• In-app surveys measuring ease-of-use ratings on numeric scales
• Moderated focus groups discussing reactions to prototype
• Diary studies capturing user challenges and delights

Metrics prove what features customers interact with most while qualitative findings explain why they choose to use or abandon certain app functions. This drives effective product refinement.

As demonstrated, thoughtfully blending quantitative and qualitative techniques can provide powerful multifaceted insights.

Tying It All Together: A Nuanced Perspective

Qualitative and quantitative research encompass differing but complementary methodological paradigms for understanding our world through data.

Qualitative research allows inquiry into the depths of human complexities – perceptions, stories, symbols and meanings. Meanwhile, quantitative methods enable us to zoom out and systematically analyze empirical patterns.

Leveraging both modes of discovery provides a nuanced perspective for unlocking insights. As analyst John Tukey noted, "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data."

Rather than blindly following statistics alone, factoring in qualitative details allows us to carefully interpret the context and meaning behind the numbers.

In closing, elegantly integrating quantitative precision with qualitative awareness offers a multilayered lens for conducting research and driving data-savvy decisions.

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

When it comes to collecting, analyzing, and interpreting research findings, two primary approaches stand out - qualitative and quantitative methods. While both are essential for gaining a deeper understanding of various topics, they differ significantly in their approach, methodology, data collection techniques, analysis procedures, and the insights they provide.

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Quantitative Research: Numbers & Statistics

Quantitative research focuses on numerical values to test hypotheses or confirm theories through statistical analyses. This type involves collecting large datasets using methods such as surveys with closed-ended questions, experiments where variables are controlled and manipulated, observations in natural environments without control over variables, literature reviews of published works by other authors.

Quantitative data collection techniques include:

• Surveys: Distributed to a sample population via online platforms or face-to-face interactions.
• Experiments: Manipulating independent variables while controlling for confounding factors.
• Observations: Recording events in natural settings without intervention.

Common quantitative biases and limitations are information bias, omitted variable bias, sampling bias, selection bias.

Qualitative Research: Words & Meanings

Qualitative research focuses on the realm of words to understand concepts, thoughts, or experiences through open-ended questions during interviews, observations described verbally, literature reviews exploring theoretical frameworks.

Key qualitative data collection methods include:

• Interviews with in-depth questioning and follow-up clarification
• Observations where events are documented using descriptive language
• Literature Reviews examining existing theories & conceptualizations

Qualitative research is susceptible to biases such as the Hawthorne effect (observer influence), observer bias, recall bias, social desirability bias.

The Differences Between Quantitative vs. Qualitative Research Methods

Quantitative and qualitative approaches differ in their data collection methods, analysis procedures, and insights gained.

Data Collection Techniques

Both quantitative & qualitative research employ various techniques for collecting information; however some are more commonly associated with one type over the other

Commonly used both: Surveys (can be open-ended or closed), Observational studies (data can represent numbers e.g., rating scales) Case Studies.

Quantitative data collection methods tend to focus on numerical representations, whereas qualitative approaches emphasize descriptive language.

When Choosing Between Qualitative vs. Quantitative Research Methods

A general guideline for selecting between these two is:

Use quantitative research if you aim to confirm or test a hypothesis/theory Use qualitative research when seeking in-depth understanding of concepts/thoughts/experiences

Most topics allow the use of either, mixed-method approaches combining both are also viable options depending on your question(s), deductive vs. inductive approach and practical considerations such as time & resources.

Research Question

Example: How satisfied are students with their studies?

Quantitative Approach:

Survey 300 university students asking questions like "on a scale from 1-5, how do you rate professors?" Statistical analysis can reveal average ratings (e.g., 4.2).

Qualitative approach:

Conduct in-depth interviews using open-ended queries such as: “How satisfied are you with your studies?” Transcribe & analyze responses to identify common themes.

Mixed Methods Approach:

Combine both approaches by first conducting qualitative research through interviews, then quantifying the findings via a survey.

Analyzing Qualitative and Quantitative Data

Data analysis is crucial for extracting meaningful insights from collected data. The approach differs significantly between quantitative (numbers) and qualitative methods (words).

Quantitative Analysis: Numbers & Statistics

Use statistical software like Excel or SPSS to discover patterns, correlations/causations in numerical datasets.

Common analyses include:

• Average scores/means
• Frequency counts of specific answers
• Correlation/coefficient analysis for relationships among variables.
• Data Analysis for Decision Making - Quantitative Technique

Qualitative Data Analysis:

Analyze text-based data through various techniques such as content tracking (word frequency), thematic identification & discourse examination.

Some common qualitative approaches are: 

• Qualitative Content Analysis - examining word occurrences, positions and meanings.
• Thematic Analysis – closely analyzing the dataset to identify main themes/patterns
• Discourse analysis- studying communication dynamics in social contexts.
• Qualitative research within management

Ultimately understanding when to use each method depends on your research question(s), whether you're taking an inductive or deductive approach & practical considerations such as time, resources and access. By combining both qualitative and quantitative methods (mixed-method approaches) researchers can gain a more comprehensive view of their subject matter.

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Enhancing Qualitative and Quantitative Data Linkages in Complex Mixed Methods Designs: Illustrations from a Multi-Phase Healthcare Delivery Study

Affiliations.

• 1 Department of Family Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI.
• 2 Department of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.
• 3 Department of Statistics, School of Public Health, University of Michigan, Ann Arbor, MI.
• 4 Strategy, Ethics, and Entrepreneurship, Darden School of Business, University of Virginia, Charlottesville, VA.
• 5 Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI.
• 6 Michigan Medicine, University of Michigan, Ann Arbor, MI.
• 7 Department of Internal Medicine, Division of Infectious Diseases, Michigan Medicine, University of Michigan, Ann Arbor, MI.
• 8 Department of Strategic Management and Entrepreneurship, Carlson School of Management, University of Minnesota, Minneapolis, MN.
• 9 Department of Internal Medicine, Division of Cardiovascular Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI.
• PMID: 39170802
• PMCID: PMC11335019
• DOI: 10.1177/15586898241257549

While mixed methods research is increasingly used to examine determinants of unwarranted variability in healthcare delivery and outcomes, novel integrative approaches are required to meet the needs of mixed methods healthcare delivery research. This article describes novel refining strategies that enhance the linkage between qualitative and quantitative dimensions of a mixed methods healthcare delivery research study. Leveraging our study experiences, this paper demonstrates several refining strategies: (1) using mediated allocation concealment to facilitate qualitative sampling; (2) informing qualitative inquiry through quantitative analytics; and (3) training and immersing multidisciplinary researchers in qualitative data collection and analysis. Developing and implementing strategies in mixed methods healthcare delivery research could advance methodological rigor and strengthen multidisciplinary collaboration.

Keywords: healthcare delivery; heart failure; mixed methods; team science.

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• Bazeley P (2009). Editorial: Integrating data analyses in mixed methods research. Journal of Mixed Methods Research, 3(3), 203–207. 10.1177/1558689809334443 - DOI
• Bernard HR (2006). Research methods in anthropology: Qualitative and quantitative approaches (4th ed.). AltaMira Press.
• Bryman A (2007). Barriers to integrating quantitative and qualitative research. Journal of Mixed Methods Research, 1(1), 8–22. 10.1177/2345678906290531 - DOI
• Castro FG, Kellison JG, Boyd SJ, & Kopak A (2010). A methodology for conducting integrative mixed methods research and data analyses. Journal of Mixed Methods Research, 4(4), 342–360. 10.1177/1558689810382916 - DOI - PMC - PubMed
• Chandanabhumma PP, Fàbregues S, Oetzel J, Duran B, & Ford C (2023a). Examining the influence of group diversity on the functioning of community-based participatory research partnerships: A mixed methods study. American Journal of Community Psychology, 71(1–2), 242–254. 10.1002/ajcp.12626 - DOI - PMC - PubMed

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• v.4(Suppl 1); 2019

Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods

1 School of Social Sciences, Bangor University, Wales, UK

Andrew Booth

2 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

Graham Moore

3 School of Social Sciences, Cardiff University, Wales, UK

Kate Flemming

4 Department of Health Sciences, The University of York, York, UK

Özge Tunçalp

5 Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland

Elham Shakibazadeh

6 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Associated Data

bmjgh-2018-000893supp001.pdf

bmjgh-2018-000893supp002.pdf

bmjgh-2018-000893supp003.pdf

bmjgh-2018-000893supp005.pdf

bmjgh-2018-000893supp004.pdf

Guideline developers are increasingly dealing with more difficult decisions concerning whether to recommend complex interventions in complex and highly variable health systems. There is greater recognition that both quantitative and qualitative evidence can be combined in a mixed-method synthesis and that this can be helpful in understanding how complexity impacts on interventions in specific contexts. This paper aims to clarify the different purposes, review designs, questions, synthesis methods and opportunities to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of guidelines developed by WHO, which incorporated quantitative and qualitative evidence, are used to illustrate possible uses of mixed-method reviews and evidence. Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Consideration is given to the opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence. Recommendations are made concerning the future development of methods to better address questions in systematic reviews and guidelines that adopt a complexity perspective.

Summary box

• When combined in a mixed-method synthesis, quantitative and qualitative evidence can potentially contribute to understanding how complex interventions work and for whom, and how the complex health systems into which they are implemented respond and adapt.
• The different purposes and designs for combining quantitative and qualitative evidence in a mixed-method synthesis for a guideline process are described.
• Questions relevant to gaining an understanding of the complexity of complex interventions and the wider health systems within which they are implemented that can be addressed by mixed-method syntheses are presented.
• The practical methodological guidance in this paper is intended to help guideline producers and review authors commission and conduct mixed-method syntheses where appropriate.
• If more mixed-method syntheses are conducted, guideline developers will have greater opportunities to access this evidence to inform decision-making.

Introduction

Recognition has grown that while quantitative methods remain vital, they are usually insufficient to address complex health systems related research questions. 1 Quantitative methods rely on an ability to anticipate what must be measured in advance. Introducing change into a complex health system gives rise to emergent reactions, which cannot be fully predicted in advance. Emergent reactions can often only be understood through combining quantitative methods with a more flexible qualitative lens. 2 Adopting a more pluralist position enables a diverse range of research options to the researcher depending on the research question being investigated. 3–5 As a consequence, where a research study sits within the multitude of methods available is driven by the question being asked, rather than any particular methodological or philosophical stance. 6

Publication of guidance on designing complex intervention process evaluations and other works advocating mixed-methods approaches to intervention research have stimulated better quality evidence for synthesis. 1 7–13 Methods for synthesising qualitative 14 and mixed-method evidence have been developed or are in development. Mixed-method research and review definitions are outlined in box 1 .

Defining mixed-method research and reviews

Pluye and Hong 52 define mixed-methods research as “a research approach in which a researcher integrates (a) qualitative and quantitative research questions, (b) qualitative research methods* and quantitative research designs, (c) techniques for collecting and analyzing qualitative and quantitative evidence, and (d) qualitative findings and quantitative results”.A mixed-method synthesis can integrate quantitative, qualitative and mixed-method evidence or data from primary studies.† Mixed-method primary studies are usually disaggregated into quantitative and qualitative evidence and data for the purposes of synthesis. Thomas and Harden further define three ways in which reviews are mixed. 53

• The types of studies included and hence the type of findings to be synthesised (ie, qualitative/textual and quantitative/numerical).
• The types of synthesis method used (eg, statistical meta-analysis and qualitative synthesis).
• The mode of analysis: theory testing AND theory building.

*A qualitative study is one that uses qualitative methods of data collection and analysis to produce a narrative understanding of the phenomena of interest. Qualitative methods of data collection may include, for example, interviews, focus groups, observations and analysis of documents.

†The Cochrane Qualitative and Implementation Methods group coined the term ‘qualitative evidence synthesis’ to mean that the synthesis could also include qualitative data. For example, qualitative data from case studies, grey literature reports and open-ended questions from surveys. ‘Evidence’ and ‘data’ are used interchangeably in this paper.

This paper is one of a series that aims to explore the implications of complexity for systematic reviews and guideline development, commissioned by WHO. This paper is concerned with the methodological implications of including quantitative and qualitative evidence in mixed-method systematic reviews and guideline development for complex interventions. The guidance was developed through a process of bringing together experts in the field, literature searching and consensus building with end users (guideline developers, clinicians and reviewers). We clarify the different purposes, review designs, questions and synthesis methods that may be applicable to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of WHO guidelines that incorporated quantitative and qualitative evidence are used to illustrate possible uses of mixed-method reviews and mechanisms of integration ( table 1 , online supplementary files 1–3 ). Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process are presented. Specific considerations when using an evidence to decision framework such as the Developing and Evaluating Communication strategies to support Informed Decisions and practice based on Evidence (DECIDE) framework 15 or the new WHO-INTEGRATE evidence to decision framework 16 at the review design and evidence to decision stage are outlined. See online supplementary file 4 for an example of a health systems DECIDE framework and Rehfuess et al 16 for the new WHO-INTEGRATE framework. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence in guidelines of complex interventions that take a complexity perspective and health systems focus.

Designs and methods and their use or applicability in guidelines and systematic reviews taking a complexity perspective

Supplementary data

Taking a complexity perspective.

The first paper in this series 17 outlines aspects of complexity associated with complex interventions and health systems that can potentially be explored by different types of evidence, including synthesis of quantitative and qualitative evidence. Petticrew et al 17 distinguish between a complex interventions perspective and a complex systems perspective. A complex interventions perspective defines interventions as having “implicit conceptual boundaries, representing a flexible, but common set of practices, often linked by an explicit or implicit theory about how they work”. A complex systems perspective differs in that “ complexity arises from the relationships and interactions between a system’s agents (eg, people, or groups that interact with each other and their environment), and its context. A system perspective conceives the intervention as being part of the system, and emphasises changes and interconnections within the system itself”. Aspects of complexity associated with implementation of complex interventions in health systems that could potentially be addressed with a synthesis of quantitative and qualitative evidence are summarised in table 2 . Another paper in the series outlines criteria used in a new evidence to decision framework for making decisions about complex interventions implemented in complex systems, against which the need for quantitative and qualitative evidence can be mapped. 16 A further paper 18 that explores how context is dealt with in guidelines and reviews taking a complexity perspective also recommends using both quantitative and qualitative evidence to better understand context as a source of complexity. Mixed-method syntheses of quantitative and qualitative evidence can also help with understanding of whether there has been theory failure and or implementation failure. The Cochrane Qualitative and Implementation Methods Group provide additional guidance on exploring implementation and theory failure that can be adapted to address aspects of complexity of complex interventions when implemented in health systems. 19

Health-system complexity-related questions that a synthesis of quantitative and qualitative evidence could address (derived from Petticrew et al 17 )

It may not be apparent which aspects of complexity or which elements of the complex intervention or health system can be explored in a guideline process, or whether combining qualitative and quantitative evidence in a mixed-method synthesis will be useful, until the available evidence is scoped and mapped. 17 20 A more extensive lead in phase is typically required to scope the available evidence, engage with stakeholders and to refine the review parameters and questions that can then be mapped against potential review designs and methods of synthesis. 20 At the scoping stage, it is also common to decide on a theoretical perspective 21 or undertake further work to refine a theoretical perspective. 22 This is also the stage to begin articulating the programme theory of the complex intervention that may be further developed to refine an understanding of complexity and show how the intervention is implemented in and impacts on the wider health system. 17 23 24 In practice, this process can be lengthy, iterative and fluid with multiple revisions to the review scope, often developing and adapting a logic model 17 as the available evidence becomes known and the potential to incorporate different types of review designs and syntheses of quantitative and qualitative evidence becomes better understood. 25 Further questions, propositions or hypotheses may emerge as the reviews progress and therefore the protocols generally need to be developed iteratively over time rather than a priori.

Following a scoping exercise and definition of key questions, the next step in the guideline development process is to identify existing or commission new systematic reviews to locate and summarise the best available evidence in relation to each question. For example, case study 2, ‘Optimising health worker roles for maternal and newborn health through task shifting’, included quantitative reviews that did and did not take an additional complexity perspective, and qualitative evidence syntheses that were able to explain how specific elements of complexity impacted on intervention outcomes within the wider health system. Further understanding of health system complexity was facilitated through the conduct of additional country-level case studies that contributed to an overall understanding of what worked and what happened when lay health worker interventions were implemented. See table 1 online supplementary file 2 .

There are a few existing examples, which we draw on in this paper, but integrating quantitative and qualitative evidence in a mixed-method synthesis is relatively uncommon in a guideline process. Box 2 includes a set of key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in mixed-methods design might ask. Subsequent sections provide more information and signposting to further reading to help address these key questions.

Key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in a mixed-methods design might ask

Compound questions requiring both quantitative and qualitative evidence?

Questions requiring mixed-methods studies?

Separate quantitative and qualitative questions?

Separate quantitative and qualitative research studies?

Related quantitative and qualitative research studies?

Mixed-methods studies?

Quantitative unpublished data and/or qualitative unpublished data, eg, narrative survey data?

Throughout the review?

Following separate reviews?

At the question point?

At the synthesis point?

At the evidence to recommendations stage?

Or a combination?

Narrative synthesis or summary?

Quantitising approach, eg, frequency analysis?

Qualitising approach, eg, thematic synthesis?

Tabulation?

Logic model?

Conceptual model/framework?

Graphical approach?

• WHICH: Which mixed-method designs, methodologies and methods best fit into a guideline process to inform recommendations?

Complexity-related questions that a synthesis of quantitative and qualitative evidence can potentially address

Petticrew et al 17 define the different aspects of complexity and examples of complexity-related questions that can potentially be explored in guidelines and systematic reviews taking a complexity perspective. Relevant aspects of complexity outlined by Petticrew et al 17 are summarised in table 2 below, together with the corresponding questions that could be addressed in a synthesis combining qualitative and quantitative evidence. Importantly, the aspects of complexity and their associated concepts of interest have however yet to be translated fully in primary health research or systematic reviews. There are few known examples where selected complexity concepts have been used to analyse or reanalyse a primary intervention study. Most notable is Chandler et al 26 who specifically set out to identify and translate a set of relevant complexity theory concepts for application in health systems research. Chandler then reanalysed a trial process evaluation using selected complexity theory concepts to better understand the complex causal pathway in the health system that explains some aspects of complexity in table 2 .

Rehfeuss et al 16 also recommends upfront consideration of the WHO-INTEGRATE evidence to decision criteria when planning a guideline and formulating questions. The criteria reflect WHO norms and values and take account of a complexity perspective. The framework can be used by guideline development groups as a menu to decide which criteria to prioritise, and which study types and synthesis methods can be used to collect evidence for each criterion. Many of the criteria and their related questions can be addressed using a synthesis of quantitative and qualitative evidence: the balance of benefits and harms, human rights and sociocultural acceptability, health equity, societal implications and feasibility (see table 3 ). Similar aspects in the DECIDE framework 15 could also be addressed using synthesis of qualitative and quantitative evidence.

Integrate evidence to decision framework criteria, example questions and types of studies to potentially address these questions (derived from Rehfeuss et al 16 )

GIS, Geographical Information System; RCT, randomised controlled trial.

Questions as anchors or compasses

Questions can serve as an ‘anchor’ by articulating the specific aspects of complexity to be explored (eg, Is successful implementation of the intervention context dependent?). 27 Anchor questions such as “How does intervention x impact on socioeconomic inequalities in health behaviour/outcome x” are the kind of health system question that requires a synthesis of both quantitative and qualitative evidence and hence a mixed-method synthesis. Quantitative evidence can quantify the difference in effect, but does not answer the question of how . The ‘how’ question can be partly answered with quantitative and qualitative evidence. For example, quantitative evidence may reveal where socioeconomic status and inequality emerges in the health system (an emergent property) by exploring questions such as “ Does patterning emerge during uptake because fewer people from certain groups come into contact with an intervention in the first place? ” or “ are people from certain backgrounds more likely to drop out, or to maintain effects beyond an intervention differently? ” Qualitative evidence may help understand the reasons behind all of these mechanisms. Alternatively, questions can act as ‘compasses’ where a question sets out a starting point from which to explore further and to potentially ask further questions or develop propositions or hypotheses to explore through a complexity perspective (eg, What factors enhance or hinder implementation?). 27 Other papers in this series provide further guidance on developing questions for qualitative evidence syntheses and guidance on question formulation. 14 28

For anchor and compass questions, additional application of a theory (eg, complexity theory) can help focus evidence synthesis and presentation to explore and explain complexity issues. 17 21 Development of a review specific logic model(s) can help to further refine an initial understanding of any complexity-related issues of interest associated with a specific intervention, and if appropriate the health system or section of the health system within which to contextualise the review question and analyse data. 17 23–25 Specific tools are available to help clarify context and complex interventions. 17 18

If a complexity perspective, and certain criteria within evidence to decision frameworks, is deemed relevant and desirable by guideline developers, it is only possible to pursue a complexity perspective if the evidence is available. Careful scoping using knowledge maps or scoping reviews will help inform development of questions that are answerable with available evidence. 20 If evidence of effect is not available, then a different approach to develop questions leading to a more general narrative understanding of what happened when complex interventions were implemented in a health system will be required (such as in case study 3—risk communication guideline). This should not mean that the original questions developed for which no evidence was found when scoping the literature were not important. An important function of creating a knowledge map is also to identify gaps to inform a future research agenda.

Table 2 and online supplementary files 1–3 outline examples of questions in the three case studies, which were all ‘COMPASS’ questions for the qualitative evidence syntheses.

Types of integration and synthesis designs in mixed-method reviews

The shift towards integration of qualitative and quantitative evidence in primary research has, in recent years, begun to be mirrored within research synthesis. 29–31 The natural extension to undertaking quantitative or qualitative reviews has been the development of methods for integrating qualitative and quantitative evidence within reviews, and within the guideline process using evidence to decision-frameworks. Advocating the integration of quantitative and qualitative evidence assumes a complementarity between research methodologies, and a need for both types of evidence to inform policy and practice. Below, we briefly outline the current designs for integrating qualitative and quantitative evidence within a mixed-method review or synthesis.

One of the early approaches to integrating qualitative and quantitative evidence detailed by Sandelowski et al 32 advocated three basic review designs: segregated, integrated and contingent designs, which have been further developed by Heyvaert et al 33 ( box 3 ).

Segregated, integrated and contingent designs 32 33

Segregated design.

Conventional separate distinction between quantitative and qualitative approaches based on the assumption they are different entities and should be treated separately; can be distinguished from each other; their findings warrant separate analyses and syntheses. Ultimately, the separate synthesis results can themselves be synthesised.

Integrated design

The methodological differences between qualitative and quantitative studies are minimised as both are viewed as producing findings that can be readily synthesised into one another because they address the same research purposed and questions. Transformation involves either turning qualitative data into quantitative (quantitising) or quantitative findings are turned into qualitative (qualitising) to facilitate their integration.

Contingent design

Takes a cyclical approach to synthesis, with the findings from one synthesis informing the focus of the next synthesis, until all the research objectives have been addressed. Studies are not necessarily grouped and categorised as qualitative or quantitative.

A recent review of more than 400 systematic reviews 34 combining quantitative and qualitative evidence identified two main synthesis designs—convergent and sequential. In a convergent design, qualitative and quantitative evidence is collated and analysed in a parallel or complementary manner, whereas in a sequential synthesis, the collation and analysis of quantitative and qualitative evidence takes place in a sequence with one synthesis informing the other ( box 4 ). 6 These designs can be seen to build on the work of Sandelowski et al , 32 35 particularly in relation to the transformation of data from qualitative to quantitative (and vice versa) and the sequential synthesis design, with a cyclical approach to reviewing that evokes Sandelowski’s contingent design.

Convergent and sequential synthesis designs 34

Convergent synthesis design.

Qualitative and quantitative research is collected and analysed at the same time in a parallel or complementary manner. Integration can occur at three points:

a. Data-based convergent synthesis design

All included studies are analysed using the same methods and results presented together. As only one synthesis method is used, data transformation occurs (qualitised or quantised). Usually addressed one review question.

b. Results-based convergent synthesis design

Qualitative and quantitative data are analysed and presented separately but integrated using a further synthesis method; eg, narratively, tables, matrices or reanalysing evidence. The results of both syntheses are combined in a third synthesis. Usually addresses an overall review question with subquestions.

c. Parallel-results convergent synthesis design

Qualitative and quantitative data are analysed and presented separately with integration occurring in the interpretation of results in the discussion section. Usually addresses two or more complimentary review questions.

Sequential synthesis design

A two-phase approach, data collection and analysis of one type of evidence (eg, qualitative), occurs after and is informed by the collection and analysis of the other type (eg, quantitative). Usually addresses an overall question with subquestions with both syntheses complementing each other.

The three case studies ( table 1 , online supplementary files 1–3 ) illustrate the diverse combination of review designs and synthesis methods that were considered the most appropriate for specific guidelines.

Methods for conducting mixed-method reviews in the context of guidelines for complex interventions

In this section, we draw on examples where specific review designs and methods have been or can be used to explore selected aspects of complexity in guidelines or systematic reviews. We also identify other review methods that could potentially be used to explore aspects of complexity. Of particular note, we could not find any specific examples of systematic methods to synthesise highly diverse research designs as advocated by Petticrew et al 17 and summarised in tables 2 and 3 . For example, we could not find examples of methods to synthesise qualitative studies, case studies, quantitative longitudinal data, possibly historical data, effectiveness studies providing evidence of differential effects across different contexts, and system modelling studies (eg, agent-based modelling) to explore system adaptivity.

There are different ways that quantitative and qualitative evidence can be integrated into a review and then into a guideline development process. In practice, some methods enable integration of different types of evidence in a single synthesis, while in other methods, the single systematic review may include a series of stand-alone reviews or syntheses that are then combined in a cross-study synthesis. Table 1 provides an overview of the characteristics of different review designs and methods and guidance on their applicability for a guideline process. Designs and methods that have already been used in WHO guideline development are described in part A of the table. Part B outlines a design and method that can be used in a guideline process, and part C covers those that have the potential to integrate quantitative, qualitative and mixed-method evidence in a single review design (such as meta-narrative reviews and Bayesian syntheses), but their application in a guideline context has yet to be demonstrated.

Points of integration when integrating quantitative and qualitative evidence in guideline development

Depending on the review design (see boxes 3 and 4 ), integration can potentially take place at a review team and design level, and more commonly at several key points of the review or guideline process. The following sections outline potential points of integration and associated practical considerations when integrating quantitative and qualitative evidence in guideline development.

Review team level

In a guideline process, it is common for syntheses of quantitative and qualitative evidence to be done separately by different teams and then to integrate the evidence. A practical consideration relates to the organisation, composition and expertise of the review teams and ways of working. If the quantitative and qualitative reviews are being conducted separately and then brought together by the same team members, who are equally comfortable operating within both paradigms, then a consistent approach across both paradigms becomes possible. If, however, a team is being split between the quantitative and qualitative reviews, then the strengths of specialisation can be harnessed, for example, in quality assessment or synthesis. Optimally, at least one, if not more, of the team members should be involved in both quantitative and qualitative reviews to offer the possibility of making connexions throughout the review and not simply at re-agreed junctures. This mirrors O’Cathain’s conclusion that mixed-methods primary research tends to work only when there is a principal investigator who values and is able to oversee integration. 9 10 While the above decisions have been articulated in the context of two types of evidence, variously quantitative and qualitative, they equally apply when considering how to handle studies reporting a mixed-method study design, where data are usually disaggregated into quantitative and qualitative for the purposes of synthesis (see case study 3—risk communication in humanitarian disasters).

Question formulation

Clearly specified key question(s), derived from a scoping or consultation exercise, will make it clear if quantitative and qualitative evidence is required in a guideline development process and which aspects will be addressed by which types of evidence. For the remaining stages of the process, as documented below, a review team faces challenges as to whether to handle each type of evidence separately, regardless of whether sequentially or in parallel, with a view to joining the two products on completion or to attempt integration throughout the review process. In each case, the underlying choice is of efficiencies and potential comparability vs sensitivity to the underlying paradigm.

Once key questions are clearly defined, the guideline development group typically needs to consider whether to conduct a single sensitive search to address all potential subtopics (lumping) or whether to conduct specific searches for each subtopic (splitting). 36 A related consideration is whether to search separately for qualitative, quantitative and mixed-method evidence ‘streams’ or whether to conduct a single search and then identify specific study types at the subsequent sifting stage. These two considerations often mean a trade-off between a single search process involving very large numbers of records or a more protracted search process retrieving smaller numbers of records. Both approaches have advantages and choice may depend on the respective availability of resources for searching and sifting.

Screening and selecting studies

Closely related to decisions around searching are considerations relating to screening and selecting studies for inclusion in a systematic review. An important consideration here is whether the review team will screen records for all review types, regardless of their subsequent involvement (‘altruistic sifting’), or specialise in screening for the study type with which they are most familiar. The risk of missing relevant reports might be minimised by whole team screening for empirical reports in the first instance and then coding them for a specific quantitative, qualitative or mixed-methods report at a subsequent stage.

Assessment of methodological limitations in primary studies

Within a guideline process, review teams may be more limited in their choice of instruments to assess methodological limitations of primary studies as there are mandatory requirements to use the Cochrane risk of bias tool 37 to feed into Grading of Recommendations Assessment, Development and Evaluation (GRADE) 38 or to select from a small pool of qualitative appraisal instruments in order to apply GRADE; Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) 39 to assess the overall certainty or confidence in findings. The Cochrane Qualitative and Implementation Methods Group has recently issued guidance on the selection of appraisal instruments and core assessment criteria. 40 The Mixed-Methods Appraisal Tool, which is currently undergoing further development, offers a single quality assessment instrument for quantitative, qualitative and mixed-methods studies. 41 Other options include using corresponding instruments from within the same ‘stable’, for example, using different Critical Appraisal Skills Programme instruments. 42 While using instruments developed by the same team or organisation may achieve a degree of epistemological consonance, benefits may come more from consistency of approach and reporting rather than from a shared view of quality. Alternatively, a more paradigm-sensitive approach would involve selecting the best instrument for each respective review while deferring challenges from later heterogeneity of reporting.

Data extraction

The way in which data and evidence are extracted from primary research studies for review will be influenced by the type of integrated synthesis being undertaken and the review purpose. Initially, decisions need to be made regarding the nature and type of data and evidence that are to be extracted from the included studies. Method-specific reporting guidelines 43 44 provide a good template as to what quantitative and qualitative data it is potentially possible to extract from different types of method-specific study reports, although in practice reporting quality varies. Online supplementary file 5 provides a hypothetical example of the different types of studies from which quantitative and qualitative evidence could potentially be extracted for synthesis.

The decisions around what data or evidence to extract will be guided by how ‘integrated’ the mixed-method review will be. For those reviews where the quantitative and qualitative findings of studies are synthesised separately and integrated at the point of findings (eg, segregated or contingent approaches or sequential synthesis design), separate data extraction approaches will likely be used.

Where integration occurs during the process of the review (eg, integrated approach or convergent synthesis design), an integrated approach to data extraction may be considered, depending on the purpose of the review. This may involve the use of a data extraction framework, the choice of which needs to be congruent with the approach to synthesis chosen for the review. 40 45 The integrative or theoretical framework may be decided on a priori if a pre-developed theoretical or conceptual framework is available in the literature. 27 The development of a framework may alternatively arise from the reading of the included studies, in relation to the purpose of the review, early in the process. The Cochrane Qualitative and Implementation Methods Group provide further guidance on extraction of qualitative data, including use of software. 40

Synthesis and integration

Relatively few synthesis methods start off being integrated from the beginning, and these methods have generally been subject to less testing and evaluation particularly in a guideline context (see table 1 ). A review design that started off being integrated from the beginning may be suitable for some guideline contexts (such as in case study 3—risk communication in humanitarian disasters—where there was little evidence of effect), but in general if there are sufficient trials then a separate systematic review and meta-analysis will be required for a guideline. Other papers in this series offer guidance on methods for synthesising quantitative 46 and qualitative evidence 14 in reviews that take a complexity perspective. Further guidance on integrating quantitative and qualitative evidence in a systematic review is provided by the Cochrane Qualitative and Implementation Methods Group. 19 27 29 40 47

Types of findings produced by specific methods

It is highly likely (unless there are well-designed process evaluations) that the primary studies may not themselves seek to address the complexity-related questions required for a guideline process. In which case, review authors will need to configure the available evidence and transform the evidence through the synthesis process to produce explanations, propositions and hypotheses (ie, findings) that were not obvious at primary study level. It is important that guideline commissioners, developers and review authors are aware that specific methods are intended to produce a type of finding with a specific purpose (such as developing new theory in the case of meta-ethnography). 48 Case study 1 (antenatal care guideline) provides an example of how a meta-ethnography was used to develop a new theory as an end product, 48 49 as well as framework synthesis which produced descriptive and explanatory findings that were more easily incorporated into the guideline process. 27 The definitions ( box 5 ) may be helpful when defining the different types of findings.

Different levels of findings

Descriptive findings —qualitative evidence-driven translated descriptive themes that do not move beyond the primary studies.

Explanatory findings —may either be at a descriptive or theoretical level. At the descriptive level, qualitative evidence is used to explain phenomena observed in quantitative results, such as why implementation failed in specific circumstances. At the theoretical level, the transformed and interpreted findings that go beyond the primary studies can be used to explain the descriptive findings. The latter description is generally the accepted definition in the wider qualitative community.

Hypothetical or theoretical finding —qualitative evidence-driven transformed themes (or lines of argument) that go beyond the primary studies. Although similar, Thomas and Harden 56 make a distinction in the purposes between two types of theoretical findings: analytical themes and the product of meta-ethnographies, third-order interpretations. 48

Analytical themes are a product of interrogating descriptive themes by placing the synthesis within an external theoretical framework (such as the review question and subquestions) and are considered more appropriate when a specific review question is being addressed (eg, in a guideline or to inform policy). 56

Third-order interpretations come from translating studies into one another while preserving the original context and are more appropriate when a body of literature is being explored in and of itself with broader or emergent review questions. 48

Bringing mixed-method evidence together in evidence to decision (EtD) frameworks

A critical element of guideline development is the formulation of recommendations by the Guideline Development Group, and EtD frameworks help to facilitate this process. 16 The EtD framework can also be used as a mechanism to integrate and display quantitative and qualitative evidence and findings mapped against the EtD framework domains with hyperlinks to more detailed evidence summaries from contributing reviews (see table 1 ). It is commonly the EtD framework that enables the findings of the separate quantitative and qualitative reviews to be brought together in a guideline process. Specific challenges when populating the DECIDE evidence to decision framework 15 were noted in case study 3 (risk communication in humanitarian disasters) as there was an absence of intervention effect data and the interventions to communicate public health risks were context specific and varied. These problems would not, however, have been addressed by substitution of the DECIDE framework with the new INTEGRATE 16 evidence to decision framework. A d ifferent type of EtD framework needs to be developed for reviews that do not include sufficient evidence of intervention effect.

Mixed-method review and synthesis methods are generally the least developed of all systematic review methods. It is acknowledged that methods for combining quantitative and qualitative evidence are generally poorly articulated. 29 50 There are however some fairly well-established methods for using qualitative evidence to explore aspects of complexity (such as contextual, implementation and outcome complexity), which can be combined with evidence of effect (see sections A and B of table 1 ). 14 There are good examples of systematic reviews that use these methods to combine quantitative and qualitative evidence, and examples of guideline recommendations that were informed by evidence from both quantitative and qualitative reviews (eg, case studies 1–3). With the exception of case study 3 (risk communication), the quantitative and qualitative reviews for these specific guidelines have been conducted separately, and the findings subsequently brought together in an EtD framework to inform recommendations.

Other mixed-method review designs have potential to contribute to understanding of complex interventions and to explore aspects of wider health systems complexity but have not been sufficiently developed and tested for this specific purpose, or used in a guideline process (section C of table 1 ). Some methods such as meta-narrative reviews also explore different questions to those usually asked in a guideline process. Methods for processing (eg, quality appraisal) and synthesising the highly diverse evidence suggested in tables 2 and 3 that are required to explore specific aspects of health systems complexity (such as system adaptivity) and to populate some sections of the INTEGRATE EtD framework remain underdeveloped or in need of development.

In addition to the required methodological development mentioned above, there is no GRADE approach 38 for assessing confidence in findings developed from combined quantitative and qualitative evidence. Another paper in this series outlines how to deal with complexity and grading different types of quantitative evidence, 51 and the GRADE CERQual approach for qualitative findings is described elsewhere, 39 but both these approaches are applied to method-specific and not mixed-method findings. An unofficial adaptation of GRADE was used in the risk communication guideline that reported mixed-method findings. Nor is there a reporting guideline for mixed-method reviews, 47 and for now reports will need to conform to the relevant reporting requirements of the respective method-specific guideline. There is a need to further adapt and test DECIDE, 15 WHO-INTEGRATE 16 and other types of evidence to decision frameworks to accommodate evidence from mixed-method syntheses which do not set out to determine the statistical effects of interventions and in circumstances where there are no trials.

When conducting quantitative and qualitative reviews that will subsequently be combined, there are specific considerations for managing and integrating the different types of evidence throughout the review process. We have summarised different options for combining qualitative and quantitative evidence in mixed-method syntheses that guideline developers and systematic reviewers can choose from, as well as outlining the opportunities to integrate evidence at different stages of the review and guideline development process.

Review commissioners, authors and guideline developers generally have less experience of combining qualitative and evidence in mixed-methods reviews. In particular, there is a relatively small group of reviewers who are skilled at undertaking fully integrated mixed-method reviews. Commissioning additional qualitative and mixed-method reviews creates an additional cost. Large complex mixed-method reviews generally take more time to complete. Careful consideration needs to be given as to which guidelines would benefit most from additional qualitative and mixed-method syntheses. More training is required to develop capacity and there is a need to develop processes for preparing the guideline panel to consider and use mixed-method evidence in their decision-making.

This paper has presented how qualitative and quantitative evidence, combined in mixed-method reviews, can help understand aspects of complex interventions and the systems within which they are implemented. There are further opportunities to use these methods, and to further develop the methods, to look more widely at additional aspects of complexity. There is a range of review designs and synthesis methods to choose from depending on the question being asked or the questions that may emerge during the conduct of the synthesis. Additional methods need to be developed (or existing methods further adapted) in order to synthesise the full range of diverse evidence that is desirable to explore the complexity-related questions when complex interventions are implemented into health systems. We encourage review commissioners and authors, and guideline developers to consider using mixed-methods reviews and synthesis in guidelines and to report on their usefulness in the guideline development process.

Handling editor: Soumyadeep Bhaumik

Contributors: JN, AB, GM, KF, ÖT and ES drafted the manuscript. All authors contributed to paper development and writing and agreed the final manuscript. Anayda Portela and Susan Norris from WHO managed the series. Helen Smith was series Editor. We thank all those who provided feedback on various iterations.

Funding: Funding provided by the World Health Organization Department of Maternal, Newborn, Child and Adolescent Health through grants received from the United States Agency for International Development and the Norwegian Agency for Development Cooperation.

Disclaimer: ÖT is a staff member of WHO. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO.

Competing interests: No financial interests declared. JN, AB and ÖT have an intellectual interest in GRADE CERQual; and JN has an intellectual interest in the iCAT_SR tool.

Patient consent: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: No additional data are available.

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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• Published: 03 September 2024

Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods

• Xiongwei Zhu 1 ,
• Dezhi Li 1 , 2 ,
• Shenghua Zhou 1 ,
• Shiyao Zhu 3 &
• Lugang Yu 1  

Scientific Reports volume  14 , Article number:  20461 ( 2024 ) Cite this article

Metrics details

• Climate-change adaptation
• Climate-change impacts
• Environmental impact
• Sustainability

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

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Introduction.

Cities, as centers of population and economy, play crucial roles in cultural exchange, social integration, transportation, communication, and disaster response in modern societal development 1 , 2 . According to the United Nations Human Settlements program’s “2022 World Cities Report”, as of 2021, the global urbanization rate has reached 56%, and it is projected that by 2050, an additional 2.2 billion people will live in cities, increasing the urbanization rate to 68% 3 . North America and European countries are approaching urbanization saturation, with little fluctuation expected, while urbanization in Asia and Africa will accelerate notably 4 . Particularly in China, the world’s second-largest economy, as of 2022, the urbanization rate is only 64.7%, ranking 96th globally, indicating significant potential for growth compared to developed countries like the USA and the UK 5 . The Chinese government places high importance on urbanization development. It was clearly stated in the “2020 State Council Government Work Report” that new urbanization is a key measure for achieving China’s modernization. Moreover, in the “14th Five-Year Plan (2021–2025) and the Long-Range Objectives Through the Year 2035”, detailed strategies are outlined for optimizing the urban layout and promoting urban–rural integration, among other policies to advance urbanization 6 . However, urbanization, as a process of continuous concentration of population and industrial elements in cities, while bringing opportunities for economic growth and social development, also presents a series of challenges such as environmental pressure, resource constraints, and increased demand for services 7 , 8 .

In 2008, the American company IBM introduced the concept of a “Smart Planet”, which garnered widespread attention globally 9 . The concept of a smart city, as a specific application within this framework, aims to enhance urban management and service efficiency through the integration and innovative application of Information and Communication Technology (ICT), thereby improving the quality of life for residents, optimizing resource use, reducing environmental impact, and promoting economic development and social progress 10 , 11 . Currently, the smart city construction model is seen as one of the effective means to advance global urbanization, improve urban governance, and solve major urban issues 12 . In 2009, IBM released the “Smart Planet: Winning in China” plan, outlining China’s five major thematic tasks in constructing a “Smart Planet” (sustainable economic development, corporate competitiveness, energy efficiency, environmental protection, and social harmony) 13 . The construction of smart cities, as a key measure to achieve these thematic tasks, has received significant attention from the Chinese government. In 2014, the Chinese government elevated smart city construction to a “national strategy”, considering it a cornerstone of China’s future economic and urban development strategies. By 2016, over 500 Chinese cities had initiated or announced smart city pilot construction plans, accounting for nearly half of all such projects planned or underway globally 14 . In recent years, with the continuous release of policy benefits related to smart city construction in China and substantial capital investment, China has become a leader in driving global smart city initiatives 15 . However, an undeniable fact is that while smart city construction models promote economic development and improve the quality of life for residents, the new infrastructure supporting the operation of smart cities, such as big data centers, 5G shared base stations, and Beidou ground-based augmentation stations, result in substantial energy consumption and significant carbon emissions 16 . Research shows that in 2018, the total electricity consumption of data centers in China supporting IT infrastructure reached 160.9 billion kilowatt-hours, exceeding the total electricity consumption of Shanghai for that year and accounting for about 2% of China’s total electricity consumption, with carbon emissions nearing 100 million tons 17 . The Environmental Defense Fund (EDF) predicts that by 2035, the total electricity consumption of China’s data centers and 5G base stations will reach 695.1–782 billion kilowatt-hours, accounting for 5–7% of China’s total electricity consumption, with total carbon emissions reaching 230–310 million tons 18 .

In 2022, global energy-related CO 2 emissions increased by 0.9%, reaching a record high of over 36.8 Gt. Concurrently, atmospheric CO 2 concentrations continued to rise, averaging 417.06 parts per million, marking the eleventh consecutive year with an increase exceeding 2 ppm 19 . According to the World Meteorological Organization (WMO), the global surface temperature in September 2023 was 1.44 °C higher than the twentieth century average, setting a new historical record 20 . The continuous rise in global temperatures has led to frequent occurrences of disastrous events such as extreme heat, torrential rains, floods, forest fires, and hurricanes in recent years, causing significant loss of life and property damage 21 . World Health Organization (WHO) data indicates that in 2022, there were at least 29 weather disaster events globally causing billions of dollars in losses, with approximately 61,672 deaths in Europe due to heatwave-related causes 22 . As global climate issues become increasingly severe, the call for global carbon emission reduction is growing louder. Cities, as highly concentrated areas of population and economic activities, according to the Global Report by the United Nations Human Settlements Programme (UN-Habitat), consume 60–80% of the global energy and contribute to over 75% of global CO 2 emissions 23 . As the largest global emitter of carbon, China’s CO 2 emissions in 2022 accounted for 27% of the global total 24 . Given China’s influence in the global economy, technological innovation, and international cooperation, international organizations and global climate policies generally believe that China’s efforts in carbon reduction are crucial to achieving the global 1.5 °C climate goal 25 . In recent years, the Chinese government has actively promoted the construction of low-carbon pilot cities. To date, three batches of low-carbon pilot cities have been implemented in China, bringing the total number of such cities to 81 26 .

However, the report “China’s Digital Infrastructure Decarburization Path: Data Centers and 5G Carbon Reduction Potential and Challenges (2020–2035)” indicates that compared to peak carbon emissions expected around 2025 in key sectors like steel, building materials, and non-ferrous metals in China, the “lock-in effect” of carbon emissions from digital infrastructure poses a significant challenge to achieving China’s peak carbon and carbon neutrality goals 27 , 28 , 29 . Given the urgency of global climate change, it raises the question of the correlation between smart cities and low-carbon cities: is it positive, negative, or non-existent? Should the pace of smart city development be slowed to achieve sustainable urban development goals, considering the significant carbon dioxide emissions resulting from current technological choices, social habits, and policy frameworks? To address these practical issues, it is first essential to conduct an objective and accurate assessment of urban SCP and LCL. However, due to the complexity and diversity of urban carbon emissions sources, current measurement and estimation techniques fail to capture all emission types. This limitation hampers the ability to obtain comprehensive, accurate, and timely city-level carbon emission data 30 , 31 . To address this challenge, this paper decomposes smart cities and low-carbon cities into their interdependent and interactive subsystems (i.e., economic, political, cultural, social, and ecological) viewed through the lens of urban complex systems. It then develops evaluation models for both city types and conducts empirical analyses in 52 representative Chinese pilot cities. Based on these analyses, the paper elucidates the coupling coordination degree between SCP and LCL and proposes a specific pathway for their coordinated development.

This paper is therefore structured as follows: “ Literature review ” section offers an overview of the relevant literature, laying the foundation for the introduction of SCP and LCL. Subsequently, SCP and LCL are identified clearly, and measurement based on a mixed method for the coupling coordination degree is established in “ Methodology ” section, followed by a case demonstration for the introduced method in “ Results ” section and the demonstration results analysis in “ Discussions and implications ” section. Finally, “ Conclusions ” section summarizes the study’s main findings and contributions, discusses its limitations, and suggests directions for future research.

Literature review

Evaluation of smart city: contents, methods, and subjects.

The evaluation of smart cities is a central research area within the smart city development field. Developing standardized evaluation criteria serves the dual purpose of defining smart city development boundaries and scientifically measuring its effectiveness. This, in turn, facilitates the achievement of development goals centered on evaluation-driven construction, improvement, and management 32 . We conducted data collection on “smart city*” AND “evaluation”, resulting in the selection of 82 articles. This involved an extensive search of the Wos Core Collection database for articles published in the period from January 2019 to January 2024.

To facilitate a clearer understanding for readers of current research on smart city evaluation, we have categorized it by evaluation contents , evaluation methods , and evaluation subjects .

Cluster1-evaluation contents (what to evaluate), including smart city evaluation dimensions and indicators. By analyzing the article content, it’s clear that most smart city evaluation approaches align with six core dimensions: economy, quality of life, governance, people, mobility, and environment 13 , 15 . Centered around these six dimensions, international organizations (ISO, ETSI, UN, and ITU) and scholars have established various sets of smart city evaluation indicators, considering the interdependencies among urban economic, environmental, and social factors, all in alignment with the goals of sustainable urban development 32 , 33 , 34 . Notably, Sharifi 35 compiled a comprehensive list of indicators incorporating a wide range of assessment schemes. This list not only covers the scope of the evaluation indicators (project/community/city) and their data types (primary/secondary) but also considers the stages of smart city development (planning/operation) and stakeholder involvement 36 . Subsequent research predominantly utilizes the same criteria as Sharifi 35 to identify indicator sets, taking into account the specific needs of each city and defining the spatial and temporal scales of the indicator sets 37 .

Cluster 2-evaluation methods (How to evaluate) , including smart city evaluation methods and tools. Research in this field focuses on three main areas: identifying evaluation indicators for smart cities, computing composite index, and developing evaluation models 38 , 39 . Methods for indicator identification mainly include literature review, case studies, brainstorming, the Delphi method, and data-driven techniques 40 , 41 . The Analytic Hierarchy Process (AHP) is commonly used for calculating composite indices, yet it faces issues like subjective biases and data size limitations 42 . Alternative methods, such as the Analytical Network Process (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL), are used to address these drawbacks by simulating inter-indicator interactions. Additionally, techniques like Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA) are applied for indicator weighting. Finally, smart city evaluation models are constructed to aggregate various dimensions and indicators into a unified score, facilitating project comparison and ranking, and highlighting areas needing improvement 43 , 44 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including smart city stakeholders and participants. Smart city evaluations involve various stakeholders and participants. These complex processes see each entity, including government agencies, international organizations, academic institutions, industry sectors, and NGOs, contributing to the smart cities’ planning, development, and management 45 , 46 . Key organizations in this realm are the International Organization for Standardization (ISO), International Telecommunication Union (ITU), United Nations Human Settlements Programme (UN-Habitat), Smart Cities Council, European Institute of Innovation and Technology (EIT Urban Mobility), and World Council on City Data (WCCD). Additionally, numerous countries have established their own smart city evaluation standards to direct and review smart city progress 11 . Notable examples are the “One New York: The Plan for a Strong and Just City” in the USA, the “BSI PAS 180” in the UK, Singapore's “Smart Nation Initiative”, and China’s “National New-type Smart City Evaluation Indicator System”.

Evaluation of low-carbon city: contents, methods, and subjects

As more countries integrate low-carbon city development into their national strategies and plans, conducting scientific evaluations of cities’ current low-carbon development levels to encourage them to adopt corresponding measures for improvement has become a key strategy in advancing cities towards a low-carbon future 47 . In the Wos Core Collection database, we conducted a search for studies spanning January 2018 to January 2023 with “low-carbon city*” AND “evaluation” as keywords, subsequently identifying 98 pertinent articles through two rounds of screening.

This section, maintaining the research framework of “ Evaluation of smart city: contents, methods, and subjects ” section ( evaluation contents, methods, and subjects ), organizes low-carbon city research to enable comparison with smart city evaluations.

Cluster 1-evaluation contents (what to evaluate), including low-carbon city evaluation systems, dimensions, and indicators. Current research focusing on low-carbon cities primarily spans six key domains: urban low-carbon scale, energy, behavior, policy, mobility, and carbon sinks. The evaluation dimensions for low-carbon cities are mainly divided into two types: single-criterion systems concentrating on specific low-carbon aspects (such as low-carbon economy, low-carbon energy, etc.), and comprehensive multi-criteria systems assessing the overall urban low-carbon development 48 , 49 . Compared to single-criterion evaluation systems, comprehensive and multi-criteria evaluation systems are increasingly gaining attention from scholars. These scholars share the view that low-carbon city construction is a diverse, dynamic, interconnected process that requires comprehensive consideration of various urban aspects, including economy, society, and environment, and involves coordinating the actions of different stakeholders to achieve sustainable urban development 50 , 51 . Additionally, international institutions and many national governments have also published low-carbon city evaluation frameworks from the perspective of comprehensive and multi-criteria evaluation systems. The most notable examples include the United Nations Commission on Sustainable Development, which set 30 indicators from four dimensions: social, environmental, economic, and institutional, to evaluate the level of urban low-carbon development. The Chinese Academy of Social Sciences proposed the “China Low Carbon City Indicator System”, covering 8 dimensions such as economy, energy, facilities, and 25 specific indicators including energy intensity, per capita carbon emissions, and forest coverage rate.

Cluster 2-evaluation methods (How to evaluate) , including low-carbon city evaluation methods and tools. Firstly, identifying evaluation indicators as the initial step in constructing a low-carbon city evaluation model, current research methods not only include traditional methods like literature review and expert interviews but also increasingly involve scholars using dynamic perspectives based on urban complex systems, applying models like DPSR (Driving forces-Pressures-State-Response), STIRPA (Stochastic Impacts by Regression on Population, Affluence, and Technology), the Environmental Kuznets Curve (EKC), and STEEP (Social, Technological, Economic, Ecological, and Political) for indicator identification 52 , 53 . Secondly, weighting evaluation indicators, an essential part of model construction, typically involves methods like subjective weighting (expert scoring, Delphi method, AHP) 54 , objective weighting (PCA, Entropy weight method, variance analysis), and combined weighting (DEA) 55 . Each method has its characteristics and suitable scenarios and should be selected according to specific circumstances. Additionally, quantitative assessment of regional carbon emissions using methods like carbon footprint analysis, baseline emission comparison, and Life Cycle Assessment (LCA) is also becoming a research focus 56 .

Cluster 3-evaluation subjects (Who performs the evaluation) , including low-carbon city stakeholders and participants. The evaluation of low-carbon cities also involves multiple stakeholders (government, enterprises, residents, etc.) 57 . Among them, international organizations like the International Organization for Standardization (ISO), the International Energy Agency (IEA), and the World Meteorological Organization (WMO) have played significant roles in establishing low-carbon city evaluation standards and promoting global low-carbon city development. Additionally, due to economic, policy, and perception factors, current low-carbon city construction relies primarily on government financial input, with social capital and public participation in low-carbon city construction noticeably lacking 58 . Therefore, how to enhance the awareness of enterprises and residents as main actors in low-carbon city construction has become a current research focus.

Coupling coordination analysis between SCP and LCL

Smart cities and low-carbon cities, as important urban development models for the future, have seen an increasing focus on their interrelation by scholars in recent years, becoming an emerging research hotspot in the field. In the Wos Core Collection database, we searched for studies from January 2018 to January 2024 using the keywords “smart city*” “low-carbon city*” “correlation analysis” “coupling coordination analysis” and “urban sustainability”. After two rounds of screening, 24 related studies were selected for analysis.

From the perspective of research results, the current research conclusions about the correlation between low-carbon cities and smart cities primarily include two main points: (i) SCP and LCL cannot achieve coupling coordination development. Some scholars argue that SCP and LCL differ in their focus: SCP emphasizes urban technological and economic development, while LCL focuses more on urban ecological construction 17 . Particularly, De Jong identified 12 urban development concepts, including smart city, low-carbon city, eco-city, and green city. He believes that a clear distinction must be made in the conceptual definition of these types of cities to more accurately guide future urban planning 59 . Furthermore, some scholars argue that the relationship between SMC and LCC is negatively correlated. Deakin believes that the direct environmental benefits of IoT technology are insufficient to achieve urban sustainability goals 60 . Barr et al. argue that the logic of smart cities often leads city administrations to prioritize superficial changes and promote individual behavioral shifts, detracting from the crucial task of reconfiguring urban infrastructure for low-carbon lifestyles 61 , 62 . (ii) SCP and LCL can achieve coupling coordination development. Some scholars believe there is a positive correlation between SCP and LCL, with SCP potentially promoting the development of LCL. Specifically, the intelligent systems built by SCP can effectively match urban energy supply and demand, reducing urban carbon emissions, such as through smart grids and intelligent transportation networks 18 . It is worth noting that most of the studies on the coupling coordination relationship between urban SCP and LCL are based on perspectives of individual urban subsystems such as technology, economy, management, industrial structure, and society. They lack a comprehensive consideration of the city as a complex system 59 , 61 , 63 .

From the perspective of research methodologies, coupling coordination analysis is a fundamental statistical approach for examining relationships between two or more variables. This analysis typically employs techniques such as Pearson’s correlation coefficient, Spearman’s rank correlation coefficient, Kendall’s tau, partial correlation, point-biserial correlation, and multiple correlations. Each technique offers unique insights into the nature and strength of the interdependencies among variables 61 . The selection of an appropriate method depends on the data type (continuous, ordinal, or categorical), its distribution (e.g., normal distribution), and the specific objectives of the research.

In summary, although existing research has made significant contributions to the independent evaluation and advancement of smart cities and low-carbon cities, including their relevant construction content, main actors, as well as some specific measures such as empowering cities with data intelligence for low-carbon economic development and transitioning industrial structure to low-carbon, there are still some important knowledge gaps. On the one hand, current research primarily analyzes the coupling coordination relationship between urban SCP and LCL from the micro-perspective of individual urban subsystems such as economic and energy systems. This approach lacks a macroscopic perspective from the complex urban system, which is detrimental to the comprehensive development of cities 60 , 64 , 65 . On the other hand, current studies often only conduct basic qualitative comparisons of the relationship between the development levels of urban SCP and LCL from a quantitative or qualitative perspective. They lack a comprehensive analytical approach that integrates both qualitative and quantitative analyses for further exploration of the coupling coordination relationship between urban SCP and LCL. This shortfall hinders the sustainable development of cities.

To fill these knowledge gaps, this study employs a mixed-methods approach, combining qualitative and quantitative analyses, to examine the model of coupling coordination between urban SCP and LCL. It also develops recommendations to enhance this coupling coordination, aiming to support sustainable development goals. Furthermore, this research selects 52 typical low-carbon and smart pilot cities in China as case studies, ensuring both scientific validity and practical applicability of the findings. Additionally, to enhance the logical coherence and readability of this study, we posit that a coupling coordination relationship exists between urban SCP and LCL and thus propose Hypothesis 1 .

Hypothesis 1

There is a substantial degree of coupling coordination between the overall urban system’s SCP and LCL, yet there are disparities in this coordination degree among the subsystems of economy, society, politics, culture, and ecology.

Methodology

Research framework.

The construction of low-carbon and smart cities, as key pathways to urban sustainability, necessitates examining their interplay and fostering their collaborative development for achieving sustainability goals 66 . This research employs a sequential framework, including Conceptual, Data, Analysis, and Decision-making Layers, to methodically explore the coupling coordination relationship between SCP and LCL, with the framework illustrated in Fig.  1 .

Research framework.

Firstly , in the Conceptual Layer, this study aligns with the United Nations’ objectives for sustainable cities, encompassing economic growth, social equity, better life conditions, and improved urban environments. Integrating these with China’s “Five-Sphere Integrated Plan (economy, politics, culture, society, and ecological environment construction)” for urban development, the research dissects the components of smart city systems (such as information infrastructure, information security, public welfare services) and low-carbon city systems (including low-carbon construction, transportation, and industry), with the aim to collect indicators. Secondly , in the Data Layer, this research develops smart city and low-carbon city evaluation systems, grounded in national standards and official statistics, to qualitatively examine the correlation between SCP and LCL from a macro perspective. Thirdly, in the Analysis Layer, this study selects 52 cities, both smart and low-carbon pilot cities in China, as samples for quantitative analysis. The process involves standardizing indicators, scoring and ranking the cities based on their smart performance and low-carbon levels, followed by employing Pearson’s correlation coefficient and coupling coordination degree model to scientifically analyze the correlation between SCP and LCL. Finally, in the Decision-making Layer, the study examines the coupling coordination relationship between urban smart performance, the overall low-carbon level, and the low-carbon level across five dimensions, which is key for us to test Hypothesis 1 . It also formulates development paths for the coupling coordination of smart and low-carbon cities.

SCP index system construction

Since the concept of smart cities was introduced in 2008, many national governments have established smart city evaluation standards. Due to varying national conditions, SCP evaluation indicators differ across countries. As the sample cities in this study are Chinese smart pilot cities, the selection of SCP evaluation indicators primarily references relevant Chinese national standards. As a global pioneer in smart city development, China released the “Evaluation indicators for new-type smart cities (GB/T 33356-2016)” in 2016 and revised it in 2022. This national standard, with its evaluative indicators, clearly defines the key construction content and development direction of new smart cities, aiming to specifically enhance the effectiveness and level of smart city construction, gaining significant recognition within the industry.

This study, grounded in the concept of a city’s “Five-in-One” sustainable development, is guided by three principles of “Inclusive well-being & Ecological harmony”, “Digital space & Physical space”, and “New IT technologies & Comprehensive services”. It also adheres to the “people-oriented concept” and adopts an “urban complex dynamic perspective” in the process of smart city construction. Additionally, it follows the principle of “similar attributes of evaluation objects”. Based on these foundations, the study establishes three criteria for selecting evaluation indicators, including scientific, coordination, and representation. Drawing on the Chinese government’s smart city evaluation standards and utilizing a literature review methodology, this research constructs an SCP evaluation indicator system for cities, as detailed in Supplementary Appendix Table A1 . The SCP index system includes six primary indicators, including smart public service (SPE), precise governance (PG), information infrastructure (II), digital economy (DE), innovative development environment (IDE), and citizen satisfaction (SCS). It also features 24 secondary indicators, such as traffic information services, grassroots smart governance, and spatio-temporal information platforms. Importantly, to explore the correlation between smart cities and low-carbon cities more effectively, the study deliberately omits “Internet + Green Ecology” related indicators from the smart city evaluation system. To ensure the accuracy and representativeness of these indicators, they were validated through expert consultation, public participation, and comprehensive statistical methods.

LCL index system construction

Current international organizations and academic perspectives on low-carbon city evaluation systems are predominantly based on the urban complex systems approach, considering the interplay and interaction of aspects such as low-carbon society, economy, and technology. Consistent with the principles for selecting SCP evaluation indicators, the choice of LCL evaluation indicators in this study primarily adheres to relevant Chinese national standards and related literature.

As a proactive practitioner in global low-carbon city development, in 2021, the Chinese government released the “Sustainable Cities and Communities—Guides for low-carbon development evaluation (GB/T 41152-2021)”. This national standard evaluates the level of urban low-carbon development, clarifying the key directions for such development, and serves as a current guide for low-carbon city construction in China. Thus, this study, grounded in the “Five-in-One” sustainable urban development framework and guided by the principles of “carbon reduction & pollution reduction”, “green economic growth”, and “enhanced carbon sequestration capacity”, combines the previously established principles of scientific, coordination, and representative for selecting evaluation indicators. It establishes an LCL index system based on the Chinese government’s evaluation standards and relevant literature. Specifically, the LCL evaluation index system constructed in this study includes five primary indicators, including low-carbon economic (LCE), low-carbon society (LCS), low-carbon environmental quality (LCEQ), low-carbon management (LCM), and low-carbon culture (LCC), as well as 22 secondary indicators such as energy consumption per unit of GDP and carbon emission intensity, as shown in Supplementary Appendix Table A2 . Similarly, to ensure the accuracy and representativeness of the indicators, the specific indicators were validated through expert consultation, public participation, and comprehensive statistical methods.

Analysis model construction

In this study, an Entropy-TOPSIS-Pearson correlation-Coupling coordination degree (ETPC) analysis model is constructed to quantitatively analyze the coupling coordination relationship between Urban SCP and LCL. The entropy method is first applied for objective weighting of evaluation indices, ensuring data objectivity and reducing subjective bias, thus enhancing the model’s accuracy and fairness. Next, the TOPSIS method is used to rank sample cities based on their smart performance and low-carbon levels, providing a straightforward and intuitive ranking mechanism. The Pearson correlation method then examines the correlation between SCP and LCL, offering data-driven insights into the dynamic relationships between these variables. Finally, the coupling coordination model calculates the degree of coordination between SCP and LCL, providing a theoretical basis for subsequent enhancement pathways and policy recommendations. The ETPC model constructed in this study has several advantages and complementarities, allowing for a comprehensive analysis and evaluation of the research question from various perspectives. Additionally, the ETPC model can be broadly applied to other multidimensional evaluation and decision analysis issues, such as the coupling coordination between various public health interventions and community health levels, and the comprehensive effects of different economic policies on regional economic development and environmental impact. Specific analysis steps are outlined as follows.

Step 1: Conduct the data normalization process.

where x ij and y ij represent respectively the original and standardized value for the indicator j in referring to the sample case i ( i  = 1,2,3,…, m; j  = 1,2,3,…, n ), max (x j ) and min (x j ) denote respectively the largest and smallest value among all m samples for the indicator j , P ij represents the value proportion of indicator j in the sample case i to the summation value of the indicator from all cases.

Step 2: Calculate the weight and measure the comprehensive level based on entropy method.

The entropy weight method, an objective approach deriving weights from sample characteristics, mitigates expert bias, enhancing the objectivity and credibility of indicator weighting 67 . This study employs this method, determining weights through the calculation of each indicator’s information entropy, and measure the comprehensive level of the subsystem.

where m is the total number of sample cases, \({e}_{j}\) demonstrates the entropy value of the j indicator and \({\omega }_{j}\) denotes the weight of indicator j , and V represent the comprehensive level.

Step 3: Conduct a ranking of evaluation objects based on TOPSIS method.

A key limitation of the entropy method is its tendency to neglect the significance of indicators. The TOPSIS method, addressing this issue, is an ideal-solution-based ranking technique that aids in multi-objective decision-making among finite options 68 . In this approach, the study first determines positive and negative ideal solutions, measures each objective’s distance to these ideals, and subsequently ranks the subjects by the proximity of each objective to the ideal solution.

where \({ V}^{+}\) and \({V}^{-}\) respectively represent the best ideal solution and the worst ideal solution, \({D}_{i}^{+}\) and \({D}_{i}^{-}\) represent the distances from the objective to the positive and negative ideal solutions, respectively. \({C}_{i}\) indicates the closeness of the evaluation objective to the optimal solution, with \({C}_{i}\in \left[\text{0,1}\right]\) . A larger \({C}_{i}\) value suggests stronger smart and low-carbon development capabilities of the sample city.

Step 4: Analyze the correlation based on Pearson correlation method.

The Pearson correlation method is commonly used to measure the correlation coefficient between two continuous random variables, thereby assessing the degree of correlation between them 69 . In this study, based on the results from Steps 1–3, two sets of data are obtained representing the smart development level and low-carbon development level of sample cities, \(A:\left\{{A}_{1},{A}_{2},\dots ,{A}_{n}\right\}\) and \(B:\left\{{B}_{1},{B}_{2},\dots ,{B}_{n}\right\}\) . The overall means and covariance of both data sets are calculated, resulting in the Pearson correlation coefficient between the two variables.

where \({A}_{i}\) and \({B}_{i}\) respectively represent the SCP and LCL of sample cities. \(E\left(A\right)\) and \(E\left(B\right)\) are the overall means of the two data sets, \({\sigma }_{A}\text{ and }{\sigma }_{B}\) are their respective standard deviations, \(cov(A,B)\) is the covariance, and \({\rho }_{AB}\) is the Pearson correlation coefficient. When the correlation coefficient approaches 0, the relationship weakens, as it nears − 1 or + 1, the correlation strengthens.

Step 5: Analyze the coupling coordination degree based on the coupling coordination model.

The coupling coordination degree characterizes the level of interaction between different systems and serves as a scientific model for measuring the coordinated development level of multiple subsystems or elements 70 . This study has developed a model to measure the coupling coordination degree between two systems.

where C defines the coupling degree, \({f}_{1}\) and \({f}_{2}\) are the evaluation values of SCP and LCL respectively. CPD represents the coupling coordination degree. \(\alpha\) , \(\beta\) are the coefficient to be determined, indicating the importance of the systems. This study assumes that each system is equally important. Thus \(\alpha =\beta =1/2.\)

In this study, building upon the framework established by a preceding study, a classification system for the coupling coordination degree was developed. This system delineates the various types of coupling-coordinated development among SCP, LCL, LCS, LCM, LCEQ, and LCC. Current research on the division of coupling coordination degree intervals often uses an average distribution within the [0, 1] range 70 . However, due to the large sample size and the wide distribution range of coupling coordination degrees in this study, we have categorized these types into ten distinct levels based on their rank, as detailed in Table 1 .

Selection of sample cities and data collection

The Chinese government has prioritized the development of smart and low-carbon cities. Since 2010, it has launched 290 smart city pilots and 81 low-carbon city pilots across various regions, reflecting different levels of development, resource allocations, and operational foundations. To maintain the scientific integrity of our study, we established stringent criteria for selecting sample cities: (i) each city must be concurrently identified as both a smart and a low-carbon city pilot, and (ii) their government agencies must have issued data on key performance indicators for these initiatives. Following these criteria, our research has ultimately selected 52 cities as samples, as detailed in Fig.  2 . It is noteworthy that these 52 typical case cities are almost all provincial capitals in China, mostly located within the Yangtze River Delta, Pearl River Delta, Jingjinji (Beijing–Tianjin–Hebei), and Western Triangle economic regions. Additionally, according to the “Globalization and World Cities Research Network (GaWC) World Cities Roster 2022 (GaWC2022)”, these cities are ranked within the top 200 globally. Therefore, given the scope of this research, these case cities offer significant representativeness and can serve as valuable models for promoting development in other urban areas. The data for this paper were sourced from the “China Low-Carbon Yearbook (2010–2023)”, the “China Environmental Statistics Yearbook (2010–2023)”, and low-carbon city data published by the governments of the sample cities. Additionally, this study addressed any missing data by averaging the data from adjacent years and applying exponential smoothing.

52 sample cities and their geographic locations.

Weighting values between evaluation indicators

The entropy weighting values between the 20 indicators of SCP and the 19 indicators of LCL are calculated by applying the data described in “ Weighting values between evaluation indicators ” section to formula ( 1 )–( 5 ), and the results are shown in Supplementary Appendix Tables A3 and A4 . Specifically, within the SCP evaluation framework, SPE and II are assigned the highest weights, while LCS and LCM are allocated the highest weights within the LCL evaluation framework. Conversely, SCS and LCC have attributed the lowest weights in their respective contexts.

Evaluation of SCP and LCL in sample cities

Utilizing the data from “ Selection of sample cities and data collection ” section and the weighting values derived in “ Weighting values between evaluation indicators ” section, we can determine the SCP and LCL of sample cities using the TOPSIS method, as outlined in formulas ( 6 )–( 9 ). The results are illustrated in Supplementary Appendix Table A5 and Fig.  3 . In this study, the value of the closeness coefficient (C i ) is used to indicate the relative closeness of a particular sample city to the negative ideal point 71 . The negative ideal point represents the worst solution of the ideal, where the individual attribute values reach their worst in each alternative. Therefore, a larger value of closeness indicates better smart city performance or a lower carbon level of a sample city 72 . C LCL and C SCP respectively represent the low-carbon level closeness coefficient and the smart city performance closeness coefficient. In referring to Supplementary Appendix Table A5 , the best three cities of SCP are Shenzhen, Shanghai, and Hangzhou, whilst the worst three cities are Yan’an, Jincheng, and Xining. Furthermore, Chengdu, Qingdao, and Beijing are the best there low-carbon level performers. Whilst Jincheng, Urumqi, and Huhehaote are the three worst.

TOPSIS-based analysis of SCP with LCL in 52 sample cities.

In referencing Fig.  3 , this study considers SCP data of sample cities as the control variable and ranks them in ascending order based on TOPSIS results. We then examine changes in LCL data to ascertain the correlation between these variables, yielding two key research conclusions: on one hand, analysis of 52 sample cities demonstrates a general ascending trend in both SCP and LCL data curves. This trend suggests a positive correlation between these two parameters. On the other hand, the LCL data, in contrast to the consistent rise in SCP, exhibits notable fluctuations and wider dispersion. This indicates that the positive correlation between SCP and LCL, while present, is not markedly robust.

Correlation results of SCP and LCL in sample cities

Correlation analysis of urban SCP and overall-LCL. This analysis employs the closeness coefficient (C i ) to assess SCP and overall-LCL in sample cities for Hypothesis 1 in Eqs. ( 10 ) and ( 11 ). The results are presented in Table 2 . Additionally, a linear regression analysis is conducted to determine the presence and magnitude of the relationship between SCP and LCL in these cities, as shown in Fig.  4 .

The scatter and regression of SCP and LCL: ( A ) SCP & Overall-LCL; ( B ) SCP & LCM; ( C ) SCP & LCS; ( D ) SCP & LCE; ( E ) SCP & LCQE; ( F ) SCP & LCC.

Considering the closeness coefficient range, correlation is categorized into five levels: very weak ( \(\left|{\rho }_{AB}\right|<0\) .1), weak ( \(0.1\le \left|{\rho }_{AB}\right|<0\) .3), moderate ( \(0.3\le \left|{\rho }_{AB}\right|<0\) .5), strong ( \(0.5\le \left|{\rho }_{AB}\right|<0\) .7), and very strong ( \(0.7\le \left|{\rho }_{AB}\right|<1.0\) ) 73 . Table 1 indicates a strong positive correlation between SCP and overall LCL. Linear regression analysis in Fig.  4 A demonstrates a significant correlation between SCP and urban LCL ( R 2  = 0.42, p  < 0.001), with notable differences exist among cities, consistent with Hypothesis 1 .

Correlation analysis of SCP and each low-carbon dimension. Pearson correlation analysis effectively measures the strength of linear relationships between two variables, but it does not identify causal relationships between them. To address this limitation and explore the interaction between the two variables, this study sets and solves the closeness coefficient for each low-carbon dimension, which are low-carbon economy (C LCE ), low-carbon society (C LCS ), low-carbon environmental quality (C LCEQ ), low-carbon management (C LCM ), and low-carbon culture (C LCC ). It then calculates the correlation analysis results for SCP and each low-carbon dimension for Hypothesis 1 , as shown in Table 1 . Furthermore, the results of the linear regression analysis are presented in Fig.  4 .

In detail, strong correlations exist between SCP and LCM, LCS, and LCEQ. The correlation is moderate with LCE and weak with LCC. Furthermore, linear regression analysis shows that the links between SCP and low-carbon levels across five dimensions are significant with minimal variance. Cities with higher SCP typically show higher values in LCM ( R 2  = 0.38, p  = 0.000), LCS ( R 2  = 0.35, p  = 0.000), and LCE ( R 2  = 0.32, p  = 0.000) as depicted in Fig.  4 B–D. However, this trend is less pronounced in LCEQ ( R 2  = 0.17, p  = 0.000) and LCC ( R 2  = 0.06, p  = 0.001), which exhibit greater dispersion as shown in Fig.  4 E,F. The lower R 2 values for LCEQ and LCC compared to other dimensions suggest a greater influence of factors not included in the model. Furthermore, to ensure the credibility and reliability of the research findings, this study conducted a sensitivity analysis by identifying and removing outliers from the sample dataset using the Z-score method, in addition to the previously mentioned Pearson correlation analysis. The Pearson correlation coefficient for the original dataset of city SCP and LCL is 0.65, with a significant P-value. After removing the outliers, the Pearson correlation coefficient is 0.61, and the P-value remained significant. Therefore, the correlation between city SCP and LCL proposed in Research Hypothesis 1 is robust.

Coupling coordination degree of SCP and LCL in sample cities

The degree of coupling coordination comprehensively considers multiple aspects of urban complex systems, including economic, social, and environmental dimensions. By systematically evaluating the coordinated development level of urban SCP and LCL, this approach enables the analysis of the coupling and coordination relationships between SCP and LCL, as well as among various subsystems such as LCM, LCS, LCE, LCEQ, and LCC. This reveals the dynamic interactions and causality between SCP and LCL within urban complex systems. The coupling coordination degrees of SCP and LCL, along with their subsystems, in 52 typical smart and low-carbon pilot cities in China, are illustrated in Fig.  5 .

Coupled coordination degree of SCP and LCL, LCS, LCEQ, LCE, LCM, LCC.

Characteristics of objective changes in the coupled coordination degree between SCP and LCL. Based on the coupling coordination model and Eqs. ( 12 ) to ( 14 ), the coupling coordination degree of the urban complex system in SCP and LCL regions is calculated for Hypothesis 1 , as illustrated in Fig.  5 .

From the holistic perspective of urban complex systems, as the level of urban SCP continuously improves, the coupling coordination degree between SCP and LCL among 52 pilot cities in China shows an upward trend. This indicates that as the functional indices of urban SCP and LCL both strengthen, their interaction and coordination also enhance. Among these, Jincheng has the lowest coupled coordination degree at 0.5201, while Beijing boasts the highest at 0.8622. Within the 52 pilot cities, 5.78% exhibit a barely coupling coordination level, 51.93% display a primary coupling coordination level, 25% achieve an intermediate coupling coordination level, and 17.31% reach a good coupling coordination level. Moreover, the average coupling coordination degree of the 52 pilot cities is 0.598, suggesting that the SCP and LCL of the pilot cities can achieve coupled coordinated development.

Characteristics of objective changes in the coupled coordination degree among SCP, LCM, LCS, LCE, LCEQ, and LCC for Hypothesis 1 are illustrated in Fig.  5 .

From the perspective of urban subsystems, the coupling coordination degrees of LCS & SCP, LCE & SCP, and LCM & SCP all exhibit characteristics of steady fluctuations with an upward trend, while the coupling coordination degree of LCC & SCP shows greater volatility in its upward trend. The coupling coordination degree of LCEQ & SCP demonstrates a trend of initially rising and then declining. Furthermore, the average values of the coupling coordination degrees for LCS & SCP, LCE & SCP, LCM & SCP, LCEQ & SCP, and LCC & SCP are 0.478, 0.761, 0.779, 0.710, and 0.485, respectively. Among these, the pilot cities’ subsystems of LCE, LCM, and LCEQ with SCP exhibit an intermediate level of coupling coordination, while the coupling coordination degrees of LCS and LCC with SCP are on the verge of a dysfunctional recession. This indicates that the causal relationships between urban SCP and the subsystems of urban LCM, LCS, LCE, LCEQ, and LCC vary. Overall, Hypothesis 1 holds true both from the perspective of the city's overall system and from the perspective of its various subsystems.

Discussions and implications

Relationship between scp and lcl of different cities.

Considering the evaluation results of the urban SCP and LCL, four grades of the overall points can be classified, namely, excellent (0.7–1.0), average (0.5–0.7), below average (0.4–0.5), and poor (0–0.4). Subsequently, the sample cities in Supplementary Appendix Table A5 were classified based on these gradations. In the sample, cities with excellent SCP constitute 9.62%, about double the proportion with excellent LCL. Cities with average SCP account for 48.08%, whereas those at average LCL represent only 26.92%. Notably, cities with poor LCL comprise 26.92%, nearly triple the rate of those with poor SCP. The findings suggest that China’s SCP currently outperforms its low-carbon city initiatives, largely attributable to the rapid advancement of the Internet and Information and Communication Technology (ICT) in recent years. What’s more, Fig.  4 illustrates that urban SCP significantly positively influences the urban LCL, though substantial variations exist among different cities. The relevant types can be summarized into the following four categories.

Quadrant I-high SCP and high LCL, including only six cities (Shenzhen, Shanghai, Beijing, Ningbo, Xiamen, and Qingdao). These cities are not only among China’s earliest smart city pilots but also recent focus areas for the government’s “Carbon Peak Pioneer Cities” initiative. By actively exploring innovative models, systems, and technologies for smart and low-carbon co-development, these cities provide valuable practical experiences for others. For instance, Shenzhen has developed a multi-level, multi-component greenhouse gas monitoring network and technology system for “carbon flux, carbon concentration, carbon emissions”, while Ningbo has constructed a “smart zero-carbon” comprehensive demonstration port area.

Quadrant II-poor SCP and poor LCL, numerous cities in Fig.  4 A, such as Jincheng, Lhasa, and Urumqi, exhibit poor SCP and LCL. Despite China having the most smart and low-carbon city pilots globally, its development level in these areas still lags significantly behind typical developed countries. While China’s infrastructure like networking and computing power has reached a certain scale, issues persist with insufficient integration and intensity in infrastructure construction and operation, as well as problems with aging infrastructure and low levels of intelligence. Furthermore, although China’s low-carbon pilot cities have made positive progress in promoting low-carbon development, most still have incomplete carbon emission statistical systems and inadequate operational mechanisms, leading to generally poor overall low-carbon development levels.

Quadrant III-high LCL but poor SCP, such as Kunming, Xining, and Guiyang. These cities possess resources conducive to low-carbon development, such as Kunming and Guiyang with their rich forest carbon sinks, and Xining with abundant clean energy sources like solar and wind power. However, they are mostly situated in China’s central and southwest areas with underdeveloped physical and economic conditions. Leveraging their abundant low-carbon resources, and utilizing big data and IoT technology, achieving sustainable green economic growth through carbon credits and trading markets, as well as green finance, represents a significant future development direction for these cities.

Quadrant IV-high SCP but poor LCL, including Suzhou, and Jinhua Zhongshan, decoupling economic development from carbon emissions presents a significant development challenge for these cities. Specifically, for Suzhou, one of the world’s largest industrial cities, the main challenge is achieving decarburization in the energy sector and transitioning high-emission manufacturing industries to low-carbon alternatives.

What’s more, as illustrated in Fig.  5 , the degree of interaction between SCP and LCL across the 52 pilot cities in China positively impacts the balanced and comprehensive performance of these cities. This, in turn, fosters the coordinated development of urban systems as a whole. Moreover, the continual increase in the coupled coordination degree between SCP and LCL with the enhancement of SCP in pilot cities indicates that smart city construction contributes to urban low-carbon development. Future urban development in China should fully leverage the industrial upgrading effect, carbon sequestration effect, and energy utilization effect of smart city construction. However, the increasing slope of the SCP & LCL coupled coordination degree curve in Fig.  5 suggests significant regional differences in the level of SCP & LCL coupled coordination development across Chinese cities. Smart city construction has a more pronounced decarburization effect in central and western cities, southern cities, non-environmentally focused cities, and resource-based cities, with cities in the northwest showing notably poorer levels of SCP & LCL coupled coordination development. This serves as a warning for future urban development in China.

Relationships between SCP and LCL in each urban subsystem

The relationship between urban SCP and LCL across five dimensions is illustrated in Fig.  4 B–F. There is a strong positive correlation between SCP and LCM, LCS, and LCE, while a moderate correlation is observed with LCEQ, and a weak correlation with LCC. Furthermore, the degree of coupling coordination between SCP and subsystems such as LCS, LCEQ, LCE, LCM, and LCC is examined in Fig.  5 . The results of the coupling coordination vividly illustrate the synergistic interactions and developmental harmony between urban SCP and various systems.

Among these, the coupling coordination degree curve fluctuation between SCP & LCM is stable, situated at an intermediate coupling coordination level, indicating the dominant role of the Chinese government in the construction of smart cities and low-carbon cities, as well as the effectiveness of policy implementation. However, this also suggests that in promoting urban smart and low-carbon construction, China faces the risk of adopting “one-size-fits-all” mandatory policies, neglecting to advance construction in phases with emphasis, tailored to the city's resource endowment and economic development status. The coupling coordination degree curve changes between SCP&LCE and SCP&LCL show the highest degree of fit, indicating that low-carbon economic development brought about by digital empowerment and upgrading of the urban industrial structure is a key driving factor for promoting the coupled coordination development of urban smart and low-carbon initiatives. Transforming traditional industrial structures and pursuing low-carbon upgrades of the economic structure present challenges for urban development in China today. The coupled coordination degree of SCP & LCS is on the verge of a dysfunctional recession, highlighting the imbalance in the development between China's SCP and LCS, especially in terms of new infrastructure construction, such as smart transportation and logistics facilities, smart energy systems, smart environmental resources facilities, etc. The current construction of new infrastructure in China is far from meeting the living needs of the broad masses of people.

It is noteworthy that with the continuous improvement of the SCP in sample cities, the coupling performance degree between SCP and LCEQ exhibits two phases: an initial stage of synergistic enhancement followed by a stage of diminished synergy. In the early phase of synergistic development, the SCP and LCEQ systems of cities, driven by shared goals of sustainable urban development, strategy adjustments, resource sharing, and technological progress, facilitated effective collaboration and integration between systems. However, upon reaching a certain stage, intensified resource competition, declining management efficiency, and environmental changes led to internal system fatigue, resulting in weakened synergy. This indicates that once the technological effects generated by smart city construction reach a certain level, it becomes crucial to enhance the city's capacity for autonomous innovation. Addressing the bottleneck issues of core technologies and transforming the development mode of smart low-carbon technology from “imitative innovation” represent significant breakthroughs for further promoting the coupled coordination of SCP and LCEQ in China’s future.

Moreover, as the SCP of sample cities continuously improves, the coupled coordination degree between SCP and LCC shows two phases: initial stable fluctuations and subsequent rapid growth. The turning point in the curve change occurs at a coupled coordination degree of 0.6, denoted as the primary coupling coordination point. Among these, the low-carbon awareness rate of urban residents, as a key indicator of LCC, shows that the majority of urban residents in China are still in the cognitive awakening stage regarding low-carbon consciousness. At this stage, residents begin to recognize the severity of climate change and environmental degradation, along with the importance of smart low-carbon lifestyles in mitigating these issues. The government continuously promotes this awareness through media reports, educational activities, official propaganda, and community initiatives. As residents gain a deeper understanding of the issues, their attitudes shift from initial indifference or skepticism to a stronger identification with and support for the values and concepts of smart low-carbon living. This shift encourages residents to experiment with new smart low-carbon lifestyles, gradually finding suitable smart low-carbon behavioral patterns that become habitual. Ultimately, when smart low-carbon lifestyles are fully internalized as part of residents’ values, they not only practice smart low-carbon living at the individual level but also actively participate in promoting society’s smart low-carbon construction. Therefore, this study posits that the emergence of the coupled coordination degree turning point between SCP and LCC is not only a process of individual behavioral change but also a reflection of social and cultural transformation. This process is time-consuming and influenced by multiple factors, including policy guidance, economic incentives, educational dissemination, and the social atmosphere.

Implications for promoting coupling coordination development between urban SCP and LCL

Low-carbon and smartness are vital features of modern, sustainable urban development and key supports for it. This study posits that urban low-carbon and smart development should not be disjointed but rather synergistic and complementary. To better achieve sustainable urban development goals, a model should be constructed with “low-carbon” as the cornerstone of sustainable development and “smartness” as the technological assurance for low-carbon growth. Specifically, this study proposes the “urban smart low-carbon co-development model”, which entails a deep integration of intelligent technologies such as the Internet of Things (IoT) and big data with urban construction, governance services, and economic development. This model leverages digitalization to facilitate decarburization, thereby achieving urban sustainable development goals such as energy-efficient and green urbanization, ecological and livable environments, and streamlined governance services.

Furthermore, to better coordinate smart development with low-carbon city construction, enhance low-carbon city building through digitalization, and explore exemplary practices and models of smart low-carbon city construction, this study finds it necessary to establish an evaluation system for smart and low-carbon urban co-development. Therefore, based on the aforementioned urban SCP and LCL evaluation indicator system, this study initially conducted a literature review of past research, selecting 5 primary indicators and 20 secondary indicators from 48 articles to evaluate the degree of coupling coordination development between urban SCP and LCL. Subsequently, the Delphi method was employed to finalize the list of evaluation indicators, with 10 experts from various regions and diverse backgrounds in China refining the list and determining the weights of each indicator, as shown in Supplementary Appendix Table A6 . The final Smart Low-Carbon City Coupling Coordination Development Evaluation Indicator System, as presented in Table 3 , comprises 5 primary indicators and 18 secondary indicators. This evaluation system aims to emphasize the utilization of next-generation information technologies such as 5G, artificial intelligence, cloud computing, and blockchain to expand urban green ecological spaces, strengthen ecological environment governance, and enhance the level of intelligent urban governance, meeting the development needs of smart low-carbon cities.

The policy implications from the analysis results suggest that actions should be taken by government departments in China to reduce the uneven performance between urban SCP and LCL across various cities. These actions include, for example: Firstly, guiding the innovative development of urban SCP and LCL through policies, such as enhancing government digital services and administrative platforms, continuously promoting the development of emerging industries and the upgrading of traditional industries, and actively promoting green energy technologies. Secondly, categorizing and advancing the coordinated development of smart and low-carbon cities—comprehensive development should be pursued simultaneously in large cities in eastern and central China, while in smaller cities in western China, priorities should include enhancing urban innovation capabilities and improving infrastructure to lay a solid foundation for the coupled coordination of urban SCP and LCL. Thirdly, constructing a multi-stakeholder governance system to maximize the leading role of the government, the main role of enterprises, and the active participation of residents. By fostering a positive social atmosphere and cultural attributes, this will enhance the sense of participation and achievement among different social groups, creating a sustainable development model for urban SCP and LCL coordination. Lastly, emphasizing the development of SCP and LCL coordination in county-level cities is crucial. While large Chinese cities have already begun to form a pattern of coordinated SCP and LCL development, county-level cities, though with weaker infrastructures, possess tremendous potential. Focusing on low-carbon production, circulation, and consumption, and strengthening smart and low-carbon constructions in county-level cities will be a vital task for future urban development in China.

Conclusions

The global urbanization process brings opportunities for economic growth and social development, but also presents a series of challenges, such as environmental pressures and resource constraints 3 . The evaluation of urban SCP and LCL creates a link between the policy-making in urban resources environment management and the objectives of sustainable development goals (SDGs 11.4, 11.6, and 11.b) at the city level 74 . Currently, there is no unified consensus on the coupling coordination development between urban SCP and LCL. This study proposes a method combining qualitative and quantitative analysis from the perspective of urban complex systems to analyze the coupling coordination relationship between SCP and LCL. This new method clearly interprets a strong positive correlation between urban smart performance and the overall low-carbon level. Specifically, there are strong correlations between SMC and LCM, LCS, and LCE, with a moderate correlation to LCQE and a weak correlation with LCC. Several innovative insights for this method are highlighted: (i) sustainable development based on SCP and LCL assessment; (ii) emphasizing the “people-centric” concept in urban development; (iii) analyzing from the perspective of urban complex systems.

This study selected 52 typical smart and low-carbon pilot cities in China as sample cities to analyze the coupled coordination relationship between urban SCP and LCL. And the main findings from this analysis can be summarized as follows: (i) smart city initiatives outperform low-carbon city development, with notable differences in SCP and LCL effectiveness across eastern, central, and non-resource-based cities versus western, peripheral, and resource-dependent ones in China. (ii) A strong positive link between urban SCP and low-carbon levels, especially between SCP and LCM, LCS, and LCE, with moderate and weak correlations to LCEQ and LCC, respectively. (iii) An increasing urban SCP levels enhance the coupling coordination within the urban SCP and LCL system. SCP & LCE, SCP & LCM, and SCP & LCS subsystems align well with the overall system, driving the coupled coordination of urban SCP and LCL. In contrast, SCP & LCC and SCP & LCEQ have lesser alignment, affected by factors like technology, policy, economic incentives, education, and societal attitudes. Based on the evaluation results, this study posits that the development of urban low-carbon and smart initiatives should not be disjointed but rather synergistic and complementary. This study constructs an evaluation indicator system for the co-development of smart low-carbon cities aimed at better guiding the future coupling coordination development of smart and low-carbon cities.

The novelty of this study not only addresses the practical dilemma of obtaining comprehensive, accurate, and timely urban-level carbon emission data, a challenge due to existing measurement and estimation technologies being unable to capture all types of carbon emissions, but also assesses the urban SCP and LCL. Simultaneously, by combining qualitative and quantitative analysis methods, it fills the research gap on the nature of the coupled coordination relationship between urban SCP and LCL. Moreover, from the perspective of urban complex systems, this study dissects the urban low-carbon level into LCC, LC, LCE, LCEQ, and LCS, exploring their respective coupled coordination relationships with SCP. This clarifies the impact mechanism between SCP and LCL, providing a theoretical basis for smart low-carbon city co-development. The limitations of the study are also appreciated. Firstly, the study only selected a sample of cities in China, and the limited number of samples may not fully substantiate the research conclusions. Secondly, the indicator system constructed by this study is still not perfect, leading to certain inaccuracies in the evaluation results. In this regard, future studies are recommended to conduct a more comprehensive comparison analysis on the coupled coordination relationship between SCP and LCL at city, regional, and national levels, which would be beneficial in better guiding the practice of urban sustainability.

Data availability

All data generated or analysed during this study are included in this published article [and its Supplementary Information files].

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Xiongwei Zhu, Dezhi Li, Shenghua Zhou & Lugang Yu

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Zhu, X., Li, D., Zhou, S. et al. Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods. Sci Rep 14 , 20461 (2024). https://doi.org/10.1038/s41598-024-68417-4

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