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15 Types of Research Methods

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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types of research methods, explained below

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Primarily Quantitative Methods	Primarily Qualitative methods
Experimental Research	Case Study
Surveys and Questionnaires	Ethnography
Longitudinal Studies	Phenomenology
Cross-Sectional Studies	Historical research
Correlational Research	Content analysis
Causal-Comparative Research	Grounded theory
Meta-Analysis	Action research
Quasi-Experimental Design	Observational research

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Pros of Ethnographic Research	Cons of Ethnographic Research
1. Provides deep cultural insights	1. Time-consuming
2. Contextually relevant findings	2. Potential researcher bias
3. Explores dynamic social processes	3. May

Example of Ethnography

Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Pros of Phenomenological Research	Cons of Phenomenological Research
1. Provides rich, detailed data	1. Limited generalizability
2. Highlights personal experience and perceptions	2. Data collection can be time-consuming
3. Allows exploration of complex phenomena	3. Requires highly skilled researchers

Example of Phenomenological Research

A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Pros of Historical Research	Cons of Historical Research
1.	1. Dependent on available sources
2. Can help understand current events or trends	2. Potential bias in source materials
3. Allows the study of change over time	3. Difficult to replicate

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Pros of Content Analysis	Cons of Content Analysis
1. Unobtrusive data collection	1. Lacks contextual information
2. Allows for large sample analysis	2. Potential coder bias
3. Replicable and reliable if done properly	3. May overlook nuances

Example of Content Analysis

How is Islam Portrayed in Western Media? by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory during and after data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Pros of Grounded Theory Research	Cons of Grounded Theory Research
1. Helps with theory development	1. Time-consuming
2. Rigorous data analysis	2. Requires iterative data collection and analysis
3. Can fill gaps in existing theories	3. Requires skilled researchers

Grounded Theory Example

Developing a Leadership Identity by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Pros of Action Research	Cons of Action Research
1. Addresses real-world problems and seeks to find solutions.	1. It is time-consuming and often hard to implement into a practitioner’s already busy schedule
2. Integrates research and action in an action-research cycle.	2. Requires collaboration between researcher, practitioner, and research participants.
3. Can bring about positive change in isolated instances, such as in a school or nursery setting.	3. Complexity of managing dual roles (where the researcher is also often the practitioner)

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Pros of Qualitative Observational Research	Cons of Qualitative Observational Research
1. Captures behavior in natural settings, allowing for interesting insights into authentic behaviors.	1. Researcher’s presence may influence behavior
2. Can provide rich, detailed data through the researcher’s vignettes.	2. Can be time-consuming
3. Non-invasive because researchers want to observe natural activities rather than interfering with research participants.	3. Requires skilled and trained observers

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

Pros of Case Study Research	Cons of Case Study Research
1. Provides detailed insights	1. Limited generalizability
2. Facilitates the study of complex phenomena	2. Can be time-consuming
3. Can test or generate theories	3. Subject to observer bias

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Pros of Experimental Research	Cons of Experimental Research
1. Allows for determination of causality	1. Might not reflect real-world conditions
2. Allows for the study of phenomena in highly controlled environments to minimize research contamination.	2. Can be costly and time-consuming to create a controlled environment.
3. Can be replicated so other researchers can test and verify the results.	3. Ethical concerns need to be addressed as the research is directly manipulating variables.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Pros of Surveys and Questionnaires	Cons of Surveys and Questionnaires
1. Data can be gathered from larger samples than is possible in qualitative research.	1. There is heavy dependence on respondent honesty
2. The data is quantifiable, allowing for comparison across subpopulations	2. There is limited depth of response as opposed to qualitative approaches.
3. Can be cost-effective and time-efficient	3. Static with no flexibility to explore responses (unlike semi- or unstrcutured interviewing)

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Pros of Longitudinal Studies	Cons of Longitudinal Studies
1. Tracks changes over time allowing for comparison of past to present events.	1. Is almost by definition time-consuming because time needs to pass between each data collection session.
2. Can identify sequences of events, but causality is often harder to determine.	2. There is high risk of participant dropout over time as participants move on with their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Pros of Cross-Sectional Studies	Cons of Cross-Sectional Studies
1. Quick and inexpensive, with no long-term commitment required.	1. Cannot determine causality because it is a simple snapshot, with no time delay between data collection points.
2. Good for descriptive analyses.	2. Does not allow researchers to follow up with research participants.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Pros of Correlational Research	Cons of Correlational Research
1. Reveals relationships between variables	1. Cannot determine causality
2. Can use existing data	2. May be
3. Can guide further experimental research	3. Correlation may be coincidental

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Pros	Cons
1. It’s more feasible to implement than true experiments.	1. Without random assignment, it’s harder to rule out confounding variables.
2. It can be conducted in real-world settings, making the findings more applicable to the real world.	2. The lack of random assignment may of the study.
3. Useful when it’s unethical or impossible to manipulate the independent variable or randomly assign participants.	3. It’s more difficult to establish a cause-effect relationship due to the potential for confounding variables.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Pros	Cons
Increased Statistical Power: By combining data from multiple studies, meta-analysis increases the statistical power to detect effects.	Publication Bias: Studies with null or negative findings are less likely to be published, leading to an overestimation of effect sizes.
Greater Precision: It provides more precise estimates of effect sizes by reducing the influence of random error.	Quality of Studies: of a meta-analysis depends on the quality of the studies included.
Resolving Discrepancies: Meta-analysis can help resolve disagreements between different studies on a topic.	Heterogeneity: Differences in study design, sample, or procedures can introduce heterogeneity, complicating interpretation of results.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

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Here's What You Need to Understand About Research Methodology

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

Analyze and understand research methodologies faster with SciSpace Copilot

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

When someone tries to duplicate or expand upon your research after few years.
If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

Observation
Experimental
Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative
Quantitative		.

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary
Secondary

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Research methods for analysing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyse data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyse the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyse data collected from interviews, focus groups or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyse large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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 .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

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.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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Research Guides

Organizing Your Social Sciences Research Paper

6. The Methodology
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II. Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
Tools and methods used to identify and collect information, and how you identified relevant variables,
The ways in which you processed the data and the procedures you used to analyze that data, and
The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE: Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE: If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III. Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
You can easily answer any questions about your research if they arise at a later stage.
A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
It also helps ensure that ethical guidelines are followed while conducting research.
A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

Sampling: The most common type is probability sampling.
Interviews: Commonly telephonic or done in-person.
Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
Document review: Reviewing existing research or documents to collect evidence for supporting the research.
Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

Measures of frequency (count, percent, frequency)
Measures of central tendency (mean, median, mode)
Measures of dispersion or variation (range, variance, standard deviation)
Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

Correlation: To understand the relationship between two or more variables.
Cross-tabulation: Analyze the relationship between multiple variables.
Regression analysis: Study the impact of independent variables on the dependent variable.
Frequency tables: To understand the frequency of data.
Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

Research objectives, aims, and questions —these would help structure the research design.
Review existing literature to identify any gaps in knowledge.
Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
Research method —this can be quantitative, qualitative, or mixed-method.
Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
Sampling —this involves selecting a representative subset of the population being studied.
Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
Data analysis —describe the data analysis methods you will use once you’ve collected the data.
Research limitations —mention any limitations you foresee while conducting your research.
Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

Participants should not be subjected to harm.
Respect for the dignity of participants should be prioritized.
Full consent should be obtained from participants before the study.
Participants’ privacy should be ensured.
Confidentiality of the research data should be ensured.
Anonymity of individuals and organizations participating in the research should be maintained.
The aims and objectives of the research should not be exaggerated.
Affiliations, sources of funding, and any possible conflicts of interest should be declared.
Communication in relation to the research should be honest and transparent.
Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation, or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Techniques or tools used for gathering research data include:

Qualitative Techniques or Tools	Quantitative Techniques or Tools
: these can be structured, semi-structured or unstructured in-depth sessions with the researcher and a participant.	Surveys or questionnaires: which ask the same questions to large numbers of participants or use Likert scales which measure opinions as numerical data.
: with several participants discussing a particular topic or a set of questions. Researchers can be facilitators or observers.	Observation: which can either involve counting the number of times a specific phenomenon occurs, or the coding of observational data in order to translate it into numbers.
: On-site, in-context or role-play options.	Document screening: sourcing numerical data from financial reports or counting word occurrences.
: Interrogation of correspondence (letters, diaries, emails etc) or reports.	Experiments: testing hypotheses in laboratories, testing cause and effect relationships, through field experiments, or via quasi- or natural experiments.
: Remembrances or memories of experiences told to the researcher.

SAGE research methods

SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

Next: Finding qualitative research >>
Last Updated: Aug 19, 2024 3:39 PM
URL: https://libguides.newcastle.edu.au/researchmethods

What Is Research Methodology?

I f you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

What exactly research methodology means
What qualitative , quantitative and mixed methods are
What sampling strategy is
What data collection methods are
What data analysis methods are
How to choose your research methodology
Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

What type of data to collect (e.g., qualitative or quantitative data )
Who to collect it from (i.e., the sampling strategy )
How to collect it (i.e., the data collection method )
How to analyse it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just what methodological choices were made, but also explains why they were made. In other words, the methodology chapter should justify the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions.

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president.

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study. There are many different sampling methods you can choose from, but the two overarching categories are probability sampling and non-probability sampling .

Probability sampling involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable to the entire population.

Non-probability sampling , on the other hand, doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

Interviews (which can be unstructured, semi-structured or structured)
Focus groups and group interviews
Surveys (online or physical surveys)
Observations (watching and recording activities)
Biophysical measurements (e.g., blood pressure, heart rate, etc.)
Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

Qualitative content analysis
Thematic analysis
Discourse analysis
Narrative analysis
Interpretative phenomenological analysis (IPA)
Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some common qualitative analysis methods, along with practical examples.

Descriptive statistics (e.g. means, medians, modes )
Inferential statistics (e.g. correlation, regression, structural equation modelling)

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis).

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components.

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Learn More About Methodology

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Research Limitations 101: What You Need To Know

Learn everything you need to know about research limitations (AKA limitations of the study). Includes practical examples from real studies.

In Vivo Coding 101: Full Explainer With Examples

Learn about in vivo coding, a popular qualitative coding technique ideal for studies where the nuances of language are central to the aims.

Process Coding 101: Full Explainer With Examples

Learn about process coding, a popular qualitative coding technique ideal for studies exploring processes, actions and changes over time.

Qualitative Coding 101: Inductive, Deductive & Hybrid Coding

Inductive, Deductive & Abductive Coding Qualitative Coding Approaches Explained...

📄 FREE TEMPLATES

Research Topic Ideation

Proposal Writing

Literature Review

Methodology & Analysis

Academic Writing

Referencing & Citing

Apps, Tools & Tricks

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Choosing the Right Research Methodology: A Guide for Researchers

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Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories.

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis.

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable.

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:

Your overall research objectives and approach
Whether you’ll rely on primary research or secondary research
Your sampling methods or criteria for selecting subjects
Your data collection methods
The procedures you’ll follow to collect data
Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative approach	Quantitative approach
	and describe frequencies, averages, and correlations about relationships between variables

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

How much time do you have to collect data and write up the research?
Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
Descriptive and correlational designs allow you to measure variables and describe relationships between them.

Type of design	Purpose and characteristics
Experimental	relationships effect on a
Quasi-experimental	)
Correlational
Descriptive

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Type of design	Purpose and characteristics


Grounded theory
Phenomenology

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling	Non-probability sampling

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Questionnaires	Interviews
	)

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Quantitative observation

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

Field	Examples of data collection methods
Media & communication	Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology	Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education	Using tests or assignments to collect data on knowledge and skills
Physical sciences	Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

Reliability	Validity
	) )

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

How many participants do you need for an adequate sample size?
What inclusion and exclusion criteria will you use to identify eligible participants?
How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

The distribution of the data (e.g., the frequency of each score on a test)
The central tendency of the data (e.g., the mean to describe the average score)
The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

Make estimates about the population based on your sample data.
Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

Approach	Characteristics
Thematic analysis
Discourse analysis

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

Your research questions and/or hypotheses
Your overall approach (e.g., qualitative or quantitative )
The type of design you’re using (e.g., a survey , experiment , or case study )
Your data collection methods (e.g., questionnaires , observations)
Your data collection procedures (e.g., operationalization , timing and data management)
Your data analysis methods (e.g., statistical tests or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative Research

What is qualitative research.

Qualitative research is a methodology focused on collecting and analyzing descriptive, non-numerical data to understand complex human behavior, experiences, and social phenomena. This approach utilizes techniques such as interviews, focus groups, and observations to explore the underlying reasons, motivations, and meanings behind actions and decisions. Unlike quantitative research, which focuses on measuring and quantifying data, qualitative research delves into the 'why' and 'how' of human behavior, providing rich, contextual insights that reveal deeper patterns and relationships.

The Basic Idea

Theory, meet practice.

TDL is an applied research consultancy. In our work, we leverage the insights of diverse fields—from psychology and economics to machine learning and behavioral data science—to sculpt targeted solutions to nuanced problems.

Ever heard of the saying “quality over quantity”? Well, some researchers feel the same way!

Imagine you are conducting a study looking at consumer behavior for buying potato chips. You’re interested in seeing which factors influence a customer’s choice between purchasing Doritos and Pringles. While you could conduct quantitative research and measure the number of bags purchased, this data alone wouldn’t explain why consumers choose one chip brand over the other; it would just tell you what they are purchasing. To gather more meaningful data, you may conduct interviews or surveys, asking people about their chip preferences and what draws them to one brand over another. Is it the taste of the chips? The font or color of the bag? This qualitative approach dives deeper to uncover why one potato chip is more popular than the other and can help companies make the adjustments that count.

Qualitative research, as seen in the example above, can provide greater insight into behavior, going beyond numbers to understand people’s experiences, attitudes, and perceptions. It helps us to grasp the meaning behind decisions, rather than just describing them. As human behavior is often difficult to qualify, qualitative research is a useful tool for solving complex problems or as a starting point to generate new ideas for research. Qualitative methods are used across all types of research—from consumer behavior to education, healthcare, behavioral science, and everywhere in between!

At its core, qualitative research is exploratory—rather than coming up with a hypothesis and gathering numerical data to support it, qualitative research begins with open-ended questions. Instead of asking “Which chip brand do consumers buy more frequently?”, qualitative research asks “Why do consumers choose one chip brand over another?”. Common methods to obtain qualitative data include focus groups, unstructured interviews, and surveys. From the data gathered, researchers then can make hypotheses and move on to investigating them.

It’s important to note that qualitative and quantitative research are not two opposing methods, but rather two halves of a whole. Most of the best studies leverage both kinds of research by collecting objective, quantitative data, and using qualitative research to gain greater insight into what the numbers reveal.

You may have heard the world is made up of atoms and molecules, but it’s really made up of stories. When you sit with an individual that’s been here, you can give quantitative data a qualitative overlay. – William Turner, 16th century British scientist 1

Quantitative Research: A research method that involves collecting and analyzing numerical data to test hypotheses, identify patterns, and predict outcomes.

Exploratory Research: An initial study used to investigate a problem that is not clearly defined, helping to clarify concepts and improve research design.

Positivism: A scientific approach that emphasizes empirical evidence and objectivity, often involving the testing of hypotheses based on observable data. 2

Phenomenology: A research approach that emphasizes the first-person point of view, placing importance on how people perceive, experience, and interpret the world around them. 3

Social Interaction Theory: A theoretical perspective that people make sense of their social worlds by the exchange of meaning through language and symbols. 4

Critical Theory: A worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts that influences reality and society. 5

Empirical research: A method of gaining knowledge through direct observation and experimentation, relying on real-world data to test theories.

Paradigm shift: A fundamental change in the basic assumptions and methodologies of a scientific discipline, leading to the adoption of a new framework. 2

Interpretive/descriptive approach: A methodology that focuses on understanding the meanings people assign to their experiences, often using qualitative methods.

Unstructured interviews: A free-flowing conversation between researcher and participant without predetermined questions that must be asked to all participants. Instead, the researcher poses questions depending on the flow of the interview. 6

Focus Group: Group interviews where a researcher asks questions to guide a conversation between participants who are encouraged to share their ideas and information, leading to detailed insights and diverse perspectives on a specific topic.

Grounded theory : A qualitative methodology that generates a theory directly from data collected through iterative analysis.

When social sciences started to emerge in the 17th and 18th centuries, researchers wanted to apply the same quantitative approach that was used in the natural sciences. At this time, there was a predominant belief that human behavior could be numerically analyzed to find objective patterns and would be generalizable to similar people and situations. Using scientific means to understand society is known as a positivist approach. However, in the early 20th century, both natural and social scientists started to criticize this traditional view of research as being too reductive. 2

In his book, The Structure of Scientific Revolutions, American philosopher Thomas Kuhn identified that a major paradigm shift was starting to occur. Earlier methods of science were being questioned and replaced with new ways of approaching research which suggested that true objectivity was not possible when studying human behavior. Rather, the importance of context meant research on one group could not be generalized to all groups. 2 Numbers alone were deemed insufficient for understanding the environment surrounding human behavior which was now seen as a crucial piece of the puzzle. Along with this paradigm shift, Western scholars began to take an interest in ethnography , wanting to understand the customs, practices, and behaviors of other cultures.

Qualitative research became more prominent throughout the 20th century, expanding beyond anthropology and ethnography to being applied across all forms of research; in science, psychology, marketing—the list goes on. Paul Felix Lazarsfield, Austrian-American sociologist and mathematician often known as the father of qualitative research, popularized new methods such as unstructured interviews and group discussions. 7 During the 1940s, Lazarfield brought attention to the fact that humans are not always rational decision-makers, making them difficult to understand through numerical data alone.

The 1920s saw the invention of symbolic interaction theory, developed by George Herbert Mead. Symbolic interaction theory posits society as the product of shared symbols such as language. People attach meanings to these symbols which impacts the way they understand and communicate with the world around them, helping to create and maintain a society. 4 Critical theory was also developed in the 1920s at the University of Frankfurt Institute for Social Research. Following the challenge of positivism, critical theory is a worldview that there is no unitary or objective “truth” about people that can be discovered, as human experience is shaped by social, cultural, and historical contexts. By shedding light on the human experience, it hopes to highlight the role of power, ideology, and social structures in shaping humans, and using this knowledge to create change. 5

Other formalized theories were proposed during the 20th century, such as grounded theory , where researchers started gathering data to form a hypothesis, rather than the other way around. This represented a stark contrast to positivist approaches that had dominated the 17th and 18th centuries.

The 1950s marked a shift toward a more interpretive and descriptive approach which factored in how people make sense of their subjective reality and attach meaning to it. 2 Researchers began to recognize that the why of human behavior was just as important as the what . Max Weber, a German sociologist, laid the foundation of the interpretive approach through the concept of Verstehen (which in English translates to understanding), emphasizing the importance of interpreting the significance people attach to their behavior. 8 With the shift to an interpretive and descriptive approach came the rise of phenomenology, which emphasizes first-person experiences by studying how individuals perceive, experience, and interpret the world around them.

Today, in the age of big data, qualitative research has boomed, as advancements in digital tools allow researchers to gather vast amounts of data (both qualitative and quantitative), helping us better understand complex social phenomena. Social media patterns can be analyzed to understand public sentiment, consumer behavior, and cultural trends to grasp how people attach subjective meaning to their reality. There is even an emerging field of digital ethnography which is entirely focused on how humans interact and communicate in virtual environments!

Thomas Kuhn

American philosopher who suggested that science does not evolve through merely an addition of knowledge by compiling new learnings onto existing theories, but instead undergoes paradigm shifts where new theories and methodologies replace old ones. In this way, Kuhn suggested that science is a reflection of a community at a particular point in time. 9

Paul Felix Lazarsfeld

Often referred to as the father of qualitative research, Austrian-American sociologist and mathematician Paul Lazarsfield helped to develop modern empirical methods of conducting research in the social sciences such as surveys, opinion polling, and panel studies. Lazarsfeld was best known for combining qualitative and quantitative research to explore America's voting habits and behaviors related to mass communication, such as newspapers, magazines, and radios. 10

German sociologist and political economist known for his sociological approach of “Verstehen” which emphasized the need to understand individuals or groups by exploring the meanings that people attach to their decisions. While previously, qualitative researchers in ethnography acted like an outside observer to explain behavior from their point of view, Weber believed that an empathetic understanding of behavior, that explored both intent and context, was crucial to truly understanding behavior. 11

George Herbert Mead

Widely recognized as the father of symbolic interaction theory, Mead was an American philosopher and sociologist who took an interest in how spoken language and symbols contribute to one’s idea of self, and to society at large. 4

Consequences

Humans are incredibly complex beings, whose behaviors cannot always be reduced to mere numbers and statistics. Qualitative research acknowledges this inherent complexity and can be used to better capture the diversity of human and social realities.

Qualitative research is also more flexible—it allows researchers to pivot as they uncover new insights. Instead of approaching the study with predetermined hypotheses, oftentimes, researchers let the data speak for itself and are not limited by a set of predefined questions. It can highlight new areas that a researcher hadn’t even thought of exploring.

By providing a deeper explanation of not only what we do, but why we do it, qualitative research can be used to inform policy-making, educational practices, healthcare approaches, and marketing tactics. For instance, while quantitative research tells us how many people are smokers, qualitative research explores what, exactly, is driving them to smoke in the first place. If the research reveals that it is because they are unaware of the gravity of the consequences, efforts can be made to emphasize the risks, such as by placing warnings on cigarette cartons.

Finally, qualitative research helps to amplify the voices of marginalized or underrepresented groups. Researchers who embrace a true “Verstehen” mentality resist applying their own worldview to the subjects they study, but instead seek to understand the meaning people attach to their own behaviors. In bringing forward other worldviews, qualitative research can help to shift perceptions and increase awareness of social issues. For example, while quantitative research may show that mental health conditions are more prevalent for a certain group, along with the access they have to mental health resources, qualitative research is able to explain the lived experiences of these individuals and uncover what barriers they are facing to getting help. This qualitative approach can support governments and health organizations to better design mental health services tailored to the communities they exist in.

Controversies

Qualitative research aims to understand an individual’s lived experience, which although provides deeper insights, can make it hard to generalize to a larger population. While someone in a focus group could say they pick Doritos over Pringles because they prefer the packaging, it’s difficult for a researcher to know if this is universally applicable, or just one person’s preference. 12 This challenge makes it difficult to replicate qualitative research because it involves context-specific findings and subjective interpretation.

Moreover, there can be bias in sample selection when conducting qualitative research. Individuals who put themselves forward to be part of a focus group or interview may hold strong opinions they want to share, making the insights gathered from their answers not necessarily reflective of the general population.13 People may also give answers that they think researchers are looking for leading to skewed results, which is a common example of the observer expectancy effect .

However, the bias in this interaction can go both ways. While researchers are encouraged to embrace “Verstehen,” there is a possibility that they project their own views onto their participants. For example, if an American researcher is studying eating habits in China and observes someone burping, they may attribute this behavior to rudeness—when in fact, burping can be a sign that you have enjoyed your meal and it is a compliment to the chef. One way to mitigate this risk is through thick description , noting a great amount of contextual detail in their observations. Another way to minimize the researcher’s bias on their observations is through member checking , returning results to participants to check if they feel they accurately capture their experience.

Another drawback of qualitative research is that it is time-consuming. Focus groups and unstructured interviews take longer and are more difficult to logistically arrange, and the data gathered is harder to analyze as it goes beyond numerical data. While advances in technology alleviate some of these labor-intensive processes, they still require more resources.

Many of these drawbacks can be mitigated through a mixed-method approach, combining both qualitative and quantitative research. Qualitative research can be a good starting point, giving depth and contextual understanding to a behavior, before turning to quantitative data to see if the results are generalizable. Or, the opposite direction can be used—quantitative research can show us the “what,” identifying patterns and correlations, and researchers can then better understand the “why” behind behavior by leveraging qualitative methods. Triangulation —using multiple datasets, methods, or theories—is another way to help researchers avoid bias.

Linking Adult Behaviors to Childhood Experiences

In the mid-1980s, an obesity program at the KP San Diego Department of Preventive Medicine had a high dropout rate. What was interesting is that a majority of the dropouts were successfully losing weight, posing the question of why they were leaving the program in the first place. In this instance, greater investigation was required to understand the why behind their behaviors.

Researchers conducted in-depth interviews with almost 200 dropouts, finding that many of them had experienced childhood abuse that had led to obesity. In this unfortunate scenario, obesity was a consequence of another problem, rather than the root problem itself. This led Dr. Vincent J. Felitti, who was working for the department, to launch the Adverse Childhood Experiences (ACE) Study, aimed at exploring how childhood experiences impact adult health status.

Felitti and the Department of Preventive Medicine studied over 17,000 adults with health plans that revealed a strong relationship between emotional experiences as children and negative health behaviors as adults, such as obesity, smoking, and intravenous drug use. This study demonstrates the importance of qualitative research to uncover correlations that would not be discovered by merely looking at numerical data. 14

Understanding Voter Turnout

Voting is usually considered an important part of political participation in a democracy. However, voter turnout is an issue in many countries, including the US. While quantitative research can tell us how many people vote, it does not provide insights into why people choose to vote or not.

With this in mind, Dawn Merdelin Johnson, a PhD student in philosophy at Walden University, explored how public corruption has impacted voter turnout in Cook County, Illinois. Johnson conducted semi-structured telephone interviews to understand factors that contribute to low voter turnout and the impact of public corruption on voting behaviors. Johnson found that public corruption leads to voters believing public officials prioritize their own well-being over the good of the people, leading to distrust in candidates and the overall political system, and thus making people less likely to vote. Other themes revealed that to increase voter turnout, voting should be more convenient and supply more information about the candidates to help people make more informed decisions.

From these findings, Johnson suggested that the County could experience greater voter turnout through the development of an anti-corruption agency, improved voter registration and maintenance, and enhanced voting accessibility. These initiatives would boost voting engagement and positively impact democratic participation. 15

Increasing HPV Vaccination in Rural Kenya

While HPV vaccines are an effective method of preventing cervical cancer, there is low intake in low and middle-income countries worldwide. Qualitative research can uncover the social and behavioral barriers to increasing HPV vaccination, revealing that misinformation, skepticism, and fear prevent people from getting the vaccine. In this article, our writer Annika Steele explores how qualitative insights can inform a two-part intervention strategy to increase HPV vaccination rates.

Versta Research. (n.d.). Bridging the quantitative-qualitative gap . Versta Research. Retrieved August 17, 2024, from https://verstaresearch.com/newsletters/bridging-the-quantitative-qualitative-gap/
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
Smith, D. W. (2018). Phenomenology. In E. N. Zalta (Ed.), Stanford Encyclopedia of Philosophy . Retrieved from https://plato.stanford.edu/entries/phenomenology/#HistVariPhen
Nickerson, C. (2023, October 16). Symbolic interaction theory . Simply Psychology. https://www.simplypsychology.org/symbolic-interaction-theory.html
DePoy, E., & Gitlin, L. N. (2016). Introduction to research (5th ed.). Elsevier.
ATLAS.ti. (n.d.). Unstructured interviews . ATLAS.ti. Retrieved August 17, 2024, from https://atlasti.com/research-hub/unstructured-interviews
O'Connor, O. (2020, August 14). The history of qualitative research . Medium. https://oliconner.medium.com/the-history-of-qualitative-research-f6e07c58e439
Sociology Institute. (n.d.). Max Weber: Interpretive sociology & legacy . Sociology Institute. Retrieved August 18, 2024, from https://sociology.institute/introduction-to-sociology/max-weber-interpretive-sociology-legacy
Kuhn, T. S. (2012). The structure of scientific revolutions (4th ed.). University of Chicago Press.
Encyclopaedia Britannica. (n.d.). Paul Felix Lazarsfeld . Encyclopaedia Britannica. Retrieved August 17, 2024, from https://www.britannica.com/biography/Paul-Felix-Lazarsfeld
Nickerson, C. (2019). Verstehen in Sociology: Empathetic Understanding . Simply Psychology. Retrieved August 18, 2024, from: https://www.simplypsychology.org/verstehen.html
Omniconvert. (2021, October 4). Qualitative research: Definition, methodology, limitations, and examples . Omniconvert. https://www.omniconvert.com/blog/qualitative-research-definition-methodology-limitation-examples/
Vaughan, T. (2021, August 5). 10 advantages and disadvantages of qualitative research . Poppulo. https://www.poppulo.com/blog/10-advantages-and-disadvantages-of-qualitative-research
Felitti, V. J. (2002). The relation between adverse childhood experiences and adult health: Turning gold into lead. The Permanente Journal, 6 (1), 44–47. https://www.thepermanentejournal.org/doi/10.7812/TPP/02.994
Johnson, D. M. (2024). Voters' perception of public corruption and low voter turnout: A qualitative case study of Cook County (Doctoral dissertation). Walden University.

About the Author

Emilie Rose Jones

Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible.

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Market research: Key steps, methods, and use cases

Ever wonder why some products fly off the shelves while others just sit there, all lonely and ignored? Or why your last marketing campaign was a massive hit while some of your others have felt like a total flop? Maybe you’re just dying to know what your customers actually want (spoiler alert: it’s not always what you think).

To answer these above questions, you’re going to need to tap into the best resource around: your customers.

That’s where market research comes in. In this complete guide, we’ll give you the 411 into the key steps, methods, and use cases so you can make smarter, data-driven decisions.

What’s Market Research?

Market research is all about getting information on your target market and customers. It helps you figure out if a new product will be a success, make tweaks to what you’ve already got, or understand how people see your brand so you can communicate your company’s value better.

Let’s take a look at a real-world example:

If you’re into TikTok (and who isn’t?), you’ve probably seen those quick surveys while scrolling your ‘For You’ feed. TikTok uses them to learn what users like and how they feel about ads.

One survey asks for your opinion on a video or ad you just watched. Another might ask if you’ve seen a specific brand’s sponsored content recently, like “Did you catch any Dove ads in the last few days?”

TikTok uses this feedback to fine-tune what pops up in your feed.

Why’s it Important?

Image Sourced from statista.com

The global market research industry hit a record high of around $84.3 billion in 2023 . Over the last decade, it’s grown steadily, even when the economy was shaky. It just goes to show how important this stuff is becoming. But why?

Well, market research helps you meet your buyers where they are (not where you wish they were). In a world that’s getting louder and more distracting by the day, that’s pretty much gold. When you really get your buyers’ problems, pain points, and what they’re dreaming of, you can design your product or service in a way that just clicks with them.

Types of Market Research

You now know what market research is and why it’s important to carry out! Next up, we’re going to teach you all the different types you can use:

Interviews: Sit down with people one-on-one either in person or via video call . Have a chat and let the conversation flow naturally using open ended questions. This’ll help you learn more about your buyers and shape your marketing strategy.
Focus groups: Another form of market research here.This one’s all about gathering a small group of people to test your product and share their thoughts.
Customer surveys: Customer surveys could be quantitative or qualitative, done online, over-the-phone or via SMS.
Product/service use research: Find out how and why people use your product or service. This tells you if it’s easy to use and how it fits their needs.
Observation-based research: Watch how your target audience interacts with your product or service. You get to see what’s working well and what needs fixing.
Market segmentation research: To do this one, simply break your audience into different groups based on specific traits (like age, interests or how much they like to spend) This’ll help you figure out the best ways to meet their needs.
Pricing research: Look at some of the products out there similar to yours. How much are they charging for their products or services? How many people are willing to actually pay that? This research helps you set the right price.
Customer satisfaction surveys and loyalty research: See how happy your current customers are, and what makes them come back. This could be anything from loyalty programs to great digital customer service .

How to Do Market Research

Alright, It’s time to get practical! Here’s our step-by-step plan on market research:

Step 1: Define Your Buyer Persona

Free to use image sourced from Stockvault

First up, you’re going to need to know who your potential customers are and how they’re making buying decisions. Now, you can do this by creating buyer personas.

Buyer personas (AKA as marketing personas or customer personas FYI) are basically fictional profiles of your ideal customers. They help you understand what your best customers look like and how to reach them. Here’s what you should include in your buyer persona:

Job title(s)
Family size
Major challenges

Think of your persona as a way of connecting with real customers in your industry. If you can, back up your persona with real data from your existing audience. For example, using phone analytics tools from your business phone service , you can check out phone patterns, customers inquiries and communications preferences to share in your persona.

Step 2: Find a Persona Group to Engage With

Now that you’ve got your buyer personas figured out, it’s time to pick a group of people to look into for your market research. You want a sample that truly represents your target customers so you can get a clear picture of their traits, challenges, and buying habits.

So, if you’re an IT company that helps businesses put together an enterprise architecture strategy , for example, you’ll want to select people who either recently interacted with your company, bought similar services, and/or even those who chose a competitor.

How to pick the right people for market research

Aim for 10 participants per persona
Pick people who’ve interacted with you in the last six months (or at least up to a year)
Include people who’ve bought from you, from your competitors, and those who decided not to buy at all.
Get people excited to spend 30-45 minutes helping you out. On a tight budget? Give them something like exclusive content as a reward instead of cash. This way, you’ll get the feedback you need to truly understand your audience.

Step 3: Collect Your Data

Start by setting up a solid plan for collecting information. Make sure your customer surveys or interview questions are clear and cover everything you need. If people skip questions or don’t fill them out properly, your research could be off.

Step 4: Analyze the Results

Now comes the fun part. It’s time to analyze all that data you’ve collected. Look beyond the numbers to find the real insights.

You’ll get some basic info like age and job titles, but the important data helps you understand their feelings and experiences. Tools like empathy maps can help you get into your customers’ heads (imagine being a mind reader, but more scientific). With good planning and a solid approach, figuring out what your data means should be super simple.

Step 5: Create the Research Report

When writing up your report, think about what you want to achieve with the report and try to tell a story with your findings.Tips for a great report:

Use the inverted pyramid style: start with the big conclusions, and then add the details. No one has time for a novel!
Kick off with key insights that your audience would be most interested in
Make it easy to read so people can get to the main points quickly

Step #6: Make Decisions

Market research helps you understand things like customer buying habits, market trends, and pricing strategies. So go ahead, make those decisions and put your findings into action! Your research wasn’t just for fun, after all.

Final Thoughts

So there you have it. Our complete market research guide, packed with everything you need to know about conducting it at your company.

Market research isn’t a one-time thing. It’s an ongoing process to keep you in tune with what your customers want and how your market’s shifting. keep it relevant, and don’t be afraid to dive back in whenever you need to adjust your game plan.

Happy researching! And remember, the more you know, the better you can do.

What can you learn by carrying out market research?

Market research can give you:

Where your target audience and current customers are doing their research
Which of your competitors your audience is turning to for advice, options, or straight-up buying
What’s trending in your industry and what your buyers are buzzing about
Who’s in your market, what their struggles are, and how you can help
What really influences your audience’s buying decisions and conversions
Consumer attitudes about specific topics, pains, products, or brands (including yours)
If there’s actually a demand for those big business ideas you’re pouring money into
Unmet or underserved needs that you could turn into a selling opportunity
How your audience feels about pricing for a particular product or service

Are there any downsides of market research?

While market research can answer big questions about your industry, it’s not a magic crystal ball. It takes time to get a clear picture of what’s really going on with your target audience. But even if you only research a small piece of the puzzle, you’ll start to get a better sense of who your buyers are and how you can offer something unique that they can’t find elsewhere.

Originally published Aug 30, 2024

Cold calling scripts: 7 examples and tips to close more sales

Cold calling gets a bad reputation sometimes, but it’s still regularly used by many sales teams. And while achieving good response rates through cold calling can be a challenge, it can be done. In this article, we take a look at seven examples of cold calling scripts that will lead you in the right direction. ...

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Simplifying the options for RNA-seq: A short guide to different methods

You know you want to explore the transcriptome of your research subject, and you’ve decided to use RNA sequencing (RNA-seq). However, you’re still faced with a number of different workflow choices.

How do you decide?

The answer depends on the questions you’re trying to answer, as well as the origin, quantity, and quality of your samples. In this article, we’ll briefly discuss several RNA-seq workflows, how they differ, and the advantages and drawbacks of each.

Regardless of the method you choose, every RNA-seq project begins with library preparation-the method for modifying the RNA in your sample into molecules that can be read by a sequencing instrument. While library preparation workflows vary by supplier and by sequencing platform*, the discussion below is relevant to most of them.

Is your primary goal to detect or measure the expression of protein-coding genes?

If so-and if your samples are from eukaryotic organisms-then mRNA-capture methods are your best bet. These workflows first capture mature, polyadenylated (poly-A) transcripts using a substrate coated with poly-T oligos (typically beads or a column), and then continue with library preparation using only those captured molecules.

mRNA-capture is often used when comparing the expression profile of different samples, such as: tumor vs normal tissue, to look for aberrant gene expression levels or to identify disease-associated transcripts; cells or organisms at different times or developmental stages, to identify changes in gene expression; comparing healthy vs diseased tissues or organisms to identify stress- or immune-mediated gene expression; and, in drug development pathways, comparing gene expression under treated vs untreated conditions.

However, mRNA capture is not effective with bacterial or viral RNA (as they typically lack polyA tails) and is not reliable with low-quality samples; fragmented, degraded RNA typically lacks full-length polyadenylated transcripts, which can lead to biased or incomplete results.

Are you interested in non-coding and nascent transcripts? Or prokaryotic samples?

If this describes your research, choose a method that uses ribosomal RNA depletion to remove the massively abundant rRNA from total RNA samples. As rRNA can comprise 90-98% of the RNA in a cell and is not relevant for most research, ribodepletion reduces noise (wasted reads) in the final data, ultimately saving on sequencing costs. This method is often referred to as whole-transcriptome sequencing (WTS).

Some advantages of ribodepletion workflows are that: (1) they are compatible with prokaryotic samples, although custom depletion oligos may be needed for organisms other than human, mouse, and rat; (2) they enable the sequencing of non-coding and immature transcripts, both of which typically lack poly-A tails, and (3) they are effective with degraded samples. In addition, other highly abundant transcripts can also be depleted using complementary oligos, such as hemoglobin transcripts in blood-derived samples-increasing the percentage of informative sequencing reads.

Are there specific transcripts you want to detect with greater precision?

Sometimes, measuring relative expression levels of a specific group of transcripts is the goal -such as those associated with a specific cancer type or stage. In cases like this, transcripts of interest can be concentrated using target enrichment methods; these include hybridization-based, primer-extension-based, and amplicon-based workflows. The common denominator for these methods is the use of sequence-specific oligos that are complementary to the transcripts of interest to capture or amplify those transcripts, while the rest are eliminated from the final sequencing library. The end result is a dataset enriched for the desired sequences.

And the list of options continues to grow…

In addition to the methods above, there are also many different workflows for sequencing RNA from individual cells (scRNA-seq) and other ultra-low-input samples. Many multiomics workflows combine RNA-seq with DNA sequencing, DNA or RNA epigenomic analysis, and/or proteomics analysis; however, even in these workflows, it’s important to choose the right RNA-seq option. All of these methods, when used in the right place at the right time, have enormous potential to advance basic research, as well as drug discovery and design.

*Many different instruments and workflows exist for preparing RNA for sequencing, including short- and long-read sequencing, sequencing-by-synthesis, sequencing-by-binding, and nanopore sequencing.

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Experience Sampling as a dietary assessment method: a scoping review towards implementation

Joke Verbeke 1 &
Christophe Matthys 1 , 2

International Journal of Behavioral Nutrition and Physical Activity volume 21 , Article number: 94 ( 2024 ) Cite this article

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Accurate and feasible assessment of dietary intake remains challenging for research and healthcare. Experience Sampling Methodology (ESM) is a real-time real-life data capturing method with low burden and good feasibility not yet fully explored as alternative dietary assessment method.

This scoping review is the first to explore the implementation of ESM as an alternative to traditional dietary assessment methods by mapping the methodological considerations to apply ESM and formulating recommendations to develop an Experience Sampling-based Dietary Assessment Method (ESDAM). The scoping review methodology framework was followed by searching PubMed (including OVID) and Web of Science from 2012 until 2024.

Screening of 646 articles resulted in 39 included articles describing 24 studies. ESM was mostly applied for qualitative dietary assessment (i.e. type of consumed foods) ( n = 12), next to semi-quantitative dietary assessment (i.e. frequency of consumption, no portion size) ( n = 7), and quantitative dietary assessment (i.e. type and portion size of consumed foods) ( n = 5). Most studies used ESM to assess the intake of selected foods. Two studies applied ESM as an alternative to traditional dietary assessment methods assessing total dietary intake quantitatively (i.e. all food groups). ESM duration ranged from 4 to 30 days and most studies applied ESM for 7 days ( n = 15). Sampling schedules were mostly semi-random ( n = 12) or fixed ( n = 9) with prompts starting at 8–10 AM and ending at 8–12 PM. ESM questionnaires were adapted from existing questionnaires, based on food consumption data or focus group discussions, and respond options were mostly presented as multiple-choice. Recall period to report dietary intake in ESM prompts varied from 15 min to 3.5 h.

Conclusions

Most studies used ESM for 7 days with fixed or semi-random sampling during waking hours and 2-h recall periods. An ESDAM can be developed starting from a food record approach (actual intake) or a validated food frequency questionnaire (long-term or habitual intake). Actual dietary intake can be measured by ESM through short intensive fixed sampling schedules while habitual dietary intake measurement by ESM allows for longer less frequent semi-random sampling schedules. ESM sampling protocols should be developed carefully to optimize feasibility and accuracy of dietary data.

Research on health and nutrition relies on accurate assessment of dietary intake [ 1 ]. However, dietary intake is a complex exposure variable with high inter- and intra-variability existing of different components ranging from micronutrients, macronutrients, food groups, meals to the dietary pattern as a whole. Therefore, measuring dietary intake accurately and feasibly is challenging for both researchers and healthcare professionals [ 2 , 3 , 4 ]. Only few established nutritional biomarkers are available and, therefore, no objective method exist to reflect true dietary intake or the dietary pattern as a whole in epidemiological research [ 2 , 3 ]. Instead, most dietary assessment methods rely on self-report. Food records, referred to as the “golden standard”, together with 24-h dietary recalls provide most detailed dietary data while Food Frequency Questionnaires (FFQ) reflects habitual (i.e. long-term usual intake) dietary intake which is the variable of interest in most diet-disease research [ 4 , 5 , 6 ]. Food records, 24-h dietary recalls, and FFQs have known limitations and challenges including recall bias, social-desirability bias, misreporting, and burdensomeness contributing to inherent measurement error in dietary intake data [ 2 , 6 ]. A review of Kirkpatrick et al . showed that feasibility, including cost-effectiveness and ease-of-use, is the main determinant for researchers in selecting a dietary assessment method instead of appropriateness for study design and purpose at the expense of data quality and accuracy [ 7 ]. To advance nutritional research and enhance the quality of dietary data, exploring the implementation of new methodologies is warranted to improve feasibility and overcome the limitations of current dietary assessment methods.

Experience Sampling Methodology (ESM), an umbrella term including Ecological Momentary Assessment (EMA), ambulatory assessment, and structured diary method, refers to intensive longitudinal assessment and real-time data-capturing methods [ 8 ]. Participants are asked to respond to short questions sent through smartphone prompt messages or beeps at random moments during the day to assess experiences or behaviors and moment-to-moment changes in daily life [ 9 ]. Originating from the field of psychology and behavioral sciences, ESM typically assesses current mood, cognitions, perceptions, or behaviors and descriptors of the momentary context (i.e. location, company) [ 9 ]. Usually, assessments are collected in a random time sampling protocol yet, assessments can also be triggered by an event (event-contingent sampling), at fixed time points, or random within fixed time intervals (semi-random). ESM questionnaires are usually designed to be completed in under 2 min consisting of open-ended questions, visual analogue scales, checklists, or self-report Likert scales. Several ESM survey applications (i.e. m-Path, PsyMate, PocketQ) are currently available in which the sampling protocol and questionnaires can be customized to the study design and aim [ 10 , 11 ]. It was shown that ESM reduces recall bias, reactivity bias, and misreporting in psychology and behavioral research by its design through unannounced, rapid, real-life, real-time repeated assessments [ 12 ]. For this reason, Experience Sampling might be an interesting new methodology to explore as an alternative dietary assessment methodology. The design of ESM could overcome recall bias, reactivity bias, social desirability bias, and misreporting seen in traditional dietary assessment methods. However, the application of ESM for dietary assessment is new. Defining and balancing ESM methodological considerations, i.e. study duration, frequency and timing of sampling (signaling technique), formulation of questions and answer options, is a delicate matter and crucial in balancing feasibility with data accuracy [ 13 ].

The application of ESM in the field of dietary assessment has not been fully explored yet. Schembre et al . reviewed ESM for dietary behavior for the first time [ 12 ]. However, it has not yet been assessed how ESM could be implemented as an alternative dietary assessment method aiming to estimate daily energy, nutrient, and food group intake quantitatively.

Therefore, this scoping review investigates how Experience Sampling Methodology can be implemented to develop an Experience Sampling-based dietary assessment method as an alternative to traditional dietary assessment methods to measure daily energy, nutrient, and food group intake quantitatively. This review aims to map ESM sampling protocols and questionnaire designs used to assess dietary intake. Additionally, the findings of this review will be combined with best practices to develop ESMs and dietary assessment methods to formulate key recommendations for the development of an Experience Sampling-based Dietary Assessment Method (ESDAM). The following questions will be answered:

How is ESM applied in literature to assess dietary intake - focusing on methodological considerations (i.e. development and formulation of questions and answers, selection and consideration of prompting schedule (timing and frequency))?

How can ESM specifically be applied for quantitative assessment of total dietary intake (i.e. as an alternative to traditional dietary assessment method)?

This scoping review followed the methodological framework for scoping reviews of Arksey and O’Malley which was further developed by Levac et al. [ 14 , 15 ]. A scoping review approach was chosen to explore and map the design aspects and considerations for developing experience sampling methods to assess dietary intake as an alternative to traditional dietary assessment methods, which is novel. Moreover, this review will formulate design recommendations to apply ESM as a dietary assessment method and will serve as starting point to develop an ESDAM. An a priori protocol was developed based on the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) and the Joanna Briggs Institute Scoping Review protocol template (Supplementary Material) [ 16 , 17 ]. According to Arksey and O’Malley methodological framework, the iterative nature of scoping reviews may include further refinement of the search strategy and the inclusion and exclusion criteria during the initial review process due to the unknown breadth of the topic [ 14 ]. Therefore, adaptations made to the methodology described in the a priori protocol based on initial searches are described below. This scoping review was reported according to the PRISMA extension for scoping reviews (PRISMA-ScR) [ 18 ].

Search strategy and screening

The search strategy was developed based on key words and Mesh terms for “dietary assessment” and “experience sampling” (Supplementary Material). The term “ecological momentary assessment” was included as a synonym of ESM. Electronic databases PubMed (including MEDLINE) and Web of Science were searched for relevant literature published between January 2012 and February 9th 2024. The year 2012 was chosen as lower limit for inclusion since this review focuses on the use of ESM by digital tools (i.e. smartphones, web-based or mobile applications) which has emerged especially since the introduction of applications for smartphones since 2008. Therefore, the time frame of this review is focused on literature published in the last 12 years. The reference lists of all included articles were screened for additional studies.

The initial search strategy described in the protocol was developed based on the assumption that research using ESM as an alternative to traditional dietary assessment was limited. Therefore, initially, research using ESM in the broader field of health research was included to obtain more evidence on methodological considerations of application of ESM. In line with the Arksey and O’Malley methodological framework, inclusion criteria were adapted following initial searches along with discussion and consensus between the reviewer (JV) and principal investigator (CM). Therefore, inclusion criteria were adapted to research applying ESM to measure dietary intake quantitatively or qualitatively since literature was also available in the field of dietary behaviour in relation to contextual factors (Table 1 ). Studies measuring dietary behaviour (i.e. cravings, hunger, eating disorder behaviour, dietary lapses) only, without assessing dietary intake, were excluded. Event-based ESM as dietary assessment method was excluded since this was deemed a similar methodology as the food record and, therefore, not serving the purpose of this review to explore a new methodology for dietary assessment to overcome limitations of traditional dietary assessment methods. All inclusion and exclusion criteria are presented in Table 1 .

All records were exported and uploaded into the review software Rayyan. Duplicates were identified through the software followed by a manual screening of the reviewer for confirmation and removal of duplicates. One reviewer (JV) screened the retrieved articles first by title and abstract followed by a full text screening [ 19 , 20 , 21 ]. In case of hesitancy on inclusion of articles, the reviewer (JV) consulted the principal investigator (CM) to reach consensus. In line with established scoping review methods, methodological quality assessment was not performed [ 14 , 18 ]. Since this review aims to shed light on design aspects and considerations of ESM and, thus focuses on the application of the methodology used in the articles rather than the study outcome, quality assessment was considered not relevant for this purpose.

Data extraction

Data were extracted in an Excel table describing the authors, title, year of publication, signalling technique, timing of prompts, study duration, dietary variables measured, answer window, (formulation of) questions, respond options, notification method, indication of qualitative or quantitative dietary assessment, delivery method, population and study name. All data were described qualitatively. Studies applying ESM for dietary assessment were categorized in separate tables for ESM used for qualitative dietary assessment (i.e. assessment of type of foods consumed without portion size, not allowing estimation of nutrient intake), ESM used for semi-quantitative dietary assessment (i.e. assessment of type of foods or frequency of consumption of foods, not allowing estimation of nutrient intake), and ESM used for quantitative dietary assessment (i.e. assessment of type of foods consumed and portion size, allowing estimation of nutrient intake).

Literature search and study characteristics

The electronic databases search resulted in 701 articles of which 55 duplicates were identified and removed. Next, 646 articles were screened by title and abstract of which 591 were excluded according to the exclusion criteria (Fig. 1 ). The remaining 55 articles were screened by full text. After exclusion of 16 articles following full text screening, 39 articles were selected for inclusion (Table 2 ). The included articles describe 24 individual studies of which the Mother’s and Their Children’s Health (MATCH) study was described most frequently ( n = 12, 25%). Most studies were published in 2018 ( n = 7), followed by 2020 ( n = 6) and 2022 ( n = 6). Students, including both high school and higher education students, were the study population in most EMA or ESM studies included ( n = 10, 43%). Two studies applied the ESM methodology to assess dietary behaviour including dietary variables of children with mothers as proxy. Five studies referred to their methodology using the terminology ‘ESM’ while the other studies used ‘EMA’ as terminology.

PRISMA flow diagram of the screening and selection process

Application of ESM for dietary assessment in literature

Dietary variables measured through esm.

Most studies assessed consumption of specific foods only [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 42 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. Table 2 , 3 and 4 provide an overview of the included studies described in the manuscripts with description of specific ESM methodology characteristics according to qualitative, semi-quantitative and quantitative dietary assessment respectively. Four studies used ESM to assess snack consumption [ 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Four studies focused on snack and sugar sweetened beverage (SBB) consumption only [ 22 , 36 , 44 , 52 , 53 ]. Piontak et al . applied ESM to assess unhealthy food consumption including fast food, caffeinated drinks and not consuming any fruit or vegetables [ 35 ]. Two studies focused on palatable food consumption of which the study of Cummings et al . assessed palatable food consumption together with highly processed food intake [ 37 , 54 ]. Lin et al . applied ESM to measure empty calorie food and beverage consumption while Boronat et al . assessed Mediterranean diet food consumption [ 39 , 55 ]. Two studies assessed the occurrence of food consumption only without assessing type of foods consumed [ 40 , 41 ]. The study of de Rivaz et al . assessed the largest type of meal consumed in between signals [ 56 ]. Three studies aimed to assess total dietary intake of which the study of Lucassen et al . evaluated approaches to assess both actual and habitual dietary intake using ESM [ 43 , 57 , 58 , 59 ].

Qualitative versus quantitative dietary assessment through ESM

As shown in Table 2 , twelve studies performed qualitative dietary assessment (i.e. assessing type of foods consumed without quantification) (Table 2 ). Seven studies performed semi-quantitative dietary assessment (i.e. assessing frequency of meals/eating occasions or number of servings of food categories not allowing nutrient calculation) [ 44 , 49 , 50 , 52 , 53 , 54 , 55 , 56 ] (Table 3 ). Quantitative dietary assessment, in line with the aim of traditional dietary assessment methods (i.e. assessment of both type and quantity of foods consumed allowing to estimate nutrient intake), was performed in four studies of which Wouters et al . and Richard et al . assessed snack intake only while Jeffers et al . and Lucassen et al . assessed overall dietary intake (i.e. all food groups) [ 45 , 46 , 47 , 48 , 51 , 57 , 58 ] (Table 4 ).

Study duration, ESM timing and signaling technique

Study duration of ESM dietary assessment varied from four to thirty days of which most studies ( n = 15) had a study duration of seven days of ESM dietary assessment. The study of Piontak et al . had the longest duration of 30 days of ESM assessment [ 35 ]. The semi-random sampling scheme (i.e. random sampling within multiple fixed time-intervals) was applied most frequently ( n = 12), followed by the fixed sampling scheme (i.e. sampling at fixed times) ( n = 9). Random sampling (i.e. completely random sampling) was chosen in three studies [ 34 , 36 , 55 ]. A mixed sampling approach was applied in three studies of which Lucassen et al . tested and compared both a fixed sampling and a semi-random sampling approach to assess overall dietary intake [ 22 , 42 , 57 , 59 ]. Two studies applied different sampling schemes during the weekend compared to weekdays [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Sampling time windows were adapted to the daily structure of the study population, i.e. shifts of shift-workers, school hours of students or (self-reported) waking hours (Table 2 ). The sampling time window of the included studies started between 6 and 10 AM and ended between 8 PM and midnight. One study applied a 24-h sampling time window since the study population were nurses working in shifts [ 39 ].

Formulation of ESM questions

Different types of questions and phrasing of questions can be identified in the studies using ESM for dietary assessment. Two studies use indirect phrasing (i.e. ‘What were you doing?’) followed by multiple-choice answer options including i.e. physical activity, eating, rest [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Seven studies use direct phrasing (i.e. ‘Did you eat?’) which is applied both as real-time prompts (i.e. ‘Were you eating or drinking anything – in this moment?’) and as retrospective prompts (i.e. ‘Did you eat anything since the last signal?’) without specifying specific food consumption [ 22 , 38 , 40 , 41 , 45 , 46 , 47 , 48 , 56 , 58 ]. Thirteen studies use direct and specific phrasing regarding consumption of specified foods (i.e. ‘Did you eat any snacks or sugar sweetened beverages since the last signal?’) [ 35 , 36 , 37 , 39 , 43 , 44 , 50 , 51 , 52 , 53 , 54 , 55 , 57 ]. The time period in retrospective prompts with direct phrasing varied. Ten studies assessed consumption since last signal, three studies during the past 2 h and one study during respectively the preceding 15 min, 1 h, 2.5 h, 3 h and 3.5 h [ 41 , 42 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 56 ]. The MATCH study used two different retrospective time periods of which the first prompt of the day requested to report since waking up and the following prompts during the last 2 h [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Forman et al . used prompts which requested to report snack intake between the last prompt of the previous day and falling asleep and between waking up and receiving the first prompt [ 49 ]. The study of Bruening et al . combined both real-time prompts, to report what participants were doing the moment before receiving the prompt, and retrospective prompts to report what they were doing the past 3 h [ 34 ].

Formulation of ESM response options

Binary (i.e. yes or no) response options are provided in eleven studies followed by open field, a built in search function or multiple-choice bullets to specify type of food or drinks consumed in five studies [ 22 , 35 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 47 , 48 , 52 , 53 , 56 , 58 ]. Food lists shown as response option to indicate food consumption were based on National Health Surveys, validated Food Frequency Questionnaires, other validated questionnaires, the National Food Composition Database or results from focus group discussions. Eight studies requested to indicate quantities of the foods consumed by open field (i.e. in grams or milliliters), Visual Analogical Scale (VAS) sliders (i.e. from zero to 100) or multiple-choice options (i.e. small, medium, large) [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 54 , 56 , 57 ].

This review reveals that ESM has been applied to assess dietary intake in various research settings using different design approaches. However, most studies assessed consumption of specific foods only focusing on the foods of interest related to the research question. Especially snack consumption and, in general, unhealthy foods were the foods of interest for which ESM was used most often to measure its consumption. Due to its momentary nature, ESM may be especially suitable to measure these specific foods which are often (unconsciously) missed or underreported using traditional dietary assessment methods. Findings from our review show that ESM applied to assess dietary intake shows both features of 24-h dietary recalls (24HRs) and food frequency questionnaires (FFQ). Aside from the recall-based reporting and multiple choice assessment of specific foods, found in 24HRs and FFQs respectively, the ESM is a new methodology compared to traditional dietary assessment methods. ESM shows to lends itself well to assess the total dietary intake quantitatively as well albeit less explored yet according to our review. Moreover, most studies using ESM for dietary assessment were behavioral science research (i.e. psychological aspects of eating behavior) which highlights the novelty and need of ESM specifically designed for dietary assessment and research on diet-health associations.

Recommendations to develop an Experience Sampling-based Dietary Assessment Method

The implementation of ESM will differ depending which health behavior is being measured and in which research field it is being applied [ 13 , 60 ]. This section describes recommendations of the methodological implementation of ESM as an alternative dietary assessment methodology to measure total dietary intake quantitatively based on the findings of this review, recommendations of the open handbook for ESM by Myin-Germeys et al. and practices in traditional dietary assessment development [ 13 ].

Recommendations for study duration, ESM timing and frequency

All ESM study characteristics (study duration, sampling frequency, timing, recall period) are interrelated and cannot be evaluated individually.

ESM study duration (i.e. number of days) and sampling frequency (i.e. number of prompts per day) should be reconciled and should be inversely adapted to one another (i.e. short study duration allows for higher sampling frequency per day and vice versa) to maintain low burden and good feasibility.

Our review showed an ESM study duration of 7 days is most common however reporting fatigue might arise from day 4 onwards in case of high sampling frequency (i.e. fixed sampling every 2 h) similarly as experienced with food records [ 61 ].

Frequency and timing of ESM prompts should be adapted to waking hours covering the typical eating episodes of the target study population. Typically, studies used waking hours starting around 7 AM till 10 PM however a preliminary short survey can identify feasible and accurate waking hours of the target study population and allow to adapt accordingly.

Waking hours, and consequently sampling frequency, could be different on weekend days (i.e. more frequent, longer waking hours) as seen in some studies in our review. Short recall periods (i.e. last hours or previous day) are suggested to be better than longer recalls of weeks or months [ 62 ]. Aiming to obtain more accurate dietary intake data, lower recall bias and social desirability bias by reducing the awareness of being measured requests short recall periods of 1 up to 3.5 h, with a 2-h recall most commonly applied, as demonstrated by our review. In this way, ESM allow for near real-time measurements of dietary intake.

Furthermore, study duration, sampling frequency and timing should be adapted and differs when aiming to measure actual dietary intake or habitual dietary intake.

Recommendations ESM signaling technique for actual versus habitual dietary intake

Measuring actual dietary intake using an intensive prompting schedule can only be performed for short periods, preferably three to four days, due to the risk of responding fatigue as seen similarly in food records. As demonstrated by Lucassen et al. actual intake can be measured by ESM applying a fixed sampling approach which samples every time-window during the waking hours (i.e. sampling every 2 h between 7 AM and 10 PM on dietary intake during past 2 h) [ 58 ].

Habitual dietary intake can be measured by ESM applying a semi-random sampling approach which samples every time window during waking hours multiple times during a longer period (i.e. sampling three time-windows per day on dietary intake during past 2 h for two weeks until every time window is sampled three times) [ 58 ]. Measuring habitual dietary intake by ESM using a less intensive sampling frequency allows for a longer study duration (i.e. multiple weeks). Lastly, a combination of fixed and (semi-)random sampling schedules can be applied. Both in case of measuring actual and habitual dietary intake, it is recommended to compose a sampling schedule with time windows covering all waking hours to ensure all eating occasions could be sampled [ 12 ]. Additionally, the sampling schedule should cover weekend days next to week days to be able to sample the variability in dietary intake. More so, to capture variability of dietary intake several waves of ESM measurement periods could be implemented alternated with no-measurement periods. On the other hand, the application of multiple waves is associated with higher dropout rates especially with increased time in-between waves [ 13 ].

In conclusion, ESM signaling technique, frequency, timing, recall period and duration of sampling should be carefully adapted to one another to ensure accurate dietary intake data, low burden and optimal feasibility. As recommended by Myin-Germeys et al., a pilot study allows to evaluate all ESM design characteristics to obtain optimal data quality yet remain feasible [ 13 ].

Recommendations for ESM questions and response options

Questionnaires for ESM should be carefully developed and request methodological rigor [ 63 ]. As stated by Myin-Germeys et al., there are currently no specific guidelines on how to develop questionnaires for ESM [ 63 ]. However, according to our review most studies adapt existing questionnaires to implement in ESM research. Still, few studies in our review describe methodologically which or how adaptations are made to fit in the ESM format. First, a timeframe should be chosen on which the question will reflect. Although ESM is ideally consisting of questions on momentary variables, this is less suitable to measure dietary intake. As dietary intake does not continuously take place, momentary questions (i.e. What are you eating in this moment?) would lead to a large amount of missing data and, consequently, large measurement error on daily dietary intake estimations. Instead, time intervals lend itself better to assess dietary intake with ESM. The time interval on which the question reflects should be clearly stated (i.e. What did you eat during the last two hours?). As mentioned previously, in case of an interval contingent (semi-random) ESM approach, constitution of contiguous time intervals that cover the complete waking hour time frame (i.e. waking hours between 7 AM and 10 PM with semi-random ESM sampling by intervals of every two hours) is recommended to reduce risk of missing eating occasions [ 12 ]. Therefore, following the latter approach, it is most feasible to choose the same time frame on which the question reflects as the time intervals of the prompts (i.e. semi random sampling in time intervals of two hours with question ‘What did you eat since the last signal?’). The time frame on which the question reflects should be chosen based on expected events of dietary intake (i.e. every two or three hours) and depends on dietary habits of the target population which is culture specific. Myin-Germeys et al. recommend to keep questions short and to the point so it fits the screen of the mobile device and allows for quick response [ 63 ]. Furthermore, implicit assessments (i.e. Have you eaten since the last signal?) are recommended over explicit assessments (i.e. Did you eat fast food since the last signal?) to inhibit reactivity bias. Questionnaire length is important to consider as it is recommended to maintain a completion time of maximum three minutes to keep the burden low [ 63 ]. Although in traditional ESM research questionnaires up to 30 items are accepted, in the field of dietary assessment, this would equivalent a short FFQ and can be considered too burdensome when presented all at once at every prompt reducing compliance. Moreover, ESM research in the field of psychology, where it originated from, uses most often scales (i.e. Likert scale, visual scales) as respond options. Unlike many psychological variables (i.e. mood, emotions), dietary intake can be assessed quantitatively and precise which allows for more specific response options.

Recommendations to develop ESM sampling scheme based on FFQ or food record

Questions and respond options for ESM dietary assessment could be adapted from existing questionnaires as demonstrated in the studies of our review. In the field of dietary assessment, ESM could therefore be applied to validated dietary assessment questionnaires such as validated Food Frequency Questionnaires (FFQ’s) or (web-based) food records as proposed in Fig. 2 .

Recommendations to implement experience sampling for actual and habitual dietary assessment

Starting from the food record approach, a general open question (i.e. Did you eat anything since the last signal?) could be followed by a question to specify the consumed foods by an open field text box or food groups part originating from a National Food Consumption Database. Portion sizes of consumed foods could be provided by an open field text box with standard units (i.e. milliliters, grams) or common household measures (i.e. table spoons, glasses).

Starting from the FFQ approach, food groups assessed in FFQ’s could be regrouped to a limited number and questions reformulated to assess dietary intake in near real time to design ESM questionnaires. Consumption of all food groups could be assessed at each prompt or consumption of a different set of food groups could be assessed at each prompt. In the latter case, the study needs to be designed so that consumption of each food group is assessed at each interval multiple times to account for unanswered prompts with missing data. Moreover, ordering of questions on consumption of food groups need to be considered as the consumption of specific food groups might need to be assessed at the same prompt to reduce ambiguity (i.e. fried food consumption needs to be assessed before consumption of fast food to avoid response overlap). Asking the same set of questions at each prompt may feel repetitive but might reduce burden [ 63 ]. A control question can be added to assess careless responding.

Application of ESM as alternative dietary assessment method in literature

Most studies used ESM to measure food consumption qualitatively (i.e. type of foods consumed) or semi-quantitatively (i.e. frequency of consumption of specific foods) as opposed to quantitatively (i.e. type and quantity of foods consumed) to serve the same purpose as traditional dietary assessment methods. Questions were most often formulated using direct phrasing and asking about consumption of specific foods since the last signal. Answers were most often binary (i.e. yes/no indicating consumption of specific foods since last signal) combined with options to specify type and/or frequency or amount of foods consumed. Only the studies of Jeffers et al . and Lucassen et al . apply ESM to measure total dietary intake quantitatively of which Lucassen et al . evaluated ESM specifically as an alternative methodology for dietary assessment [ 57 , 58 ].

Although both event-contingent and signal-contingent approaches are being used for dietary assessment, signal-contingent ESM approaches might provide auspicious opportunities to overcome the limitations and biases of traditional dietary assessment methods [ 12 ]. The near-real time data collection combined with (semi-)random sampling shows potential to reduce the burden for the participant both by its low intensity of registering and by its shorter questions with easy respond options. Moreover, the (semi-)random sampling technique might make the participant less aware of being measured resulting in possibly lower social-desirability bias leading, together with the short recall period, to more accurate data. In combination with modern technology such as mobile applications feasibility could be enhanced as well. Adapting questions and response options from either a validated FFQ or food record allow for relatively easy implementation of ESM as alternative dietary assessment method for total dietary intake (i.e. all food groups). However, validity and reliability need to be evaluated in the target population, similarly as traditional dietary assessment methods.

The systematic review and meta-analysis of Perski et al . states to have reviewed the use of ESM to assess five key health behaviors including dietary behavior [ 60 ]. Similar to our findings, all four studies described by Perski et al . are assessing dietary intake through ESM of specific foods only instead of the total dietary pattern (i.e. all food groups). Moreover, Perski et al . included event-contingent sampling (i.e. registering dietary intake as it occurs) approaches as well. As highlighted by Schembre et al . event-contingent sampling entails similar limitations and biases such as social desirability bias and burden as the traditional dietary assessment methods [ 27 ]. Not surprising, as event-contingent sampling can be seen as a similar approach as the traditional food record and serves for this reason not the purpose of this review to define a new methodology to overcome the limitations of current traditional dietary assessment methods. Similarly, photo-based methodologies (i.e. using images as food diary by event-based sampling) are unlikely to overcome the limitations of traditional dietary assessment methods due to the large measurement error in estimation of portion sizes and types of foods and were for this reason excluded in our review [ 3 ]. Most importantly, the four included reviews on dietary behavior in the meta-analysis of Perski et al . lacked specific details on ESM design characteristics or methodological implication of ESM as alternative dietary assessment method. Still, the potential of ESM to obtain more accurate and reliable dietary data is highlighted together with the need for proper validation.

Altogether, the lacking details on important methodological aspects of ESM hinders drawing conclusions on common practices for implementation of ESM for quantitative dietary assessment. Nevertheless, Perski et al . emphasize the need for more elaboration on the methodological aspects in order to provide a summary of best practices on implementation of ESM for specific health behaviors including dietary behavior [ 60 ]. Our scoping review meets this need with key methodological recommendations for developing an experience sampling dietary assessment method for total dietary intake next to elaboration on commonly applied ESM design characteristics.

Limitations and strengths

An important limitation of this scoping review is, inherent to scoping reviews, the less rigor search strategy and screening process. This will have resulted in an incomplete overview of studies describing ESM for dietary assessment. Still, this review has not the aim to assess outcomes of studies but rather evaluate how ESM can be applied for dietary assessment methodologically. Therefore, its strength lies in the assessment and description of ESM approaches specifically to provide insight in its use for quantitative dietary assessment as an alternative method for the traditional dietary assessment methods. To our knowledge, this has only been performed by Schembre et al. previously [ 12 ]. However, our scoping review is, to our knowledge, the first to describe practical recommendations for developing an ESM for total dietary assessment (i.e. all food groups). Additionally, only two studies were identified to have applied ESM for total dietary assessment. Consequently, limited evidence-based information was available in literature on the development of ESM characteristics (prompting schedule, duration, questionnaire design) for quantitative dietary assessment of total dietary intake. Nevertheless, studies on qualitative and semi-quantitative dietary assessment using ESM were described and form, together with the guidelines of Myin-Germeys et al., the base of practical guidelines of designing an ESM protocol for quantitative dietary assessment of total dietary intake. To our knowledge, this review is the first to discuss recommendations on the implementation of ESM for quantitative dietary assessment as an alternative for traditional dietary assessment methods.

This review shows that ESM is increasingly being applied in research to measure dietary intake. However, few studies applied ESM to assess total dietary intake quantitatively with the same purpose of traditional dietary assessment methods. Still, the methodological characteristics of ESM show auspicious possibilities to overcome limitations of the classic dietary assessment methods. This paper provides guidance and is the starting point for the development of an Experience Sampling Dietary Assessment Method to assess total dietary intake quantitatively based on recent literature and theoretical background. Thorough evaluation and validation studies are needed to test the full potential of ESM as a feasible and accurate alternative for traditional dietary assessment methods.

Availability of data and materials

The data that support the findings of this manuscript are available from the corresponding author upon reasonable request. The review protocol can be downloaded at: KU Leuven repository.

Abbreviations

Ecological Momentary Assessment

Experience Sampling-based Dietary Assessment Method

Experience Sampling Method

Food Frequency Questionnaire

Mother’s and Their Children’s Health

Preferred Reporting Items for Systematic review and Meta-Analysis Protocols

Preferred Reporting Items for Systematic review and Meta-Analysis extension for scoping reviews

Sugar Sweetened Beverages

Visual Analog Scale

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This work was supported by a PhD fellowship Strategic Basic research grant (1S96721N) of Research Foundation Flanders (FWO) and KU Leuven Internal Funds (C3/22/50). The funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being

Adrienne Yang 1 na1 ,
Simone Langness 2 na1 ,
Lara Chehab 1 na1 ,
Nikhil Rajapuram 3 ,
Li Zhang 4 &
Amanda Sammann 1

BMC Medical Education volume 24 , Article number: 947 ( 2024 ) Cite this article

Metrics details

Nearly three in four U.S. medical students graduate with debt in six-figure dollar amounts which impairs students emotionally and academically and impacts their career choices and lives long after graduation. Schools have yet to develop systems-level solutions to address the impact of debt on students’ well-being. The objectives of this study were to identify students at highest risk for debt-related stress, define the impact on medical students’ well-being, and to identify opportunities for intervention.

This was a mixed methods, cross-sectional study that used quantitative survey analysis and human-centered design (HCD). We performed a secondary analysis on a national multi-institutional survey on medical student wellbeing, including univariate and multivariate logistic regression, a comparison of logistic regression models with interaction terms, and analysis of free text responses. We also conducted semi-structured interviews with a sample of medical student respondents and non-student stakeholders to develop insights and design opportunities.

Independent risk factors for high debt-related stress included pre-clinical year (OR 1.75), underrepresented minority (OR 1.40), debt $20–100 K (OR 4.85), debt >$100K (OR 13.22), private school (OR 1.45), West Coast region (OR 1.57), and consideration of a leave of absence for wellbeing (OR 1.48). Mental health resource utilization ( p = 0.968) and counselors ( p = 0.640) were not protective factors against debt-related stress. HCD analysis produced 6 key insights providing additional context to the quantitative findings, and associated opportunities for intervention.

Conclusions

We used an innovative combination of quantitative survey analysis and in-depth HCD exploration to develop a multi-dimensional understanding of debt-related stress among medical students. This approach allowed us to identify significant risk factors impacting medical students experiencing debt-related stress, while providing context through stakeholder voices to identify opportunities for system-level solutions.

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Introduction

Over the past few decades, it has become increasingly costly for aspiring physicians to attend medical school and pursue a career in medicine. Most recent data shows that 73% of medical students graduate with debt often amounting to six-Fig [ 1 ]. – an amount that is steadily increasing every year [ 2 ]. In 2020, the median cost of a four-year medical education in the United States (U.S.) was $250,222 for public and $330,180 for private school students [ 1 ] – a price that excludes collateral costs such as living, food, and lifestyle expenses. To meet these varied costs, students typically rely on financial support from their families, personal means, scholarships, or loans. Students are thereby graduating with more debt than ever before and staying indebted for longer, taking 10 to 20 years to repay their student loans regardless of specialty choice or residency length [ 1 ].

Unsurprisingly, higher debt burden has been negatively correlated with generalized severe distress among medical students [ 3 , 4 ], in turn jeopardizing their academic performance and potentially impacting their career choices [ 5 ]. Studies have found that medical students with higher debt relative to their peers were more likely to choose a specialty with a higher average annual income [ 5 ], less likely to plan to practice in underserved locations, and less likely to choose primary care specialties [ 4 ]. However, a survey of 2019 graduating medical students from 142 medical schools found that, when asked to rank factors that influenced their specialty choice, students ranked economic factors, including debt and income, at the bottom of the list. With this inconsistency in the literature, authors Youngclaus and Fresne declare that further studies and analysis are required to better understand this important relationship [ 1 ].

Unfortunately, debt and its negative effects disproportionately impact underrepresented minority (URM) students, including African Americans, Hispanic Americans, American Indian, Native Hawaiian, and Alaska Native [ 6 ], who generally have more debt than students who are White or Asian American [ 1 ]. In 2019, among medical school graduates who identified as Black, 91% reported having education debt, in comparison to the 73% reported by all graduates [ 1 ]. Additionally, Black medical school graduates experience a higher median education debt amount relative to other groups of students, with a median debt of $230,000 [ 1 ]. This inequitable distribution of debt disproportionately places financial-related stress on URM students [ 7 ], discouraging students from pursuing a medical education [ 8 ]. These deterring factors can lead to a physician workforce that lacks diversity and compromises health equity outcomes [ 9 ].

Limited literature exists to identify the impact of moderating variables on the relationship between debt and debt-related stress. Financial knowledge is found to be a strong predictor of self-efficacy and confidence in students’ financial management, leading to financial optimism and potentially alleviating debt stress [ 10 , 11 , 12 ]. Numerous studies list mindfulness practices, exercise, and connecting with loved ones as activities that promote well-being and reduce generalized stress among students [ 13 , 14 , 15 ]. However, to date, no studies have examined whether these types of stress-reducing activities, by alleviating generalized stress, reduce debt-related stress. Studies have not examined whether resources such as physician role models may act as a protective factor against debt-related stress.

Despite the growing recognition that debt burdens medical students emotionally and academically, we have yet to develop systemic solutions that target students’ unmet needs in this space. We performed the first multi-institutional national study on generalized stress among medical students, and found that debt burden was one of several risk factors for generalized stress among medical students [ 3 ]. The goal of this study is to build upon our findings by using a mixed methods approach combining rigorous survey analysis and human-centered design to develop an in-depth understanding of the impact that education debt has on medical students’ emotional and academic well-being and to identify opportunities for intervention.

We conducted a mixed methods, cross-sectional study that explored the impact of debt-related stress on US medical students’ well-being and professional development. This study was conducted at the University of California, San Francisco (UCSF). All activities were approved by the UCSF institutional review board, and informed consent was obtained verbally from participants prior to interviews. We performed a secondary analysis of the quantitative and qualitative results of the Medical Student Wellbeing Survey (MSWS), a national multi-institutional survey on medical student wellbeing administered between 2019 and 2020, to determine risk factors and moderating variables of debt-related stress. To further explore these variables, we used human-centered design (HCD), an approach to problem-solving that places users at the center of the research process in order to determine key pain points and unmet needs, and co-design solutions tailored to their unique context [ 16 ]. In this study, we performed in-depth, semi-structured interviews with a purposefully sampled cohort of medical students and a convenience sample of non-student stakeholders to determine key insights representing students’ unmet needs, and identified opportunities to ameliorate the impact of debt-related stress on medical students.

Quantitative data: the medical student wellbeing survey

The MSWS is a survey to assess medical student wellbeing that was administered from September 2019 to February 2020 to medical students actively enrolled in accredited US or Caribbean medical schools [ 3 ]. Respondents of the MSWS represent a national cohort of > 3,000 medical students from > 100 unique medical school programs. The MSWS utilizes a combination of validated survey questions, such as the Medical Student Wellbeing Index (MS-WBI), and questions based on foundations established from previously validated wellbeing survey methods [ 3 ]. Questions generally focused on student demographics, sources of stress during medical school, specialty consideration, and frequency in activities that promote wellbeing. Some questions ask students to rate physical, emotional, and social domains of wellbeing using a five-point Likert scale. Questions of interest from the MSWS included debt-related stress, generalized stress, intended specialty choice, and utilization of well-being resources and counselors. An additional variable investigated was average school tuition, which was determined by a review of publicly available data for each student’s listed medical school [ 17 ]. All data from the MSWS was de-identified for research purposes.

Stress: debt-related and generalized stress

Debt stress was assessed by the question, “How does financial debt affect your stress level?” Students responded using a five-point Likert scale from − 2 to 2: significant increase in stress (-2), mild increase (-1), no change (0), mild decrease (1), or significant decrease (2). Responses for this question were evaluated as a binary index of ‘high debt stress,’ defined as a response of − 2, versus ‘low debt stress,’ defined as a response of − 1 or 0. In addition, generalized stress from the MSWS was assessed by questions from the embedded MS-WBI, which produced a score. Previous studies have shown that the score can be used to create a binary index of distress: a score ≥ 4 has been associated with severe distress, and a score < 4 has been associated with no severe distress [ 18 ].

Intended specialty

We categorized students’ responses to intended specialty choice by competitiveness, using the 2018 National Resident Match Program data [ 19 ]. ‘High’ and ‘low’ competitiveness were defined as an average United States Medical Licensing Examination (USMLE) Step 1 score of > 240 or ≤ 230, respectively, or if > 18% or < 4% of applicants were unmatched, respectively. ‘Moderate’ competition was defined as any specialty not meeting criteria for either ‘high’ or ‘low’ competitiveness.

Resource utilization

The MSWS assessed the utilization of well-being resources by the question, “At your institution, which of the following well-being resources have you utilized? (Select all that apply)” Students responded by selecting each of the resource(s) they used: Mental Health and Counseling Services, Peer Mentorship, Self-Care Education, Mindfulness/Meditation Classes, Community Building Events, and Other. The number of choices that the student selected was calculated, allowing for placement into a category depending on the amount of resource utilization: 0–20%, 20–40%, 40–60%, 60–80%, 80–100%. Responses for this question were evaluated as a binary index of ‘high resource utilization,’ defined as a response of 80–100% resource utilization, versus ‘low resource utilization,’ defined as a response of < 80% resource utilization. The co-authors collaboratively decided upon this “top-box score approach,” [ 20 ] which is the sum of percentages for the most favorable top one, two or three highest categories on a scale, to assess if the most extreme users (80–100%) of these supportive resources experienced a decrease in debt-related stress. Additionally, use of a counselor for mental health support was assessed by the question, “Which of the following activities do you use to cope with difficult situations (or a difficult day on clinical rotation)? (Select all that apply).” Students responded by selecting the activities that they use from a list (e.g., listen to music, mindfulness practice, meet with a counselor, exercise). Responses for this question were evaluated as a binary index of ‘Meeting with a Counselor,’ defined by selection of that option, versus ‘Not Meeting with a Counselor,’ defined as not selecting that option.

Quantitative data analysis

We performed a secondary analysis of quantitative data from the MSWS to calculate frequencies and odds ratios for the five quantitative variables described above (debt-related stress, generalized stress, intended specialty, resource utilization, and school tuition). Tests performed are summarized in Table 1 (“Secondary Analysis Tests Performed”). Univariate analysis and multivariate logistic regression were performed among students in the high debt stress (-2) and low debt stress (0 or − 1) for select variables, such as clinical phase, URM, debt burden, specialty competitiveness, and average school tuition, to identify risk factors for high debt stress. To determine if ‘high resource utilization’ or ‘meeting with a counselor’ were moderating variables on the relationship between debt burden and debt stress, we applied the logistic regression with the interaction terms of ‘debt’ and ‘resource utilization’ (high vs. low). Then, we performed a similar analysis but replaced the interaction term with ‘debt’ and ‘meeting with a counselor’ (yes vs. no). We also performed Chi-squared tests to determine the degree to which severe distress increases as debt burden increases, if specialty competitiveness varied by debt stress, and if the proportion of students who identified as URM, in comparison to non-URM, differed by debt level. All statistical tests were two-sided and p < 0.05 was considered significant. Statistical analyses were performed using SAS version 9.4 and R version 4.0.5.

Qualitative data: interviews and MSWS free text responses

Free-text entries.

At the conclusion of the 2019–2020 MSWS, respondents had unlimited text space to provide comments to two prompts. The first prompt read, “What well-being resource(s), if offered at your school, do you feel would be most useful?” The second prompt read “If you have any further comments to share, please write them below.” Answers to either prompt that pertained to debt, cost of medical school, or finances were extracted for the purpose of this study and analyzed with the other qualitative data subsequently described.

Interview selection & purposive sampling

Interview participants were identified from a repository of respondents to the MSWS who had attached their email address and expressed willingness at the time of the survey to be contacted for an interview [ 3 ]. Our recruitment period was between April 19, 2021 to July 2, 2021. The recruitment process involved sending invitations to all of the email addresses in the list to participate in a 45-minute interview on the topic of student debt and wellbeing. The invitation included a brief screening questionnaire asking students to report updates to questions that were previously asked in the MSWS (i.e.: clinical training year, marital status, dependents). Additional novel questions included primary financial support system, estimate of financial support systems’ household income in the last year, estimate of educational financial debt at conclusion of medical school, student’s plan for paying off debt, and degree of stress (using a Likert scale from 0 to 10) over current and future education debt.

Purposeful sampling of medical student stakeholders for interviews allowed us to maximize heterogeneity. We utilized the students’ responses to the brief screening questionnaire with their corresponding responses to demographic questions from the MSWS to select interviewees that varied by gender, race, presence of severe distress, type of medical school (public vs. private), region of school, and tuition level of school. The sampling ensured a diverse representation, in accordance with HCD methodology [ 21 ]. Brief descriptions of participant experiences are listed in Table 2 (“Interviewee Descriptors”). Students who were selected for interviews were sent a confirmation email to participate. Interviews were to be conducted until thematic saturation was reached. In addition, to include representation from the entire ecosystem, we interviewed a financial aid counselor at a medical school and a pre-medical student, chosen through convenience sampling. We directly contacted those two individuals for interviews.

Semi-structured interviews

All interviews were conducted between April 2021 and July 2021 over Zoom. A single researcher conducted interviews over an average of 45 min. Informed consent was obtained verbally from participants prior to interviews; interviews and their recordings only proceeded following verbal consent. The interview guide (S1 File) included open-ended questions about students’ experience of debt-related stress and their reflections on its consequences. The audio recordings were transcribed using Otter.ai, a secure online transcription service that converts audio files to searchable text files. Interview responses were redacted to preserve anonymity of respondent identity.

Qualitative data analysis

Interview data was analyzed using a general inductive approach to thematic analysis. Specifically, two researchers (SL and AY) independently inductively analyzed transcripts from the first three semi-structured interviews to come up with themes relating to the experiences and consequences of debt-related stress. They reconciled discrepancies in themes through discussion to create the codebook (S2 File), which included 18 themes. SL and AY independently coded each subsequent interview transcript as well as the free text responses from the survey, meeting to reach a consensus on representative quotes for applicable themes.

Following the HCD methodology, two researchers met with the core team to discuss the themes from the interviews and translate them into “insight statements”, which reflect key tensions and challenges experienced by stakeholders. Insight statements carefully articulate stakeholders’ unique perspectives and motivations in a way that is actionable for solution development [ 22 ]. As such, these insight statements are reframed into design opportunities, which suggest that multiple solutions are possible [ 23 , 24 ]. For example, discussion about themes 1a and 1b (“Questionable Job Security” and “Disappointing MD salary and Satisfaction Payoff”) revealed that they were related in the way that they led students to wonder whether the investment in medical school would be offset by the salary payoff. This led to the identification of the tension for low-income students in particular, who have to weigh this tradeoff earlier in their medical school journey than other students who are less financially-constrained (insight: “Medical school is a risky investment for low-income students”.) The design opportunity logically translates into a call to action for brainstorming and solution development: “Support low-income students to make values-based tradeoffs when considering a career in medicine.”

MSWS respondents and quantitative analysis

A total of 3,162 students responded to the MSWS and their sociodemographic characteristics have been described previously [ 3 ]. A total of 2,771 respondents (87.6%) responded to our study’s variables of interest, including a response for ‘high debt stress’ (–2) or ‘low debt stress’ (–1 or 0). Table 3 lists the distribution of debt-related stress across different variables for all respondents.

Risk factors for debt-related stress

Factors that were independently associated with higher debt-related stress included being in pre-clinical year (OR 1.75, 95% CI 1.30–2.36, p < 0.001), identifying as URM (OR 1.40, 95% CI 1.03–1.88), p = 0.029), having debt $20–100 K (OR 4.85, 95% CI 3.32–7.30, p < 0.001), debt > 100 K (OR 13.22, 95% CI 9.05–19.90, p < 0.001), attending a private medical school (OR 1.45, 95% CI 1.06–1.98, p = 0.019), attending medical school on the West Coast (OR 1.57, 95% CI 1.17–2.13, p = 0.003), and having considered taking a leave of absence for wellbeing (OR 1.48, 95% CI 1.13–1.93, p = 0.004) (Table 4 , S1 Table).

Severe distress by debt amount

Levels of generalized severe distress differed across debt burden groups. As debt level increased, the percentage of individuals with “severe” distress increased ( p < 0.001).

Debt and career decisions

There were significant differences between the high debt stress versus low debt stress groups and plans to pursue highly vs. moderately vs. minimally competitive specialties ( p = 0.027) (Fig. 1 ) A greater percentage of low debt stress students were pursuing a highly competitive specialty or a minimally competitive specialty. A greater percentage of high debt stress students were pursuing a moderately competitive specialty. As shown in Table 4 , there were no differences in debt-associated stress between students who choose different specialties, such as medical versus surgical versus mixed (medical/surgical).

Debt stress by specialty competitiveness

URM students’ experience of debt

URM identity was an independent risk factor for higher debt-related stress (Table 4 ) In addition, debt levels varied between those who identify as URM versus non-URM ( p < 0.001). Students identifying as URM tended to have higher debt than those who did not. Although the percentage of non-URM students was higher than that of URM students within the lowest debt burden category (<$20k), among all higher debt burden categories, including $20–100 K, $100–300 K, and >$300K, the percentage of URM students was higher than the percentage of non-URM students.

Moderating factors on the relationship between debt and debt stress

Protective factors such as high degree of mental health resource utilization and meeting with a counselor did not reduce the impact of debt burden on debt stress. Among students who reported a high degree of mental health resource utilization, there was no impact on the relationship between debt and debt stress ( p = 0.968). Similarly, meeting with a counselor had no impact on the relationship between debt and debt stress ( p = 0.640).

Interview respondents and qualitative analysis

We conducted in-depth, semi-structured interviews with 11 medical students, who are briefly described in Table 2 . We reached thematic saturation with 11 interviews, a point at which we found recurring themes. Therefore, no further interviews were needed. Among the medical student interviewees, there was representation from all regions, including the Northeast ( n = 3), West Coast ( n = 5), Midwest ( n = 2), and South ( n = 1). Students were also from all clinical phases, including pre-clinical ( n = 3), clinical ( n = 4), gap year/other ( n = 2), and post-clinical ( n = 2). Most interviewees were female ( n = 8) and 5 of the interviewees identified as URM. Financial support systems were diverse, including self ( n = 3), spouse/partner ( n = 3), and parents/other ( n = 5). Most interviewees reported low debt stress ( n = 8), as opposed to high debt stress ( n = 3). 55% of interviewees planned to pursue specialties that pay <$300K ( n = 6), with some pursuing specialties that pay $300–400 K ( n = 2) and >$400K ( n = 3).

Among the MSWS free-text responses, to the prompt, “What well-being resource(s), if offered at your school, do you feel would be most useful?” 20 of 118 respondents (16.9%) provided free-text responses that pertained to debt, cost of medical school, or finances. To the prompt “If you have any further comments to share, please write them below” 11 of 342 students (3.2%) provided relevant free-text responses. Analysis of the free-text responses and semi-structured interviews revealed 6 distinct insights (Table 5 ), with each insight translated into an actionable design opportunity.

Medical school is a risky investment for low-income students.

Description

The personal and financial sacrifices required for low-income students to attend medical school and pursue a career in medicine outweigh the benefits of becoming a physician. When considering a career in medicine, students feel discouraged by questionable job security (theme 1a) and reduced financial compensation (theme 1b) – a combination that jeopardizes immediate and long-term job satisfaction. Some students feel hopeful that their decision to pursue medicine will be personally rewarding (1b.6) and their salaries will stabilize (1a.1, 1a.5), but many low-income students experience doubt about whether they made the right career choice (1b.2, 1b.4, 1b.6), and feel stressed that they will be in debt for longer than they expected (1a.3, 1a.4, 1b.1, 1b.5). Support low-income students to make values-based tradeoffs when considering a career in medicine.

Design opportunity

Support low-income students to make values-based tradeoffs when considering a career in medicine.

Medical schools lack the adaptive infrastructure to be welcoming to low-income students.

Students face financial challenges from the moment they apply to medical school (theme 2a), a costly process that limits admissions options for low-income students due to their inability to pay for numerous application fees (2a.1) and expensive test preparation courses (2a.2, 2a.3). Once students begin medical school, they feel unsupported in their varied responsibilities towards their families (theme 2b) and additional financial needs (theme 2c), requiring them to make tradeoffs with their education and personal lives (2b.2, 2c.1).

Design opportunity 2

Develop flexible systems that can recognize and accommodate students’ complex financial needs during medical school.

Students worry about the impact that their medical school debt has on their present and future families, which compounds feelings of guilt and anxiety.

For students who need to take loans, the decision to pursue a career in medicine is a collective investment with their families. Students feel guilty about the sacrifices their families have to make for the sake of their career (theme 3a) and feel pressure to continue to provide financially for their family while having debt (theme 3b). Students are stressed about acquiring more debt throughout their training (3a.1) and the impact that has on loved ones who are dependent on them (3a.4, 3a.5, 3b.2), especially with respect to ensuring their financial security in the future (3b.4).

Design opportunity 3

Create an environment that acknowledges and accounts for the burden of responsibility that students face towards their families.

Without the appropriate education about loans, the stress of debt is exponentially worse.

Students feel the greatest fear around loans when they do not understand them, including the process of securing loans and paying off debt (theme 4a). Students are overwhelmed by their loan amounts (4a.5) and lack the knowledge or resources to manage their debt (4a.1, 4a.2), making them uncertain about how they will become debt-free in the future (4a.3, 4a.4). Students reported that various resources helped to alleviate those burgeoning fears (theme 4b), including financial aid counselors (4b.2, 4b.3) and physician role models (4b.5, 4b.6) that generally increase knowledge and skills related to debt management (4b.1).

Design opportunity 4

Empower students to become experts in managing their debt by making loan-related resources more available and accessible.

The small, daily expenses are the most burdensome and cause the greatest amount of stress.

Students with educational debt are mentally unprepared for the burden of managing their daily living expenses (theme 5a), causing them to make significant lifestyle adjustments in the hopes to ease their resulting anxiety (theme 5b). These costs are immediate and tangible, compared to tuition costs which are more distant and require less frequent management (5a.3) Students learn to temper their expectations for living beyond a bare minimum during medical school (5a.1, 5b.2, 5b.4) and develop strategies to ensure that their necessary expenses are as low as possible (5b.1, 5b.2, 5b.3, 5b.4).

Design opportunity 5

Develop and distribute resources to support both short- and long-term financial costs for medical students.

Students view debt as a dark cloud that constrains their mental health and dictates their career trajectory.

The constant burden of educational debt constrains students’ abilities to control their mental health (theme 6a) and pursue their desired career path in medicine (themes 6b & 6c). Students feel controlled by their debt (6a.3) and concerned that it will impact their [ability] to live a personally fulfilling life (6a.1, 6a.2, 6c.6), especially with respect to pursuing their desired medical specialties (6b.1, 6c.3, 6c.5, 6c.6). Students with scholarships, as opposed to loans, felt more able to choose specialties that prioritized their values rather than their finances (6c.1, 6c.2), an affordance that impacts long-term career growth and satisfaction.

Design opportunity 6

Create a culture of confidence for managing debt and debt-stress among medical students.

This is the first multi-institutional national study to explore the impact of debt-related stress on medical students’ well-being in the United States. We used an innovative, mixed methods approach to better understand the factors that significantly affect debt-related stress, and propose opportunities for improving medical student well-being.

URM students

Analysis of survey results found that students who identify as URM are more likely to experience higher levels of debt-related stress than non-URM students. Our study also found that among all higher debt burden categories, debt levels were higher for URM students, findings consistent with studies that have shown the disproportionate burden of debt among URM students [ 1 ]. Our semi-structured interviews illuminated that students from low-income backgrounds feel unsupported by their medical schools in these varied financial stressors that extend beyond tuition costs (insight 2), leaving their needs unmet and increasing financial stress over time: “We don’t have different socio-economic classes in medicine because there’s constantly a cost that [isn’t] even factored into tuition cost [and] that we can’t take student loans for.” Many URM students feel especially stressed by their financial obligations towards their families (insight 3), and describe the decision to enter into medicine as one that is collective ( “the family’s going to school” ) rather than individual, placing additional pressure on themselves to succeed in their career: “ Being of low SES , the most significant stressor for me is the financing of medical school and the pull of responsibility for my family.” Several other studies from the literature confirm that students who identify as URM and first generation college or medical students are at higher risk for financial stress compared to their counterparts [ 7 ], and report that they feel as though it is their responsibility to honor their families through their educational and career pursuits [ 25 ]. Our study demonstrates and describes how low-income and URM students face numerous financial barriers in medical school, resulting in medical trainees that are less diverse than the patient populations they are serving [ 1 , 8 ].

Debt amount

Our quantitative analysis found that students with debt amounts over $100,000 are at much higher risk for experiencing severe stress than students with debt less than that amount. Although this finding may seem intuitive, it is important to highlight the degree to which this risk differs between these two cohorts. Students with debt amounts between $20,000 and $100,000 are approximately 5 times more likely to experience high stress than students with debt less than $20,000, while students with debt amounts over $100,000 are approximately 13 times more likely to experience severe stress when compared to the same cohort. Interview participants describe that the more debt they have, the less hopeful they feel towards achieving financial security (insight 1): “There are other healthcare professionals that will not accrue the same amount of loans that we will , and then may or may not have the same salary or privileges […] makes me question , did I do the right thing?” Students internalize this rising stress so as not to shift the feelings of guilt onto their families (insight 3), thereby compounding the psychological burden associated with large amounts of debt (insight 6): “As long as you’re in debt , you’re owned by someone or something and the sooner you can get out of it , the better; the sooner I can get started with my life.”

Pre-clinical students

According to our survey analysis, students who are in their pre-clinical years are at higher risk for stress than students in their clinical years. Our interview findings from insight 4 suggest that students feel initially overwhelmed and unsure about what questions to ask ( “One of my fears is that I don’t know what I don’t know”) or how to manage their loans so that it doesn’t have a permanent impact on their lives: “The biggest worry is , what if [the debt] becomes so large that I am never able to pay it off and it ends up ruining me financially.” Pre-clinical students may therefore feel unsure or ill-equipped to manage their loans, making them feel overwhelmed by the initial stimulus of debt. By the time students reach their clinical years, they may have had time to develop strategies for managing stress, acquire more financial knowledge, and/or normalize the idea of having debt.

Medical school characteristics

Our survey analysis found several risk factors related to medical school characteristics. First, we found that students who attended a private school were at higher risk for debt-related stress than students who attended a public school. Not only is the median 4-year cost of attendance in 2023 almost $100,000 higher in private compared to public medical schools [ 26 ], but it is also the case that financial aid packages are more liberally available for public schools due to state government funding [ 27 ]. This not only relieves students from having higher amounts of debt, but it also creates a more inclusive cohort of medical students. Insight 2 from our interviews suggests that private medical schools without the infrastructure to meet students’ varying financial needs force low-income students to make tradeoffs between their education and personal lives.

Another characteristic that was found to be a risk factor for debt stress was attending a medical school on the West Coast (compared to a non-coastal school.) This was a surprising finding given that tuition rates for both private and public schools on the West Coast are no higher than those in other regions [ 17 ]. The distribution of survey respondents did not vary significantly across regional categories, so no bias in sample size is suspected. While these interviews were not designed to address the reasoning behind students’ choice of medical school matriculation, there is a potential explanation for this finding. Historically, students match for residency programs that are in their home state or not far from their home state; [ 28 , 29 ] therefore, we speculate that students may prefer to settle on the West Coast, and may be willing to take on more financial debt in pursuit of their long-term practice and lifestyle goals.

Our quantitative analysis found that students who reported having considered taking a leave of absence for well-being purposes were at higher risk for debt-related stress. This cohort of students likely experience higher levels of stress as they are conscious of the negative impact it has on their life, and have already ruminated on leaving medical school. A study by Fallar et al. found that the period leading up to a leave of absence is particularly stressful for students because they are unfamiliar with the logistics of taking time off, and don’t feel as though leaving medical school is encouraged or normalized for students [ 30 ]. An interview with a student who did a joint MD and PhD program expressed having more time for herself during her PhD program, and described using money for activities that could alleviate stress (“I took figure skating during my PhD”) rather than create more stress by compromising on their lifestyle during medical school (insight 5). More research may be needed to better understand and support students considering taking a leave of absence from medical school.

Specialty choice

Our study found that students with high debt stress pursue moderately competitive specialties compared to students with low debt stress. This may be explained by the fact that low debt stress gives students the freedom to pursue minimally competitive specialties, which may be more fulfilling to them but typically have lower salaries. Insight 6 further elaborates upon this finding that students with high debt stress deprioritize specialties for which they are passionate in favor of higher paying specialties that might alleviate their debt: “I love working with kids…but being an outpatient pediatrician just wasn’t going to be enough to justify the [private school] price tag.” Students with lower debt stress describe having the freedom to choose specialties that align with their values, regardless of anticipated salary: “Scholarships give me the freedom to do [specialties] that maybe are a little bit less well-paying in medicine.” Interestingly, certain studies examining the relationship between specialty choice and debt stress have found that high debt stress is associated with a higher likelihood of pursuing a more competitive, and presumably higher paying, specialty [ 5 ]. More research investigating the relationship between debt stress and specialty choice could illuminate opportunities for increasing a sense of agency and overall satisfaction among students for their career choices.

In our exploration of potential protective factors against the effects of debt-related stress, our survey analysis found that the two variables measured (high mental health resource utilization and meeting with a counselor) did not have any impact on reducing debt-related stress. This finding is inconsistent with the literature, which considers these activities to promote general well-being among students but has never been studied in the context of debt-related stress [ 13 , 14 , 15 ]. A potential explanation is that the survey questions that assessed these activities were imperfect. For example, the question of meeting with a counselor was not a standalone question, but instead, was at the bottom of a list of other wellbeing activities; therefore, students may have been fatigued by the time they got to the bottom of the list and not selected it. Additionally, our definition of “high” mental health resource utilization may have been perceived as too strict (i.e.: 80–100%) and perhaps we would have seen effects at lower percentages of utilization (i.e.: 40–60%). Despite this finding, students describe in their interviews that having access to certain resources such as financial knowledge and physician role models can help to alleviate stress by helping them feel confident in managing their loans in the immediate and more distant future (insight 4): “I’ve had explicit discussions with physicians who went to med school , had debt , paid it off [.] the debt hasn’t hindered their life in any way. I think that just makes me feel a lot calmer.” This finding aligns with previous studies that suggest that financial knowledge, such as knowledge about loans and a payoff plan, confers confidence in students’ financial management [ 11 , 12 ]. These factors are also aligned with previous studies that suggest financial optimism, such as with a physician role model who successfully paid off loans, is associated with less financial stress [ 10 ].

Our quantitative analysis of risk factors helped us to identify which areas might significantly impact debt-related stress among medical students, while our qualitative analysis provided more in-depth insight into those risk factors for more human-centered intervention design. The HCD process not only provides additional context from the perspective of medical students, but also proposes distinct design opportunities upon which interventions may be designed and tested. Drawing from the six design opportunities outlined in this paper, we propose a solution on a national scale: lowering the cost of the MCAT and medical school applications to reduce the financial barrier to applying to medical school [ 31 ]. We also propose the following solutions that can be implemented at the level of medical schools to better support medical students facing debt-related stress: (1) providing adequate financial aid that prevents low-income students from needing to work while being in medical school [ 32 ], (2) providing targeted financial planning classes and counseling for first-year medical students who have taken loans [ 33 ], and (3) creating mentorship programs that pair medical students with debt with physician role models who had also had debt but successfully paid it off [ 34 ]. We encourage medical schools to consider these suggestions, choosing the ideas from the list that make sense and tailoring them as necessary for their students and their unique needs. Additionally, given that our quantitative portion of the study was a secondary analysis of a survey focused on general medical student well-being, a nationwide study is needed that is specifically designed to explore the topic of debt-related stress among medical students. Furthermore, more research is needed that assesses the impact of activities that promote well-being (e.g., access to therapy, mindfulness practices, exercise) on debt-related stress among medical students.

Limitations

Our study had some notable limitations. One potential limitation is that our data collection occurred between 2019 and 2021 for this publication in 2023. Additionally, as described in the original study [ 3 ], a limitation of the MSWS is the inability to determine a response rate of students due to the survey distribution by medical student liaisons from each medical school; under the reasonable assumption that the survey was distributed to every US allopathic medical student, the response rate was estimated to have been 8.7%. 3 An additional limitation is the potential for response bias [ 3 ]. A limitation of the qualitative interviews is the potential for response bias among the interviewees. Although we purposely sampled, the students who accepted the invitation to interview may have been students with extreme views, either very negative views of debt or very neutral views of debt. Additionally, the interviewees were not representative of all possible financial situations, given that most students were from private schools, which typically have higher tuition rates. Also, all students had debt amounts in the middle and high categories, with none in the low category. Finally, our model of risk factors for debt-related stress suggested the presence of negative confounding factors, which exerted effects on specific variables (i.e.: pre-clinical year, West Coast) for which univariate analysis found no significant associations but multivariate analysis did. We did not perform further analysis to identify which variables served as the negative confounding variables.

In conclusion, our mixed methods, cross-sectional study exploring debt-related stress and its impact on US medical students’ wellbeing and professional development revealed a set of risk factors and design opportunities for intervention. By using a combined quantitative and qualitative HCD approach, we were able to develop a broad, in-depth understanding of the challenges and opportunities facing medical students with education debt. With these efforts to support the well-being and academic success of students at higher risk of debt-related stress, medical education institutions can develop and nurture a more diverse medical field that can best support the needs of future patients.

Data availability

Data is provided within the supplementary information files.

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Acknowledgements

We thank the members of The Better Lab, including Devika Patel, Christiana Von Hippel, and Marianna Salvatori, for their support. We appreciate Pamela Derish (UCSF) for assistance in manuscript editing and the UCSF Clinical and Translational Science Institute (CTSI) for assistance in statistical analysis. This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Funding was not obtained for this project.

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Adrienne Yang, Simone Langness and Lara Chehab contributed equally to this work.

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Department of Surgery, University of California, San Francisco, CA, USA

Adrienne Yang, Lara Chehab & Amanda Sammann

Department of Trauma Surgery, Sharp HealthCare, San Diego, CA, USA

Simone Langness

Department of Pediatrics, Stanford University, Stanford, CA, USA

Nikhil Rajapuram

Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA

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A.Y. and L.C. wrote the main manuscript text and prepared the figures. S.L. created the study design. All authors reviewed the manuscript.

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Yang, A., Langness, S., Chehab, L. et al. Medical students in distress: a mixed methods approach to understanding the impact of debt on well-being. BMC Med Educ 24 , 947 (2024). https://doi.org/10.1186/s12909-024-05927-9

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Muhammad Hassan

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Evaluation of macro- and micro-geometry of models made of photopolymer resins using the polyjet method.

1. Introduction

2. materials and methods, 3.1. macro-geometry analysis, 3.2. micro-geometry analysis, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Material	Mean Absolute Deviation Mean_abs, mm		First Quartile Q1, mm		Median Q2, mm		Third Quartile Q3, mm		Interquartile Range IQR, mm
Material	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
Digital ABS Plus	0.052	0.021	−0.060	0.034	−0.002	0.006	0.023	0.011	0.083	0.039
RGD 720	0.059	0.017	−0.074	0.025	−0.030	0.011	0.008	0.016	0.082	0.030
Vero Clear	0.052	0.018	−0.059	0.035	−0.004	0.008	0.024	0.009	0.083	0.036

Dependent Variable	p-Value
Mean absolute deviation (mean_abs)	0.002
Interquartile range (IQR)	0.998
First quartile (Q1)	0.016
Median (Q2)	2.07 × 10
Third quartile (Q3)	4.21 × 10

Dependent Variable	Contrast	A	B	p-Value	Common Language Effect Size (CLES)
Mean absolute deviation (mean_abs)	Material	Digital ABS Plus	RGD 720	0.011	0.40
		Digital ABS Plus	Vero Clear	0.758	0.52
		RGD 720	Vero Clear	0.006	0.60
First quartile (Q1)	Material	Digital ABS Plus	RGD 720	0.054	0.63
		Digital ABS Plus	Vero Clear	0.831	0.49
		RGD 720	Vero Clear	0.013	0.38
Median (Q2)	Material	Digital ABS Plus	RGD 720	3.44 × 10	0.99
		Digital ABS Plus	Vero Clear	0.359	0.65
		RGD 720	Vero Clear	3.41 × 10	0.03
Third quartile (Q3)	Material	Digital ABS Plus	RGD 720	0.006	0.87
		Digital ABS Plus	Vero Clear	0.765	0.51
		RGD 720	Vero Clear	0.001	0.10

Parameter	Statistics	Material
Parameter	Statistics	Digital ABS Plus	RGD 720	Vero Clear
Arithmetical mean height (Sa)	mean, μm	1.63	15.86	1.96
	median, μm	1.43	16.27	1.69
	std, μm	0.74	7.06	0.62
	CV, %	45.3	44.5	31.65
Reduced peak height (Spk) + core height (Sk) + reduced dale depth (Svk)	mean, μm	9.10	101.88	10.51
	median, μm	7.74	111.21	9.04
	std, μm	3.92	44.10	3.40
	CV, %	43.1	43.29	32.33

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Turek, P.; Bazan, A.; Budzik, G.; Dziubek, T.; Przeszłowski, Ł. Evaluation of Macro- and Micro-Geometry of Models Made of Photopolymer Resins Using the PolyJet Method. Materials 2024 , 17 , 4315. https://doi.org/10.3390/ma17174315

Turek P, Bazan A, Budzik G, Dziubek T, Przeszłowski Ł. Evaluation of Macro- and Micro-Geometry of Models Made of Photopolymer Resins Using the PolyJet Method. Materials . 2024; 17(17):4315. https://doi.org/10.3390/ma17174315

Turek, Paweł, Anna Bazan, Grzegorz Budzik, Tomasz Dziubek, and Łukasz Przeszłowski. 2024. "Evaluation of Macro- and Micro-Geometry of Models Made of Photopolymer Resins Using the PolyJet Method" Materials 17, no. 17: 4315. https://doi.org/10.3390/ma17174315

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15 Types of Research Methods

Types of Research Methods

Qualitative Research Methods

1. Ethnographic Research

2. Phenomenological Research

3. Historical Research

4. Content Analysis

5. Grounded Theory Research

6. Action Research

7. Natural Observational Research

8. Case Study Research

Quantitative Research Methods

9. Experimental Research

10. Surveys and Questionnaires

11. Longitudinal Studies

12. Cross-Sectional Studies

13. Correlational Research

14. Quasi-Experimental Design Research

15. Meta-Analysis Research

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Here's What You Need to Understand About Research Methodology

Table of Contents

What is the definition of a research methodology?

The need for a good research methodology

What is the basic structure of a research methodology?

1. Your research procedure

2. Provide the rationality behind your chosen approach

3. Explain your mechanism

4. Significance of outcomes

5. Reader’s advice

6. Explain your sample space

7. Challenges and limitations

The importance of a good research methodology

Instruments you could use while writing a good research methodology

a. Interviews (One-on-One or a Group)

c. Sample Groups

d. Observations

Types of research methodology

1. Qualitative research methodology

2. Quantitative research methodology

3. Amalgam methodology

Final words: How to decide which is the best research methodology?

Frequently Asked Questions (FAQs) about Research Methodology

2. What are the types of research methodology?

3. What is the true meaning of research methodology?

4. Where lies the importance of research methodology?

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Table of contents

Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

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Qualitative analysis methods

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Types of Research – Explained with Examples

Types of Research

Classification of Types of Research

According to its Purpose

Applied Research

According to your Depth of Scope

Descriptive Research

Explanatory Research

Correlational Research

According to the Type of Data Used

Quantitative Research

According to the Degree of Manipulation of Variables

Non-Experimental Research

Quasi-Experimental Research

According to the Type of Inference

Inductive Research

Hypothetical-Deductive Investigation

According to the Time in Which it is Carried Out

Cross-Sectional Study (also referred to as Synchronous Research)