15 Types of Research Methods

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 MethodsPrimarily Qualitative methods
Experimental ResearchCase Study
Surveys and QuestionnairesEthnography
Longitudinal StudiesPhenomenology
Cross-Sectional StudiesHistorical research
Correlational ResearchContent analysis
Causal-Comparative ResearchGrounded theory
Meta-AnalysisAction research
Quasi-Experimental DesignObservational 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 ResearchCons of Ethnographic Research
1. Provides deep cultural insights1. Time-consuming
2. Contextually relevant findings2. Potential researcher bias
3. Explores dynamic social processes3. May raise ethical issues

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 ResearchCons of Phenomenological Research
1. Provides rich, detailed data1. Limited generalizability
2. Highlights personal experience and perceptions2. Data collection can be time-consuming
3. Allows exploration of complex phenomena3. 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 ResearchCons of Historical Research
1. Provides historical context1. Dependent on available sources
2. Can help understand current events or trends2. Potential bias in source materials
3. Allows the study of change over time3. 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 AnalysisCons of Content Analysis
1. Unobtrusive data collection1. Lacks contextual information
2. Allows for large sample analysis2. Potential coder bias
3. Replicable and reliable if done properly3. 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 ResearchCons of Grounded Theory Research
1. Helps with theory development1. Time-consuming
2. Rigorous data analysis2. Requires iterative data collection and analysis
3. Can fill gaps in existing theories3. 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 ResearchCons 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 ResearchCons 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
(For more, see my guide on qualitative observational research)

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 ResearchCons of Case Study Research
1. Provides detailed insights1. Limited generalizability
2. Facilitates the study of complex phenomena2. Can be time-consuming
3. Can test or generate theories3. 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 ResearchCons of Experimental Research
1. Allows for determination of causality1. 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 QuestionnairesCons 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 subpopulations2. There is limited depth of response as opposed to qualitative approaches.
3. Can be cost-effective and time-efficient3. 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 StudiesCons 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 StudiesCons 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 ResearchCons of Correlational Research
1. Reveals relationships between variables1. Cannot determine causality
2. Can use existing data2. May be influenced by third variables
3. Can guide further experimental research3. 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 of Quasi-Experimental ResearchCons of Quasi-Experimental Research
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 reduce the internal validity 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 of Meta-Analysis ResearchCons of Meta-Analysis Research
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: The accuracy 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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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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