Research design refers to the strategies and methods researchers employ to carry out their research and reach valid and reliable results.
It can refer to the collection, interpretation, and analysis of the dataset.
While various sources claim there are between 4 and 5 types of research design (each list, it seems, differs in its arguments), under each type are sub-types, representing the diversity of ways of going about conducting research.
For example, Jalil (2015) identified five types: descriptive, correlational, experimental, and meta-analytic. But the farther we broaden our scope to include the wide array of fields of study in academic research, the more we can incorporate – for example, in cultural studies, thematic content analysis is a very common, albeit somewhat alternative, way of designing a study of empirical data.
So, below, I present 25 potential forms of research design that can be employed in an academic empirical study.
Types of Research Designs
1. Experimental Research Design
The experimental research design involves manipulating one variable to determine if changes in one variable lead to changes in another variable.
An experimental research design tends to split research participants into two groups, known as the control group and experimental group(Abbott & McKinney, 2013). The control group receives nothing, or, a placebo (e.g. sugar pill), while the experimental group is provided the dependant variable (e.g. a new medication).
Participants are typically assigned to groups at random in order to control for any extraneous variables that could influence the results. Furthermore, the study may occur in a controlled environment where extraneous variables can be controlled and minimized, allowing for the analysis of cause-and-effect.
Example of Experimental Research Design
In a study exploring the effects of sleep deprivation on cognitive performance, the researcher might take two groups of people. One group is deprived of sleep for 24 hours (experimental group), while the other group is allowed a full night’s sleep (control group). The researcher then measures the cognitive performance of both groups. If the sleep-deprived group performs significantly worse, it could be inferred that sleep deprivation negatively affects cognitive performance.
2. Causal Research Design
Causal research design is used when the goal is to find a cause-and-effect relationship between two variables – an independent vs dependent variable.
This design is used to determine whether one variable influences another variable (Ortiz & Greene, 2007).
Causal research involves conducting experiments where one or more variables are manipulated and the effects are measured.
It seeks to isolate cause-and-effect relationships by holding all factors constant except for the one under investigation (the independent variable). Researchers then observe if changes to the manipulated variables cause changes to the variable they are measuring (the dependent variable).
There are three criteria that must be met to determine causality in a causal research design:
- Temporal Precedence: This means the cause (independent variable) must occur before the effect (dependent variable). For example, if you are studying the impact of studying on test scores, the studying must occur before the test.
- Covariation of the Cause and Effect: Observing that a change in the independent variable is accompanied by a change in the dependent variable. For example, decreased class sizes (cause) might lead to improved test scores (effect), which we could plot on a chart.
- No Plausible Alternative Explanations: The researcher must be able to rule out other factors or variables that might be causing the observed effect. This is often the most challenging criteria to meet and is typically addressed through the use of control groups and random assignment in experimental designs (Ortiz & Greene, 2007)..
Example of Causal Research Design
Consider a study that aims to investigate the impact of classroom size on academic achievement. The researchers choose a causal research design, where they collect data on the size of each classroom (independent variable) and then compare that to the average academic performance of each class group (dependent variable). They would then be bale to determine whether students in smaller classes perform at any different rate, on average, compared to larger class groups. If there is a difference, they may be able to demonstrate a causal relationship between classroom size and academic performance.
3. Correlational Research Design
A correlational research design is used when researchers want to determine if there is a relationship between two variables, but it does not necessarily mean that one variable causes changes in the other (Marczyk, DeMatteo & Festinger, 2010).
The primary goal is to identify whether two variables are related and if they move together, i.e., change in one variable is associated with the change in another variable (Abbott & McKinney, 2013; Marczyk, DeMatteo & Festinger, 2010). This relationship can be positive (both variables increase or decrease together), negative (one variable increases while the other decreases), or nonexistent (no connection between the variables).
However, unlike causal research design that we looked at above, correlation does not imply causation. Just because two variables correlate doesn’t mean that changing one variable will change the other.
Example of Correlational Research Design
For example, researchers could be interested in finding out if there is a relationship between the amount of time spent on homework (variable one) and academic performance (variable two). If students who spend more time on homework tend to have better academic performance, then there is a positive correlation between these two variables. However, they may not be able to determine that this correlation implies causation. Other factors could be at play. To make it causal design, they may need to employ control and experimental groups in the study.
Also See: 15 Examples of Random Assignment
4. Diagnostic Research Design
Diagnostic research is a type of research that is conducted to identify and understand the nature of a phenomenon or to develop a profile of characteristics related to a certain issue (Abbott & McKinney, 2013; Leavy, 2022).
It is more precise and focused than exploratory research and goes further to provide additional insights about the specifics of the problem.
In the context of medical or psychological research, diagnostic research often involves detailed examinations or tests to identify the nature of a disease or disorder, its causes, symptoms, and effects. The objective of this research is to gain a deep understanding of the problem in order to provide a diagnosis or create an intervention (Leavy, 2022).
In non-clinical research, diagnostic research still focuses on understanding a particular issue or phenomenon in depth. Researchers collect data and investigate to determine the source of particular problems, behaviors, attitudes, or market trends. This could involve conducting detailed interviews, observations, surveys, or reviewing existing records.
Example of Diagnostic Research Design
Suppose a teacher is curious about why students in her class are struggling with reading comprehension. She may conduct a diagnostic study where she individually assesses each student’s reading skills, looking for patterns of common difficulties. She may find that many of the students struggle with vocabulary, identifying main ideas, or making inferences. This insight can then guide her teaching strategies to improve students’ reading comprehension.
5. Exploratory research design
Exploratory research is a type of research conducted to clarify ambiguous problems or discover ideas that can be potential research topics.
This type of research is usually conducted when a problem is not clearly defined. It is the preliminary stage of research and helps to define the problem statement, understand the underlying phenomena, or set the stage for further research (Abbott & McKinney, 2013).
Exploratory research design does not aim to provide conclusive results or decide a course of action. Instead, it focuses on gaining insights and familiarity with the subject.
It’s typically characterized by its flexibility, as it allows researchers to shift their focus as new data and insights are collected. The main methods of data collection for exploratory research are survey research, qualitative research, literature reviews, case studies, and focus groups.
Exploratory Research Example Design
Consider a business that is noticing a decline in its customer retention rates. They are not sure of the cause, so they decide to conduct exploratory research. They may start with open-ended surveys or interviews with their customers to understand their needs and challenges. Based on the initial feedback, they might find several possible causes – poor customer service, outdated product features, or increased competition. These insights can help define further research to fully understand and address the identified issues.
6. Observational research design
Observational research, as the name suggests, involves observing subjects in their natural environment without any manipulation or control by the researcher.
This can be done in a number of ways including direct observation, participant observation, unobtrusive observation, and structured observation (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).
Observational research is particularly valuable when researchers want to study behavior as it naturally occurs, without interference or intervention. It can provide a high degree of ecological validity, which means the behavior is likely a reflection of real life because it’s observed in a natural setting. However, observational research may be influenced by observer bias and can be time-consuming and difficult to replicate.
Example of Observational Research Design
A child psychologist may want to study the impact of playground design on children’s social interactions. Using observational research, they could spend time watching children play in different playground environments, recording their interactions and behaviors. This could reveal patterns such as more cooperative play on playgrounds with particular features, which could inform future playground design.
7. Descriptive research design
Descriptive research is a form of research design aims to accurately and systematically describe a situation, problem, phenomenon, service, or program, or provides information about, say, the living conditions of a community, or describes attitudes towards an issue (Abbott & McKinney, 2013;).
It provides a snapshot of the variables included in the study at a particular point in time.
Descriptive research does not fit neatly into the definition of either quantitative or qualitative research methodologies, but instead, it can utilize elements of both, often within the same study.
The descriptive function of research relies on instrumentation for measurement and observations. The descriptive research results in our ability to carefully describe the phenomena, events, or case under study.
Example of Descriptive Research
A market research company is hired to understand the types of customers frequenting a new shopping mall. They may conduct descriptive research using methods such as surveys, interviews, and observations. This could result in a detailed description of customer demographics, preferences, and behaviors. The information could then be used by the mall’s management to make strategic business decisions.
8. Case study
Case study research is a design that involves studying a specific phenomenon, person, or group of people in a specific context (Bennett, 2004).
This allows you to go into depth in the study, gaining strong insights into a specific instance.
Case studies tend to be qualitative, not quantitative. The knowledge that can be generated via a case study project can reveal high-quality insights, but is not generalizable because there is not sufficient breadth of subjects or contexts in order to get a good grasp of whether the case study is representative of a broader experience.
Example of a Case Study
A researcher conducts a case study in one classroom, examining a new teaching method that the teachers have implemented. The study focuses on how the teacher and students adapt to the new method, conducting semi-structured interviews with the teachers and students. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other educational settings, as statistical significance has not been established to achieve generalization.
See Also: Case Study Advantages and Disadvantages
9. Action research design
Action research is a research design that involves using the scientific method to study professional practice in the workplace and improve upon it.
The defining purpose of action research is to improve workplace practice. In this sense, it’s extremely practical, designed to achieve tangible results for a specific practitioner in a specific setting.
Gillis and Jackson (2002) offer a very concise definition of action research:
“systematic collection and analysis of data for the purpose of taking action and making change” (p.264).
Action research is often participatory, meaning the practitioner is both a participant in the research and the person studying the phenomenon (Macdonald, 2012).
This design is often cyclical, meaning the practitioner implements a change, studies it, then uses the feedback to implement another change, and so forth, until substantive change is made.
Example of Action Research Design
I supervised one research student, Mark, who completed an action research study 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. You can read his study here (Ellison & Drew, 2019).
10. Cross-sectional research design
A cross-sectional research design involves collecting data on a sample of individuals at one specific point in time (Levin, 2006).
Unlike longitudinal studies, which examine variables across a time horizon, a cross-sectional design will only collect data at one point in time.
The researchers will generally collect various datapoints at the one time to study how they are interrelated, the predominance of some other others, and so on.
A cross-sectional research is descriptive only, painting a picture of a sub-population being analyzed, but cannot determine cause and effect.
Cross-Sectional Research Example
Psychologists could collect data on people’s socioeconomic status (for example, their current income levels, education, and occupation). During the study, they may also gather data on self-reported mental health status using validated Likert scales. Based on this dataset, the researchers then explored the relationship between socioeconomic status and profession and mental health. While this provided excellent descriptive insights about which professions and SES groups tend to have higher mental health concerns, the researchers could not determine causal factors through the cross-sectional study alone.
11. Sequential research design
Sequential research design is a method that combines both quantitative and qualitative research approaches, in a sequence, to gain a broader understanding of a research problem (Abbott & McKinney, 2013; Leavy, 2022).
This approach allows the researcher to take the benefits of both methods, using one method to enhance or inform the other.
It may take the form of:
- QUAN→QUAL: This design involves conducting quantitative analysis first, then supplementing it with a qualitative study.
- QUAL→QUAN: This design goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.
This type of research design allows for flexibility and is particularly effective when the researcher doesn’t have a clear idea of the problems that will arise during the research.
It also allows the researcher to adapt the study according to the emerging results, which can lead to a more nuanced and informed understanding of a research problem. However, this research design can be time-consuming and requires substantial resources, as it involves two phases of research.
Sequential Research Example
A researcher interested in understanding the effectiveness of a new teaching method could first conduct quantitative research, such as a survey, to measure the overall student performance. Then, in the second phase, the researcher could conduct qualitative research, such as focus group discussions or interviews, to understand the students’ experiences with the new teaching method.
12. Cohort research design
Cohort research is a form of longitudinal study design that observes a defined group, or cohort, over a period of time.
The cohort can be defined by a common characteristic or set of characteristics. Cohort studies are often used in life sciences, social sciences, and health research (Marczyk, DeMatteo & Festinger, 2010; Ortiz & Greene, 2007).
Cohort research allows for the analysis of sequences and patterns in life events. It can be retrospective (observing historical data) or prospective (collecting data forward in time).
The major advantage of cohort research is its ability to study causation, i.e., to make definitive statements about cause-and-effect relationships. However, it can be time-consuming and expensive to conduct.
Cohort Research Example
A health researcher could study a cohort of smokers and non-smokers over a period of 20 years to understand the long-term effects of smoking on lung health. The researcher could gather data at regular intervals, tracking changes in the participants’ health over time.
13. Historical research design
Historical research design involves studying the past to draw conclusions that are relevant to the present or the future (Danto, 2008).
This research method involves a deep dive into historical data to gain a clear understanding of past events, contexts, or phenomena.
Historical research helps us understand how past events inform current circumstances. It can include the examination of records, documents, artifacts, and other archival material (Danto, 2008).
However, the reliability of historical research is often challenged due to the accuracy of past records, potential bias in recorded histories, and the interpretive nature of the analysis.
Historical Research Example
A historian might conduct research on the economic impact of the Great Depression on the United States. They would likely analyze data from that era, such as economic indicators, governmental policies, and personal accounts to form a comprehensive understanding of the economic climate of the time.
14. Field research design
Field research is a qualitative method of research concerned with understanding and interpreting the social interactions, behaviors, and perceptions within a specific social or environmental setting.
It involves collecting data ‘in the field’, i.e., in a natural or social setting, and often involves direct and prolonged contact with participants.
Field research can include observations, interviews, and document review. The goal is to gain insights into a group’s practices, behaviors, and culture by observing and interacting with them in their natural environment. This method can provide rich, contextual data but is also time-intensive and requires significant planning to ensure representative sampling and accurate recording of data.
Field Research Example
An anthropologist studying the social practices of a remote indigenous tribe may live with the tribe for several months, participating in their daily activities, observing, and documenting their practices and rituals. Through this field research, they can understand the tribe’s social structure, beliefs, and customs in
15. Systematic review
A systematic review is a type of research design that involves a comprehensive and structured overview of existing literature on a specific topic (Jalil, 2015).
This research method aims to collate all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question.
The systematic review follows a transparent and replicable methodology to minimize bias and ensure reliability.
It involves identifying, evaluating, and interpreting all available research relevant to the research question.
However, it can be time-consuming and resource-intensive and relies heavily on the availability and quality of existing studies.
Systematic Review Example
A health researcher interested in the impact of a plant-based diet on heart disease might conduct a systematic review of all published studies on the topic. They would gather, analyze, and synthesize data from these studies to draw a comprehensive understanding of the current evidence base on this issue.
A survey research design involves gathering information from a sample of individuals using a standardized questionnaire or interview format (Fowler, 2013).
Surveys can be used to describe, compare, or explain individual and societal phenomena. Surveys allow for data collection from a large population, in a cost-effective and efficient manner (Fowler, 2013).
They can be delivered in various formats, such as online, telephone, mail, or in-person.
However, the reliability of survey data can be affected by several factors, such as response bias and sample representativeness.
A market research company might use a survey to understand consumer preferences for a new product. They could distribute the survey to a representative sample of their target market, asking questions about preferences, behaviors, and demographics to inform the product’s development and marketing strategy.
17. Meta-analysis research design
A meta-analysis is a type of research design that involves looking over the current literature on a topic and assessing its quality, trends, and collective insights (Borenstein et al., 2021).
Meta-analysis doesn’t involve collecting first-hand data, but rather using secondary data in the form of the results of other peoples’ studies.
It then analyzes the quality and findings of each study in-depth, comparing and contrasting each study, and synthesizing the data from the collective studies deemed of sufficient quality, to see what collective knowledge these studies can provide (Borenstein et al., 2021).
Meta-analyses are considered some of the most valuable and respected research designs because they can demonstrate that there is sufficient data from the scientific community for an authoritative scientific account of a phenomenon or topic.
In the early 2000s, a few small studies arguing that vaccines caused autism caused moral panic in the media. In response, several meta-analyses emerged that combined the collective data from the scientific community. These meta-analyses demonstrated that, across the scientifically rigorous studies, overwhelming consensus showed there was no correlation between vaccines and autism (see: Taylor, Swerdfeger & Eslick, 2014).
18. Mixed-method research design
Mixed-method research design is a method that combines both quantitative (numerical data) and qualitative (non-numerical data) research techniques, methods, approaches, concepts or language into a single study.
This approach to research allows for the capturing of a more complete, holistic picture of the phenomena being studied (Leavy, 2022; Marczyk, DeMatteo & Festinger, 2010).
Mixed-method research can provide a more in-depth understanding of a research problem or question. It allows the researcher to explore complex phenomena and validate the findings.
However, it requires a thorough understanding of both quantitative and qualitative research methods and can be time-consuming.
An education researcher interested in student motivation might use a mixed-method approach. They could distribute a survey (quantitative method) to measure levels of motivation, and then conduct interviews (qualitative method) to gain a deeper understanding of factors influencing student motivation.
19. Longitudinal research design
Longitudinal studies take place over a long period of time to explore changes to the research subjects or variables over time (Neale, 2020).
This sort of study is often valuable in detecting correlations between variables over the course of an intervention.
By examining multiple data points at different period, it’s possible to record continuous changes within things like consumer behavior or demographics of a society (Vogl, 2023).
This makes a detailed analysis of change possible.
For example, a national census, conducted every 5 years, can be considered longitudinal. It gathers comparative demographic data that can show how the demographics of an area have changed over time.
Longitudinal Study Example
The famous Minnesota Twins study examined identical twins who were raised in separate environments to examine whether behavioral and personality traits were genetic or environmental. The study by Thomas J Bouchard, which took place between 1979 to 1990, argued that identical twins who grew up separate and in different environments did not display any greater chances of being different from each other than twins that were raised together in the same house. The study indicated that similarities in personality and behavior between twins are likely genetic rather than environmental in nature, giving sway to the argument that nature is more powerful than nurture (Bouchard et. al., 1990).
20. Philosophical research design
Philosophical research is a research design that uses philosophical methods to address broad questions about issues such as reality, morality, existence, truth, justice, and freedom (Novikov & Novikov, 2013).
This type of research often involves broad, abstract thinking and deep contemplation on the fundamental nature of human existence.
Philosophical research often relies on the critical analysis of texts, argumentation, and the formulation of theories. It requires abstract thinking and logical reasoning, but it doesn’t usually involve empirical studies.
However, it’s invaluable for underpinning other research methods and for informing our understanding of fundamental principles and theories.
Philosophical Research Example
A researcher studying ethics might use a philosophical research design to explore the concept of ‘justice’ in various societies. They would likely examine a variety of texts, historical contexts, and moral frameworks, before formulating a comprehensive theory of justice.
21. Grounded Theory
Grounded theory is characterized by a research study where no hypothesis is being tested. Instead, a hypothesis or ‘theory’ emerges out of the study (Tracy, 2019).
This goes against most research designs, where a researcher starts with a hypothesis and then they create a study to test the hypothesis. Then, they would usually come to a result affirming or debunking the study.
But in grounded theory, we start with a phenomenon, and then we go about studying it to identify themes and insights that emerge from the data. At the end of the study, the researchers would come up with a theory or hypothesis.
This has the strength of remaining open-minded about the possible outcomes of the study, and not being restricted to only studying a specifically noted hypothesis from the beginning.
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.
22. Ethnographic Research Design
Ethnographic research is a qualitative research design that aims to explore and understand the culture, social interactions, behaviors, and perceptions of a group of people (Stokes & Wall, 2017).
The methodology is derived from the field of anthropology where researchers immerse themselves in the culture they’re studying to gather in-depth insights.
An ethnographic study is usually conducted over an extended period of time and involves observing and interacting with the participants in their natural setting (Stokes & Wall, 2017).
This method can provide rich, detailed, and nuanced data. However, it is time-consuming, and its success heavily relies on the skill and sensitivity of the researcher to understand and interpret the cultural nuances of the group.
Ethnographic Research Example
A researcher interested in understanding the impact of digital technology on the daily life of a remote indigenous tribe might spend several months living with the tribe. The researcher would observe and participate in their daily activities, conduct informal interviews, and take detailed field notes to capture the changes and influences brought about by digital technology.
23. Quasi-Experimental Research Design
A quasi-experimental research design resembles an experimental design but lacks the element of random assignment to treatment or control (Abbott & McKinney, 2013; Leavy, 2022).
Instead, subjects are assigned to groups based on non-random criteria. Quasi-experiments are often used in social sciences where it’s difficult or ethically problematic to manipulate independent variables and randomly assign participants (Ortiz & Greene, 2007).
While quasi-experimental designs help establish causal relationships, they can be subject to confounding variables, which may impact the validity of the results. Also, the lack of random assignment can result in selection bias.
Quasi-Experimental Design Example
A researcher studying the impact of an educational program on students’ performance might compare the test scores of students who chose to participate in the program (the treatment group) with those who did not (the control group). The researcher could control for factors such as gender, age, and previous performance, but without random assignment, there could be other differences between the groups that impact the results.
24. Comparative Research Design
Comparative research is a research design that involves comparing two or more groups, cultures, variables, or phenomena to identify similarities and differences (Abbott & McKinney, 2013).
The comparison can be cross-sectional (comparing at a single point in time) or longitudinal (comparing over time).
Comparative research can provide insight into the effects of different variables and contribute to understanding social, economic, political, or cultural issues across different contexts. However, ensuring comparability can be challenging as factors influencing the variables being studied can vary widely between contexts.
Comparative Research Design Example
A social scientist studying gender inequality might compare the wage gap, educational attainment, and political representation in several countries. The researcher would collect data from each country and conduct a comparative analysis to identify patterns, trends, and differences, contributing to a broader understanding of gender inequality globally.
25. Thematic Content Analysis
A content analysis will involve systematic and objective coding and interpreting of text or media to identify patterns, biases , themes, ideologies, and so on (Schweigert, 2021).
They may focus on newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.
The design is often thematic, involving deductive or inductive coding, whereby researchers look through the data for ‘codes’ such as word choice, word repetition, and other meaning-making elements which, combined, can give insights into themes that emerge throughout the texts.
Content Analysis Example
Poorebrahim and Zarei (2013) employ a popular type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research) to study newspapers in their study titled How is Islam Portrayed in Western Media?. This study combs through a group of media texts to explore the language and symbolism that is used in relation to Islam and Muslims. The study demonstrates how media content has the capacity to stereotype Muslims, representing anti-Islam bias or failure to understand the Islamic world.
Abbott, M. L., & McKinney, J. (2013). Understanding and applying research design. John Wiley & Sons.
Bennett, A. (2004). Case study methods: Design, use, and comparative advantages. Models, numbers, and cases: Methods for studying international relations, 2(1), 19-55.
Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021). Introduction to meta-analysis. John Wiley & Sons.
Danto, E. A. (2008). Historical research. Oxford University Press.
Fowler Jr, F. J. (2013). Survey research methods. London: Sage publications.
Gillis, A., & Jackson, W. (2002). Research Methods for Nurses: Methods and Interpretation. Philadelphia: F.A. Davis Company.
Jalil, M. M. (2013). Practical guidelines for conducting research-Summarising good research practice in line with the DCED standard. Available at SSRN 2591803.
Leavy, P. (2022). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. Guilford Publications.
Levin, K. A. (2006). Study design III: Cross-sectional studies. Evidence-based Dentistry, 7(1), 24-25.
Macdonald, C. (2012). Understanding participatory action research: A qualitative research methodology option. Canadian Journal of Action Research, 13, 34-50. https://doi.org/10.33524/cjar.v13i2.37 Mertler, C. A. (2008). Action Research: Teachers as Researchers in the Classroom. London: Sage.
Marczyk, G. R., DeMatteo, D., & Festinger, D. (2010). Essentials of research design and methodology (Vol. 2). John Wiley & Sons.
Neale, B. (2020). Qualitative longitudinal research: Research methods. Bloomsbury Publishing.
Novikov, A. M., & Novikov, D. A. (2013). Research methodology: From philosophy of science to research design (Vol. 2). CRC Press.
Ortiz, D., & Greene, J. (2007). Research design: qualitative, quantitative, and mixed methods approaches. Qualitative Research Journal, 6(2), 205-208.
Stokes, P., & Wall, T. (2017). Research methods. New York: Bloomsbury Publishing.
Taylor, L. E., Swerdfeger, A. L., & Eslick, G. D. (2014). Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies. Vaccine, 32(29), 3623-3629.
Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. London: John Wiley & Sons.
Vogl, S. (2023). Mixed methods longitudinal research. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 24, No. 1).
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]