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Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Organizing Your Social Sciences Research Paper: Writing a Case Study

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Multiple Book Review Essay
• Reviewing Collected Essays
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Bibliography

The term case study refers to both a method of analysis and a specific research design for examining a problem, both of which are used in most circumstances to generalize across populations. This tab focuses on the latter--how to design and organize a research paper in the social sciences that analyzes a specific case.

A case study research paper examines a person, place, event, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or among more than two subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies . Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in this writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a single case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• Does the case represent an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• Does the case provide important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• Does the case challenge and offer a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in practice. A case may offer you an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to the study a case in order to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• Does the case provide an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings in order to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• Does the case offer a new direction in future research? A case study can be used as a tool for exploratory research that points to a need for further examination of the research problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of Uganda. A case study of how women contribute to saving water in a particular village can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community throughout rural regions of east Africa. The case could also point to the need for scholars to apply feminist theories of work and family to the issue of water conservation.

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work. In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What was I studying? Describe the research problem and describe the subject of analysis you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why was this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the research problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would include summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to study the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in the context of explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular subject of analysis to study and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that frames your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; c) what were the consequences of the event.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experience he or she has had that provides an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of his/her experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using him or her as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem.

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, cultural, economic, political, etc.], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, why study Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research reveals Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut? How might knowing the suppliers of these trucks from overseas reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should be linked to the findings from the literature review. Be sure to cite any prior studies that helped you determine that the case you chose was appropriate for investigating the research problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is more common to combine a description of the findings with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings It is important to remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations for the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and needs for further research.

The function of your paper's conclusion is to: 1)  restate the main argument supported by the findings from the analysis of your case; 2) clearly state the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place for you to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in and your professor's preferences, the concluding paragraph may contain your final reflections on the evidence presented applied to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were on social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood differently than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis.

Case Studies . Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent knowledge is more valuable than concrete, practical (context-dependent) knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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What is a Case Study and Why should I Use It in My PhD Dissertation? Understanding the purpose of case studies in research.

A case study can provide appropriate research design in a qualitative or quantitative study to to gain concrete, contextual, in-depth knowledge and multi-faceted understanding of a complex issue in its real-life context. The case study can be a great tool for providing insight and developing theories in the avenue of present research.

What is a case study?

A case study is a structured, focused, in-depth look at a particular issue or topic that reports on the findings and conclusions of researchers who have studied the case. A case study is not intended to be an exploratory analysis or to make sweeping generalizations. Rather, it is an objective, focused, and structured approach to understand the specific circumstances of an issue or the people, places, and things that make up a society. Prepared to the best of the researchers knowledge, a case study is a unique research method. In fact, case studies are the most common type of qualitative research. A case study is often a combination of interviews, focus groups, documentation, and research documentation.

Why do researchers use case study?

Case studies often contain detailed, in-depth examination of a particular issue or topic that reports on the findings and conclusions of researchers who have studied the case. Case studies are excellent for gaining insight and understanding in the following ways: – 

• Focus on one aspect of the phenomena being studied
• Give the researcher a window into the participants’ world 
• Explain the decision making process involved in data collection, analysis, and presentation 
• Allow for in-depth examination of the methodology

How to do a case study in PhD research

To conduct a case study, the students must first adhere to the university requirements and establish a strong subject knowledge in the domain of study. This is to help the researcher design the case study in a controlled environment. The case study must:

Be descriptive – Be specific – Be analytical – Be critical – Be narrative – Be written within required format

Key elements of a successful case study

The basic elements of a successful case study include: – 

• The phenomenon 
• The context 
• The methodology 
• The conclusions 
• The recommendations 
• The detail of resources used  

Research design using cases

Research designs that incorporate the use of case studies are often qualitative or quantitative in nature. A quantitative approach to the study of a phenomenon could involve questionnaires to collect data about the respondents’ experiences. On the other hand, a qualitative approach could involve in-depth interviews to understand the thoughts, feelings, and experiences of people who are experiencing the same phenomenon as the research team. Case studies can also be used in the analysis of the validity of a plan. For example, a case study examining the feasibility of a new project could examine the cost, risks, and benefits of undertaking the study and the study’s design.

The case study can be used in various design settings:

• Explanatory case studies aim to answer ‘how’ or ’why’ questions and involve an accurate description of the event or situation under study rather than the researcher’s thoughts.
• A descriptive case study is one that is focused and detailed to illustrate an unfamiliar subject that the researcher wants the audience to understand and the phenomenon under study is carefully scrutinized.
• Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. This is often conducted on the premise of a larger research problem to narrow the focus of the study and reach a specific research objective.

Should I be using case studies in my research?

Eisenhardt (1989) says that case studies are:

“Particularly well suited to new research areas or research areas for

which existing theory seems inadequate. This type of work is highly

complementary to incremental theory building from normal science

research. The former is useful in early stages of research on a topic or

when a fresh perspective is needed, whilst the latter is useful in later

stages of knowledge” (pp.548-549).

There is a certain skepticism about the usefulness of case studies as an objective research method considering its many limitations like the reliability of the respondents and the behavioral differences that can contribute to the inadequacy of the information provided by the subjects of the case, the issue of possibility of generalization of the findings of a case study considering the narrow and focussed approach that case studied utilize and finally the researcher bias which forms the main disadvantage to an objective study. The researcher should also consider the efficiency of using case studies in his or her research because of the detailed nature of this method that is not only dependent on factors beyond control but also intensive in preparation and deduction of conclusions.

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Is a case study a type of research paper?

I can’t find an answer that explicitly says that a case study is a type of research (e.g. analytical research paper, persuasive research paper, definition research paper, etc.). There’s this site that says that a case study is a research methodology (it’s the third chapter of a research paper).

• research-process
• research-undergraduate

• 1 You need to be a bit clearer. Most case studies used in education are at least a bit contrived. –  Buffy Commented Aug 30, 2021 at 11:57
• 1 The term is applied differently in different disciplines, it seems. What discipline is your question referring to? –  henning no longer feeds AI Commented Aug 30, 2021 at 12:27
• Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. –  Community Bot Commented Aug 30, 2021 at 12:30

2 Answers 2

The case study is a qualitative research method in several disciplines, mostly in the social sciences. 1 A research paper that builds on this method might also be referred to as a "case study". 2

1 See e.g. Gerring, J. (2007) Case Study Research: Principles and Practices (Cambridge: Cambridge University Press).

2 See e.g. Hooghe, L. (2005) ‘Several Roads Lead to International Norms, but Few Via International Socialization: A Case Study of the European Commission’. International Organization, Vol. 59, No. 4, pp. 861–898.

• Thank you so much for clarifying. –  CottonTheButton Commented Aug 30, 2021 at 12:30

A "case study" can mean several things:

A small[*] piece of original research that was published as part of another research paper or review. For example: a paper describes a theory and subsequently applies it to a small and well-defined subset (a case) of possible applications of the theory, thereby providing anecdotal evidence that the theory is useful,

Particularly in the social sciences, a "case study" may be described in a separate paper, and present anecdotal evidence (or contradiction) of a theory that was published elsewhere. (So similar to (1), but the "case study" is now a separate publication)

A study that is not published in a peer-reviewed journal, but used for example to promote new equipment from a commercial manufacturer by demonstrating its usefulness for the given "case" (this is also called "application note").

Note also that some journals have very specific requirements for the publication types they accept, and that those types are defined by the journal in question.

[*] see commments

• 2 +1 Most qualitative social science researchers would argue that case studies can be designed to provide much more than anecdotal evidence, however! (Even most quants would agree.) And (especially comparative) case studies are not necessarily "small" pieces of research. The three case studies by Theda Skocpol in "States and Social Revolutions" span some 300 pages and go to profound depths. –  henning no longer feeds AI Commented Aug 30, 2021 at 12:21
• 1 @henning Just to clarify: "small" here is meant with respect to the set containing all the other possible cases, not to describe the amount of effort invested in the research or length of the paper or something like that. –  Louic Commented Aug 30, 2021 at 12:39
• I see, "small n" then. Thanks for clarifying. –  henning no longer feeds AI Commented Aug 30, 2021 at 12:49

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How to Write a Case Study: A Breakdown of Requirements

It can take months to develop a case study. First, a topic must be chosen. Then the researcher must state his hypothesis, and make certain it lines up with the chosen topic. Then all the research must be completed. The case study can require both quantitative and qualitative research, as well as interviews with subjects. Once that is all done, it is time to write the case study.

Not all case studies are written the same. Depending on the size and topic of the study, it could be hundreds of pages long. Regardless of the size, the case study should have four main sections. These sections are:

1. Introduction

2. Background

3. Presentation of Findings

4. Conclusion

The Introduction

The introduction should set the stage for the case study, and state the thesis for the report. The intro must clearly articulate what the study's intention is, as well as how you plan on explaining and answering the thesis.

Again, remember that a case study is not a formal scientific research report that will only be read by scientists. The case study must be able to be read and understood by the layperson, and should read almost as a story, with a clear narrative.

As the reader reads the introduction, they should fully understand what the study is about, and why it is important. They should have a strong foundation for the background they will learn about in the next section.

The introduction should not be long. You must be able to introduce your topic in one or two paragraphs. Ideally, the introduction is one paragraph of about 3-5 sentences.

The Background

The background should detail what information brought the researcher to pose his hypothesis. It should clearly explain the subject or subjects, as well as their background information. And lastly, the background must give the reader a full understanding of the issue at hand, and what process will be taken with the study. Photos and videos are always helpful when applicable.

When writing the background, the researcher must explain the research methods used, and why. The type of research used will be dependent on the type of case study. The reader should have a clear idea why a particular type of research is good for the field and type of case study.

For example, a case study that is trying to determine what causes PTSD in veterans will heavily use interviews as a research method. Directly interviewing subjects garners invaluable research for the researcher. If possible, reference studies that prove this.

Again, as with the introduction, you do not want to write an extremely long background. It is important you provide the right amount of information, as you do not want to bore your readers with too much information, and you don't want them under-informed.

How much background information should a case study provide? What would happen if the case study had too much background info?

What would happen if the case study had too little background info?

The Presentation of Findings

While a case study might use scientific facts and information, a case study should not read as a scientific research journal or report. It should be easy to read and understand, and should follow the narrative determined in the first step.

The presentation of findings should clearly explain how the topic was researched, and summarize what the results are. Data should be summarized as simply as possible so that it is understandable by people without a scientific background. The researcher should describe what was learned from the interviews, and how the results answered the questions asked in the introduction.

When writing up the report, it is important to set the scene. The writer must clearly lay out all relevant facts and detail the most important points. While this section may be lengthy, you do not want to overwhelm the reader with too much information.

The Conclusion

The final section of the study is the conclusion. The purpose of the study isn't necessarily to solve the problem, only to offer possible solutions. The final summary should be an end to the story.

Remember, the case study is about asking and answering questions. The conclusion should answer the question posed by the researcher, but also leave the reader with questions of his own. The researcher wants the reader to think about the questions posed in the study, and be free to come to their own conclusions as well.

When reading the conclusion, the reader should be able to have the following takeaways:

Was there a solution provided? If so, why was it chosen?

Was the solution supported with solid evidence?

Did the personal experiences and interviews support the solution?

The conclusion should also make any recommendations that are necessary. What needs to be done, and you exactly should do it? In the case of the vets with PTSD, once a cause is determined, who is responsible for making sure the needs of the veterans are met?

English Writing Standards For Case Studies

When writing the case study, it is important to follow standard academic and scientific rules when it comes to spelling and grammar.

Spelling and Grammar

It should go without saying that a thorough spell check should be done. Remember, many case studies will require words or terms that are not in standard online dictionaries, so it is imperative the correct spelling is used. If possible, the first draft of the case study should be reviewed and edited by someone other than yourself.

Case studies are normally written in the past tense, as the report is detailing an event or topic that has since passed. The report should be written using a very logical and clear tone. All case studies are scientific in nature and should be written as such.

The First Draft

You do not sit down and write the case study in one day. It is a long and detailed process, and it must be done carefully and with precision. When you sit down to first start writing, you will want to write in plain English, and detail the what, when and how.

When writing the first draft, note any relevant assumptions. Don't immediately jump to any conclusions; just take notes of any initial thoughts. You are not looking for solutions yet. In the first draft use direct quotes when needed, and be sure to identify and qualify all information used.

If there are any issues you do not understand, the first draft is where it should be identified. Make a note so you return to review later. Using a spreadsheet program like Excel or Google Sheets is very valuable during this stage of the writing process, and can help keep you and your information and data organized.

The Second Draft

To prepare the second draft, you will want to assemble everything you have written thus far. You want to reduce the amount of writing so that the writing is tightly written and cogent. Remember, you want your case study to be interesting to read.

When possible, you should consider adding images, tables, maps, or diagrams to the text to make it more interesting for the reader. If you use any of these, make sure you have permission to use them. You cannot take an image from the Internet and use it without permission.

Once you have completed the second draft, you are not finished! It is imperative you have someone review your work. This could be a coworker, friend, or trusted colleague. You want someone who will give you an honest review of your work, and is willing to give you feedback, whether positive or negative.

Remember, you cannot proofread enough! You do not want to risk all of your hard work and research, and end up with a final case study that has spelling or grammatical errors. One typo could greatly hurt your project and damage your reputation in your field.

All case studies should follow LIT – Logical – Inclusive – Thorough.

The case study obviously must be logical. There can be no guessing or estimating. This means that the report must state what was observed, but cannot include any opinion or assumptions that might come from such an observation.

For example, if a veteran subject arrives at an interview holding an empty liquor bottle and is slurring his words, that observation must be made. However, the researcher cannot make the inference that the subject was intoxicated. The report can only include the facts.

With the Genie case, researchers witnessed Genie hitting herself and practicing self-harm. It could be assumed that she did this when she was angry. However, this wasn't always the case. She would also hit herself when she was afraid, bored or apprehensive. It is essential that researchers not guess or infer.

In order for a report to be inclusive, it must contain ALL data and findings. The researcher cannot pick and choose which data or findings to use in the report.

Using the example above, if a veteran subject arrives for an interview holding an empty liquor bottle and is slurring his words; any and all additional information that can be garnered should be recorded. For instance, what the subject was wearing, what was his demeanor, was he able to speak and communicate, etc.

When observing a man who might be drunk, it can be easy to make assumptions. However, the researcher cannot allow personal biases or beliefs to sway the findings. Any and all relevant facts must be included, regardless of size or perceived importance. Remember, small details might not seem relevant at the time of the interview. But once it is time to catalog the findings, small details might become important.

The last tip is to be thorough. It is important to delve into every observation. The researcher shouldn't just write down what they see and move on. It is essential to detail as much as possible.

For example, when interviewing veteran subjects, there interview responses are not the only information that should be garnered from the interview. The interviewer should use all senses when detailing their subject.

How does the subject appear? Is he clean? How is he dressed?

How does his voice sound? Is he speaking clearly and making cohesive thoughts? Does his voice sound raspy? Does he speak with a whisper, or does he speak too loudly?

Does the subject smell? Is he wearing cologne, or can you smell that he hasn't bathed or washed his clothes? What do his clothes look like? Is he well dressed, or does he wear casual clothes?

What is the background of the subject? What are his current living arrangements? Does he have supportive family and friends? Is he a loner who doesn't have a solid support system? Is the subject working? If so, is he happy with the job? If he is not employed, why is that? What makes the subject unemployable?

Case Studies in Marketing

We have already determined that case studies are very valuable in the business world. This is particularly true in the marketing field, which includes advertising and public relations. While case studies are almost all the same, marketing case studies are usually more dependent on interviews and observations.

Well-Known Marketing Case Studies

DeBeers is a diamond company headquartered in Luxembourg, and based in South Africa. It is well known for its logo, "A diamond is forever", which has been voted the best advertising slogan of the 20 th century.

Many studies have been done about DeBeers, but none are as well known as their marketing case study, and how they positioned themselves to be the most successful and well-known diamond company in the world.

DeBeers developed the idea for a diamond engagement ring. They also invented the "eternity band", which is a ring that has diamonds going all around it, signifying that long is forever.

They also invented the three-stone ring, signifying the past, present and future. De Beers was the first company to attribute their products, diamonds to the idea of love and romance. They originated the idea that an engagement ring should cost two-months salary.

The two-month salary standard is particularly unique, in that it is totally subjective. A ring should mean the same whether the man makes $25,000 a year or $250,000. And yet, the standard sticks due to DeBeers incredible marketing skills.

The De Beers case study is one of the most famous studies when it comes to both advertising and marketing, and is used worldwide as the ultimate example of a successful ongoing marketing campaign.

Planning the Market Research

The most important parts of the marketing case study are:

1. The case study's questions

2. The study's propositions

3. How information and data will be analyzed

4. The logic behind what is being proposed

5. How the findings will be interpreted

The study's questions should be either "how" or "why" questions, and their definitions are the researchers first job. These questions will help determine the study's goals.

Not every case study has a proposition. If you are doing an exploratory study, you will not have propositions. Instead, you will have a stated purpose, which will determine whether your study is successful, or not.

How the information will be analyzed will depend on what the topic is. This would vary depending on whether it was a person, group, or organization. Event and place studies are done differently.

When setting up your research, you will want to follow case study protocol. The protocol should have the following sections:

1. An overview of the case study, including the objectives, topic and issues.

2. Procedures for gathering information and conducting interviews.

3. Questions that will be asked during interviews and data collection.

4. A guide for the final case study report.

When deciding upon which research methods to use, these are the most important:

1. Documents and archival records

2 . Interviews

3. Direct observations (and indirect when possible)

4. Indirect observations, or observations of subjects

5. Physical artifacts and tools

Documents could include almost anything, including letters, memos, newspaper articles, Internet articles, other case studies, or any other document germane to the study.

Developing the Case Study

Developing a marketing case study follows the same steps and procedures as most case studies. It begins with asking a question, "what is missing?"

1. What is the background of the case study? Who requested the study to be done and why? What industry is the study in, and where will the study take place? What marketing needs are you trying to address?

2. What is the problem that needs a solution? What is the situation, and what are the risks? What are you trying to prove?

3. What questions are required to analyze the problem? What questions might the reader of the study have?

4. What tools are required to analyze the problem? Is data analysis necessary? Can the study use just interviews and observations, or will it require additional information?

5. What is your current knowledge about the problem or situation? How much background information do you need to procure? How will you obtain this background info?

6. What other information do you need to know to successfully complete the study?

7. How do you plan to present the report? Will it be a simple written report, or will you add PowerPoint presentations or images or videos? When is the report due? Are you giving yourself enough time to complete the project?

Formulating the Marketing Case Study

1. What is the marketing problem? Most case studies begin with a problem that management or the marketing department is facing. You must fully understand the problem and what caused it. That is when you can start searching for a solution.

However, marketing case studies can be difficult to research. You must turn a marketing problem into a research problem. For example, if the problem is that sales are not growing, you must translate that to a research problem.

What could potential research problems be?

Research problems could be poor performance or poor expectations. You want a research problem because then you can find an answer. Management problems focus on actions, such as whether to advertise more, or change advertising strategies. Research problems focus on finding out how to solve the management problem.

Method of Inquiry

As with the research for most case studies, the scientific method is standard. It allows you to use existing knowledge as a starting point. The scientific method has the following steps:

1. Ask a question – formulate a problem

2. Do background research

3. Formulate a problem

4. Develop/construct a hypothesis

5. Make predictions based on the hypothesis

6. Do experiments to test the hypothesis

7 . Conduct the test/experiment

8 . Analyze and communicate the results

The above terminology is very similar to the research process. The main difference is that the scientific method is objective and the research process is subjective. Quantitative research is based on impartial analysis, and qualitative research is based on personal judgment.

Research Method

After selecting the method of inquiry, it is time to decide on a research method. There are two main research methodologies, experimental research and non-experimental research.

Experimental research allows you to control the variables and to manipulate any of the variables that influence the study.

Non-experimental research allows you to observe, but not intervene. You just observe and then report your findings.

Research Design

The design is the plan for how you will conduct the study, and how you will collect the data. The design is the scientific method you will use to obtain the information you are seeking.

Data Collection

There are many different ways to collect data, with the two most important being interviews and observation.

Interviews are when you ask people questions and get a response. These interviews can be done face-to-face, by telephone, the mail, email, or even the Internet. This category of research techniques is survey research. Interviews can be done in both experimental and non-experimental research.

Observation is watching a person or company's behavior. For example, by observing a persons buying behavior, you could predict how that person will make purchases in the future.

When using interviews or observation, it is required that you record your results. How you record the data will depend on which method you use. As with all case studies, using a research notebook is key, and will be the heart of the study.

Sample Design

When developing your case study, you won't usually examine an entire population; those are done by larger research projects. Your study will use a sample, which is a small representation of the population. When designing your sample, be prepared to answer the following questions:

1. From which type of population should the sample be chosen?

2. What is the process for the selection of the sample?

3. What will be the size of the sample?

There are two ways to select a sample from the general population; probability and non-probability sampling. Probability sampling uses random sampling of everyone in the population. Non-probability sampling uses the judgment of the researcher.

The last step of designing your sample is to determine the sample size. This can depend on cost and accuracy. Larger samples are better and more accurate, but they can also be costly.

Analysis of the Data

In order to use the data, it first must be analyzed. How you analyze the data should be decided upon as early in the process as possible, and will vary depending on the type of info you are collecting, and the form of measurement being used. As stated repeatedly, make sure you keep track of everything in the research notebook.

The Marketing Case Study Report

The final stage of the process is the marketing case study. The final study will include all of the information, as well as detail the process. It will also describe the results, conclusions, and any recommendations. It must have all the information needed so that the reader can understand the case study.

As with all case studies, it must be easy to read. You don't want to use info that is too technical; otherwise you could potentially overwhelm your reader. So make sure it is written in plain English, with scientific and technical terms kept to a minimum.

Using Your Case Study

Once you have your finished case study, you have many opportunities to get that case study in front of potential customers. Here is a list of the ways you can use your case study to help your company's marketing efforts.

1. Have a page on your website that is dedicated to case studies. The page should have a catchy name and list all of the company's case studies, beginning with the most recent. Next to each case study list its goals and results.

2. Put the case study on your home page. This will put your study front and center, and will be immediately visible when customers visit your web page. Make sure the link isn't hidden in an area rarely visited by guests. You can highlight the case study for a few weeks or months, or until you feel your study has received enough looks.

3. Write a blog post about your case study. Obviously you must have a blog for this to be successful. This is a great way to give your case study exposure, and it allows you to write the post directly addressing your audience's needs.

4 . Make a video from your case study. Videos are more popular than ever, and turning a lengthy case study into a brief video is a great way to get your case study in front of people who might not normally read a case study.

5. Use your case study on a landing page. You can pull quotes from the case study and use those on product pages. Again, this format works best when you use market segmentation.

6. Post about your case studies on social media. You can share links on Twitter, Facebook and LinkedIn. Write a little interesting tidbit, enough to capture your client's interest, and then place the link.

7 . Use your case study in your email marketing. This is most effective if your email list is segmented, and you can direct your case study to those most likely to be receptive to it.

8. Use your case studies in your newsletters. This can be especially effective if you use segmentation with your newsletters, so you can gear the case study to those most likely to read and value it.

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How to use a case study in your masters dissertation

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Frequently asked questions.

Yes, in fact a case study is a very good option in your dissertation. There are multiple ways to implement a case study in your thesis. For instance, one main study which is in depth and complex or you could feature multiple case studies.

Case studies are a way to research a particular field, group, people and situation. The topic of research is studied deeply and thoroughly in order to solve a problem or uncover information. Case studies are a type of qualitative research.

If you are ready to find a masters course check out Masters Compare.

Prof Martyn Denscombe, author of “ The Good Research Guide, 6th edition ”, gives expert advice on how to use a case study in your masters dissertation. 

There are two main examples for how to use a case study in your masters dissertation, namely quantitative and qualitative case studies.

First, a case study provides a platform that allows you to study a situation in depth and produce the level of academic inquiry that is expected in a master’s degree. In the context of any master’s programme the dissertation operates as something of a showcase for a student’s abilities.

It can easily make the difference between getting a merit and a distinction in the final award of degree. It is important, therefore, to base the work on an approach that allows things to be explored in sufficient depth and detail to warrant a good grade.

Second, case studies can be useful in a practical sense. It is possible to complete a case study in a relatively short period of intense study and so it is the kind of research that is feasible in terms of the kind of time constraints that face master’s students as they enter the final stages of their programme of study.

Added to which a case study can also be a rather convenient form of research, avoiding the time and costs of travel to multiple research sites. The use of case studies, then, would appear to be an attractive proposition. But it is not an approach that should be used naively without consideration of its limitations or potential pitfalls.

To be a good case study the research needs to consider certain key issues. If they are not addressed it will considerably lower the value of the master’s degree. For instance, a good case study needs to:

• Be crystal clear about the purpose for which the research is being conducted
• Justify the selection of the particular case being studied
• Describe how the chosen case compares with others of its type
• Explain the basis on which any generalizations can be made from the findings

This is where The Good Research Guide, 6th edition becomes so valuable. It not only identifies the key points that need to be addressed in order to conduct a competent questionnaire survey.

It gets right to the heart of the matter with plenty of practical guidance on how to deal with issues. Using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects. It outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe’s advice on using a questionnaire survey for your postgraduate dissertation

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Action Research vs. Case Study

What's the difference.

Action research and case study are both research methodologies used in social sciences to investigate and understand complex phenomena. However, they differ in their approach and purpose. Action research is a collaborative and participatory approach that involves researchers and practitioners working together to identify and solve practical problems in real-world settings. It aims to bring about positive change and improvement in the context being studied. On the other hand, case study is an in-depth and detailed examination of a particular individual, group, or situation. It focuses on understanding the unique characteristics and dynamics of the case being studied and often involves extensive data collection and analysis. While action research emphasizes practical application and problem-solving, case study emphasizes detailed exploration and understanding of a specific case.

Further Detail

Introduction.

Action research and case study are two widely used research methodologies in various fields. While both approaches aim to gain insights and understanding, they differ in their focus, design, and implementation. This article will explore the attributes of action research and case study, highlighting their similarities and differences.

Action Research

Action research is a participatory approach that involves collaboration between researchers and practitioners to address real-world problems. It emphasizes the active involvement of stakeholders in the research process, aiming to bring about practical change and improvement. Action research typically follows a cyclical process, consisting of planning, action, observation, and reflection.

One of the key attributes of action research is its focus on generating knowledge that is directly applicable to the context in which it is conducted. It aims to bridge the gap between theory and practice by actively involving practitioners in the research process. This participatory nature allows for a deeper understanding of the complexities and nuances of the problem being investigated.

Action research often involves multiple iterations, with each cycle building upon the insights gained from the previous one. This iterative approach allows for continuous learning and adaptation, enabling researchers to refine their interventions and strategies based on the feedback received. It also promotes a sense of ownership and empowerment among the participants, as they actively contribute to the research process.

Furthermore, action research is characterized by its emphasis on collaboration and co-learning. It encourages the exchange of ideas and knowledge between researchers and practitioners, fostering a sense of shared responsibility and collective action. This collaborative approach not only enhances the quality of the research but also increases the likelihood of successful implementation of the findings.

In summary, action research is a participatory and iterative approach that aims to generate practical knowledge through collaboration between researchers and practitioners. It focuses on addressing real-world problems and promoting positive change within specific contexts.

Case study, on the other hand, is an in-depth investigation of a particular phenomenon, event, or individual. It involves the detailed examination of a specific case or cases to gain a comprehensive understanding of the subject under study. Case studies can be conducted using various research methods, such as interviews, observations, and document analysis.

One of the key attributes of case study research is its ability to provide rich and detailed insights into complex phenomena. By focusing on a specific case, researchers can delve deep into the intricacies and unique aspects of the subject, uncovering valuable information that may not be easily captured through other research methods.

Case studies are often used to explore and understand real-life situations in their natural settings. They allow researchers to examine the context and dynamics surrounding the case, providing a holistic view of the phenomenon under investigation. This contextual understanding is crucial for gaining a comprehensive and nuanced understanding of the subject.

Furthermore, case studies are particularly useful when the boundaries between the phenomenon and its context are not clearly defined. They allow for the exploration of complex and multifaceted issues, enabling researchers to capture the interplay of various factors and variables. This holistic approach enhances the validity and reliability of the findings.

Moreover, case studies can be exploratory, descriptive, or explanatory in nature, depending on the research questions and objectives. They can be used to generate hypotheses, provide detailed descriptions, or test theoretical frameworks. This versatility makes case study research applicable in various fields, including psychology, sociology, business, and education.

In summary, case study research is an in-depth investigation of a specific phenomenon, providing rich and detailed insights into complex situations. It focuses on understanding the context and dynamics surrounding the case, allowing for a comprehensive exploration of multifaceted issues.

Similarities

While action research and case study differ in their focus and design, they also share some common attributes. Both approaches aim to gain insights and understanding, albeit through different means. They both involve the collection and analysis of data to inform decision-making and improve practice.

Furthermore, both action research and case study can be conducted in naturalistic settings, allowing for the examination of real-life situations. They both emphasize the importance of context and seek to understand the complexities and nuances of the phenomena under investigation.

Moreover, both methodologies can involve multiple data collection methods, such as interviews, observations, and document analysis. They both require careful planning and design to ensure the validity and reliability of the findings.

Additionally, both action research and case study can contribute to theory development. While action research focuses on generating practical knowledge, it can also inform and contribute to theoretical frameworks. Similarly, case studies can provide empirical evidence that can be used to refine and expand existing theories.

In summary, action research and case study share common attributes, including their aim to gain insights and understanding, their focus on real-life situations, their emphasis on context, their use of multiple data collection methods, and their potential contribution to theory development.

Action research and case study are two distinct research methodologies that offer unique approaches to gaining insights and understanding. Action research emphasizes collaboration, participation, and practical change, while case study focuses on in-depth investigation and contextual understanding. Despite their differences, both approaches contribute to knowledge generation and have the potential to inform theory and practice. Researchers should carefully consider the nature of their research questions and objectives to determine which approach is most suitable for their study.

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Distinguishing case study as a research method from case reports as a publication type

The purpose of this editorial is to distinguish between case reports and case studies. In health, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. As a qualitative methodology, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. The depth and richness of case study description helps readers understand the case and whether findings might be applicable beyond that setting.

Single-institution descriptive reports of library activities are often labeled by their authors as “case studies.” By contrast, in health care, single patient retrospective descriptions are published as “case reports.” Both case reports and case studies are valuable to readers and provide a publication opportunity for authors. A previous editorial by Akers and Amos about improving case studies addresses issues that are more common to case reports; for example, not having a review of the literature or being anecdotal, not generalizable, and prone to various types of bias such as positive outcome bias [ 1 ]. However, case study research as a qualitative methodology is pursued for different purposes than generalizability. The authors’ purpose in this editorial is to clearly distinguish between case reports and case studies. We believe that this will assist authors in describing and designating the methodological approach of their publications and help readers appreciate the rigor of well-executed case study research.

Case reports often provide a first exploration of a phenomenon or an opportunity for a first publication by a trainee in the health professions. In health care, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. Another type of study categorized as a case report is an “N of 1” study or single-subject clinical trial, which considers an individual patient as the sole unit of observation in a study investigating the efficacy or side effect profiles of different interventions. Entire journals have evolved to publish case reports, which often rely on template structures with limited contextualization or discussion of previous cases. Examples that are indexed in MEDLINE include the American Journal of Case Reports , BMJ Case Reports, Journal of Medical Case Reports, and Journal of Radiology Case Reports . Similar publications appear in veterinary medicine and are indexed in CAB Abstracts, such as Case Reports in Veterinary Medicine and Veterinary Record Case Reports .

As a qualitative methodology, however, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. Distinctions include the investigator’s definitions and delimitations of the case being studied, the clarity of the role of the investigator, the rigor of gathering and combining evidence about the case, and the contextualization of the findings. Delimitation is a term from qualitative research about setting boundaries to scope the research in a useful way rather than describing the narrow scope as a limitation, as often appears in a discussion section. The depth and richness of description helps readers understand the situation and whether findings from the case are applicable to their settings.

CASE STUDY AS A RESEARCH METHODOLOGY

Case study as a qualitative methodology is an exploration of a time- and space-bound phenomenon. As qualitative research, case studies require much more from their authors who are acting as instruments within the inquiry process. In the case study methodology, a variety of methodological approaches may be employed to explain the complexity of the problem being studied [ 2 , 3 ].

Leading authors diverge in their definitions of case study, but a qualitative research text introduces case study as follows:

Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system (a case) or multiple bound systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes. The unit of analysis in the case study might be multiple cases (a multisite study) or a single case (a within-site case study). [ 4 ]

Methodologists writing core texts on case study research include Yin [ 5 ], Stake [ 6 ], and Merriam [ 7 ]. The approaches of these three methodologists have been compared by Yazan, who focused on six areas of methodology: epistemology (beliefs about ways of knowing), definition of cases, design of case studies, and gathering, analysis, and validation of data [ 8 ]. For Yin, case study is a method of empirical inquiry appropriate to determining the “how and why” of phenomena and contributes to understanding phenomena in a holistic and real-life context [ 5 ]. Stake defines a case study as a “well-bounded, specific, complex, and functioning thing” [ 6 ], while Merriam views “the case as a thing, a single entity, a unit around which there are boundaries” [ 7 ].

Case studies are ways to explain, describe, or explore phenomena. Comments from a quantitative perspective about case studies lacking rigor and generalizability fail to consider the purpose of the case study and how what is learned from a case study is put into practice. Rigor in case studies comes from the research design and its components, which Yin outlines as (a) the study’s questions, (b) the study’s propositions, (c) the unit of analysis, (d) the logic linking the data to propositions, and (e) the criteria for interpreting the findings [ 5 ]. Case studies should also provide multiple sources of data, a case study database, and a clear chain of evidence among the questions asked, the data collected, and the conclusions drawn [ 5 ].

Sources of evidence for case studies include interviews, documentation, archival records, direct observations, participant-observation, and physical artifacts. One of the most important sources for data in qualitative case study research is the interview [ 2 , 3 ]. In addition to interviews, documents and archival records can be gathered to corroborate and enhance the findings of the study. To understand the phenomenon or the conditions that created it, direct observations can serve as another source of evidence and can be conducted throughout the study. These can include the use of formal and informal protocols as a participant inside the case or an external or passive observer outside of the case [ 5 ]. Lastly, physical artifacts can be observed and collected as a form of evidence. With these multiple potential sources of evidence, the study methodology includes gathering data, sense-making, and triangulating multiple streams of data. Figure 1 shows an example in which data used for the case started with a pilot study to provide additional context to guide more in-depth data collection and analysis with participants.

Key sources of data for a sample case study

VARIATIONS ON CASE STUDY METHODOLOGY

Case study methodology is evolving and regularly reinterpreted. Comparative or multiple case studies are used as a tool for synthesizing information across time and space to research the impact of policy and practice in various fields of social research [ 9 ]. Because case study research is in-depth and intensive, there have been efforts to simplify the method or select useful components of cases for focused analysis. Micro-case study is a term that is occasionally used to describe research on micro-level cases [ 10 ]. These are cases that occur in a brief time frame, occur in a confined setting, and are simple and straightforward in nature. A micro-level case describes a clear problem of interest. Reporting is very brief and about specific points. The lack of complexity in the case description makes obvious the “lesson” that is inherent in the case; although no definitive “solution” is necessarily forthcoming, making the case useful for discussion. A micro-case write-up can be distinguished from a case report by its focus on briefly reporting specific features of a case or cases to analyze or learn from those features.

DATABASE INDEXING OF CASE REPORTS AND CASE STUDIES

Disciplines such as education, psychology, sociology, political science, and social work regularly publish rich case studies that are relevant to particular areas of health librarianship. Case reports and case studies have been defined as publication types or subject terms by several databases that are relevant to librarian authors: MEDLINE, PsycINFO, CINAHL, and ERIC. Library, Information Science & Technology Abstracts (LISTA) does not have a subject term or publication type related to cases, despite many being included in the database. Whereas “Case Reports” are the main term used by MEDLINE’s Medical Subject Headings (MeSH) and PsycINFO’s thesaurus, CINAHL and ERIC use “Case Studies.”

Case reports in MEDLINE and PsycINFO focus on clinical case documentation. In MeSH, “Case Reports” as a publication type is specific to “clinical presentations that may be followed by evaluative studies that eventually lead to a diagnosis” [ 11 ]. “Case Histories,” “Case Studies,” and “Case Study” are all entry terms mapping to “Case Reports”; however, guidance to indexers suggests that “Case Reports” should not be applied to institutional case reports and refers to the heading “Organizational Case Studies,” which is defined as “descriptions and evaluations of specific health care organizations” [ 12 ].

PsycINFO’s subject term “Case Report” is “used in records discussing issues involved in the process of conducting exploratory studies of single or multiple clinical cases.” The Methodology index offers clinical and non-clinical entries. “Clinical Case Study” is defined as “case reports that include disorder, diagnosis, and clinical treatment for individuals with mental or medical illnesses,” whereas “Non-clinical Case Study” is a “document consisting of non-clinical or organizational case examples of the concepts being researched or studied. The setting is always non-clinical and does not include treatment-related environments” [ 13 ].

Both CINAHL and ERIC acknowledge the depth of analysis in case study methodology. The CINAHL scope note for the thesaurus term “Case Studies” distinguishes between the document and the methodology, though both use the same term: “a review of a particular condition, disease, or administrative problem. Also, a research method that involves an in-depth analysis of an individual, group, institution, or other social unit. For material that contains a case study, search for document type: case study.” The ERIC scope note for the thesaurus term “Case Studies” is simple: “detailed analyses, usually focusing on a particular problem of an individual, group, or organization” [ 14 ].

PUBLICATION OF CASE STUDY RESEARCH IN LIBRARIANSHIP

We call your attention to a few examples published as case studies in health sciences librarianship to consider how their characteristics fit with the preceding definitions of case reports or case study research. All present some characteristics of case study research, but their treatment of the research questions, richness of description, and analytic strategies vary in depth and, therefore, diverge at some level from the qualitative case study research approach. This divergence, particularly in richness of description and analysis, may have been constrained by the publication requirements.

As one example, a case study by Janke and Rush documented a time- and context-bound collaboration involving a librarian and a nursing faculty member [ 15 ]. Three objectives were stated: (1) describing their experience of working together on an interprofessional research team, (2) evaluating the value of the librarian role from librarian and faculty member perspectives, and (3) relating findings to existing literature. Elements that signal the qualitative nature of this case study are that the authors were the research participants and their use of the term “evaluation” is reflection on their experience. This reads like a case study that could have been enriched by including other types of data gathered from others engaging with this team to broaden the understanding of the collaboration.

As another example, the description of the academic context is one of the most salient components of the case study written by Clairoux et al., which had the objectives of (1) describing the library instruction offered and learning assessments used at a single health sciences library and (2) discussing the positive outcomes of instruction in that setting [ 16 ]. The authors focus on sharing what the institution has done more than explaining why this institution is an exemplar to explore a focused question or understand the phenomenon of library instruction. However, like a case study, the analysis brings together several streams of data including course attendance, online material page views, and some discussion of results from surveys. This paper reads somewhat in between an institutional case report and a case study.

The final example is a single author reporting on a personal experience of creating and executing the role of research informationist for a National Institutes of Health (NIH)–funded research team [ 17 ]. There is a thoughtful review of the informationist literature and detailed descriptions of the institutional context and the process of gaining access to and participating in the new role. However, the motivating question in the abstract does not seem to be fully addressed through analysis from either the reflective perspective of the author as the research participant or consideration of other streams of data from those involved in the informationist experience. The publication reads more like a case report about this informationist’s experience than a case study that explores the research informationist experience through the selection of this case.

All of these publications are well written and useful for their intended audiences, but in general, they are much shorter and much less rich in depth than case studies published in social sciences research. It may be that the authors have been constrained by word counts or page limits. For example, the submission category for Case Studies in the Journal of the Medical Library Association (JMLA) limited them to 3,000 words and defined them as “articles describing the process of developing, implementing, and evaluating a new service, program, or initiative, typically in a single institution or through a single collaborative effort” [ 18 ]. This definition’s focus on novelty and description sounds much more like the definition of case report than the in-depth, detailed investigation of a time- and space-bound problem that is often examined through case study research.

Problem-focused or question-driven case study research would benefit from the space provided for Original Investigations that employ any type of quantitative or qualitative method of analysis. One of the best examples in the JMLA of an in-depth multiple case study that was authored by a librarian who published the findings from her doctoral dissertation represented all the elements of a case study. In eight pages, she provided a theoretical basis for the research question, a pilot study, and a multiple case design, including integrated data from interviews and focus groups [ 19 ].

We have distinguished between case reports and case studies primarily to assist librarians who are new to research and critical appraisal of case study methodology to recognize the features that authors use to describe and designate the methodological approaches of their publications. For researchers who are new to case research methodology and are interested in learning more, Hancock and Algozzine provide a guide [ 20 ].

We hope that JMLA readers appreciate the rigor of well-executed case study research. We believe that distinguishing between descriptive case reports and analytic case studies in the journal’s submission categories will allow the depth of case study methodology to increase. We also hope that authors feel encouraged to pursue submitting relevant case studies or case reports for future publication.

Editor’s note: In response to this invited editorial, the Journal of the Medical Library Association will consider manuscripts employing rigorous qualitative case study methodology to be Original Investigations (fewer than 5,000 words), whereas manuscripts describing the process of developing, implementing, and assessing a new service, program, or initiative—typically in a single institution or through a single collaborative effort—will be considered to be Case Reports (formerly known as Case Studies; fewer than 3,000 words).

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Qualitative Research Designs

Case study design, using case study design in the applied doctoral experience (ade), applicability of case study design to applied problem of practice, case study design references.

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The field of qualitative research there are a number of research designs (also referred to as “traditions” or “genres”), including case study, phenomenology, narrative inquiry, action research, ethnography, grounded theory, as well as a number of critical genres including Feminist theory, indigenous research, critical race theory and cultural studies. The choice of research design is directly tied to and must be aligned with your research problem and purpose. As Bloomberg & Volpe (2019) explain:

Choice of research design is directly tied to research problem and purpose. As the researcher, you actively create the link among problem, purpose, and design through a process of reflecting on problem and purpose, focusing on researchable questions, and considering how to best address these questions. Thinking along these lines affords a research study methodological congruence (p. 38).

Case study is an in-depth exploration from multiple perspectives of a bounded social phenomenon, be this a social system such as a program, event, institution, organization, or community (Stake, 1995, 2005; Yin, 2018). Case study is employed across disciplines, including education, health care, social work, sociology, and organizational studies. The purpose is to generate understanding and deep insights to inform professional practice, policy development, and community or social action (Bloomberg 2018).

Yin (2018) and Stake (1995, 2005), two of the key proponents of case study methodology, use different terms to describe case studies. Yin categorizes case studies as exploratory or descriptive . The former is used to explore those situations in which the intervention being evaluated has no clear single set of outcomes. The latter is used to describe an intervention or phenomenon and the real-life context in which it occurred. Stake identifies case studies as intrinsic or instrumental , and he proposes that a primary distinction in designing case studies is between single and multiple (or collective) case study designs. A single case study may be an instrumental case study (research focuses on an issue or concern in one bounded case) or an intrinsic case study (the focus is on the case itself because the case presents a unique situation). A longitudinal case study design is chosen when the researcher seeks to examine the same single case at two or more different points in time or to capture trends over time. A multiple case study design is used when a researcher seeks to determine the prevalence or frequency of a particular phenomenon. This approach is useful when cases are used for purposes of a cross-case analysis in order to compare, contrast, and synthesize perspectives regarding the same issue. The focus is on the analysis of diverse cases to determine how these confirm the findings within or between cases, or call the findings into question.

Case study affords significant interaction with research participants, providing an in-depth picture of the phenomenon (Bloomberg & Volpe, 2019). Research is extensive, drawing on multiple methods of data collection, and involves multiple data sources. Triangulation is critical in attempting to obtain an in-depth understanding of the phenomenon under study and adds rigor, breadth, and depth to the study and provides corroborative evidence of the data obtained. Analysis of data can be holistic or embedded—that is, dealing with the whole or parts of the case (Yin, 2018). With multiple cases the typical analytic strategy is to provide detailed description of themes within each case (within-case analysis), followed by thematic analysis across cases (cross-case analysis), providing insights regarding how individual cases are comparable along important dimensions. Research culminates in the production of a detailed description of a setting and its participants, accompanied by an analysis of the data for themes or patterns (Stake, 1995, 2005; Yin, 2018). In addition to thick, rich description, the researcher’s interpretations, conclusions, and recommendations contribute to the reader’s overall understanding of the case study.

Analysis of findings should show that the researcher has attended to all the data, should address the most significant aspects of the case, and should demonstrate familiarity with the prevailing thinking and discourse about the topic. The goal of case study design (as with all qualitative designs) is not generalizability but rather transferability —that is, how (if at all) and in what ways understanding and knowledge can be applied in similar contexts and settings. The qualitative researcher attempts to address the issue of transferability by way of thick, rich description that will provide the basis for a case or cases to have relevance and potential application across a broader context.

Qualitative research methods ask the questions of "what" and "how" a phenomenon is understood in a real-life context (Bloomberg & Volpe, 2019). In the education field, qualitative research methods uncover educational experiences and practices because qualitative research allows the researcher to reveal new knowledge and understanding. Moreover, qualitative descriptive case studies describe, analyze and interpret events that explain the reasoning behind specific phenomena (Bloomberg, 2018). As such, case study design can be the foundation for a rigorous study within the Applied Doctoral Experience (ADE).

Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate information that is current. This fits well with the ADE program, as students are typically exploring a problem of practice. Because of the flexibility of the methods used, a descriptive design provides the researcher with the opportunity to choose data collection methods that are best suited to a practice-based research purpose, and can include individual interviews, focus groups, observation, surveys, and critical incident questionnaires. Methods are triangulated to contribute to the study’s trustworthiness. In selecting the set of data collection methods, it is important that the researcher carefully consider the alignment between research questions and the type of data that is needed to address these. Each data source is one piece of the “puzzle,” that contributes to the researcher’s holistic understanding of a phenomenon. The various strands of data are woven together holistically to promote a deeper understanding of the case and its application to an educationally-based problem of practice.

Research studies within the Applied Doctoral Experience (ADE) will be practical in nature and focus on problems and issues that inform educational practice.  Many of the types of studies that fall within the ADE framework are exploratory, and align with case study design. Case study design fits very well with applied problems related to educational practice, as the following set of examples illustrate:

Elementary Bilingual Education Teachers’ Self-Efficacy in Teaching English Language Learners: A Qualitative Case Study

The problem to be addressed in the proposed study is that some elementary bilingual education teachers’ beliefs about their lack of preparedness to teach the English language may negatively impact the language proficiency skills of Hispanic ELLs (Ernst-Slavit & Wenger, 2016; Fuchs et al., 2018; Hoque, 2016). The purpose of the proposed qualitative descriptive case study was to explore the perspectives and experiences of elementary bilingual education teachers regarding their perceived lack of preparedness to teach the English language and how this may impact the language proficiency of Hispanic ELLs.

Exploring Minority Teachers Experiences Pertaining to their Value in Education: A Single Case Study of Teachers in New York City

The problem is that minority K-12 teachers are underrepresented in the United States, with research indicating that school leaders and teachers in schools that are populated mainly by black students, staffed mostly by white teachers who may be unprepared to deal with biases and stereotypes that are ingrained in schools (Egalite, Kisida, & Winters, 2015; Milligan & Howley, 2015). The purpose of this qualitative exploratory single case study was to develop a clearer understanding of minority teachers’ experiences concerning the under-representation of minority K-12 teachers in urban school districts in the United States since there are so few of them.

Exploring the Impact of an Urban Teacher Residency Program on Teachers’ Cultural Intelligence: A Qualitative Case Study

The problem to be addressed by this case study is that teacher candidates often report being unprepared and ill-equipped to effectively educate culturally diverse students (Skepple, 2015; Beutel, 2018). The purpose of this study was to explore and gain an in-depth understanding of the perceived impact of an urban teacher residency program in urban Iowa on teachers’ cultural competence using the cultural intelligence (CQ) framework (Earley & Ang, 2003).

Qualitative Case Study that Explores Self-Efficacy and Mentorship on Women in Academic Administrative Leadership Roles

The problem was that female school-level administrators might be less likely to experience mentorship, thereby potentially decreasing their self-efficacy (Bing & Smith, 2019; Brown, 2020; Grant, 2021). The purpose of this case study was to determine to what extent female school-level administrators in the United States who had a mentor have a sense of self-efficacy and to examine the relationship between mentorship and self-efficacy.

Suburban Teacher and Administrator Perceptions of Culturally Responsive Teaching to Promote Connectedness in Students of Color: A Qualitative Case Study

The problem to be addressed in this study is the racial discrimination experienced by students of color in suburban schools and the resulting negative school experience (Jara & Bloomsbury, 2020; Jones, 2019; Kohli et al., 2017; Wandix-White, 2020). The purpose of this case study is to explore how culturally responsive practices can counteract systemic racism and discrimination in suburban schools thereby meeting the needs of students of color by creating positive learning experiences. 

As you can see, all of these studies were well suited to qualitative case study design. In each of these studies, the applied research problem and research purpose were clearly grounded in educational practice as well as directly aligned with qualitative case study methodology. In the Applied Doctoral Experience (ADE), you will be focused on addressing or resolving an educationally relevant research problem of practice. As such, your case study, with clear boundaries, will be one that centers on a real-life authentic problem in your field of practice that you believe is in need of resolution or improvement, and that the outcome thereof will be educationally valuable.

Bloomberg, L. D. (2018). Case study method. In B. B. Frey (Ed.), The SAGE Encyclopedia of educational research, measurement, and evaluation (pp. 237–239). SAGE. https://go.openathens.net/redirector/nu.edu?url=https%3A%2F%2Fmethods.sagepub.com%2FReference%2Fthe-sage-encyclopedia-of-educational-research-measurement-and-evaluation%2Fi4294.xml

Bloomberg, L. D. & Volpe, M. (2019). Completing your qualitative dissertation: A road map from beginning to end . (4th Ed.). SAGE.

Stake, R. E. (1995). The art of case study research. SAGE.

Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin and Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (3rd ed., pp. 443–466). SAGE.

Yin, R. (2018). Case study research and applications: Designs and methods. SAGE.

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Case Study Thesis Statement

Ai generator.

A case study is a deep and comprehensive study of a specific subject, such as individuals, groups, or events, in their real-life context. Crafting a compelling thesis statement for a case study ensures that readers are primed to engage with the detailed analysis that follows. It sets the tone and provides a roadmap for what’s to be explored. Whether you’re examining a business scenario, a societal issue, or a psychological condition, a well-constructed thesis sets the foundation. Let’s delve into examples, writing techniques, and tips to perfect this art.

What is a Case Study Thesis Statement? – Definition

A case study thesis statement is a concise summary that outlines the central point or argument of a case study. It encapsulates the primary findings, insights, or conclusions drawn from the detailed analysis of a particular subject or situation in its real-life context. This statement serves as a guide for readers, offering a snapshot of what the case study will explore and the significance of its findings.

What is an example of a Case Study thesis statement?

“In the analysis of XYZ Corporation’s marketing strategies during the fiscal year 2020-2021, it’s evident that the company’s innovative use of social media advertising not only boosted its brand visibility among millennials but also led to a 15% increase in sales, demonstrating the power of digital platforms in modern business models.”

This Specific thesis statement provides a clear insight into the focus of the case study (XYZ Corporation’s marketing strategies) and highlights the primary conclusion (success in using social media advertising to boost sales).

100 Case Study Thesis Statement Examples

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Case study thesis statements provide a concise encapsulation of the primary conclusions or insights gleaned from an in-depth analysis of a subject. They serve as a roadmap for readers, informing them of the study’s focal points and key findings. To craft an effective case study thesis, it’s imperative to be specific, evidence-based, and relevant to the subject being explored. Below are 100 examples spanning various fields and scenarios:

• Analyzing the success of Apple’s iPhone X launch, it’s evident that the blend of technological innovation and targeted marketing resulted in record-breaking sales figures globally.
• A deep dive into London’s urban planning post-2000 reveals a significant push towards sustainable infrastructure, reducing the city’s carbon footprint by 12%.
• In studying patient recovery rates at the ABC Rehabilitation Center, it becomes clear that personalized therapy programs yield a 25% faster recovery time compared to generic methods.
• A review of Brazil’s reforestation efforts in the last decade demonstrates that community involvement is a pivotal factor, with local engagement accelerating afforestation by 18%.
• Exploring the financial collapse of Company XYZ in 2019, mismanagement of funds and a lack of internal audits were the predominant causes leading to its bankruptcy.
• The rise in mental health issues among high school students from 2015-2020, as examined in Region A, strongly correlates with increased social media usage and cyberbullying incidents.
• A detailed analysis of Japan’s public transport system reveals that timely investments in technology and maintenance are primary reasons for its 99% punctuality rate.
• Studying the diet patterns of Mediterranean regions provides insights into lower cardiovascular disease rates, highlighting the benefits of olive oil, fish, and whole grains.
• The decline in print media sales from 2000-2020, as evident in the case of Magazine ABC, is largely due to the surge in digital content consumption and changing reader habits.
• In assessing the success of the ‘Clean River’ campaign in City B, it’s observed that public awareness drives and stricter industrial regulations reduced water pollution by 30%
• An examination of solar energy adoption in Rural Region X indicates that governmental subsidies coupled with community workshops played a pivotal role in increasing installations by 40% in five years.
• By delving into the cultural revival in City Y, it’s apparent that grassroots movements and local art festivals were instrumental in rejuvenating traditional art forms and bolstering tourism.
• A study of telecommuting trends during the 2020 pandemic reveals that companies with pre-existing digital infrastructure reported a smoother transition and a mere 5% drop in productivity.
• Through analyzing the public health response in Country Z during the measles outbreak, it’s clear that rapid immunization drives and public awareness campaigns curbed the spread by 60%.
• A review of the organic farming movement in Region P shows that farmer cooperatives and government-backed training sessions were crucial in tripling organic produce output in a decade.
• Assessing the success factors behind Brand Q’s viral ad campaign, a blend of humor, social relevance, and effective online targeting resulted in a 300% ROI.
• An in-depth look at the urban wildlife conservation initiative in City R suggests that integrating green corridors and public education were key to increasing urban biodiversity by 20%.
• Studying the economic turnaround of City S post-recession, it emerges that a combination of SME incentives, infrastructure investments, and tourism promotions led to a steady 7% GDP growth.
• Exploring the education overhaul in District T, the introduction of experiential learning methods and teacher training programs significantly improved student performance metrics across all grades.
• The analysis of e-commerce trends in Country U during the festive season underscores that localized marketing campaigns and easy return policies boosted sales by an unprecedented 45%
• An exploration of the rehabilitation programs in Prison V reveals that the integration of vocational training reduced recidivism rates by 15% over three years.
• Investigating the decline of traditional crafts in Region W, it becomes apparent that globalized market pressures and a generational shift in career preferences were primary contributors.
• The analysis of startup ecosystem growth in City X demonstrates that mentorship programs and venture capital accessibility were crucial drivers, leading to a 50% increase in successful startup launches.
• In evaluating the healthcare system of Country Y, the strategic placement of clinics and telemedicine integration were central to achieving a 90% accessibility rate in remote areas.
• Studying the architectural evolution in City Z, the emphasis on eco-friendly designs and green spaces has significantly enhanced residents’ quality of life and reduced energy consumption.
• A detailed assessment of the digital literacy program in District A1 indicates that hands-on workshops and collaboration with tech companies led to a 30% increase in digital skills among the elderly.
• The case study of the MNO Music Festival shows that blending international and local artists, along with immersive cultural experiences, resulted in a tripling of international attendees.
• In examining the rebranding strategy of Company B2, leveraging user-generated content and transparency in production processes garnered a 60% boost in brand loyalty.
• Exploring the impact of the ‘Green School’ initiative in Region C3, schools that integrated environmental education witnessed a marked increase in student-led sustainability projects.
• By delving into the tourism dynamics of Island D4, it’s observed that the emphasis on eco-tourism and cultural preservation led to sustained tourism growth without ecological degradation.
• A deep dive into the public transport upgrades in City E5 reveals that the inclusion of smart ticketing systems and real-time tracking improved user satisfaction rates by 25%.
• Analyzing the performance of the XYZ sports team over a decade, the focus on grassroots talent recruitment and continuous training regimes was key to their championship victories.
• A study of the fast-food industry shifts in Region F6 showcases that the introduction of plant-based menu options was instrumental in capturing a new health-conscious demographic.
• Through assessing the cybersecurity reforms in Organization G7, proactive threat monitoring and employee training drastically reduced security breaches by 80%.
• An examination of the ‘Urban Forest’ project in City H8 underlines that community participation and periodic maintenance drives ensured a 90% survival rate of planted trees.
• Investigating the cultural festival in Village I9, the collaboration with local artisans and digital promotions drew an unprecedented global audience, revitalizing the local economy
• The scrutiny of e-learning trends in School J10 revealed that blending video tutorials with interactive assignments resulted in higher student engagement and a 20% improvement in test scores.
• In studying the revamp of the K11 shopping mall, the introduction of experiential retail spaces and diversified dining options significantly increased footfall and monthly sales.
• By analyzing the success of the L12 mobile banking app, user-friendly interfaces combined with robust security measures led to a user adoption rate surpassing 70% within the first year.
• The comprehensive review of NGO M13’s outreach programs indicates that localized content and leveraging social media influencers amplified awareness, doubling donations received.
• An in-depth study of the transportation overhaul in City N14 highlights that integrating cycling lanes and pedestrian zones reduced vehicular traffic by 15% and enhanced urban livability.
• A case study on the O15 biotech startup’s rapid growth identifies that collaborations with academic institutions and a focus on sustainable solutions were critical success factors.
• Investigating the wildlife conservation measures in Park P16, the integration of community-based surveillance and eco-tourism initiatives resulted in a 10% rise in endangered species populations.
• Exploring the dynamics of the Q17 film festival, the embrace of indie filmmakers and diversification into virtual screenings expanded the global audience base by threefold.
• Through a detailed assessment of the R18 smart city project, data-driven decision-making and public-private partnerships accelerated infrastructure development and improved resident satisfaction.
• A study of the resurgence of traditional crafts in Village S19 underscores that governmental grants combined with e-commerce platforms enabled artisans to reach global markets and triple their income.
• By analyzing the mental health initiative in University T20, the introduction of peer counseling and mindfulness workshops led to a 30% decrease in reported student stress levels.
• In evaluating the U21 sustainable farming project, the practice of crop rotation and organic pest control methods doubled yields without compromising soil health.
• A deep dive into the V22 robotics industry shows that investments in research and development, coupled with industry-academia partnerships, positioned the region as a global leader in automation solutions.
• The case study of the W23 urban renewal initiative reveals that preserving historical sites while integrating modern amenities revitalized the district and boosted tourism by 40%
• Exploring the telehealth revolution in Hospital X24, it’s evident that user-centric design coupled with real-time patient support drastically reduced waiting times and enhanced patient satisfaction.
• A review of the Z25 green tech startup’s rise showcases how tapping into emerging markets and prioritizing local adaptations enabled a 250% growth rate over two years.
• By analyzing the Y26 literary festival’s global success, forging partnerships with international publishers and leveraging livestreamed sessions captured a diversified and engaged global readership.
• In evaluating the urban art projects of City A27, integrating community artists and sourcing local materials led to culturally resonant artworks and rejuvenated public spaces.
• The detailed study of B28’s freshwater conservation strategies highlights that community education, combined with sustainable fishing practices, restored marine life balance within a decade.
• Through a comprehensive look at the C29 space tech firm’s accomplishments, early investments in satellite miniaturization positioned it as a front-runner in commercial space solutions.
• By delving into the digital transformation of Retailer D30, the integration of augmented reality for virtual try-ons significantly boosted online sales and reduced return rates.
• A study of the E31 desert afforestation initiative reveals that harnessing native drought-resistant flora and community-based irrigation systems successfully greened over 10,000 hectares.
• Exploring F32’s inclusive education reforms, a curriculum designed with multi-modal teaching techniques led to improved learning outcomes for differently-abled students.
• In examining the eco-tourism drive of Island G33, maintaining a balance between visitor volume and ecological sustainability ensured steady revenue without environmental degradation.
• Analyzing the H34 online gaming platform’s surge in popularity, community engagement features and regional game localization were instrumental in its global user base expansion.
• A review of the I35 urban cycling initiative shows that creating cyclist-friendly infrastructure, coupled with public awareness campaigns, led to a 20% increase in daily cycling commuters.
• In studying J36’s public library modernization project, the fusion of digital archives with interactive learning zones increased visitor numbers and enhanced community learning.
• By evaluating the K37 corporate wellness program, a holistic approach encompassing mental health, fitness, and nutrition resulted in a 15% reduction in employee sick days.
• A detailed look at the L38 organic coffee farming cooperative identifies that fair-trade certifications and eco-friendly processing techniques doubled farmer profits and market reach.
• Exploring the M39 microfinance model in developing regions shows that leveraging mobile technology and community leaders made financial services accessible to previously unbanked populations.
• The case study of N40’s anti-pollution drive reveals that using technology for real-time air quality monitoring and public alerts led to actionable civic interventions and clearer skies.
• Analyzing the O41 cultural dance revival initiative, collaborations with schools and televised events reintroduced traditional dances to younger generations, preserving cultural heritage.
• Through studying the P42 renewable energy project, community-owned solar and wind farms not only achieved energy self-sufficiency but also created local employment opportunities.
• By examining Q43’s digital archival project, crowdsourcing contributions and integrating multimedia storytelling resurrected historical narratives for a global digital audience.
• In reviewing the R44 disaster response initiative, utilizing drones and AI-driven analytics for real-time situation assessment led to a 30% faster rescue response.
• Exploring the success of the S45 women’s empowerment project, localized workshops and financial literacy programs led to the establishment of over 500 women-led businesses.
• Analyzing the T46 urban farming revolution, rooftop gardens and vertical farming technologies not only reduced the carbon footprint but also bolstered local food security.
• Through a detailed examination of U47’s mental health awareness campaign, leveraging celebrity ambassadors and social media channels destigmatized mental health discussions among young adults.
• The study of V48’s coastal conservation initiative reveals that coral transplantation and sustainable tourism practices significantly enhanced marine biodiversity and local livelihoods.
• By scrutinizing the W49 digital arts program, collaborations with global tech firms and virtual exhibitions brought contemporary art to a wider and more diversified audience.
• In evaluating the X50 grassroots sports initiative, talent scouting at school levels and offering specialized training camps led to a surge in regional sports achievements.
• Exploring the Y51 urban greenery project, the symbiotic integration of flora with urban structures, like bus stops and building facades, transformed the cityscape and improved air quality.
• Through analyzing the Z52 elderly wellness initiative, mobile health check-ups and community gathering events significantly improved the well-being and social connectedness of seniors.
• A deep dive into A53’s tech literacy drive for rural regions showcases that mobile classrooms and gamified learning tools bridged the digital divide, empowering communities.
• Investigating B54’s smart waste management project, sensor-fitted bins and data-driven route optimization for collection trucks minimized operational costs and improved city cleanliness.
• The case study of C55’s heritage restoration initiative highlights that a blend of traditional craftsmanship with modern conservation techniques revitalized historical landmarks, boosting tourism.
• In studying D56’s alternative education model, experiential outdoor learning and community projects fostered holistic student development and real-world problem-solving skills.
• By analyzing E57’s urban transit solution, electric buses paired with dynamic route algorithms resulted in reduced traffic congestion and a decrease in emissions.
• The examination of F58’s sustainable fashion movement indicates that upcycling workshops and eco-conscious designer collaborations led to a greener fashion industry with reduced waste.
• Through a deep dive into G59’s wildlife rehabilitation project, mobile veterinary units and habitat restoration measures significantly increased the population of endangered species.
• In assessing H60’s collaborative workspace model, creating modular designs and fostering community events led to increased startup incubation and knowledge exchange.
• Studying the I61 teletherapy initiative, the integration of wearable tech for biometric feedback and real-time counseling support made mental health care more accessible and tailored.
• The review of J62’s community theater resurgence underlines that offering free training workshops and forging school partnerships enriched cultural landscapes and nurtured local talent.
• By evaluating K63’s clean water initiative in remote areas, solar-powered desalination units and community-led maintenance ensured uninterrupted access to potable water.
• Exploring the L64 sustainable architecture movement, it’s evident that the incorporation of passive solar design and green roofs reduced building energy consumption by up to 40%.
• Through a detailed analysis of the M65 virtual reality (VR) in education program, integrating VR expeditions and interactive simulations led to a 20% increase in student comprehension.
• The study of N66’s eco-village development project reveals that community-owned renewable energy systems and permaculture designs fostered self-sufficiency and resilience.
• By reviewing the O67’s inclusive playground initiative, universally designed play equipment and sensory-friendly zones catered to children of all abilities, promoting inclusivity and joy.
• Investigating the P68’s digital heritage preservation, utilizing 3D scanning and augmented reality brought ancient monuments and artifacts to life for global audiences.
• By scrutinizing the Q69’s local organic produce movement, direct farmer-to-consumer platforms and community-supported agriculture initiatives revitalized local economies and promoted healthy living.
• A deep dive into the R70’s urban beekeeping project indicates that rooftop apiaries and bee-friendly green spaces boosted pollinator populations, benefiting both biodiversity and urban agriculture.
• In evaluating the S71’s community radio station initiative, platforms that prioritized local news and indigenous languages fostered civic participation and cultural pride.
• Exploring the success of T72’s renewable energy transition, investments in grid-tied wind and solar farms led to the region achieving carbon neutrality within a decade.
• The review of U73’s zero-waste community challenge highlights that community workshops on composting, recycling, and upcycling drastically reduced landfill contributions and elevated environmental consciousness.

These statements encompass a diverse range of endeavors, from technological innovations and educational transformations to environmental conservation and cultural preservation. Each thesis offers a concise yet compelling entry point, illustrating the multifaceted nature of case studies and their potential to drive change across various sectors.

Case Study Thesis Statement Example for Argumentative Essay

An argumentative essay’s thesis statement presents a debatable claim about a particular scenario or situation, seeking to persuade the reader of its validity. It combines evidence from the case study with a clear stance on the matter, aiming to convince through both factual data and logical reasoning.

• Despite the surge in e-commerce, a case study on Brick & Mortar Retail Y1 reveals that experiential in-store shopping can significantly boost customer loyalty and overall sales.
• Examining the X2 city’s public transport model, it’s evident that prioritizing bicycles over cars results in healthier urban environments and happier citizens.
• By studying vegan diets through the Z3 health initiative, there is undeniable evidence that plant-based diets lead to improved overall health metrics when compared to omnivorous diets.
• Through a deep dive into the A4’s shift to remote work, productivity levels and employee well-being evidently increase when offered flexible work arrangements.
• In the debate over renewable versus fossil fuels, the B5 country’s successful transition showcases the undeniable economic and environmental advantages of renewable energy.
• Analyzing the C6 city’s urban greening project, it’s clear that community gardens play a pivotal role in crime reduction and social cohesion.
• A study on the D7’s educational reforms reveals that continuous assessment, as opposed to one-off exams, offers a more comprehensive understanding of student capabilities.
• By evaluating the E8’s plastic ban initiative, environmental rejuvenation and improved public health metrics affirm the necessity of eliminating single-use plastics.
• Exploring the F9’s universal healthcare model, there’s a robust argument that public health services lead to more equitable societies and better health outcomes.
• The success of the G10’s work-life balance policies underscores that a shorter workweek can lead to heightened productivity and enhanced employee satisfaction.

Case Study Thesis Statement Example for Research Paper

Case Study for  research paper thesis statement serves as a central hypothesis or primary insight derived from the chosen case. It succinctly captures the essence of the research findings and the implications they might hold, offering a foundation upon which the paper’s arguments and conclusions are built.

• An extensive analysis of the H11 city’s water conservation techniques presents innovative methodologies that have achieved a 30% reduction in urban water consumption.
• Investigating the I12’s coral reef restoration projects, recent advancements in marine biology have been instrumental in rejuvenating dying reef ecosystems.
• The in-depth research on J13’s forest management strategies reveals the successful intersection of indigenous knowledge and modern conservation techniques.
• A comprehensive study on the K14’s biodynamic farming practices demonstrates their impact on soil health and crop yield enhancement.
• Researching L15’s approach to mental health, community-based interventions, and localized therapy models have shown significant efficacy.
• By delving into M16’s urban waste management, innovative recycling technologies are revolutionizing urban sustainability and waste reduction.
• The examination of N17’s digital literacy programs for seniors demonstrates adaptive pedagogies tailored for older learners, resulting in improved tech proficiency.
• In-depth research on O18’s tidal energy projects presents groundbreaking advancements in harnessing marine energy for sustainable power generation.
• A study of P19’s green building materials showcases the potential for sustainable construction without compromising on durability or aesthetics.
• Extensive research on Q20’s citizen science initiatives has shed light on the profound impact of public engagement in scientific discoveries.

Case Study Essay Thesis Statement Example for Essay Writing

In essay writing, the case study thesis statement offers a central idea or perspective about the case at hand. It provides a roadmap for readers, indicating the essay’s direction and focus, and typically draws on the unique aspects of the case study to make broader observations or arguments.

• The revitalization of the R21 town square serves as a testament to the profound impact of urban design on community engagement and cultural preservation.
• Exploring the journey of S22’s artisanal chocolate brand offers insights into the nuances of combining traditional recipes with modern marketing.
• The success story of the T23’s community library initiative illustrates the timeless importance of books and shared spaces in fostering community spirit.
• Through a narrative on U24’s eco-tourism model, the delicate balance between conservation, commerce, and community involvement comes to the fore.
• V25’s transformation from a tech-averse community to a digital hub showcases the ripple effects of targeted tech education and infrastructure investment.
• The tale of W26’s fight against deforestation illuminates the intertwining of grassroots activism, governmental policy, and global collaboration.
• X27’s journey in preserving endangered languages paints a vivid picture of the role of technology in safeguarding cultural heritage.
• Diving into Y28’s transition from coal to solar energy portrays the challenges, victories, and transformative power of collective will.
• The story of Z29’s grassroots sports academy gives a glimpse into the potential of talent nurtured through community support and dedication.
• A narrative on A30’s urban art movement elucidates the transformative power of public art in redefining cityscapes and fostering local talent.

Does a case study have a thesis statement?

Yes, a case study often has a thesis statement, especially if it is intended for academic or formal publication. While the nature of case studies is to explore, analyze, and present specific situations or phenomena in detail, a thesis statement helps provide direction, focus, and clarity to the study. It serves as a clear indication of the main point or argument the author wishes to make, derived from their analysis of the case.

What is a thesis statement for a case study analysis?

A thesis statement for a case study analysis is a concise summary of the main insight or argument derived from reviewing and analyzing a particular case. It should be specific and based on the evidence found within the study, aiming to encapsulate the core findings or implications. This statement will guide the reader’s understanding of what the case study is ultimately trying to convey or the conclusions the author has drawn from their analysis.

How do you write a thesis statement for a case study? – Step by Step Guide

• Select Your Case: Before you can write a thesis statement, you need to choose a case that offers enough substance and relevance. Your case should be representative or unique enough to provide meaningful insights.
• Conduct Thorough Research: Dive deep into the details of your case. Understand its history, the key players involved, its significance, and its outcomes.
• Identify Key Themes or Patterns: As you research, note down recurring themes or patterns that emerge. These will often hint at the broader implications of the case.
• Formulate Your Argument: Based on your observations, craft an argument or insight about the case. Ask yourself what the case reveals about a broader phenomenon or what makes this case particularly significant.
• Be Specific: Your thesis statement should be precise. Avoid vague or overly broad statements. Instead, focus on the specific insights or conclusions you’ve drawn from the case.
• Write and Refine: Draft your thesis statement. It should be one or two sentences long, capturing the essence of your argument. Revisit and refine it to ensure clarity and conciseness.

Tips for Writing a Case Study Thesis Statement

• Keep it Focused: Your thesis statement should be concise and directly related to the case in question. Avoid generalities or unrelated observations.
• Be Evidence-Based: Ensure that your thesis statement can be backed up with evidence from the case study. It should be a result of your analysis, not a preconceived notion.
• Avoid Jargon: Keep your thesis statement accessible. It should be understandable even to those unfamiliar with the specifics of the case.
• Stay Objective: While your thesis statement will represent your analysis and perspective, it’s crucial to base it on facts and avoid unnecessary biases.
• Seek Feedback: Once you’ve crafted your thesis statement, share it with peers or mentors. Their feedback can help refine your thesis and ensure it captures the essence of your case study effectively.

In conclusion, while a case study delves deep into specific instances, having a clear thesis statement is crucial to give direction to your study and offer readers a concise understanding of the case’s significance and your analysis.

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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2017-102845

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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

• Gustafsson J
• Calanzaro M
• Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Thesis Guide

How to Write a Case Study

Most good empirical software engineering papers that contain a study follow the same structure for its presentation. As far as I know, this structure was not invented by a single researcher, but developed gradually over the course of many publications.

Professional readers expect your case study to follow this structure, too. The audience that really matters for your publication—your thesis supervisor, his PhD advisor or program committee members—all are professional readers.

The goal of this article is to describe this structure: the basic building blocks of thesis chapters or paper sections that make up case study presentations. It is meant as an introduction and thus necessarily skips details. For further reading, this article contains links at the end.

Basic Anatomy

The typical structure comprises these sections:

• Research questions

Study Objects

Study design.

Study Procedure

Results & Interpretation

Threats to Validity.

As a reader, I expect each section to answer a specific set of questions. In the following, I describe the gist of each section, its set of questions and common mistakes.

To make the sections more tangible, I use part of a study from one of our papers . The study investigates inconsistencies in code clones.

Research Questions

This section states the questions that the study aims to answer and their rationale. It should contain:

• What the questions are. In my paper, RQ 1 is Are clones changed inconsistently? .

Why the research questions are relevant.

A frequent mistake is missing rationale. In such papers, the motivation behind the research question often remains unclear or unconvincing.

Some background on the example paper: code clones are duplicated pieces of source code in a software systems. Clones are typically created by copy & paste. They hinder software maintenance, since changes must often be made to all clone instances. If a clone gets forgotten during such a change, the code becomes inconsistent. This inconsistency can be a bug.

What I wanted to investigate with my study, was how big of a problem this is in practice. One the one hand, I had seen some instances of inconsistent clones that suspiciously looked like bugs. On the other hand, I had no idea how frequently this occurred, and if this really was problematic in practice. My study goal was to quantify this by analyzing clones (and their inconsistencies) in real systems.

The rationale of the first research question was to understand if inconsistent changes to clones happen at all, and how often. If they are very rare, they probably do not deserve further investigation (which is performed by the later research questions in the paper).

This section outlines the study objects (e.g. software systems), which the study analyzes to answer its research questions. It should contain:

• The names of the study objects and their characteristics (those properties that are relevant for the study). In the example, the study objects were the 5 systems that I searched for clones. The relevant characteristics comprise programming language, size, age, number of developers and a short description of their functionality.

Why (and maybe how) those objects were chosen. This is relevant, since choice can influence study result validity. For the example, a large number of study objects (and ideally their random selection from a large pool of potential study objects), would increase the generalizability of the study results.

In the clone paper, however, I needed to do interviews with the system’s developers for later questions. I thus had to rely on our industry contacts to get hold of these developers. This limited my choices and thus potentially affects generalizability of the results (which is mentioned in the threats to validity section).

A frequent mistake is to not mention why those objects were chosen and what the consequences of the choice are. As a reader, this makes me wonder if the selection was manipulated to better produce the answers the author was looking for.

If a study involves data from industry, the study object names are often anonymized (e.g. replaced by A , B , C , …). As a reader, I don’t care about this, since the names of proprietary industrial systems are meaningless to me anyway. For the authors, however, it makes it much easier to get clearance to publish these results.

This section describes how the study, using the information from the study objects, attempts to answer the research questions.

For the clone study, I computed the percentage of inconsistent clones among all clones. For this, I defined two sets:

• C : The set C of consistent clones. The clones in each clone group are consistent (i.e. contain no differences or only small ones, like renamed variables).

IC : Set of inconsistent clones , i.e. clone groups with substantial differences between clones, such as missing statements.

As the answer to the research question, I computed the inconsistent clone ratio as |C| / |IC|. Intuitively, it denotes the probability that a clone group in the system contains at least one inconsistency.

A common mistake is to interleave study design, procedure and implementation details.

This section describes the nitty gritty details required to implement the study design in reality. In principle, they could also be included directly in the description of the study design. However, it is easier for the reader to first understand the general idea, and then the details.

For the clone study, this section states detection parameters (like minimal clone length and number of allowed differences between clones). It also treats handling of false positives, generated code and overlapping clone groups.

This section describes the results and interprets them with respect to the research questions. Since there is often a lot of data, this section should guide the reader through the results. In studies with large amounts of data, it is often easier to read to separate description of the data from its interpretation.

In the example, the paper presents the results for each study object and then the aggregated ratio. On average, 52% of the clone groups contained inconsistencies. The paper thus answers the question positively: yes, clones are changed inconsistently.

A common mistake is to mix the results with the discussion. This makes it harder for the reader to separate backed-up results from speculation.

Interpretation of the results that go further than the research questions. This can, e.g., contain implications for software development.

The clone paper (based on the above presented and further questions) concludes, that clones are a threat to program correctness, implying that their proper management deserves more attention.

Threats to Validity

This section lists all threats, i.e. reasons why the study results could be wrong. Ideally, it then treats every single threat and describes what you did to make sure that this threat does not invalidate your study results.

Threats to validity are often classified into internal and external threats.

Internal threats are reasons why the results could be invalid for your study objects. In the example, the parameter values of the clone detector have a strong impact on the detected clones. The section states that we mitigated the threat through a pre-study we performed in order to validate the chosen parameter values.

(To be honest, this is a weak mitigation. What it really says is that we tinkered with the values until they felt good and then did the study. A stronger mitigation would be to also perform the study with different parameter values and investigate whether the general results still hold. Since this distracts from the main study, such back-up studies are often only described in a much abbreviated fashion in the threats section itself.)

External threats are reasons why the results encountered for the study objects might not be transferable to other objects. In the example, the way we chose the study objects (through our personal network) might bias our results. To mitigate this threat, we at least chose systems that had different characteristics, such as programming language, development contractor and age.

The most common mistake is to ignore threats entirely. Much better (but still improvable) is to state a threat without giving a mitigation or an estimation of its severity.

Variation Points

The case study structure described in this article can be used in two different decomposition styles. The most common one is described in this article. It orders by section first and by research question second:

• Research questions 1.1: RQ 1 … 1.2: RQ 2 …

Study Objects 2.1 For RQ 1: … 2.2 For RQ 2: …

Study Design 2.1 For RQ 1: … 2.2 For RQ 2: …

Is most frequent alternative, however, is to order by research question first and by section second:

• RQ 1 1.1 Research question 1 … 1.2 Study Objects for RQ 1 … 1.3 Study Design for RQ 1… …

RQ 2 2.1 Research question 2 … 2.2 Study Object for RQ 2 … 2.3 Study Design for RQ 2 … …

Both decomposition styles have advantages and drawbacks. I use these heuristics to select the decomposition level:

This is the case in the clone paper example. Research questions two and three ask whether the inconsistencies between clones are unintentional, and if so, whether they represent a fault. RQ n thus builds upon the results of RQ n-1 . Since the study sections share so much, describing them in isolation would create a lot of redundancy. They are thus easier to read all at once. Decomposition by study section facilitates this.

By research questions : when each study has its own study objects, design and procedure.

In this paper we wrote , the study objects, design and procedure of research questions one and three have nothing in common. Since there is little synergy between them, it is easier to read a complete study—from question to results interpretation—before reading the next one.

Apart from the above examples, there are mixed cases as well (where some RQs share objects and design, but others in the same paper don’t). For them, simply choose the decomposition style that feels right, but stick to it for the entire study description. Don’t mix decomposition styles, since this confuses the reader.

From my experience, you only really get to feel if a style feels right, once you write it down, often two times, once in each decomposition style. This is tedious, but pays off, since a suitable decomposition style strongly increases the readability of your study.

Further reading:

• Guidelines for conducting and reporting case study research in software engineering by Per Runeson & Martin Höst.

Case Study Research. Design and Methods by Robert K. Yin.

Thanks to Rainer Koschke and Stefan Wagner for literature suggestions and to Daniela Steidl for reading drafts of this.

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Informatiker, Software-Analyst, Sprecher, Wein- und Biertrinker. View all posts by ElmarJuergens

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Home » Education » Difference Between Action Research and Case Study

Difference Between Action Research and Case Study

Main difference – action research vs case study.

Research is the careful study of a given field or problem in order to discover new facts or principles. Action research and case study are two types of research, which are mainly used in the field of social sciences and humanities. The main difference between action research and case study is their purpose; an action research study aims to solve an immediate problem whereas a case study aims to provide an in-depth analysis of a situation or case over a long period of time.

1. What is Action Research?      – Definition, Features, Purpose, Process

2. What is Case Study?      – Definition, Features, Purpose, Process

What is Action Research

Action research is a type of a research study that is initiated to solve an immediate problem. It may involve a variety of analytical, investigative and evaluative research methods designed to diagnose and solve problems. It has been defined as “a disciplined process of inquiry conducted by and for those taking the action. The primary reason for engaging in action research is to assist the “actor” in improving and/or refining his or her actions” (Sagor, 2000). This type of research is typically used in the field of education. Action research studies are generally conductors by educators, who also act as participants.

Here, an individual researcher or a group of researchers identify a problem, examine its causes and try to arrive at a solution to the problem. The action research process is as follows.

Action Research Process

• Identify a problem to research
• Clarify theories
• Identify research questions
• Collect data on the problem
• Organise, analyse, and interpret the data
• Create a plan to address the problem
• Implement the above-mentioned plan
• Evaluate the results of the actions taken

The above process will keep repeating. Action research is also known as cycle of inquiry or cycle of action since it follows a specific process that is repeated over time.

What is a Case Study

A case study is basically an in-depth examination of a particular event, situation or an individual. It is a type of research that is designed to explore and understand complex issues; however, it involves detailed contextual analysis of only a limited number of events or situations. It has been defined as “an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.” (Yin, 1984)

Case studies are used in a variety of fields, but fields like sociology and education seem to use them the most. They can be used to probe into community-based problems such as illiteracy, unemployment, poverty, and drug addiction. 

Case studies involve both quantitative and qualitative data and allow the researchers to see beyond statistical results and understand human conditions. Furthermore, case studies can be classified into three categories, known as exploratory, descriptive and explanatory case studies.

However, case studies are also criticised since the study of a limited number of events or cases cannot easily establish generality or reliability of the findings. The process of a case study is generally as follows:

Case Study Process

• Identifying and defining the research questions
• Selecting the cases and deciding techniques for data collection and analysis
• Collecting data in the field
• Evaluating and analysing the data
• Preparing the report

Action Research : Action research is a type of a research study that is initiated to solve an immediate problem.

Case Study : Case study is an in-depth analysis of a particular event or case over a long period of time.                         

Action Research : Action research involves solving a problem.

Case Study : Case studies involve observing and analysing a situation.

Action Research : Action research studies are mainly used in the field of education.

Case Study : Case studies are used in many fields; they can be specially used with community problems such as unemployment, poverty, etc.

Action Research : Action research always involve providing a solution to a problem.

Case Study : Case studies do not provide a solution to a problem.

Participants

Action Research : Researchers can also act as participants of the research.

Case Study : Researchers generally don’t take part in the research study.

Zainal, Zaidah.  Case study as a research method . N.p.: n.p., 7 June 2007. PDF.

 Soy, Susan K. (1997).  The case study as a research method . Unpublished paper, University of Texas at Austin.

Sagor, Richard.  Guiding school improvement with action research . Ascd, 2000.

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Frequently asked questions

What’s the difference between action research and a case study.

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

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.

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.

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 sampling methods or criteria for selecting subjects
• 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 research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

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

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

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

There are five common approaches to qualitative research :

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

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

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.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

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

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

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

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

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

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

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.

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

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

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 analyze data (for example, 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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Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

Related handouts

• Student Title Page Guide (PDF, 263KB)
• Student Paper Setup Guide (PDF, 3MB)

Student papers do not include a running head unless requested by the instructor or institution.

Follow the guidelines described next to format each element of the student title page.

Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

Follow the guidelines described next to format each element of the professional title page.