in single case research the main goal is to determine a

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• Perspective
• Published: 22 November 2022

Single case studies are a powerful tool for developing, testing and extending theories

• Lyndsey Nickels   ORCID: orcid.org/0000-0002-0311-3524 1 , 2 ,
• Simon Fischer-Baum   ORCID: orcid.org/0000-0002-6067-0538 3 &
• Wendy Best   ORCID: orcid.org/0000-0001-8375-5916 4  

Nature Reviews Psychology volume  1 ,  pages 733–747 ( 2022 ) Cite this article

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Psychology embraces a diverse range of methodologies. However, most rely on averaging group data to draw conclusions. In this Perspective, we argue that single case methodology is a valuable tool for developing and extending psychological theories. We stress the importance of single case and case series research, drawing on classic and contemporary cases in which cognitive and perceptual deficits provide insights into typical cognitive processes in domains such as memory, delusions, reading and face perception. We unpack the key features of single case methodology, describe its strengths, its value in adjudicating between theories, and outline its benefits for a better understanding of deficits and hence more appropriate interventions. The unique insights that single case studies have provided illustrate the value of in-depth investigation within an individual. Single case methodology has an important place in the psychologist’s toolkit and it should be valued as a primary research tool.

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The authors thank all of those pioneers of and advocates for single case study research who have mentored, inspired and encouraged us over the years, and the many other colleagues with whom we have discussed these issues.

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Nickels, L., Fischer-Baum, S. & Best, W. Single case studies are a powerful tool for developing, testing and extending theories. Nat Rev Psychol 1 , 733–747 (2022). https://doi.org/10.1038/s44159-022-00127-y

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, 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 analyze the case, other interesting articles.

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

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

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.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

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

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

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.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

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 analyze 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.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The Family of Single-Case Experimental Designs

Leonard h. epstein.

1 Jacobs School of Medicine and Biomedical Sciences, Division of Behavioral Medicine, Department of Pediatrics, University at Buffalo, Buffalo, New York, United States of America,

Jesse Dallery

2 Department of Psychology, University of Florida, Gainesville, Florida, United States of America

Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case—which can be an individual, clinic, or community—ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, translational research, personalized interventions, and the study of rare diseases and disorders. This article provides a broad overview of the family of single-case experimental designs with corresponding examples, including reversal designs, multiple baseline designs, combined multiple baseline/reversal designs, and integration of single-case designs to identify optimal treatments for individuals into larger randomized controlled trials (RCTs). Personalized N-of-1 trials can be considered a subcategory of SCEDs that overlaps with reversal designs. Relevant issues for each type of design—including comparisons of treatments, design issues such as randomization and blinding, standards for designs, and statistical approaches to complement visual inspection of single-case experimental designs—are also discussed.

1. Introduction

Single-case experimental designs (SCEDs) represent a family of experimental designs to examine the relationship between one or more treatments or levels of treatment and changes in biological or behavioral outcomes. These designs originated in early experimental psychology research ( Boring, 1929 ; Ebbinghaus, 1913 ; Pavlov, 1927 ), and were later expanded and formalized in the fields of basic and applied behavior analysis ( Morgan & Morgan, 2001 ; Sidman, 1960 ). SCEDs have been extended to a number of fields, including medicine ( Lillie et al., 2011 ; Schork, 2015 ), public health ( Biglan et al., 2000 ; Duan et al., 2013 ), education ( Horner et al., 2005 ), counseling psychology ( Lundervold & Belwood, 2000 ), clinical psychology ( Vlaeyen et al., 2020 ), health behavior ( McDonald et al., 2017 ), and neuroscience ( Soto, 2020 ).

SCEDs provide a framework to determine whether changes in a target behavior(s) or symptom are in fact a function of the intervention. The fundamentals of an SCED involve repeated measurement, replication of conditions (e.g., baseline and intervention conditions), and the analysis of effects with respect to each individual serving as his or her own control. This process can be useful for identifying the optimal treatment for an individual ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ), treating rare diseases ( Abrahamyan et al., 2016 ), and implementing early phase translational research ( Czajkowski et al., 2015 ). SCEDs can be referred to as ‘personalized (N-of-1) trials’ when used this way, but they also have broad applicability to a range of scientific questions. Results from SCEDs can be aggregated using meta-analytic techniques to establish generalizable methods and treatment guidelines ( Shadish, 2014 ; Vannest et al., 2018 ). Figure 1 presents the main family of SCEDs, and shows how personalized (N-of-1) trials fit into these designs ( Vohra et al., 2016 ). The figure also distinguishes between experimental and nonexperimental single-case designs. In the current article, we provide an overview of SCEDs and thus a context for the articles in this special issue focused on personalized (N-of-1) trials. Our focus is to provide the fundamentals of these designs, and more detailed treatments of data analysis ( Moeyaert & Fingerhut, 2022 ; Schork, 2022 ) conduct and reporting standards ( Kravitz & Duan, 2022 ; Porcino & Vohra, 2022 ), and other methodological considerations are provided in this special issue. Our hope is that this article will inspire a diverse array of students, engineers, scientists, and practitioners to further explore the utility, rigor, and flexibility of these designs.

A = Baseline, B and C refer to different treatments.

The most common approach to evaluating the effectiveness of interventions on outcomes is using randomized controlled trials (RCTs). RCTs provide an idea of the average effect of an intervention on outcomes. People do not all change at the same rate or in the same way, however; variability in both how people change and the effect of the intervention is inevitable ( Fisher et al., 2018 ; Normand, 2016 ; Roustit et al., 2018 ). These sources of variability are conflated in a typical RCT, leading to heterogeneity of treatment effects (HTE). Research on HTE has shown variability in outcomes in RCTs, and in some studies very few people actually exhibit the benefits of that treatment ( Williams, 2010 ). One approach in RCTs is to assess moderators of treatment response to identify individual differences that may predict response to a treatment. This approach may not limit variability in response, and substantial reduction in variability of treatment for subgroups in comparison to the group as a whole is far from assured. Even if variability is reduced, the average effect for that subgroup may not be representative of individual members of the subgroup.

SCEDs can identify the optimal treatment for an individual person rather than the average person in a group ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Hekler et al., 2020 ). SCEDs are multiphase experimental designs in which a great deal of data is collected on a single person, said person serves as his or her own control ( Kazdin, 2011 , 2021 ), and the order of presentation of conditions can be randomized to enhance experimental control. That is, a person’s outcomes in one phase are compared to outcomes in another phase. In a typical study, replications are achieved within and/or across several individuals; this allows for strong inferences about causation between behavior and the treatment (or levels thereof). Achieving replications is synonymous with achieving experimental control.

We provide an overview of three experimental designs that can be adapted for personalized medicine: reversal, multiple baseline, and combined reversal and multiple baseline designs, and we discuss how SCEDs can be integrated into RCTs. These designs focus on demonstrating experimental control of the relationship between treatment and outcome. Several general principles common to all of the designs are noteworthy ( Lobo et al., 2017 ). First, in many studies, treatment effects are compared with control conditions with a no- intervention baseline as the initial condition. To reduce threats to internal validity of the study, the order of assignment of interventions can be randomized ( Kratochwill & Levin, 2010 ) and, when possible, the intervention and data collection can be blinded. The demonstration of experimental control across conditions or people needs to be replicated several times (three replications is the minimum) to ensure confidence of the relationship between treatment and outcome ( Kratochwill et al., 2010 ; Kratochwill & Levin, 2015 ). Demonstrating stability of data within a phase or, otherwise, no trend in the direction of treatment effects prior to starting treatment is particularly important. Stability refers to the degree of variability in the data path over time (e.g., data points must fall within a 15% range of the median for a condition). Thus, phase length needs to be flexible for the sake of determining stability and trend within a phase, but a minimum of 5 data points per phase has been recommended ( Kratochwill et al., 2013 ). The focus of the intervention’s effects is on clinically rather than statistically significant effects with the target effect prespecified and considered in interpretation of the relevance of the effect for clinical practice ( Epstein et al., 2021 ). In addition, multiple dependent outcomes can be simultaneously measured ( Epstein et al., 2021 ). SCEDs can be used to test whether a variable mediates the effect of a treatment on symptoms or behavior ( Miočević et al., 2020 ; Riley & Gaynor, 2014 ). Visual inspection of graphical data is typically used to determine treatment effects, and statistical methods are commonly used to assist in interpretation of graphical data ( Epstein et al., 2021 ). Furthermore, a growing number of statistical approaches can summarize treatment effects and provide effect sizes ( Kazdin, 2021 ; Moeyaert & Fingerhut, this issue; Pustejovsky, 2019 ; Shadish et al., 2014 ). Data across many SCED trials can be aggregated to assess the generality of the treatment effects to help address for whom and under what conditions an intervention is effective ( Branch & Pennypacker, 2013 ; Shadish, 2014 ; Van den Noortgate & Onghena, 2003 ).

2. Reversal Designs

A reversal design collects behavioral or biological outcome data in at least two phases: a baseline or no treatment phase (labeled as ‘A’) and the experimental or treatment phase (labeled as ‘B’). The design is called a reversal design because there must be reversals or replications of phases for each individual; for example, in an ABA design, the baseline phase is replicated ( Kazdin, 2011 ). Ideally, three replications of treatment effects are used to demonstrate experimental control ( Kratochwill et al., 2010 ; Kratochwill & Levin, 1992 ). Figure 2 shows hypothetical results from an A1B1A2B2 design. The graph shows three replications of treatment effects (A1 versus B1, B1 versus A2, A2 versus B2) across four participants. Each phase was carried out until stability was evident from visual inspection of the data as well as absence of trends in the direction of the desired effect. The replication across participants increases the confidence in the effectiveness of the intervention. Extension of this design is possible by comparing multiple interventions, as well. The order of the treatments should be randomized, especially when the goal is to combine SCEDs across participants.

A1 = First Baseline, B1 First Treatment, A2 = Return to Baseline, B2 = Return to Treatment. P1–P4 represent different hypothetical participants.

Reversal designs can be more dynamic and compare several treatments. A common approach in personalized medicine would be to compare two or more doses of or different components of the same treatment ( Ward-Horner & Sturmey, 2010 ). For example, two drug doses could be compared using an A1B1C1B2C2 design, where A represents placebo and B and C represent the different drug doses ( Guyatt et al., 1990 ). In the case of drug studies, the drug/placebo administration can be double blinded. A more complex design could be A1B1A2C1A3C2A4B2, which would yield multiple replications of the comparison between drug and placebo. Based on the kinetics of the drug and the need for a washout period, the design could also be A1B1C1B2C2. This would provide three demonstration of treatment effects: B1 to C1, C1 to B2, and B2 to C2. Other permutations could be planned strategically to identify the optimal dose for each individual.

Advantages of SCED reversal designs are their ability to experimentally show that a particular treatment was functionally related to a particular change in an outcome variable for that person . This is the core principle of personalized medicine: an optimal treatment for an individual can be identified ( Dallery & Raiff, 2014 ; Davidson et al., 2021 ; Guyatt et al., 1990 ; Hekler et al., 2020 ; Lillie et al., 2011 ). These designs can work well for studying the effect of interventions on rare diseases in which collecting enough participants with similar characteristics for an RCT would be unlikely. An additional strength is the opportunity for the clinical researcher who also delivers clinical care to translate basic science findings or new findings from RCTs to their patients, who can potentially benefit ( Dallery & Raiff, 2014 ; Hayes, 1981 ). Research suggests that the trickledown of new developments and hypotheses to their support in RCTs can take more than 15 years; many important advancements in the medical and behavior sciences are likely not to be implemented rapidly enough ( Riley et al., 2013 ). The ability to test new intervention developments using scientific principles could speed up their translation into practice.

Limitations to SCED designs, however, are worth noting. Firstly, in line with the expectation that the outcome returns to baseline levels, reversals may require removal of the treatment. If the effect is not quickly reversible, then the designs are not relevant. A washout period may be placed in-between phases if the effect is not immediately reversible; for example, a drug washout period could be planned based on the half-life of drug. Secondly, the intervention should have a relatively immediate effect on the outcome. If many weeks to months are needed for some interventions to show effects, a reversal design may not be optimal unless the investigator is willing to plan a lengthy study. Thirdly, the design depends on comparing stable data over conditions. If achieving stability due to uncontrolled sources of biological or environmental variation is not possible, a reversal design may not be appropriate to evaluate a treatment, though it may be useful to identify the sources of variability ( Sidman, 1960 ). Finally, for a reversal to a baseline, a no-treatment phase may be inappropriate in investigating treatment effects for a very ill patient.

3. Multiple Baseline Designs

An alternative to a reversal design is the multiple baseline design, which does not require reversal of conditions to establish experimental control. There are three types of multiple baseline designs: multiple baseline across people, behaviors, and settings. The most popular is the multiple baseline across people, in which baselines are established for three or more people for the same outcome ( Cushing et al., 2011 ; Meredith et al., 2011 ). Treatment is implemented after different durations of baseline across individuals. The order of treatment implementation across people can be randomized ( Wen et al., 2019 ). Figure 3 shows an example across three individuals. In this hypothetical example, baseline data for each person are relatively stable and not decreasing, and reductions in the dependent variable are only observed after introduction of the intervention. Inclusion of one control person, who remains in baseline throughout the study and provides a control for extended monitoring, is also possible. Another variation is to collect baseline data intermittently in a ‘probe’ design, which can minimize burden associated with simultaneous and repeated measurement of outcomes ( Byiers et al., 2012 ; Horner & Baer, 1978 ). If the outcomes do not change during baseline conditions and the changes only occur across participants after the treatment has been implemented—and this sequence is replicated across several people—change in the outcome may be safely attributed to the treatment. The length of the baselines still must be long enough to show stability and no trend toward improvement until the treatment is implemented.

P1–P3 represent different hypothetical participants.

The two other multiple baseline designs focus on individual people: the multiple baseline across settings and the multiple baseline across behaviors ( Boles et al., 2008 ; Lane-Brown & Tate, 2010 ). An example of a multiple baseline across settings would be a dietary intervention implemented across meals. An intervention that targets a reduction in consumption of high–glycemic index foods, or foods with added sugar across meals, could be developed with the order of meals randomized. For example, someone may be randomized to reduce sugar-added or high–glycemic index foods for breakfast without any implementation at lunch or dinner. Implementation of the diet at lunch and then dinner would occur after different durations of baselines in these settings. An example of multiple baseline across behaviors might be to use feedback to develop a comprehensive exercise program that involves stretching, aerobic exercise, and resistance training. Feedback could target improvement in one of these randomly selected behaviors, implemented in a staggered manner.

The main limitation to a multiple baseline design is that some people (or behaviors) may be kept in baseline or control conditions for extended periods before treatment is implemented. Of course, failure to receive an effective treatment is common in RCTs for people who are randomized to control conditions, but unlike control groups in RCTs, all participants eventually receive treatment.

Finally, while the emphasis in personalized medicine is the identification of an optimal treatment plan for an individual person, situations in which multiple baselines across people prove relevant for precision medicine may arise. For example, identification of a small group of people with common characteristics—perhaps with a rare disease and for which a multiple-baseline-across-people design could be used to test an intervention more effectively than a series of personalized designs—is possible. In a similar vein, differential response to a common treatment in a multiple-baseline-across-people design can help to identify individual differences that can compromise the response to a treatment.

4. Integrating Multiple Baseline and Reversal Designs

While reversal designs can be used to compare effects of interventions, multiple baseline designs provide experimental control for testing one intervention but do not compare different interventions. One way to take advantage of the strengths of both designs is to combine them. For example, the effects of a first treatment could be studied using a multiple-baseline format and, after experimental control has been established, return to baseline prior to the commencement of a different treatment, which may be introduced in a different order. These comparisons can be made for several different interventions with the combination of both designs to demonstrate experimental control and compare effects of the interventions.

Figure 4 shows a hypothetical example of a combined approach to identify the best drug to decrease blood pressure. Baseline blood pressures are established for three people under placebo conditions before new drug X is introduced across participants in a staggered fashion to establish relative changes in blood pressure. All return to placebo after blood pressures reach stability, drug Y is introduced in a staggered sequence, participants are returned to placebo, and the most effective intervention for each individual (drug X or Y) is reintroduced to replicate the most important result: the most effective medication. This across-subjects design establishes experimental control for two different new drug interventions across three people while also establishing experimental control for five comparisons within subjects (placebo—drug X, drug Y—placebo, placebo—drug Y, drug Y—placebo, placebo—more effective drug). Though this combined design strengthens confidence beyond either reversal or multiple baseline designs, in many situations, experimental control demonstrated using a reversal design is sufficient.

BL = Baseline. Drug X and Drug Y represent hypothetical drugs to lower blood pressure, and Best Drug represents a reversal to the most effective drug as identified for each hypothetical participant, labeled P1–P3.

5. Other Varieties of Single-Case Experimental Designs

Other less commonly used designs within the family of SCEDs may be useful for personalized medicine. One of the most relevant may be the alternating treatment design ( Barlow & Hayes, 1979 ; Manolov et al., 2021 ), in which people are exposed to baseline and one or more treatments for very brief periods without the concern about stability before changing conditions. While the treatment period may be short, many more replications of treatments—and ineffective treatments—can be identified quickly. This type of design may be relevant for drugs that have rapid effects with a short half-life and behavioral interventions that have rapid effects ( Coyle & Robertson, 1998 )—for example, the effects of biofeedback on heart rate ( Weems, 1998 ). Another design is the changing criterion design, in which experimental control is demonstrated when the outcome meets certain preselected criteria that can be systematically increased or decreased over time ( Hartmann & Hall, 1976 ). The design is especially useful when learning a new skill or when outcomes change slowly over time ( Singh & Leung, 1988 )—for example, gradually increasing the range of foods chosen in a previously highly selective eater ( Russo et al., 2019 ).

6. Integrating Single-Case Experimental Designs Into Randomized Controlled Trials

SCEDs can be integrated into RCTs to compare the efficacy of treatments chosen for someone based on SCEDs versus a standardized or usual care treatment ( Epstein et al., 2021 ; Schork & Goetz, 2017 ). Such innovative designs may capture the best in SCEDs and randomized controlled designs. Kravitz et al. (2018) used an RCT in which one group ( n = 108) experienced a series of reversal AB conditions, or a personalized (N-of-1) trial. The specific conditions were chosen for each patient from among eight categories of treatments to reduce chronic musculoskeletal pain (e.g., acetaminophen, any nonsteroidal anti-inflammatory drug, acetaminophen/oxycodone, tramadol). The other group ( n = 107) received usual care. The study also incorporated mobile technology to record pain-related data daily (see Dallery et al., 2013 , for a discussion of technology and SCEDs). The results suggested that the N-of-1 approach was feasible and acceptable, but it did not yield statistically significant superior results in pain measures compared to the usual care group. However, as noted by Vohra and Punja (2019) , the results do not indicate a flaw in the methodological approach: finding that two treatments do not differ in superiority is a finding worth knowing.

Another example of a situation where an integrated approach may be useful is selecting a diet for weight control. Many diets for weight control that vary in their macronutrient intake—such as low carb, higher fat versus low fat, and higher carb—have their proponents and favorable biological mechanisms. However, direct comparisons of these diets basically show that they achieve similar weight control with large variability in outcome. Thus, while the average person on a low-fat diet does about the same as the average person on a lowcarb diet, some people on the low-carb diet do very well, while some fail. Some of the people who fail on the low-fat diet would undoubtedly do well on the low-carb diet, and some who fail on the low-fat diet would do well on the low-carb diet. Further, some would fail on both diets due to general problems in adherence.

Personalized medicine suggests that diets should be individualized to achieve the best results. SCEDs would be one way to show ‘proof of concept’ that a particular diet is better than a standard healthy diet. First, people would be randomized to experimental (including SCEDs) or control (not basing diet on SCEDs). Subject selection criteria would proceed as in any RCT. For the first 3 months, people in the experimental group would engage in individual reversal designs in which 2-week intervals of low-carb and low-fat diets would be interspersed with their usual eating, and weight loss, diet adherence, food preferences, and the reinforcing value of foods in the diet would be measured to assess biological, behavioral, and subjective changes.

Participants in the control group would experience a similar exposure to the different types of diets, but the diet to which they are assigned would be randomly chosen rather than chosen using SCED methods. In this way, they would have similar exposure to diets during the first 3 months of the study, but this experience would not impact group assignment. As with any RCT, the study would proceed with regular measures (e.g., 6, 12, 24 months) and the hypothesis that those assigned to a diet that results in better initial weight loss, and that they like and are motivated to continue, would do better than those receiving a randomly selected diet. The study could also be designed with three groups: a single-case design experimental group similar to the approach in the hypothetical study above and two control groups, one low-fat and one low-carb.

An alternative design would be to have everyone experience SCEDs for the first 3 months and then be randomized to either the optimal treatment identified during the first 3 months or an intervention randomly chosen among the interventions to be studied. This design has the advantage of randomization being after 3 months of study so that dropouts and non-adherers within the first 3 months would not be randomized in an intent-to-treat format.

The goal of either hypothesized study, or any study that attempts to incorporate SCEDs into RCTs, is that matching participants to treatments will provide superior results in comparison to providing the same treatment to everyone in a group. Two hypotheses can be generated in these types of designs: first, that the mean changes will differ between groups, and second, that the variability will differ between groups with less variability in outcome for people who have treatment selected after a single-case trial than people who have a treatment randomly selected. A reduction in variability plus mean differences in outcome should increase the effect size for people treated using individualized designs, increase power, and allow for a smaller sample size to ensure confidence about the differences observed between groups.

7. Limitations of Single-Case Experimental Designs

Single-case experimental designs have their common limitations. If a measure changes with repeated testing without intervention, it may not be useful for an SCED unless steps can be taken to mitigate such reactivity, such as more unobtrusive monitoring ( Kazdin, 2021 ). Given that the effects of interventions are evaluated over time, systematic environmental changes or maturation could influence the relationship between a treatment and outcome and thereby obscure the effect of a treatment. However, the design logic of reversal and multiple baseline designs largely control for such influences. Since SCEDs rely on repeated measures and a detailed study of the relationship between treatment and outcome, studies that use dependent measures that cannot be sampled frequently are not candidates for SCEDs. Likewise, the failure to identify a temporal relationship between the introduction of treatment and initiation of change in the outcome can make attribution of changes to the intervention challenging. A confounding variable’s association with introduction or removal of the intervention, which may cause inappropriate decisions about the effects of the intervention, is always possible. Dropout or uncontrolled events that occur to individuals can introduce confounding variables to the SCED. These problems are not unique to SCEDs and also occur with RCTs.

8. Single-Case Experimental Designs in Early Stage Translational Research

The emphasis of a research program may be on translating basic science findings to clinical interventions. The goal may be to collect early phase translational research as a step toward a fully powered RCT—( Epstein et al., 2021 ). The fact that a large amount of basic science does not get translated into clinical interventions is well known ( Butler, 2008 ; Seyhan, 2019 ); this served in part as the stimulus for the National Institutes of Health (NIH) to develop a network of clinical and translational science institutes in medical schools and universities throughout the United States. A common approach to early phase translational research is to implement a small, underpowered RCT to secure a ‘signal’ of a treatment effect and an effect size. This is a problematic approach to pilot research, and it is not advocated by the NIH as an approach to early phase translational research ( National Center for Complementary and Integrative Health, 2020 ). The number of participants needed for a fully powered RCT may be substantially different from the number projected from a small-sample RCT. These small, underpowered, early phase translational studies may provide too large an estimate of an effect size, leading to an underpowered RCT. Likewise, a small-sample RCT can lead to a small effect size that can, in turn, lead to a failure to implement a potentially effective intervention ( Kraemer et al., 2006 ). Therefore, SCEDs—especially reversal and multiple baseline designs—are evidently ideally suited to early phase translational research. This use complements the utility of SCEDs for identifying the optimal treatment for an individual or small group of individuals.

9. Conclusion

Single-case experimental designs provide flexible, rigorous, and cost-effective approaches that can be used in personalized medicine to identify the optimal treatment for an individual patient. SCEDs represent a broad array of designs, and personalized (N-of-1) designs are a prominent example, particularly in medicine. These designs can be incorporated into RCTs, and they can be integrated using meta-analysis techniques. SCEDs should become a standard part of the toolbox for clinical researchers to improve clinical care for their patients, and they can lead to the next generation of interventions that show maximal effects for individual cases as well as for early phase translational research to clinical practice.

Acknowledgments

We thank Lesleigh Stinson and Andrea Villegas for preparing the figures.

Disclosure Statement

Preparation of this special issue was supported by grants R01LM012836 from the National Library of Medicine of the National Institutes of Health and P30AG063786 from the National Institute on Aging of the National Institutes of Health. Funding to authors of this article was supported by grants U01 HL131552 from the National Heart, Lung, and Blood Institute, UH3 DK109543 from the National Institute of Diabetes, Digestive and Kidney Diseases, and RO1HD080292 and RO1HD088131 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. The views expressed in this paper are those of the authors and do not represent the views of the National Institutes of Health, the U.S. Department of Health and Human Services, or any other government entity.

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Generality of Findings From Single-Case Designs: It’s Not All About the “ N ”

• Technical and Tutorials
• Published: 16 February 2021
• Volume 14 , pages 991–995, ( 2021 )

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• Seth G. Walker 1 &
• James E. Carr   ORCID: orcid.org/0000-0002-6445-2992 1  

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There is a common misconception in applied research that generalizations from a study to a specific client can only be made with a large sample size. In single-case design research, however, generalizations are made from a line of replication studies rather than from a single large- N study. In this brief tutorial, we summarize how generalizations are made from single-case design research, and provide a model elevator speech to assist behavior analysts in talking about single-case design research with others.

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Advancing the Application and Use of Single-Case Research Designs: Reflections on Articles from the Special Issue

Using single-case designs in practical settings: is within-subject replication always necessary.

Single-Case Designs

We should note that generality also has another meaning in applied behavior analysis. Baer, Wolf, and Risley ( 1968 ) use generality to refer to the durability and breadth of the effects of a particular intervention implementation.

Birnbrauer ( 1981 ) proposed a different way of thinking about generality, in which its basis is the functional difference between the behavior’s controlling variables during baseline and intervention, rather than participant or setting characteristics. Birnbrauer stated, “We should look for similarities in baseline conditions, the functional relations that appear to be operative during those pretreatment conditions, and the functional changes that implementation of treatment entailed for previous subjects” (p. 126). We do not disagree with this approach to generality. However, it can only be applied when experimental articles include such information; most do not. In addition, the larger scientific community still discusses generality in terms of differences between participants, settings, and other variables across studies. Thus, this is the framework we use in the current article.

A line of research generally produces a large N , but it is the various systematic replication conditions that ultimately enhance generalization and not the large N itself.

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Acknowledgement

We thank April Kisamore for her assistance during the early stage of this project.

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Walker, S.G., Carr, J.E. Generality of Findings From Single-Case Designs: It’s Not All About the “ N ”. Behav Analysis Practice 14 , 991–995 (2021). https://doi.org/10.1007/s40617-020-00547-3

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Accepted : 21 December 2020

Published : 16 February 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s40617-020-00547-3

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