methods for case study analysis

• Privacy Policy

Home » Case Study – Methods, Examples and Guide

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

You may also like

Textual Analysis – Types, Examples and Guide

Ethnographic Research -Types, Methods and Guide

Quasi-Experimental Research Design – Types...

Exploratory Research – Types, Methods and...

Questionnaire – Definition, Types, and Examples

Descriptive Research Design – Types, Methods and...

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.

Whatever field you're in, ATLAS.ti puts your data to work for you

Download a free trial of ATLAS.ti to turn your data into insights.

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.

Ready to jumpstart your research with ATLAS.ti?

Conceptualize your research project with our intuitive data analysis interface. Download a free trial today.

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.

Ready to analyze your data with ATLAS.ti?

See how our intuitive software can draw key insights from your data with a free trial today.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

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.

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 7 June 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, 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 research 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 more 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 the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a 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:

• The case represents 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.
• The case provides 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.
• The case challenges and offers 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 current practice. A case study analysis may offer 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 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.
• The case provides 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 so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the 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 east central Africa. A case study of how women contribute to saving water in a rural village of Uganda 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. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying 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 is being studied? Describe the research problem and describe the subject of analysis [the case] 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 is 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 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 involve 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 investigate 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 use of a 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 relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] 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 constitutes 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; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide 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 their 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 them 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 [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

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, historical, cultural, economic, political], 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, explain why you are studying 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 suggests 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 off? How might knowing the suppliers of these trucks 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 clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the 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 analysis of the 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 common to combine a description of the results 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 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 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 revealed by 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 if that is how the findings can be interpreted from your case.

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 any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly 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 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 or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies 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 engaged with 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 more in terms of managing access rather 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 that leave the reader questioning the results.

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.

• << Previous: Writing a Case Analysis Paper
• Next: Writing a Field Report >>
• Last Updated: Jun 3, 2024 9:44 AM
• URL: https://libguides.usc.edu/writingguide/assignments

Research Writing and Analysis

• NVivo Group and Study Sessions
• SPSS This link opens in a new window
• Statistical Analysis Group sessions
• Using Qualtrics
• Dissertation and Data Analysis Group Sessions
• Defense Schedule - Commons Calendar This link opens in a new window
• Research Process Flow Chart
• Research Alignment Chapter 1 This link opens in a new window
• Step 1: Seek Out Evidence
• Step 2: Explain
• Step 3: The Big Picture
• Step 4: Own It
• Step 5: Illustrate
• Annotated Bibliography
• Literature Review This link opens in a new window
• Systematic Reviews & Meta-Analyses
• How to Synthesize and Analyze
• Synthesis and Analysis Practice
• Synthesis and Analysis Group Sessions
• Problem Statement
• Purpose Statement
• Conceptual Framework
• Theoretical Framework
• Locating Theoretical and Conceptual Frameworks This link opens in a new window
• Quantitative Research Questions
• Qualitative Research Questions
• Trustworthiness of Qualitative Data
• Analysis and Coding Example- Qualitative Data
• Thematic Data Analysis in Qualitative Design
• Dissertation to Journal Article This link opens in a new window
• International Journal of Online Graduate Education (IJOGE) This link opens in a new window
• Journal of Research in Innovative Teaching & Learning (JRIT&L) This link opens in a new window

Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

Was this resource helpful?

• << Previous: Thematic Data Analysis in Qualitative Design
• Next: Journal Article Reporting Standards (JARS) >>
• Last Updated: May 29, 2024 8:05 AM
• URL: https://resources.nu.edu/researchtools

• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

784k Accesses

1048 Citations

37 Altmetric

Metrics details

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Yin RK: Case study research, design and method. 2009, London: Sage Publications Ltd., 4

Google Scholar  

Keen J, Packwood T: Qualitative research; case study evaluation. BMJ. 1995, 311: 444-446.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J, et al: Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009, 6 (10): 1-11.

Article   Google Scholar  

Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, et al: The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO). 2008, [ http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf ]

Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al: Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010, 41: c4564-

Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P, the Patient Safety Education Study Group: Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010, 15: 4-10. 10.1258/jhsrp.2009.009052.

Article   PubMed   Google Scholar  

van Harten WH, Casparie TF, Fisscher OA: The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002, 60 (1): 17-37. 10.1016/S0168-8510(01)00187-7.

Stake RE: The art of case study research. 1995, London: Sage Publications Ltd.

Sheikh A, Smeeth L, Ashcroft R: Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002, 52 (482): 746-51.

PubMed   PubMed Central   Google Scholar  

King G, Keohane R, Verba S: Designing Social Inquiry. 1996, Princeton: Princeton University Press

Doolin B: Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998, 13: 301-311. 10.1057/jit.1998.8.

George AL, Bennett A: Case studies and theory development in the social sciences. 2005, Cambridge, MA: MIT Press

Eccles M, the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG): Designing theoretically-informed implementation interventions. Implementation Science. 2006, 1: 1-8. 10.1186/1748-5908-1-1.

Article   PubMed Central   Google Scholar  

Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A: Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005, 365 (9456): 312-7.

Sheikh A, Panesar SS, Lasserson T, Netuveli G: Recruitment of ethnic minorities to asthma studies. Thorax. 2004, 59 (7): 634-

CAS   PubMed   PubMed Central   Google Scholar  

Hellström I, Nolan M, Lundh U: 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005, 4: 7-22. 10.1177/1471301205049188.

Som CV: Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005, 18: 463-477. 10.1108/09513550510608903.

Lincoln Y, Guba E: Naturalistic inquiry. 1985, Newbury Park: Sage Publications

Barbour RS: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. BMJ. 2001, 322: 1115-1117. 10.1136/bmj.322.7294.1115.

Mays N, Pope C: Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000, 320: 50-52. 10.1136/bmj.320.7226.50.

Mason J: Qualitative researching. 2002, London: Sage

Brazier A, Cooke K, Moravan V: Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008, 7: 5-17. 10.1177/1534735407313395.

Miles MB, Huberman M: Qualitative data analysis: an expanded sourcebook. 1994, CA: Sage Publications Inc., 2

Pope C, Ziebland S, Mays N: Analysing qualitative data. Qualitative research in health care. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Cresswell KM, Worth A, Sheikh A: Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010, 10 (1): 67-10.1186/1472-6947-10-67.

Article   PubMed   PubMed Central   Google Scholar  

Malterud K: Qualitative research: standards, challenges, and guidelines. Lancet. 2001, 358: 483-488. 10.1016/S0140-6736(01)05627-6.

Article   CAS   PubMed   Google Scholar  

Yin R: Case study research: design and methods. 1994, Thousand Oaks, CA: Sage Publishing, 2

Yin R: Enhancing the quality of case studies in health services research. Health Serv Res. 1999, 34: 1209-1224.

Green J, Thorogood N: Qualitative methods for health research. 2009, Los Angeles: Sage, 2

Howcroft D, Trauth E: Handbook of Critical Information Systems Research, Theory and Application. 2005, Cheltenham, UK: Northampton, MA, USA: Edward Elgar

Book   Google Scholar  

Blakie N: Approaches to Social Enquiry. 1993, Cambridge: Polity Press

Doolin B: Power and resistance in the implementation of a medical management information system. Info Systems J. 2004, 14: 343-362. 10.1111/j.1365-2575.2004.00176.x.

Bloomfield BP, Best A: Management consultants: systems development, power and the translation of problems. Sociological Review. 1992, 40: 533-560.

Shanks G, Parr A: Positivist, single case study research in information systems: A critical analysis. Proceedings of the European Conference on Information Systems. 2003, Naples

Pre-publication history

The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1471-2288/11/100/prepub

Download references

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

Author information

Authors and affiliations.

Division of Primary Care, The University of Nottingham, Nottingham, UK

Sarah Crowe & Anthony Avery

Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Kathrin Cresswell, Ann Robertson & Aziz Sheikh

School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sarah Crowe .

Additional information

Competing interests.

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article.

Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

Download citation

Received : 29 November 2010

Accepted : 27 June 2011

Published : 27 June 2011

DOI : https://doi.org/10.1186/1471-2288-11-100

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Case Study Approach
• Electronic Health Record System
• Case Study Design
• Case Study Site
• Case Study Report

BMC Medical Research Methodology

ISSN: 1471-2288

• General enquiries: [email protected]

• Article Writing Affordable Article Writing Services
• Blog Writing Blogs that optimise your visibility
• Product Description Website that optimise your visibility
• Website Writing Website that optimise your visibility
• Proofreading Website that optimise your visibility
• Translation Website that optimise your visibility
• Agriculture Affordable Article Writing Services
• Health & Beauty Blogs that optimise your visibility
• Automotive Website that optimise your visibility
• Sports & fitness Website that optimise your visibility
• Real Estate Website that optimise your visibility
• Entertainment Website that optimise your visibility
• Blogs Affordable Article Writing Services
• Samples Blogs that optimise your visibility
• Case Study Website that optimise your visibility

“How to Write Case Studies: A Comprehensive Guide”

Case studies are essential for marketing and research, offering in-depth insights into successes and problem-solving methods. This blog explains how to write case studies, including steps for creating them, tips for analysis, and case study examples. You'll also find case study templates to simplify the process. Effective case studies establish credibility, enhance marketing efforts, and provide valuable insights for future projects.

Case studies are detailed examinations of subjects like businesses, organizations, or individuals. They are used to highlight successes and problem-solving methods. They are crucial in marketing, education, and research to provide concrete examples and insights.

This blog will explain how to write case studies and their importance. We will cover different applications of case studies and a step-by-step process to create them. You’ll find tips for conducting case study analysis, along with case study examples and case study templates.

Effective case studies are vital. They showcase success stories and problem-solving skills, establishing credibility. This guide will teach you how to create a case study that engages your audience and enhances your marketing and research efforts.

What are Case Studies?

1. Definition and Purpose of a Case Study

Case studies are in-depth explorations of specific subjects to understand dynamics and outcomes. They provide detailed insights that can be generalized to broader contexts.

2. Different Types of Case Studies

• Exploratory: Investigates an area with limited information.
• Explanatory: Explains reasons behind a phenomenon.
• Descriptive: Provides a detailed account of the subject.
• Intrinsic : Focuses on a unique subject.
• Instrumental: Uses the case to understand a broader issue.

3. Benefits of Using Case Studies

Case studies offer many benefits. They provide real-world examples to illustrate theories or concepts. Businesses can demonstrate the effectiveness of their products or services. Researchers gain detailed insights into specific phenomena. Educators use them to teach through practical examples. Learning how to write case studies can enhance your marketing and research efforts.

Understanding how to create a case study involves recognizing these benefits. Case study examples show practical applications. Using case study templates can simplify the process.

5 Steps to Write a Case Study

1. Identifying the Subject or Case

Choose a subject that aligns with your objectives and offers valuable insights. Ensure the subject has a clear narrative and relevance to your audience. The subject should illustrate key points and provide substantial learning opportunities. Common subjects include successful projects, client stories, or significant business challenges.

2. Conducting Thorough Research and Data Collection

Gather comprehensive data from multiple sources. Conduct interviews with key stakeholders, such as clients, team members, or industry experts. Use surveys to collect quantitative data. Review documents, reports, and any relevant records. Ensure the information is accurate, relevant, and up-to-date. This thorough research forms the foundation for how to write case studies that are credible and informative.

3. Structuring the Case Study

Organize your case study into these sections:

• Introduction: Introduce the subject and its significance. Provide an overview of what will be covered.
• Background: Provide context and background information. Describe the subject’s history, environment, and any relevant details.
• Case Presentation: Detail the case, including the problem or challenge faced. Discuss the actions taken to address the issue.
• Analysis: Analyze the data and discuss the findings. Highlight key insights, patterns, and outcomes.
• Conclusion: Summarize the outcomes and key takeaways. Reflect on the broader implications and lessons learned.

4. Writing a Compelling Introduction

The introduction should grab the reader’s attention. Start with a hook, such as an interesting fact, quote, or question. Provide a brief overview of the subject and its importance. Explain why this case is relevant and worth studying. An engaging introduction sets the stage for how to create a case study that keeps readers interested.

5. Providing Background Information and Context

Give readers the necessary background to understand the case. Include details about the subject’s history, environment, and any relevant circumstances. Explain the context in which the case exists, such as the industry, market conditions, or organizational culture. Providing a solid foundation helps readers grasp the significance of the case and enhances the credibility of your study.

Understanding how to write a case study involves meticulous research and a clear structure. Utilizing case study examples and templates can guide you through the process, ensuring you present your findings effectively. These steps are essential for writing informative, engaging, and impactful case studies. 

How to Write Case Study Analysis

1. Analyzing the Data Collected

Examine the data to identify patterns, trends, and key findings. Use qualitative and quantitative methods to ensure a comprehensive analysis. Validate the data’s accuracy and relevance to the subject. Look for correlations and causations that can provide deeper insights.

2. Identifying Key Issues and Problems

Pinpoint the main issues or challenges faced by the subject. Determine the root causes of these problems. Use tools like SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) to get a clear picture. Prioritize the issues based on their impact and urgency.

3. Discussing Possible Solutions and Their Implementation

Explore various solutions that address the identified issues. Compare the potential effectiveness of each solution. Discuss the steps taken to implement the chosen solutions. Highlight the decision-making process and the rationale behind it. Include any obstacles faced during implementation and how they were overcome.

4. Evaluating the Results and Outcomes

Assess the outcomes of the implemented solutions. Use metrics and KPIs (Key Performance Indicators) to measure success. Compare the results with the initial objectives and expectations. Discuss any deviations and their reasons. Provide evidence to support your evaluation, such as before-and-after data or testimonials.

5. Providing Insights and Lessons Learned

Reflect on the insights gained from the case study. Discuss what worked well and what didn’t. Highlight lessons that can be applied to similar situations. Provide actionable recommendations for future projects. This section should offer valuable takeaways for the readers, helping them understand how to create a case study that is insightful and practical.

Mastering how to write case studies involves understanding each part of the analysis. Use case study examples to see how these elements are applied. Case study templates can help you structure your work. Knowing how to make a case study analysis will make your findings clear and actionable.

Case Study Examples and Templates

1. Showcasing Successful Case Studies

Georgia tech athletics increase season ticket sales by 80%.

Georgia Tech Athletics aimed to enhance their season ticket sales and engagement with fans. Their initial strategy involved multiple outbound phone calls without targeting. They partnered with Salesloft to improve their sales process with a more structured inbound approach. This allowed sales reps to target communications effectively. As a result, Georgia Tech saw an 80% increase in season ticket sales, with improved employee engagement and fan relationships​.

WeightWatchers Revamps Enterprise Sales Process with HubSpot

WeightWatchers sought to improve their sales efficiency. Their previous system lacked automation, requiring extensive manual effort. By adopting HubSpot’s CRM, WeightWatchers streamlined their sales process. The automation capabilities of HubSpot allowed them to manage customer interactions more effectively. This transition significantly enhanced their operational efficiency and sales performance​.

2. Breakdown of What Makes These Examples Effective

These case study examples are effective due to their clear structure and compelling storytelling. They:

• Identify the problem: Each case study begins by outlining the challenges faced by the client.
• Detail the solution: They explain the specific solutions implemented to address these challenges.
• Showcase the results: Quantifiable results and improvements are highlighted, demonstrating the effectiveness of the solutions.
• Use visuals and quotes: Incorporating images, charts, and client testimonials enhances engagement and credibility.

3. Providing Case Study Templates

To assist in creating your own case studies, here are some recommended case study templates:

1. General Case Study Template

• Suitable for various industries and applications.
• Includes sections for background, problem, solution, and results.
• Helps provide a structured narrative for any case study.

2. Data-Driven Case Study Template

• Focuses on presenting metrics and data.
• Ideal for showcasing quantitative achievements.
• Structured to highlight significant performance improvements and achievements.

3. Product-Specific Case Study Template

• Emphasizes customer experiences and satisfaction with a specific product.
• Highlights benefits and features of the product rather than the process.

4. Tips for Customizing Templates to Fit Your Needs

When using case study templates, tailor them to match the specific context of your study. Consider the following tips:

• Adapt the language and tone: Ensure it aligns with your brand voice and audience.
• Include relevant visuals: Add charts, graphs, and images to support your narrative.
• Personalize the content: Use specific details about the subject to make the case study unique and relatable.

Utilizing these examples and templates will guide you in how to write case studies effectively. They provide a clear framework for how to create a case study that is engaging and informative. Learning how to make a case study becomes more manageable with these resources and examples​.

Tips for Creating Compelling Case Studies

1. Using Storytelling Techniques to Engage Readers

Incorporate storytelling techniques to make your case study engaging. A compelling narrative holds the reader’s attention.

2. Including Quotes and Testimonials from Participants

Add quotes and testimonials to add credibility. Participant feedback enhances the authenticity of your study.

3. Visual Aids: Charts, Graphs, and Images to Support Your Case

Use charts, graphs, and images to illustrate key points. Visual aids help in better understanding and retention.

4. Ensuring Clarity and Conciseness in Writing

Write clearly and concisely to maintain reader interest. Avoid jargon and ensure your writing is easy to follow.

5. Highlighting the Impact and Benefits

Emphasize the positive outcomes and benefits. Show how the subject has improved or achieved success.

Understanding how to write case studies involves using effective storytelling and visuals. Case study examples show how to engage readers, and case study templates help organize your content. Learning how to make a case study ensures that it is clear and impactful.

Benefits of Using Case Studies

1. Establishing Authority and Credibility

How to write case studies can effectively establish your authority. Showcasing success stories builds credibility in your field.

2. Demonstrating Practical Applications of Your Product or Service

Case study examples demonstrate how your product or service solves real-world problems. This practical evidence is convincing for potential clients.

3. Enhancing Marketing and Sales Efforts

Use case studies to support your marketing and sales strategies. They highlight your successes and attract new customers.

4. Providing Valuable Insights for Future Projects

Case studies offer insights that can guide future projects. Learning how to create a case study helps in applying these lessons effectively.

5. Engaging and Educating Your Audience

Case studies are engaging and educational. They provide detailed examples and valuable lessons. Using case study templates can make this process easier and more effective. Understanding how to make a case study ensures you can communicate these benefits clearly.

Writing effective case studies involves thorough research, clear structure, and engaging content. By following these steps, you’ll learn how to write case studies that showcase your success stories and problem-solving skills. Use the case study examples and case study templates provided to get started. Well-crafted case studies are valuable tools for marketing, research, and education. Start learning how to make a case study today and share your success stories with the world.

What is the purpose of a case study?

A case study provides detailed insights into a subject, illustrating successes and solutions. It helps in understanding complex issues.

How do I choose a subject for my case study?

Select a subject that aligns with your objectives and offers valuable insights. Ensure it has a clear narrative.

What are the key components of a case study analysis?

A case study analysis includes data collection, identifying key issues, discussing solutions, evaluating outcomes, and providing insights.

Where can I find case study templates?

You can find downloadable case study templates online. They simplify the process of creating a case study.

How can case studies benefit my business?

Case studies establish credibility, demonstrate practical applications, enhance marketing efforts, and provide insights for future projects. Learning how to create a case study can significantly benefit your business.

I am currently pursuing my Masters in Communication and Journalism from University of Mumbai. I am the author of four self published books. I am interested inv writing for films and TV. I run a blog where I write about film reviews.

More details for blogs

How to Create Interactive Content and Boost Engagement

Learn how to create interactive content to engage your audience. Discover tools, strategies, and benefits of using interactive elements.

How to Use Canva to Mass Produce Viral Content

Discover how to use Canva to mass produce viral content with these expert tips and strategies. Boost your content creation game today!

SGE Ranking Strategies: How to Get Rankings on SGEs

Learn how to create answers that rank for SGE with our comprehensive guide on SGE ranking strategies. Powerful tips and examples!

Need assistance with something

Speak with our expert right away to receive free service-related advice.

• Search Menu
• Sign in through your institution
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical Literature
• Classical Reception
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Archaeology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Variation
• Language Families
• Language Acquisition
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Modernism)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Culture
• Music and Religion
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Science
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Society
• Law and Politics
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Medical Ethics
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Games
• Computer Security
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business History
• Business Strategy
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Methodology
• Economic Systems
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• Ethnic Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Theory
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Politics and Law
• Politics of Development
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

• < Previous chapter
• Next chapter >

28 Case Selection for Case‐Study Analysis: Qualitative and Quantitative Techniques

John Gerring is Professor of Political Science, Boston University.

• Published: 02 September 2009
• Cite Icon Cite
• Permissions Icon Permissions

This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

Case ‐study analysis focuses on one or several cases that are expected to provide insight into a larger population. This presents the researcher with a formidable problem of case selection: Which cases should she or he choose?

In large‐sample research, the task of case selection is usually handled by some version of randomization. However, in case‐study research the sample is small (by definition) and this makes random sampling problematic, for any given sample may be wildly unrepresentative. Moreover, there is no guarantee that a few cases, chosen randomly, will provide leverage into the research question of interest.

In order to isolate a sample of cases that both reproduces the relevant causal features of a larger universe (representativeness) and provides variation along the dimensions of theoretical interest (causal leverage), case selection for very small samples must employ purposive (nonrandom) selection procedures. Nine such methods are discussed in this chapter, each of which may be identified with a distinct case‐study “type:” typical, diverse, extreme, deviant, influential, crucial, pathway, most‐similar , and most‐different . Table 28.1 summarizes each type, including its general definition, a technique for locating it within a population of potential cases, its uses, and its probable representativeness.

While each of these techniques is normally practiced on one or several cases (the diverse, most‐similar, and most‐different methods require at least two), all may employ additional cases—with the proviso that, at some point, they will no longer offer an opportunity for in‐depth analysis and will thus no longer be “case studies” in the usual sense ( Gerring 2007 , ch. 2 ). It will also be seen that small‐ N case‐selection procedures rest, at least implicitly, upon an analysis of a larger population of potential cases (as does randomization). The case(s) identified for intensive study is chosen from a population and the reasons for this choice hinge upon the way in which it is situated within that population. This is the origin of the terminology—typical, diverse, extreme, et al. It follows that case‐selection procedures in case‐study research may build upon prior cross‐case analysis and that they depend, at the very least, upon certain assumptions about the broader population.

In certain circumstances, the case‐selection procedure may be structured by a quantitative analysis of the larger population. Here, several caveats must be satisfied. First, the inference must pertain to more than a few dozen cases; otherwise, statistical analysis is problematic. Second, relevant data must be available for that population, or a significant sample of that population, on key variables, and the researcher must feel reasonably confident in the accuracy and conceptual validity of these variables. Third, all the standard assumptions of statistical research (e.g. identification, specification, robustness) must be carefully considered, and wherever possible, tested. I shall not dilate further on these familiar issues except to warn the researcher against the unreflective use of statistical techniques. 1 When these requirements are not met, the researcher must employ a qualitative approach to case selection.

The point of this chapter is to elucidate general principles that might guide the process of case selection in case‐study research, building upon earlier work by Harry Eckstein, Arend Lijphart, and others. Sometimes, these principles can be applied in a quantitative framework and sometimes they are limited to a qualitative framework. In either case, the logic of case selection remains quite similar, whether practiced in small‐ N or large‐ N contexts.

Before we begin, a bit of notation is necessary. In this chapter “ N ” refers to cases, not observations. Here, I am concerned primarily with causal inference, rather than inferences that are descriptive or predictive in nature. Thus, all hypotheses involve at least one independent variable ( X ) and one dependent variable ( Y ). For convenience, I shall label the causal factor of special theoretical interest X   1 , and the control variable, or vector of controls (if there are any), X   2 . If the writer is concerned to explain a puzzling outcome, but has no preconceptions about its causes, then the research will be described as Y‐centered . If a researcher is concerned to investigate the effects of a particular cause, with no preconceptions about what these effects might be, the research will be described as X‐centered . If a researcher is concerned to investigate a particular causal relationship, the research will be described as X   1 / Y‐centered , for it connects a particular cause with a particular outcome. 2   X ‐ or Y ‐centered research is exploratory; its purpose is to generate new hypotheses. X   1 / Y‐centered research, by contrast, is confirmatory/disconfirmatory; its purpose is to test an existing hypothesis.

1 Typical Case

In order for a focused case study to provide insight into a broader phenomenon it must be representative of a broader set of cases. It is in this context that one may speak of a typical‐case approach to case selection. The typical case exemplifies what is considered to be a typical set of values, given some general understanding of a phenomenon. By construction, the typical case is also a representative case.

Some typical cases serve an exploratory role. Here, the author chooses a case based upon a set of descriptive characteristics and then probes for causal relationships. Robert and Helen Lynd (1929/1956) selected a single city “to be as representative as possible of contemporary American life.” Specifically, they were looking for a city with

1) a temperate climate; 2) a sufficiently rapid rate of growth to ensure the presence of a plentiful assortment of the growing pains accompanying contemporary social change; 3) an industrial culture with modern, high‐speed machine production; 4) the absence of dominance of the city's industry by a single plant (i.e., not a one‐industry town); 5) a substantial local artistic life to balance its industrial activity …; and 6) the absence of any outstanding peculiarities or acute local problems which would mark the city off from the midchannel sort of American community. ( Lynd and Lynd 1929/1956 , quoted in Yin 2004 , 29–30)

After examining a number of options the Lynds decided that Muncie, Indiana, was more representative than, or at least as representative as, other midsized cities in America, thus qualifying as a typical case.

This is an inductive approach to case selection. Note that typicality may be understood according to the mean, median, or mode on a particular dimension; there may be multiple dimensions (as in the foregoing example); and each may be differently weighted (some dimensions may be more important than others). Where the selection criteria are multidimensional and a large sample of potential cases is in play, some form of factor analysis may be useful in identifying the most‐typical case(s).

However, the more common employment of the typical‐case method involves a causal model of some phenomenon of theoretical interest. Here, the researcher has identified a particular outcome ( Y ), and perhaps a specific X   1 / Y hypothesis, which she wishes to investigate. In order to do so, she looks for a typical example of that causal relationship. Intuitively, one imagines that a case selected according to the mean values of all parameters must be a typical case relative to some causal relationship. However, this is by no means assured.

Suppose that the Lynds were primarily interested in explaining feelings of trust/distrust among members of different social classes (one of the implicit research goals of the Middletown study). This outcome is likely to be affected by many factors, only some of which are included in their six selection criteria. So choosing cases with respect to a causal hypothesis involves, first of all, identifying the relevant parameters. It involves, secondly, the selection of a case that has a “typical” value relative to the overall causal model; it is well explained. Cases with untypical scores on a particular dimension (e.g. very high or very low) may still be typical examples of a causal relationship. Indeed, they may be more typical than cases whose values lie close to the mean. Thus, a descriptive understanding of typicality is quite different from a causal understanding of typicality. Since it is the latter version that is more common, I shall adopt this understanding of typicality in the remainder of the discussion.

From a qualitative perspective, causal typicality involves the selection of a case that conforms to expectations about some general causal relationship. It performs as expected. In a quantitative setting, this notion is measured by the size of a case's residual in a large‐ N cross‐case model. Typical cases lie on or near the regression line; their residuals are small. Insofar as the model is correctly specified, the size of a case's residual (i.e. the number of standard deviations that separate the actual value from the fitted value) provides a helpful clue to how representative that case is likely to be. “Outliers” are unlikely to be representative of the target population.

Of course, just because a case has a low residual does not necessarily mean that it is a representative case (with respect to the causal relationship of interest). Indeed, the issue of case representativeness is an issue that can never be definitively settled. When one refers to a “typical case” one is saying, in effect, that the probability of a case's representativeness is high, relative to other cases. This test of typicality is misleading if the statistical model is mis‐specified. And it provides little insurance against errors that are purely stochastic. A case may lie directly on the regression line but still be, in some important respect, atypical. For example, it might have an odd combination of values; the interaction of variables might be different from other cases; or additional causal mechanisms might be at work. For this reason, it is important to supplement a statistical analysis of cases with evidence drawn from the case in question (the case study itself) and with our deductive knowledge of the world. One should never judge a case solely by its residual. Yet, all other things being equal, a case with a low residual is less likely to be unusual than a case with a high residual, and to this extent the method of case selection outlined here may be a helpful guide to case‐study researchers faced with a large number of potential cases.

By way of conclusion, it should be noted that because the typical case embodies a typical value on some set of causally relevant dimensions, the variance of interest to the researcher must lie within that case. Specifically, the typical case of some phenomenon may be helpful in exploring causal mechanisms and in solving identification problems (e.g. endogeneity between X   1 and Y , an omitted variable that may account for X   1   and Y , or some other spurious causal association). Depending upon the results of the case study, the author may confirm an existing hypothesis, disconfirm that hypothesis, or reframe it in a way that is consistent with the findings of the case study. These are the uses of the typical‐case study.

2 Diverse Cases

A second case‐selection strategy has as its primary objective the achievement of maximum variance along relevant dimensions. I refer to this as a diverse‐case method. For obvious reasons, this method requires the selection of a set of cases—at minimum, two—which are intended to represent the full range of values characterizing X   1 , Y , or some particular X   1 / Y relationship. 3

Where the individual variable of interest is categorical (on/off, red/black/blue, Jewish/Protestant/Catholic), the identification of diversity is readily apparent. The investigator simply chooses one case from each category. For a continuous variable, the choices are not so obvious. However, the researcher usually chooses both extreme values (high and low), and perhaps the mean or median as well. The researcher may also look for break‐points in the distribution that seem to correspond to categorical differences among cases. Or she may follow a theoretical hunch about which threshold values count, i.e. which are likely to produce different values on Y .

Another sort of diverse case takes account of the values of multiple variables (i.e. a vector), rather than a single variable. If these variables are categorical, the identification of causal types rests upon the intersection of each category. Two dichotomous variables produce a matrix with four cells. Three trichotomous variables produce a matrix of eight cells. And so forth. If all variables are deemed relevant to the analysis, the selection of diverse cases mandates the selection of one case drawn from within each cell. Let us say that an outcome is thought to be affected by sex, race (black/white), and marital status. Here, a diverse‐case strategy of case selection would identify one case within each of these intersecting cells—a total of eight cases. Things become slightly more complicated when one or more of the factors is continuous, rather than categorical. Here, the diversity of case values do not fall neatly into cells. Rather, these cells must be created by fiat—e.g. high, medium, low.

It will be seen that where multiple variables are under consideration, the logic of diverse‐case analysis rests upon the logic of typological theorizing—where different combinations of variables are assumed to have effects on an outcome that vary across types ( Elman 2005 ; George and Bennett 2005 , 235; Lazarsfeld and Barton 1951 ). George and Smoke, for example, wish to explore different types of deterrence failure—by “fait accompli,” by “limited probe,” and by “controlled pressure.” Consequently, they wish to find cases that exemplify each type of causal mechanism. 4

Diversity may thus refer to a range of variation on X or Y , or to a particular combination of causal factors (with or without a consideration of the outcome). In each instance, the goal of case selection is to capture the full range of variation along the dimension(s) of interest.

Since diversity can mean many things, its employment in a large‐ N setting is necessarily dependent upon how this key term is defined. If it is understood to pertain only to a single variable ( X   1 or Y ), then the task is fairly simple. A categorical variable mandates the choice of at least one case from each category—two if dichotomous, three if trichotomous, and so forth. A continuous variable suggests the choice of at least one “high” and “low” value, and perhaps one drawn from the mean or median. But other choices might also be justified, according to one's hunch about the underlying causal relationship or according to natural thresholds found in the data, which may be grouped into discrete categories. Single‐variable traits are usually easy to discover in a large‐ N setting through descriptive statistics or through visual inspection of the data.

Where diversity refers to particular combinations of variables, the relevant cross‐ case technique is some version of stratified random sampling (in a probabilistic setting) or Qualitative Comparative Analysis (in a deterministic setting) ( Ragin 2000 ). If the researcher suspects that a causal relationship is affected not only by combinations of factors but also by their sequencing , then the technique of analysis must incorporate temporal elements ( Abbott 2001 ; Abbott and Forrest 1986 ; Abbott and Tsay 2000 ). Thus, the method of identifying causal types rests upon whatever method of identifying causal relationships is employed in the large‐ N sample.

Note that the identification of distinct case types is intended to identify groups of cases that are internally homogeneous (in all respects that might affect the causal relationship of interest). Thus, the choice of cases within each group should not be problematic, and may be accomplished through random sampling or purposive case selection. However, if there is suspected diversity within each category, then measures should be taken to assure that the chosen cases are typical of each category. A case study should not focus on an atypical member of a subgroup.

Indeed, considerations of diversity and typicality often go together. Thus, in a study of globalization and social welfare systems, Duane Swank (2002) first identifies three distinctive groups of welfare states: “universalistic” (social democratic), “corporatist conservative,” and “liberal.” Next, he looks within each group to find the most‐typical cases. He decides that the Nordic countries are more typical of the universalistic model than the Netherlands since the latter has “some characteristics of the occupationally based program structure and a political context of Christian Democratic‐led governments typical of the corporatist conservative nations” ( Swank 2002 , 11; see also Esping‐Andersen 1990 ). Thus, the Nordic countries are chosen as representative cases within the universalistic case type, and are accompanied in the case‐study portion of his analysis by other cases chosen to represent the other welfare state types (corporatist conservative and liberal).

Evidently, when a sample encompasses a full range of variation on relevant parameters one is likely to enhance the representativeness of that sample (relative to some population). This is a distinct advantage. Of course, the inclusion of a full range of variation may distort the actual distribution of cases across this spectrum. If there are more “high” cases than “low” cases in a population and the researcher chooses only one high case and one low case, the resulting sample of two is not perfectly representative. Even so, the diverse‐case method probably has stronger claims to representativeness than any other small‐ N sample (including the standalone typical case). The selection of diverse cases has the additional advantage of introducing variation on the key variables of interest. A set of diverse cases is, by definition, a set of cases that encompasses a range of high and low values on relevant dimensions. There is, therefore, much to recommend this method of case selection. I suspect that these advantages are commonly understood and are applied on an intuitive level by case‐study researchers. However, the lack of a recognizable name—and an explicit methodological defense—has made it difficult for case‐study researchers to utilize this method of case selection, and to do so in an explicit and self‐conscious fashion. Neologism has its uses.

3 Extreme Case

The extreme‐case method selects a case because of its extreme value on an independent ( X   1 ) or dependent ( Y ) variable of interest. Thus, studies of domestic violence may choose to focus on extreme instances of abuse ( Browne 1987 ). Studies of altruism may focus on those rare individuals who risked their lives to help others (e.g. Holocaust resisters) ( Monroe 1996 ). Studies of ethnic politics may focus on the most heterogeneous societies (e.g. Papua New Guinea) in order to better understand the role of ethnicity in a democratic setting ( Reilly 2000–1 ). Studies of industrial policy often focus on the most successful countries (i.e. the NICS) ( Deyo 1987 ). And so forth. 5

Often an extreme case corresponds to a case that is considered to be prototypical or paradigmatic of some phenomena of interest. This is because concepts are often defined by their extremes, i.e. their ideal types. Italian Fascism defines the concept of Fascism, in part, because it offered the most extreme example of that phenomenon. However, the methodological value of this case, and others like it, derives from its extremity (along some dimension of interest), not its theoretical status or its status in the literature on a subject.

The notion of “extreme” may now be defined more precisely. An extreme value is an observation that lies far away from the mean of a given distribution. This may be measured (if there are sufficient observations) by a case's “Z score”—the number of standard deviations between a case and the mean value for that sample. Extreme cases have high Z scores, and for this reason may serve as useful subjects for intensive analysis.

For a continuous variable, the distance from the mean may be in either direction (positive or negative). For a dichotomous variable (present/absent), extremeness may be interpreted as unusual . If most cases are positive along a given dimension, then a negative case constitutes an extreme case. If most cases are negative, then a positive case constitutes an extreme case. It should be clear that researchers are not simply concerned with cases where something “happened,” but also with cases where something did not. It is the rareness of the value that makes a case valuable, in this context, not its positive or negative value. 6 Thus, if one is studying state capacity, a case of state failure is probably more informative than a case of state endurance simply because the former is more unusual. Similarly, if one is interested in incest taboos a culture where the incest taboo is absent or weak is probably more useful than a culture where it is present or strong. Fascism is more important than nonfascism. And so forth. There is a good reason, therefore, why case studies of revolution tend to focus on “revolutionary” cases. Theda Skocpol (1979) had much more to learn from France than from Austro‐Hungary since France was more unusual than Austro‐Hungary within the population of nation states that Skocpol was concerned to explain. The reason is quite simple: There are fewer revolutionary cases than nonrevolutionary cases; thus, the variation that we explore as a clue to causal relationships is encapsulated in these cases, against a background of nonrevolutionary cases.

Note that the extreme‐case method of case selection appears to violate the social science folk wisdom warning us not to “select on the dependent variable.” 7 Selecting cases on the dependent variable is indeed problematic if a number of cases are chosen, all of which lie on one end of a variable's spectrum (they are all positive or negative), and if the researcher then subjects this sample to cross‐case analysis as if it were representative of a population. 8 Results for this sort of analysis would almost assuredly be biased. Moreover, there will be little variation to explain since the values of each case are explicitly constrained.

However, this is not the proper employment of the extreme‐case method. (It is more appropriately labeled an extreme‐ sample method.) The extreme‐case method actually refers back to a larger sample of cases that lie in the background of the analysis and provide a full range of variation as well as a more representative picture of the population. It is a self‐conscious attempt to maximize variance on the dimension of interest, not to minimize it. If this population of cases is well understood— either through the author's own cross‐case analysis, through the work of others, or through common sense—then a researcher may justify the selection of a single case exemplifying an extreme value for within‐case analysis. If not, the researcher may be well advised to follow a diverse‐case method, as discussed above.

By way of conclusion, let us return to the problem of representativeness. It will be seen that an extreme case may be typical or deviant. There is simply no way to tell because the researcher has not yet specified an X   1 / Y causal proposition. Once such a causal proposition has been specified one may then ask whether the case in question is similar to some population of cases in all respects that might affect the X   1 / Y relationship of interest (i.e. unit homogeneous). It is at this point that it becomes possible to say, within the context of a cross‐case statistical model, whether a case lies near to, or far from, the regression line. However, this sort of analysis means that the researcher is no longer pursuing an extreme‐case method. The extreme‐case method is purely exploratory—a way of probing possible causes of Y , or possible effects of X , in an open‐ended fashion. If the researcher has some notion of what additional factors might affect the outcome of interest, or of what relationship the causal factor of interest might have with Y , then she ought to pursue one of the other methods explored in this chapter. This also implies that an extreme‐case method may transform into a different kind of approach as a study evolves; that is, as a more specific hypothesis comes to light. Useful extreme cases at the outset of a study may prove less useful at a later stage of analysis.

4 Deviant Case

The deviant‐case method selects that case(s) which, by reference to some general understanding of a topic (either a specific theory or common sense), demonstrates a surprising value. It is thus the contrary of the typical case. Barbara Geddes (2003) notes the importance of deviant cases in medical science, where researchers are habitually focused on that which is “pathological” (according to standard theory and practice). The New England Journal of Medicine , one of the premier journals of the field, carries a regular feature entitled Case Records of the Massachusetts General Hospital. These articles bear titles like the following: “An 80‐Year‐Old Woman with Sudden Unilateral Blindness” or “A 76‐Year‐Old Man with Fever, Dyspnea, Pulmonary Infiltrates, Pleural Effusions, and Confusion.” 9 Another interesting example drawn from the field of medicine concerns the extensive study now devoted to a small number of persons who seem resistant to the AIDS virus ( Buchbinder and Vittinghoff 1999 ; Haynes, Pantaleo, and Fauci 1996 ). Why are they resistant? What is different about these people? What can we learn about AIDS in other patients by observing people who have built‐in resistance to this disease?

Likewise, in psychology and sociology case studies may be comprised of deviant (in the social sense) persons or groups. In economics, case studies may consist of countries or businesses that overperform (e.g. Botswana; Microsoft) or underperform (e.g. Britain through most of the twentieth century; Sears in recent decades) relative to some set of expectations. In political science, case studies may focus on countries where the welfare state is more developed (e.g. Sweden) or less developed (e.g. the United States) than one would expect, given a set of general expectations about welfare state development. The deviant case is closely linked to the investigation of theoretical anomalies. Indeed, to say deviant is to imply “anomalous.” 10

Note that while extreme cases are judged relative to the mean of a single distribution (the distribution of values along a single variable), deviant cases are judged relative to some general model of causal relations. The deviant‐case method selects cases which, by reference to some (presumably) general relationship, demonstrate a surprising value. They are “deviant” in that they are poorly explained by the multivariate model. The important point is that deviant‐ness can only be assessed relative to the general (quantitative or qualitative) model. This means that the relative deviant‐ness of a case is likely to change whenever the general model is altered. For example, the United States is a deviant welfare state when this outcome is gauged relative to societal wealth. But it is less deviant—and perhaps not deviant at all—when certain additional (political and societal) factors are included in the model, as discussed in the epilogue. Deviance is model dependent. Thus, when discussing the concept of the deviant case it is helpful to ask the following question: Relative to what general model (or set of background factors) is Case A deviant?

Conceptually, we have said that the deviant case is the logical contrary of the typical case. This translates into a directly contrasting statistical measurement. While the typical case is one with a low residual (in some general model of causal relations), a deviant case is one with a high residual. This means, following our previous discussion, that the deviant case is likely to be an un representative case, and in this respect appears to violate the supposition that case‐study samples should seek to reproduce features of a larger population.

However, it must be borne in mind that the primary purpose of a deviant‐case analysis is to probe for new—but as yet unspecified—explanations. (If the purpose is to disprove an extant theory I shall refer to the study as crucial‐case, as discussed below.) The researcher hopes that causal processes identified within the deviant case will illustrate some causal factor that is applicable to other (more or less deviant) cases. This means that a deviant‐case study usually culminates in a general proposition, one that may be applied to other cases in the population. Once this general proposition has been introduced into the overall model, the expectation is that the chosen case will no longer be an outlier. Indeed, the hope is that it will now be typical , as judged by its small residual in the adjusted model. (The exception would be a circumstance in which a case's outcome is deemed to be “accidental,” and therefore inexplicable by any general model.)

This feature of the deviant‐case study should help to resolve questions about its representativeness. Even if it is not possible to measure the new causal factor (and thus to introduce it into a large‐ N cross‐case model), it may still be plausible to assert (based on general knowledge of the phenomenon) that the chosen case is representative of a broader population.

5 Influential Case

Sometimes, the choice of a case is motivated solely by the need to verify the assumptions behind a general model of causal relations. Here, the analyst attempts to provide a rationale for disregarding a problematic case or a set of problematic cases. That is to say, she attempts to show why apparent deviations from the norm are not really deviant, or do not challenge the core of the theory, once the circumstances of the special case or cases are fully understood. A cross‐case analysis may, after all, be marred by several classes of problems including measurement error, specification error, errors in establishing proper boundaries for the inference (the scope of the argument), and stochastic error (fluctuations in the phenomenon under study that are treated as random, given available theoretical resources). If poorly fitting cases can be explained away by reference to these kinds of problems, then the theory of interest is that much stronger. This sort of deviant‐case analysis answers the question, “What about Case A (or cases of type A)? How does that, seemingly disconfirming, case fit the model?”

Because its underlying purpose is different from the usual deviant‐case study, I offer a new term for this method. The influential case is a case that casts doubt upon a theory, and for that reason warrants close inspection. This investigation may reveal, after all, that the theory is validated—perhaps in some slightly altered form. In this guise, the influential case is the “case that proves the rule.” In other instances, the influential‐case analysis may contribute to disconfirming, or reconceptualizing, a theory. The key point is that the value of the case is judged relative to some extant cross‐case model.

A simple version of influential‐case analysis involves the confirmation of a key case's score on some critical dimension. This is essentially a question of measurement. Sometimes cases are poorly explained simply because they are poorly understood. A close examination of a particular context may reveal that an apparently falsifying case has been miscoded. If so, the initial challenge presented by that case to some general theory has been obviated.

However, the more usual employment of the influential‐case method culminates in a substantive reinterpretation of the case—perhaps even of the general model. It is not just a question of measurement. Consider Thomas Ertman's (1997) study of state building in Western Europe, as summarized by Gerardo Munck. This study argues

that the interaction of a) the type of local government during the first period of statebuilding, with b) the timing of increases in geopolitical competition, strongly influences the kind of regime and state that emerge. [Ertman] tests this hypothesis against the historical experience of Europe and finds that most countries fit his predictions. Denmark, however, is a major exception. In Denmark, sustained geopolitical competition began relatively late and local government at the beginning of the statebuilding period was generally participatory, which should have led the country to develop “patrimonial constitutionalism.” But in fact, it developed “bureaucratic absolutism.” Ertman carefully explores the process through which Denmark came to have a bureaucratic absolutist state and finds that Denmark had the early marks of a patrimonial constitutionalist state. However, the country was pushed off this developmental path by the influence of German knights, who entered Denmark and brought with them German institutions of local government. Ertman then traces the causal process through which these imported institutions pushed Denmark to develop bureaucratic absolutism, concluding that this development was caused by a factor well outside his explanatory framework. ( Munck 2004 , 118)

Ertman's overall framework is confirmed insofar as he has been able to show, by an in‐depth discussion of Denmark, that the causal processes stipulated by the general theory hold even in this apparently disconfirming case. Denmark is still deviant, but it is so because of “contingent historical circumstances” that are exogenous to the theory ( Ertman 1997 , 316).

Evidently, the influential‐case analysis is similar to the deviant‐case analysis. Both focus on outliers. However, as we shall see, they focus on different kinds of outliers. Moreover, the animating goals of these two research designs are quite different. The influential‐case study begins with the aim of confirming a general model, while the deviant‐case study has the aim of generating a new hypothesis that modifies an existing general model. The confusion stems from the fact that the same case study may fulfill both objectives—qualifying a general model and, at the same time, confirming its core hypothesis.

Thus, in their study of Roberto Michels's “iron law of oligarchy,” Lipset, Trow, and Coleman (1956) choose to focus on an organization—the International Typographical Union—that appears to violate the central presupposition. The ITU, as noted by one of the authors, has “a long‐term two‐party system with free elections and frequent turnover in office” and is thus anything but oligarchic ( Lipset 1959 , 70). As such, it calls into question Michels's grand generalization about organizational behavior. The authors explain this curious result by the extraordinarily high level of education among the members of this union. Michels's law is shown to be true for most organizations, but not all. It is true, with qualifications. Note that the respecification of the original model (in effect, Lipset, Trow, and Coleman introduce a new control variable or boundary condition) involves the exploration of a new hypothesis. In this instance, therefore, the use of an influential case to confirm an existing theory is quite similar to the use of a deviant case to explore a new theory.

In a quantitative idiom, influential cases are those that, if counterfactually assigned a different value on the dependent variable, would most substantially change the resulting estimates. They may or may not be outliers (high‐residual cases). Two quantitative measures of influence are commonly applied in regression diagnostics ( Belsey, Kuh, and Welsch 2004 ). The first, often referred to as the leverage of a case, derives from what is called the hat matrix . Based solely on each case's scores on the independent variables, the hat matrix tells us how much a change in (or a measurement error on) the dependent variable for that case would affect the overall regression line. The second is Cook's distance , a measure of the extent to which the estimates of all the parameters would change if a given case were omitted from the analysis. Cases with a large leverage or Cook's distance contribute quite a lot to the inferences drawn from a cross‐case analysis. In this sense, such cases are vital for maintaining analytic conclusions. Discovering a significant measurement error on the dependent variable or an important omitted variable for such a case may dramatically revise estimates of the overall relationships. Hence, it may be quite sensible to select influential cases for in‐depth study.

Note that the use of an influential‐case strategy of case selection is limited to instances in which a researcher has reason to be concerned that her results are being driven by one or a few cases. This is most likely to be true in small to moderate‐sized samples. Where N is very large—greater than 1,000, let us say—it is extremely unlikely that a small set of cases (much less an individual case) will play an “influential” role. Of course, there may be influential sets of cases, e.g. countries within a particular continent or cultural region, or persons of Irish extraction. Sets of influential observations are often problematic in a time‐series cross‐section data‐set where each unit (e.g. country) contains multiple observations (through time), and hence may have a strong influence on aggregate results. Still, the general rule is: the larger the sample, the less important individual cases are likely to be and, hence, the less likely a researcher is to use an influential‐case approach to case selection.

6 Crucial Case

Of all the extant methods of case selection perhaps the most storied—and certainly the most controversial—is the crucial‐case method, introduced to the social science world several decades ago by Harry Eckstein. In his seminal essay, Eckstein (1975 , 118) describes the crucial case as one “that must closely fit a theory if one is to have confidence in the theory's validity, or, conversely, must not fit equally well any rule contrary to that proposed.” A case is crucial in a somewhat weaker—but much more common—sense when it is most, or least, likely to fulfill a theoretical prediction. A “most‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted to achieve a certain outcome, and yet does not. It is therefore used to disconfirm a theory. A “least‐likely” case is one that, on all dimensions except the dimension of theoretical interest, is predicted not to achieve a certain outcome, and yet does so. It is therefore used to confirm a theory. In all formulations, the crucial‐case offers a most‐difficult test for an argument, and hence provides what is perhaps the strongest sort of evidence possible in a nonexperimental, single‐case setting.

Since the publication of Eckstein's influential essay, the crucial‐case approach has been claimed in a multitude of studies across several social science disciplines and has come to be recognized as a staple of the case‐study method. 11 Yet the idea of any single case playing a crucial (or “critical”) role is not widely accepted among most methodologists (e.g. Sekhon 2004 ). (Even its progenitor seems to have had doubts.)

Let us begin with the confirmatory (a.k.a. least‐likely) crucial case. The implicit logic of this research design may be summarized as follows. Given a set of facts, we are asked to contemplate the probability that a given theory is true. While the facts matter, to be sure, the effectiveness of this sort of research also rests upon the formal properties of the theory in question. Specifically, the degree to which a theory is amenable to confirmation is contingent upon how many predictions can be derived from the theory and on how “risky” each individual prediction is. In Popper's (1963 , 36) words, “Confirmations should count only if they are the result of risky predictions ; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—and event which would have refuted the theory. Every ‘good’ scientific theory is a prohibition; it forbids certain things to happen. The more a theory forbids, the better it is” (see also Popper 1934/1968 ). A risky prediction is therefore one that is highly precise and determinate, and therefore unlikely to be achieved by the product of other causal factors (external to the theory of interest) or through stochastic processes. A theory produces many such predictions if it is fully elaborated, issuing predictions not only on the central outcome of interest but also on specific causal mechanisms, and if it is broad in purview. (The notion of riskiness may also be conceptualized within the Popperian lexicon as degrees of falsifiability .)

These points can also be articulated in Bayesian terms. Colin Howson and Peter Urbach explain: “The degree to which h [a hypothesis] is confirmed by e [a set of evidence] depends … on the extent to which P(eČh) exceeds P (e) , that is, on how much more probable e is relative to the hypothesis and background assumptions than it is relative just to background assumptions.” Again, “confirmation is correlated with how much more probable the evidence is if the hypothesis is true than if it is false” ( Howson and Urlbach 1989 , 86). Thus, the stranger the prediction offered by a theory—relative to what we would normally expect—the greater the degree of confirmation that will be afforded by the evidence. As an intuitive example, Howson and Urbach (1989 , 86) offer the following:

If a soothsayer predicts that you will meet a dark stranger sometime and you do in fact, your faith in his powers of precognition would not be much enhanced: you would probably continue to think his predictions were just the result of guesswork. However, if the prediction also gave the correct number of hairs on the head of that stranger, your previous scepticism would no doubt be severely shaken.

While these Popperian/Bayesian notions 12 are relevant to all empirical research designs, they are especially relevant to case‐study research designs, for in these settings a single case (or, at most, a small number of cases) is required to bear a heavy burden of proof. It should be no surprise, therefore, that Popper's idea of “riskiness” was to be appropriated by case‐study researchers like Harry Eckstein to validate the enterprise of single‐case analysis. (Although Eckstein does not cite Popper the intellectual lineage is clear.) Riskiness, here, is analogous to what is usually referred to as a “most‐ difficult” research design, which in a case‐study research design would be understood as a “least‐likely” case. Note also that the distinction between a “must‐fit” case and a least‐likely case—that, in the event, actually does fit the terms of a theory—is a matter of degree. Cases are more or less crucial for confirming theories. The point is that, in some circumstances, a paucity of empirical evidence may be compensated by the riskiness of the theory.

The crucial‐case research design is, perforce, a highly deductive enterprise; much depends on the quality of the theory under investigation. It follows that the theories most amenable to crucial‐case analysis are those which are lawlike in their precision, degree of elaboration, consistency, and scope. The more a theory attains the status of a causal law, the easier it will be to confirm, or to disconfirm, with a single case. Indeed, risky predictions are common in natural science fields such as physics, which in turn served as the template for the deductive‐nomological (“covering‐law”) model of science that influenced Eckstein and others in the postwar decades (e.g. Hempel 1942 ).

A frequently cited example is the first important empirical demonstration of the theory of relativity, which took the form of a single‐event prediction on the occasion of the May 29, 1919, solar eclipse ( Eckstein 1975 ; Popper 1963 ). Stephen Van Evera (1997 , 66–7) describes the impact of this prediction on the validation of Einstein's theory.

Einstein's theory predicted that gravity would bend the path of light toward a gravity source by a specific amount. Hence it predicted that during a solar eclipse stars near the sun would appear displaced—stars actually behind the sun would appear next to it, and stars lying next to the sun would appear farther from it—and it predicted the amount of apparent displacement. No other theory made these predictions. The passage of this one single‐case‐study test brought the theory wide acceptance because the tested predictions were unique—there was no plausible competing explanation for the predicted result—hence the passed test was very strong.

The strength of this test is the extraordinary fit between the theory and a set of facts found in a single case, and the corresponding lack of fit between all other theories and this set of facts. Einstein offered an explanation of a particular set of anomalous findings that no other existing theory could make sense of. Of course, one must assume that there was no—or limited—measurement error. And one must assume that the phenomenon of interest is largely invariant; light does not bend differently at different times and places (except in ways that can be understood through the theory of relativity). And one must assume, finally, that the theory itself makes sense on other grounds (other than the case of special interest); it is a plausible general theory. If one is willing to accept these a priori assumptions, then the 1919 “case study” provides a very strong confirmation of the theory. It is difficult to imagine a stronger proof of the theory from within an observational (nonexperimental) setting.

In social science settings, by contrast, one does not commonly find single‐case studies offering knockout evidence for a theory. This is, in my view, largely a product of the looseness (the underspecification) of most social science theories. George and Bennett point out that while the thesis of the democratic peace is as close to a “law” as social science has yet seen, it cannot be confirmed (or refuted) by looking at specific causal mechanisms because the causal pathways mandated by the theory are multiple and diverse. Under the circumstances, no single‐case test can offer strong confirmation of the theory ( George and Bennett 2005 , 209).

However, if one adopts a softer version of the crucial‐case method—the least‐likely (most difficult) case—then possibilities abound. Indeed, I suspect that, implicitly , most case‐study work that makes a positive argument focusing on a single case (without a corresponding cross‐case analysis) relies largely on the logic of the least‐ likely case. Rarely is this logic made explicit, except perhaps in a passing phrase or two. Yet the deductive logic of the “risky” prediction is central to the case‐study enterprise. Whether a case study is convincing or not often rests on the reader's evaluation of how strong the evidence for an argument might be, and this in turn—wherever cross‐ case evidence is limited and no manipulated treatment can be devised—rests upon an estimation of the degree of “fit” between a theory and the evidence at hand, as discussed.

Lily Tsai's (2007) investigation of governance at the village level in China employs several in‐depth case studies of villages which are chosen (in part) because of their least‐likely status relative to the theory of interest. Tsai's hypothesis is that villages with greater social solidarity (based on preexisting religious or familial networks) will develop a higher level of social trust and mutual obligation and, as a result, will experience better governance. Crucial cases, therefore, are villages that evidence a high level of social solidarity but which, along other dimensions, would be judged least likely to develop good governance, e.g. they are poor, isolated, and lack democratic institutions or accountability mechanisms from above. “Li Settlement,” in Fujian province, is such a case. The fact that this impoverished village nonetheless boasts an impressive set of infrastructural accomplishments such as paved roads with drainage ditches (a rarity in rural China) suggests that something rather unusual is going on here. Because her case is carefully chosen to eliminate rival explanations, Tsai's conclusions about the special role of social solidarity are difficult to gainsay. How else is one to explain this otherwise anomalous result? This is the strength of the least‐likely case, where all other plausible causal factors for an outcome have been minimized. 13

Jack Levy (2002 , 144) refers to this, evocatively, as a “Sinatra inference:” if it can make it here, it can make it anywhere (see also Khong 1992 , 49; Sagan 1995 , 49; Shafer 1988 , 14–6). Thus, if social solidarity has the hypothesized effect in Li Settlement it should have the same effect in more propitious settings (e.g. where there is greater economic surplus). The same implicit logic informs many case‐study analyses where the intent of the study is to confirm a hypothesis on the basis of a single case.

Another sort of crucial case is employed for the purpose of dis confirming a causal hypothesis. A central Popperian insight is that it is easier to disconfirm an inference than to confirm that same inference. (Indeed, Popper doubted that any inference could be fully confirmed, and for this reason preferred the term “corroborate.”) This is particularly true of case‐study research designs, where evidence is limited to one or several cases. The key proviso is that the theory under investigation must take a consistent (a.k.a. invariant, deterministic) form, even if its predictions are not terrifically precise, well elaborated, or broad.

As it happens, there are a fair number of invariant propositions floating around the social science disciplines (Goertz and Levy forthcoming; Goertz and Starr 2003 ). It used to be argued, for example, that political stability would occur only in countries that are relatively homogeneous, or where existing heterogeneities are mitigated by cross‐cutting cleavages ( Almond 1956 ; Bentley 1908/1967 ; Lipset 1960/1963 ; Truman 1951 ). Arend Lijphart's (1968) study of the Netherlands, a peaceful country with reinforcing social cleavages, is commonly viewed as refuting this theory on the basis of a single in‐depth case analysis. 14

Granted, it may be questioned whether presumed invariant theories are really invariant; perhaps they are better understood as probabilistic. Perhaps, that is, the theory of cross‐cutting cleavages is still true, probabilistically, despite the apparent Dutch exception. Or perhaps the theory is still true, deterministically, within a subset of cases that does not include the Netherlands. (This sort of claim seems unlikely in this particular instance, but it is quite plausible in many others.) Or perhaps the theory is in need of reframing; it is true, deterministically, but applies only to cross‐ cutting ethnic/racial cleavages, not to cleavages that are primarily religious. One can quibble over what it means to “disconfirm” a theory. The point is that the crucial case has, in all these circumstances, provided important updating of a theoretical prior.

Heretofore, I have treated causal factors as dichotomous. Countries have either reinforcing or cross‐cutting cleavages and they have regimes that are either peaceful or conflictual. Evidently, these sorts of parameters are often matters of degree. In this reading of the theory, cases are more or less crucial. Accordingly, the most useful—i.e. most crucial—case for Lijphart's purpose is one that has the most segregated social groups and the most peaceful and democratic track record. In these respects, the Netherlands was a very good choice. Indeed, the degree of disconfirmation offered by this case study is probably greater than the degree of disconfirmation that might have been provided by other cases such as India or Papua New Guinea—countries where social peace has not always been secure. The point is that where variables are continuous rather than dichotomous it is possible to evaluate potential cases in terms of their degree of crucialness .

Note that the crucial‐case method of case‐selection, whether employed in a confirmatory or disconfirmatory mode, cannot be employed in a large‐ N context. This is because an explicit cross‐case model would render the crucial‐case study redundant. Once one identifies the relevant parameters and the scores of all cases on those parameters, one has in effect constructed a cross‐case model that confirms or disconfirms the theory in question. The case study is thenceforth irrelevant, at least as a means of decisive confirmation or disconfirmation. 15 It remains highly relevant as a means of exploring causal mechanisms, of course. Yet, because this objective is quite different from that which is usually associated with the term, I enlist a new term for this technique.

7 Pathway Case

One of the most important functions of case‐study research is the elucidation of causal mechanisms. But which sort of case is most useful for this purpose? Although all case studies presumably shed light on causal mechanisms, not all cases are equally transparent. In situations where a causal hypothesis is clear and has already been confirmed by cross‐case analysis, researchers are well advised to focus on a case where the causal effect of X   1 on Y can be isolated from other potentially confounding factors ( X   2 ). I shall call this a pathway case to indicate its uniquely penetrating insight into causal mechanisms. In contrast to the crucial case, this sort of method is practicable only in circumstances where cross‐case covariational patterns are well studied and where the mechanism linking X   1 and Y remains dim. Because the pathway case builds on prior cross‐case analysis, the problem of case selection must be situated within that sample. There is no standalone pathway case.

The logic of the pathway case is clearest in situations of causal sufficiency—where a causal factor of interest, X   1 , is sufficient by itself (though perhaps not necessary) to account for Y 's value (0 or 1). The other causes of Y , about which we need make no assumptions, are designated as a vector, X   2 .

Note that wherever various causal factors are substitutable for one another, each factor is conceptualized (individually) as sufficient ( Braumoeller 2003 ). Thus, situations of causal equifinality presume causal sufficiency on the part of each factor or set of conjoint factors. An example is provided by the literature on democratization, which stipulates three main avenues of regime change: leadership‐initiated reform, a controlled opening to opposition, or the collapse of an authoritarian regime ( Colomer 1991 ). The case‐study format constrains us to analyze one at a time, so let us limit our scope to the first one—leadership‐initiated reform. So considered, a causal‐pathway case would be one with the following features: (a) democratization, (b) leadership‐initiated reform, (c) no controlled opening to the opposition, (d) no collapse of the previous authoritarian regime, and (e) no other extraneous factors that might affect the process of democratization. In a case of this type, the causal mechanisms by which leadership‐initiated reform may lead to democratization will be easiest to study. Note that it is not necessary to assume that leadership‐initiated reform always leads to democratization; it may or may not be a deterministic cause. But it is necessary to assume that leadership‐initiated reform can sometimes lead to democratization on its own (given certain background features).

Now let us move from these examples to a general‐purpose model. For heuristic purposes, let us presume that all variables in that model are dichotomous (coded as 0 or 1) and that the model is complete (all causes of Y are included). All causal relationships will be coded so as to be positive: X   1 and Y covary as do X   2 and Y . This allows us to visualize a range of possible combinations at a glance.

Recall that the pathway case is always focused, by definition, on a single causal factor, denoted X   1 . (The researcher's focus may shift to other causal factors, but may only focus on one causal factor at a time.) In this scenario, and regardless of how many additional causes of Y there might be (denoted X   2 , a vector of controls), there are only eight relevant case types, as illustrated in Table 28.2 . Identifying these case types is a relatively simple matter, and can be accomplished in a small‐ N sample by the construction of a truth‐table (modeled after Table 28.2 ) or in a large‐ N sample by the use of cross‐tabs.

Notes : X   1 = the variable of theoretical interest. X   2 = a vector of controls (a score of 0 indicates that all control variables have a score of 0, while a score of 1 indicates that all control variables have a score of 1). Y = the outcome of interest. A–H = case types (the N for each case type is indeterminate). G, H = possible pathway cases. Sample size = indeterminate.

Assumptions : (a) all variables can be coded dichotomously (a binary coding of the concept is valid); (b) all independent variables are positively correlated with Y in the general case; ( c ) X   1 is (at least sometimes) a sufficient cause of Y .

Note that the total number of combinations of values depends on the number of control variables, which we have represented with a single vector, X   2 . If this vector consists of a single variable then there are only eight case types. If this vector consists of two variables ( X   2a , X   2b ) then the total number of possible combinations increases from eight (2 3 ) to sixteen (2 4 ). And so forth. However, none of these combinations is relevant for present purposes except those where X   2a and X   2b have the same value (0 or 1). “Mixed” cases are not causal pathway cases, for reasons that should become clear.

The pathway case, following the logic of the crucial case, is one where the causal factor of interest, X   1 , correctly predicts Y while all other possible causes of Y (represented by the vector, X   2 ) make “wrong” predictions. If X   1 is—at least in some circumstances—a sufficient cause of Y , then it is these sorts of cases that should be most useful for tracing causal mechanisms. There are only two such cases in Ta b l e 28.2—G and H. In all other cases, the mechanism running from X   1 to Y would be difficult to discern either because X   1 and Y are not correlated in the usual way (constituting an unusual case, in the terms of our hypothesis) or because other confounding factors ( X   2 ) intrude. In case A, for example, the positive value on Y could be a product of X   1 or X   2 . An in‐depth examination of this case is not likely to be very revealing.

Keep in mind that because the researcher already knows from her cross‐case examination what the general causal relationships are, she knows (prior to the case‐ study investigation) what constitutes a correct or incorrect prediction. In the crucial‐ case method, by contrast, these expectations are deductive rather than empirical. This is what differentiates the two methods. And this is why the causal pathway case is useful principally for elucidating causal mechanisms rather than verifying or falsifying general propositions (which are already more or less apparent from the cross‐case evidence). Of course, we must leave open the possibility that the investigation of causal mechanisms would invalidate a general claim, if that claim is utterly contingent upon a specific set of causal mechanisms and the case study shows that no such mechanisms are present. However, this is rather unlikely in most social science settings. Usually, the result of such a finding will be a reformulation of the causal processes by which X   1 causes Y —or, alternatively, a realization that the case under investigation is aberrant (atypical of the general population of cases).

Sometimes, the research question is framed as a unidirectional cause: one is interested in why 0 becomes 1 (or vice versa) but not in why 1 becomes 0. In our previous example, we asked why democracies fail, not why countries become democratic or authoritarian. So framed, there can be only one type of causal‐pathway case. (Whether regime failure is coded as 0 or 1 is a matter of taste.) Where researchers are interested in bidirectional causality—a movement from 0 to 1 as well as from 1 to 0—there are two possible causal‐pathway cases, G and H. In practice, however, one of these case types is almost always more useful than the other. Thus, it seems reasonable to employ the term “pathway case” in the singular. In order to determine which of these two case types will be more useful for intensive analysis the researcher should look to see whether each case type exhibits desirable features such as: (a) a rare (unusual) value on X   1 or Y (designated “extreme” in our previous discussion), (b) observable temporal variation in X   1 , ( c ) an X   1 / Y relationship that is easier to study (it has more visible features; it is more transparent), or (d) a lower residual (thus indicating a more typical case, within the terms of the general model). Usually, the choice between G and H is intuitively obvious.

Now, let us consider a scenario in which all (or most) variables of concern to the model are continuous, rather than dichotomous. Here, the job of case selection is considerably more complex, for causal “sufficiency” (in the usual sense) cannot be invoked. It is no longer plausible to assume that a given cause can be entirely partitioned, i.e. rival factors eliminated. However, the search for a pathway case may still be viable. What we are looking for in this scenario is a case that satisfies two criteria: (1) it is not an outlier (or at least not an extreme outlier) in the general model and (2) its score on the outcome ( Y ) is strongly influenced by the theoretical variable of interest ( X   1 ), taking all other factors into account ( X   2 ). In this sort of case it should be easiest to “see” the causal mechanisms that lie between X   1 and Y .

Achieving the second desiderata requires a bit of manipulation. In order to determine which (nonoutlier) cases are most strongly affected by X   1 , given all the other parameters in the model, one must compare the size of the residuals for each case in a reduced form model, Y = Constant + X   2 + Res reduced , with the size of the residuals for each case in a full model, Y = Constant + X   2 + X   1 + Res full . The pathway case is that case, or set of cases, which shows the greatest difference between the residual for the reduced‐form model and the full model (ΔResidual). Thus,

Note that the residual for a case must be smaller in the full model than in the reduced‐ form model; otherwise, the addition of the variable of interest ( X   1 ) pulls the case away from the regression line. We want to find a case where the addition of X   1 pushes the case towards the regression line, i.e. it helps to “explain” that case.

As an example, let us suppose that we are interested in exploring the effect of mineral wealth on the prospects for democracy in a society. According to a good deal of work on this subject, countries with a bounty of natural resources—particularly oil—are less likely to democratize (or once having undergone a democratic transition, are more likely to revert to authoritarian rule) ( Barro 1999 ; Humphreys 2005 ; Ross 2001 ). The cross‐country evidence is robust. Yet as is often the case, the causal mechanisms remain rather obscure. In order to better understand this phenomenon it may be worthwhile to exploit the findings of cross‐country regression models in order to identify a country whose regime type (i.e. its democracy “score” on some general index) is strongly affected by its natural‐research wealth, all other things held constant. An analysis of this sort identifies two countries— the United Arab Emirates and Kuwait—with high Δ Residual values and modest residuals in the full model (signifying that these cases are not outliers). Researchers seeking to explore the effect of oil wealth on regime type might do well to focus on these two cases since their patterns of democracy cannot be well explained by other factors—e.g. economic development, religion, European influence, or ethnic fractionalization. The presence of oil wealth in these countries would appear to have a strong independent effect on the prospects for democratization in these cases, an effect that is well modeled by general theory and by the available cross‐case evidence.

To reiterate, the logic of causal “elimination” is much more compelling where variables are dichotomous and where causal sufficiency can be assumed ( X   1 is sufficient by itself, at least in some circumstances, to cause Y ). Where variables are continuous, the strategy of the pathway case is more dubious, for potentially confounding causal factors ( X   2 ) cannot be neatly partitioned. Even so, we have indicated why the selection of a pathway case may be a logical approach to case‐study analysis in many circumstances.

The exceptions may be briefly noted. Sometimes, where all variables in a model are dichotomous, there are no pathway cases, i.e. no cases of type G or H (in Table 28.2 ). This is known as the “empty cell” problem, or a problem of severe causal multicollinearity. The universe of observational data does not always oblige us with cases that allow us to independently test a given hypothesis. Where variables are continuous, the analogous problem is that of a causal variable of interest ( X   1 ) that has only minimal effects on the outcome of interest. That is, its role in the general model is quite minor. In these situations, the only cases that are strongly affected by X   1 —if there are any at all—may be extreme outliers, and these sorts of cases are not properly regarded as providing confirmatory evidence for a proposition, for reasons that are abundantly clear by now.

Finally, it should be clarified that the identification of a causal pathway case does not obviate the utility of exploring other cases. One might, for example, want to compare both sorts of potential pathway cases—G and H—with each other. Many other combinations suggest themselves. However, this sort of multi‐case investigation moves beyond the logic of the causal‐pathway case.

8 Most‐similar Cases

The most‐similar method employs a minimum of two cases. 16 In its purest form, the chosen pair of cases is similar in all respects except the variable(s) of interest. If the study is exploratory (i.e. hypothesis generating), the researcher looks for cases that differ on the outcome of theoretical interest but are similar on various factors that might have contributed to that outcome, as illustrated in Table 28.3 (A) . This is a common form of case selection at the initial stage of research. Often, fruitful analysis begins with an apparent anomaly: two cases are apparently quite similar, and yet demonstrate surprisingly different outcomes. The hope is that intensive study of these cases will reveal one—or at most several—factors that differ across these cases. These differing factors ( X   1 ) are looked upon as putative causes. At this stage, the research may be described by the second diagram in Table 28.3 (B) . Sometimes, a researcher begins with a strong hypothesis, in which case her research design is confirmatory (hypothesis testing) from the get‐go. That is, she strives to identify cases that exhibit different outcomes, different scores on the factor of interest, and similar scores on all other possible causal factors, as illustrated in the second (hypothesis‐testing) diagram in Table 28.3 (B) .

The point is that the purpose of a most‐similar research design, and hence its basic setup, often changes as a researcher moves from an exploratory to a confirmatory mode of analysis. However, regardless of where one begins, the results, when published, look like a hypothesis‐testing research design. Question marks have been removed: (A) becomes (B) in Table 28.3 .

As an example, let us consider Leon Epstein's classic study of party cohesion, which focuses on two “most‐similar” countries, the United States and Canada. Canada has highly disciplined parties whose members vote together on the floor of the House of Commons while the United States has weak, undisciplined parties, whose members often defect on floor votes in Congress. In explaining these divergent outcomes, persistent over many years, Epstein first discusses possible causal factors that are held more or less constant across the two cases. Both the United States and Canada inherited English political cultures, both have large territories and heterogeneous populations, both are federal, and both have fairly loose party structures with strong regional bases and a weak center. These are the “control” variables. Where they differ is in one constitutional feature: Canada is parliamentary while the United States is presidential. And it is this institutional difference that Epstein identifies as the crucial (differentiating) cause. (For further examples of the most‐similar method see Brenner 1976 ; Hamilton 1977 ; Lipset 1968 ; Miguel 2004 ; Moulder 1977 ; Posner 2004 .)

X   1 = the variable of theoretical interest. X   2 = a vector of controls. Y = the outcome of interest.

Several caveats apply to any most‐similar analysis (in addition to the usual set of assumptions applying to all case‐study analysis). First, each causal factor is understood as having an independent and additive effect on the outcome; there are no “interaction” effects. Second, one must code cases dichotomously (high/low, present/absent). This is straightforward if the underlying variables are also dichotomous (e.g. federal/unitary). However, it is often the case that variables of concern in the model are continuous (e.g. party cohesion). In this setting, the researcher must “dichotomize” the scoring of cases so as to simplify the two‐case analysis. (Some flexibility is admissible on the vector of controls ( X   2 ) that are “held constant” across the cases. Nonidentity is tolerable if the deviation runs counter to the predicted hypothesis. For example, Epstein describes both the United States and Canada as having strong regional bases of power, a factor that is probably more significant in recent Canadian history than in recent American history. However, because regional bases of power should lead to weaker parties, rather than stronger parties, this element of nonidentity does not challenge Epstein's conclusions. Indeed, it sets up a most‐difficult research scenario, as discussed above.)

In one respect the requirements for case control are not so stringent. Specifically, it is not usually necessary to measure control variables (at least not with a high degree of precision) in order to control for them. If two countries can be assumed to have similar cultural heritages one needn't worry about constructing variables to measure that heritage. One can simply assert that, whatever they are, they are more or less constant across the two cases. This is similar to the technique employed in a randomized experiment, where the researcher typically does not attempt to measure all the factors that might affect the causal relationship of interest. She assumes, rather, that these unknown factors have been neutralized across the treatment and control groups by randomization or by the choice of a sample that is internally homogeneous.

The most useful statistical tool for identifying cases for in‐depth analysis in a most‐ similar setting is probably some variety of matching strategy—e.g. exact matching, approximate matching, or propensity‐score matching. 17 The product of this procedure is a set of matched cases that can be compared in whatever way the researcher deems appropriate. These are the “most‐similar” cases. Rosenbaum and Silber (2001 , 223) summarize:

Unlike model‐based adjustments, where [individuals] vanish and are replaced by the coefficients of a model, in matching, ostensibly comparable patterns are compared directly, one by one. Modern matching methods involve statistical modeling and combinatorial algorithms, but the end result is a collection of pairs or sets of people who look comparable, at least on average. In matching, people retain their integrity as people, so they can be examined and their stories can be told individually.

Matching, conclude the authors, “facilitates, rather than inhibits, thick description” ( Rosenbaum and Silber 2001 , 223).

In principle, the same matching techniques that have been used successfully in observational studies of medical treatments might also be adapted to the study of nation states, political parties, cities, or indeed any traditional paired cases in the social sciences. Indeed, the current popularity of matching among statisticians—relative, that is, to garden‐variety regression models—rests upon what qualitative researchers would recognize as a “case‐based” approach to causal analysis. If Rosenbaum and Silber are correct, it may be perfectly reasonable to appropriate this large‐ N method of analysis for case‐study purposes.

As with other methods of case selection, the most‐similar method is prone to problems of nonrepresentativeness. If employed in a qualitative fashion (without a systematic cross‐case selection strategy), potential biases in the chosen case must be addressed in a speculative way. If the researcher employs a matching technique of case selection within a large‐ N sample, the problem of potential bias can be addressed by assuring the choice of cases that are not extreme outliers, as judged by their residuals in the full model. Most‐similar cases should also be “typical” cases, though some scope for deviance around the regression line may be acceptable for purposes of finding a good fit among cases.

X   1 = the variable of theoretical interest. X   2a–d = a vector of controls. Y = the outcome of interest.

9 Most‐different Cases

A final case‐selection method is the reverse image of the previous method. Here, variation on independent variables is prized, while variation on the outcome is eschewed. Rather than looking for cases that are most‐similar, one looks for cases that are most‐ different . Specifically, the researcher tries to identify cases where just one independent variable ( X   1 ), as well as the dependent variable ( Y ), covary, while all other plausible factors ( X   2a–d ) show different values. 18

The simplest form of this two‐case comparison is illustrated in Table 28.4 . Cases A and B are deemed “most different,” though they are similar in two essential respects— the causal variable of interest and the outcome.

As an example, I follow Marc Howard's (2003) recent work, which explores the enduring impact of Communism on civil society. 19 Cross‐national surveys show a strong correlation between former Communist regimes and low social capital, controlling for a variety of possible confounders. It is a strong result. Howard wonders why this relationship is so strong and why it persists, and perhaps even strengthens, in countries that are no longer socialist or authoritarian. In order to answer this question, he focuses on two most‐different cases, Russia and East Germany. These two countries were quite different—in all ways other than their Communist experience— prior to the Soviet era, during the Soviet era (since East Germany received substantial subsidies from West Germany), and in the post‐Soviet era, as East Germany was absorbed into West Germany. Yet, they both score near the bottom of various cross‐ national indices intended to measure the prevalence of civic engagement in the current era. Thus, Howard's (2003 , 6–9) case selection procedure meets the requirements of the most‐different research design: Variance is found on all (or most) dimensions aside from the key factor of interest (Communism) and the outcome (civic engagement).

What leverage is brought to the analysis from this approach? Howard's case studies combine evidence drawn from mass surveys and from in‐depth interviews of small, stratified samples of Russians and East Germans. (This is a good illustration, incidentally, of how quantitative and qualitative evidence can be fruitfully combined in the intensive study of several cases.) The product of this analysis is the identification of three causal pathways that, Howard (2003 , 122) claims, help to explain the laggard status of civil society in post‐Communist polities: “the mistrust of communist organizations, the persistence of friendship networks, and the disappointment with post‐communism.” Simply put, Howard (2003 , 145) concludes, “a great number of citizens in Russia and Eastern Germany feel a strong and lingering sense of distrust of any kind of public organization, a general satisfaction with their own personal networks (accompanied by a sense of deteriorating relations within society overall), and disappointment in the developments of post‐communism.”

The strength of this most‐different case analysis is that the results obtained in East Germany and Russia should also apply in other post‐Communist polities (e.g. Lithuania, Poland, Bulgaria, Albania). By choosing a heterogeneous sample, Howard solves the problem of representativeness in his restricted sample. However, this sample is demonstrably not representative across the population of the inference, which is intended to cover all countries of the world.

More problematic is the lack of variation on key causal factors of interest— Communism and its putative causal pathways. For this reason, it is difficult to reach conclusions about the causal status of these factors on the basis of the most‐different analysis alone. It is possible, that is, that the three causal pathways identified by Howard also operate within polities that never experienced Communist rule.

Nor does it seem possible to conclusively eliminate rival hypotheses on the basis of this most‐different analysis. Indeed, this is not Howard's intention. He wishes merely to show that whatever influence on civil society might be attributed to economic, cultural, and other factors does not exhaust this subject.

My considered judgment is that the most‐different research design provides minimal leverage into the problem of why Communist systems appear to suppress civic engagement, years after their disappearance. Fortunately, this is not the only research design employed by Howard in his admirable study. Indeed, the author employs two other small‐ N cross‐case methods, as well as a large‐ N cross‐country statistical analysis. These methods do most of the analytic work. East Germany may be regarded as a causal pathway case (see above). It has all the attributes normally assumed to foster civic engagement (e.g. a growing economy, multiparty competition, civil liberties, a free press, close association with Western European culture and politics), but nonetheless shows little or no improvement on this dimension during the post‐ transition era ( Howard 2003 , 8). It is plausible to attribute this lack of change to its Communist past, as Howard does, in which case East Germany should be a fruitful case for the investigation of causal mechanisms. The contrast between East and West Germany provides a most‐similar analysis since the two polities share virtually everything except a Communist past. This variation is also deftly exploited by Howard.

I do not wish to dismiss the most‐different research method entirely. Surely, Howard's findings are stronger with the intensive analysis of Russia than they would be without. Yet his book would not stand securely on the empirical foundation provided by most‐different analysis alone. If one strips away the pathway‐case (East Germany) and the most‐similar analysis (East/West Germany) there is little left upon which to base an analysis of causal relations (aside from the large‐ N cross‐national analysis). Indeed, most scholars who employ the most‐different method do so in conjunction with other methods. 20 It is rarely, if ever, a standalone method. 21

Generalizing from this discussion of Marc Howard's work, I offer the following summary remarks on the most‐different method of case analysis. (I leave aside issues faced by all case‐study analyses, issues that are explored in Gerring 2007 .)

Let us begin with a methodological obstacle that is faced by both Millean styles of analysis—the necessity of dichotomizing every variable in the analysis. Recall that, as with most‐similar analysis, differences across cases must generally be sizeable enough to be interpretable in an essentially dichotomous fashion (e.g. high/low, present/absent) and similarities must be close enough to be understood as essentially identical (e.g. high/high, present/present). Otherwise the results of a Millean style analysis are not interpretable. The problem of “degrees” is deadly if the variables under consideration are, by nature, continuous (e.g. GDP). This is a particular concern in Howard's analysis, where East Germany scores somewhat higher than Russia in civic engagement; they are both low, but Russia is quite a bit lower. Howard assumes that this divergence is minimal enough to be understood as a difference of degrees rather than of kinds, a judgment that might be questioned. In these respects, most‐different analysis is no more secure—but also no less—than most‐similar analysis.

In one respect, most‐different analysis is superior to most‐similar analysis. If the coding assumptions are sound, the most‐different research design may be quite useful for eliminating necessary causes . Causal factors that do not appear across the chosen cases—e.g. X   2a–d in Table 28.4 —are evidently unnecessary for the production of Y . However, it does not follow that the most‐different method is the best method for eliminating necessary causes. Note that the defining feature of this method is the shared element across cases— X   1 in Table 28.4 . This feature does not help one to eliminate necessary causes. Indeed, if one were focused solely on eliminating necessary causes one would presumably seek out cases that register the same outcomes and have maximum diversity on other attributes. In Table 28.4 , this would be a set of cases that satisfy conditions X   2a–d , but not X   1 . Thus, even the presumed strength of the most‐different analysis is not so strong.

Usually, case‐study analysis is focused on the identification (or clarification) of causal relations, not the elimination of possible causes. In this setting, the most‐ different technique is useful, but only if assumptions of causal uniqueness hold. By “causal uniqueness,” I mean a situation in which a given outcome is the product of only one cause: Y cannot occur except in the presence of X . X is necessary, and in some situations (given certain background conditions) sufficient, to cause Y . 22

Consider the following hypothetical example. Suppose that a new disease, about which little is known, has appeared in Country A. There are hundreds of infected persons across dozens of affected communities in that country. In Country B, located at the other end of the world, several new cases of the disease surface in a single community. In this setting, we can imagine two sorts of Millean analyses. The first examines two similar communities within Country A, one of which has developed the disease and the other of which has not. This is the most‐similar style of case comparison, and focuses accordingly on the identification of a difference between the two cases that might account for variation across the sample. A second approach focuses on communities where the disease has appeared across the two countries and searches for any similarities that might account for these similar outcomes. This is the most‐different research design.

Both are plausible approaches to this particular problem, and we can imagine epidemiologists employing them simultaneously. However, the most‐different design demands stronger assumptions about the underlying factors at work. It supposes that the disease arises from the same cause in any setting. This is often a reasonable operating assumption when one is dealing with natural phenomena, though there are certainly many exceptions. Death, for example, has many causes. For this reason, it would not occur to us to look for most‐different cases of high mortality around the world. In order for the most‐different research design to effectively identify a causal factor at work in a given outcome, the researcher must assume that X   1 —the factor held constant across the diverse cases—is the only possible cause of Y (see Table 28.4 ). This assumption rarely holds in social‐scientific settings. Most outcomes of interest to anthropologists, economists, political scientists, and sociologists have multiple causes. There are many ways to win an election, to build a welfare state, to get into a war, to overthrow a government, or—returning to Marc Howard's work—to build a strong civil society. And it is for this reason that most‐different analysis is rarely applied in social science work and, where applied, is rarely convincing.

If this seems a tad severe, there is a more charitable way of approaching the most‐different method. Arguably, this is not a pure “method” at all but merely a supplement, a way of incorporating diversity in the sub‐sample of cases that provide the unusual outcome of interest. If the unusual outcome is revolutions, one might wish to encompass a wide variety of revolutions in one's analysis. If the unusual outcome is post‐Communist civil society, it seems appropriate to include a diverse set of post‐Communist polities in one's sample of case studies, as Marc Howard does. From this perspective, the most‐different method (so‐called) might be better labeled a diverse‐case method, as explored above.

10 Conclusions

In order to be a case of something broader than itself, the chosen case must be representative (in some respects) of a larger population. Otherwise—if it is purely idiosyncratic (“unique”)—it is uninformative about anything lying outside the borders of the case itself. A study based on a nonrepresentative sample has no (or very little) external validity. To be sure, no phenomenon is purely idiosyncratic; the notion of a unique case is a matter that would be difficult to define. One is concerned, as always, with matters of degree. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. (The one exception, as noted, is the influential case.)

Of all the problems besetting case‐study analysis, perhaps the most persistent— and the most persistently bemoaned—is the problem of sample bias ( Achen and Snidal 1989 ; Collier and Mahoney 1996 ; Geddes 1990 ; King, Keohane, and Verba 1994 ; Rohlfing 2004 ; Sekhon 2004 ). Lisa Martin (1992 , 5) finds that the overemphasis of international relations scholars on a few well‐known cases of economic sanctions— most of which failed to elicit any change in the sanctioned country—“has distorted analysts view of the dynamics and characteristics of economic sanctions.” Barbara Geddes (1990) charges that many analyses of industrial policy have focused exclusively on the most successful cases—primarily the East Asian NICs—leading to biased inferences. Anna Breman and Carolyn Shelton (2001) show that case‐study work on the question of structural adjustment is systematically biased insofar as researchers tend to focus on disaster cases—those where structural adjustment is associated with very poor health and human development outcomes. These cases, often located in sub‐Saharan Africa, are by no means representative of the entire population. Consequently, scholarship on the question of structural adjustment is highly skewed in a particular ideological direction (against neoliberalism) (see also Gerring, Thacker, and Moreno 2005) .

These examples might be multiplied many times. Indeed, for many topics the most‐studied cases are acknowledged to be less than representative. It is worth reflecting upon the fact that our knowledge of the world is heavily colored by a few “big” (populous, rich, powerful) countries, and that a good portion of the disciplines of economics, political science, and sociology are built upon scholars' familiarity with the economics, political science, and sociology of one country, the United States. 23 Case‐study work is particularly prone to problems of investigator bias since so much rides on the researcher's selection of one (or a few) cases. Even if the investigator is unbiased, her sample may still be biased simply by virtue of “random” error (which may be understood as measurement error, error in the data‐generation process, or as an underlying causal feature of the universe).

There are only two situations in which a case‐study researcher need not be concerned with the representativeness of her chosen case. The first is the influential case research design, where a case is chosen because of its possible influence on a cross‐case model, and hence is not expected to be representative of a larger sample. The second is the deviant‐case method, where the chosen case is employed to confirm a broader cross‐case argument to which the case stands as an apparent exception. Yet even here the chosen case is expected to be representative of a broader set of cases—those, in particular, that are poorly explained by the extant model.

In all other circumstances, cases must be representative of the population of interest in whatever ways might be relevant to the proposition in question. Note that where a researcher is attempting to disconfirm a deterministic proposition the question of representativeness is perhaps more appropriately understood as a question of classification: Is the chosen case appropriately classified as a member of the designated population? If so, then it is fodder for a disconfirming case study.

If the researcher is attempting to confirm a deterministic proposition, or to make probabilistic arguments about a causal relationship, then the problem of representativeness is of the more usual sort: Is case A unit‐homogeneous relative to other cases in the population? This is not an easy matter to test. However, in a large‐ N context the residual for that case (in whatever model the researcher has greatest confidence in) is a reasonable place to start. Of course, this test is only as good as the model at hand. Any incorrect specifications or incorrect modeling procedures will likely bias the results and give an incorrect assessment of each case's “typicality.” In addition, there is the possibility of stochastic error, errors that cannot be modeled in a general framework. Given the explanatory weight that individual cases are asked to bear in a case‐study analysis, it is wise to consider more than just the residual test of representativeness. Deductive logic and an in‐depth knowledge of the case in question are often more reliable tools than the results of a cross‐case model.

In any case, there is no dispensing with the question. Case studies (with the two exceptions already noted) rest upon an assumed synecdoche: The case should stand for a population. If this is not true, or if there is reason to doubt this assumption, then the utility of the case study is brought severely into question.

Fortunately, there is some safety in numbers. Insofar as case‐study evidence is combined with cross‐case evidence the issue of sample bias is mitigated. Indeed, the suspicion of case‐study work that one finds in the social sciences today is, in my view, a product of a too‐literal interpretation of the case‐study method. A case study tout court is thought to mean a case study tout seul . Insofar as case studies and cross‐case studies can be enlisted within the same investigation (either in the same study or by reference to other studies in the same subfield), problems of representativeness are less worrisome. This is the virtue of cross‐level work, a.k.a. “triangulation.”

11 Ambiguities

Before concluding, I wish to draw attention to two ambiguities in case‐selection strategies in case‐study research. The first concerns the admixture of several case‐ selection strategies. The second concerns the changing status of a case as a study proceeds.

Some case studies follow only one strategy of case selection. They are typical , diverse , extreme , deviant , influential , crucial , pathway , most‐similar , or most‐different research designs, as discussed. However, many case studies mix and match among these case‐selection strategies. Indeed, insofar as all case studies seek representative samples, they are always in search of “typical” cases. Thus, it is common for writers to declare that their case is, for example, both extreme and typical; it has an extreme value on X   1 or Y but is not, in other respects, idiosyncratic. There is not much that one can say about these combinations of strategies except that, where the cases allow for a variety of empirical strategies, there is no reason not to pursue them. And where the same cases can serve several functions at once (without further effort on the researcher's part), there is little cost to a multi‐pronged approach to case analysis.

The second issue that deserves emphasis is the changing status of a case during the course of a researcher's investigation—which may last for years, if not decades. The problem is acute wherever a researcher begins in an exploratory mode and proceeds to hypothesis‐testing (that is, she develops a specific X   1 / Y proposition) or where the operative hypothesis or key control variable changes (a new causal factor is discovered or another outcome becomes the focus of analysis). Things change. And it is the mark of a good researcher to keep her mind open to new evidence and new insights. Too often, methodological discussions give the misleading impression that hypotheses are clear and remain fixed over the course of a study's development. Nothing could be further from the truth. The unofficial transcripts of academia— accessible in informal settings, where researchers let their guards down (particularly if inebriated)—are filled with stories about dead‐ends, unexpected findings, and drastically revised theory chapters. It would be interesting, in this vein, to compare published work with dissertation prospectuses and fellowship applications. I doubt if the correlation between these two stages of research is particularly strong.

Research, after all, is about discovery, not simply the verification or falsification of static hypotheses. That said, it is also true that research on a particular topic should move from hypothesis generating to hypothesis‐testing. This marks the progress of a field, and of a scholar's own work. As a rule, research that begins with an open‐ended ( X ‐ or Y ‐centered) analysis should conclude with a determinate X   1 / Y hypothesis.

The problem is that research strategies that are ideal for exploration are not always ideal for confirmation. The extreme‐case method is inherently exploratory since there is no clear causal hypothesis; the researcher is concerned merely to explore variation on a single dimension ( X or Y ). Other methods can be employed in either an open‐ ended (exploratory) or a hypothesis‐testing (confirmatory/disconfirmatory) mode. The difficulty is that once the researcher has arrived at a determinate hypothesis the originally chosen research design may no longer appear to be so well designed.

This is unfortunate, but inevitable. One cannot construct the perfect research design until (a) one has a specific hypothesis and (b) one is reasonably certain about what one is going to find “out there” in the empirical world. This is particularly true of observational research designs, but it also applies to many experimental research designs: Usually, there is a “good” (informative) finding, and a finding that is less insightful. In short, the perfect case‐study research design is usually apparent only ex post facto .

There are three ways to handle this. One can explain, straightforwardly, that the initial research was undertaken in an exploratory fashion, and therefore not constructed to test the specific hypothesis that is—now—the primary argument. Alternatively, one can try to redesign the study after the new (or revised) hypothesis has been formulated. This may require additional field research or perhaps the integration of additional cases or variables that can be obtained through secondary sources or through consultation of experts. A final approach is to simply jettison, or de‐emphasize, the portion of research that no longer addresses the (revised) key hypothesis. A three‐case study may become a two‐case study, and so forth. Lost time and effort are the costs of this downsizing.

In the event, practical considerations will probably determine which of these three strategies, or combinations of strategies, is to be followed. (They are not mutually exclusive.) The point to remember is that revision of one's cross‐case research design is normal and perhaps to be expected. Not all twists and turns on the meandering trail of truth can be anticipated.

12 Are There Other Methods of Case Selection?

At the outset of this chapter I summarized the task of case selection as a matter of achieving two objectives: representativeness (typicality) and variation (causal leverage). Evidently, there are other objectives as well. For example, one wishes to identify cases that are independent of each other. If chosen cases are affected by each other (sometimes known as Galton's problem or a problem of diffusion), this problem must be corrected before analysis can take place. I have neglected this issue because it is usually apparent to the researcher and, in any case, there are no simple techniques that might be utilized to correct for such biases. (For further discussion of this and other factors impinging upon case selection see Gerring 2001 , 178–81.)

I have also disregarded pragmatic/logistical issues that might affect case selection. Evidently, case selection is often influenced by a researcher's familiarity with the language of a country, a personal entrée into that locale, special access to important data, or funding that covers one archive rather than another. Pragmatic considerations are often—and quite rightly—decisive in the case‐selection process.

A final consideration concerns the theoretical prominence of a particular case within the literature on a subject. Researchers are sometimes obliged to study cases that have received extensive attention in previous studies. These are sometimes referred to as “paradigmatic” cases or “exemplars” ( Flyvbjerg 2004 , 427).

However, neither pragmatic/logistical utility nor theoretical prominence qualifies as a methodological factor in case selection. That is, these features of a case have no bearing on the validity of the findings stemming from a study. As such, it is appropriate to grant these issues a peripheral status in this chapter.

One final caveat must be issued. While it is traditional to distinguish among the tasks of case selection and case analysis, a close look at these processes shows them to be indistinct and overlapping. One cannot choose a case without considering the sort of analysis that it might be subjected to, and vice versa. Thus, the reader should consider choosing cases by employing the nine techniques laid out in this chapter along with any considerations that might be introduced by virtue of a case's quasi‐experimental qualities, a topic taken up elsewhere ( Gerring 2007 , ch. 6 ).

Abadie, A. , Drukker, D. , Herr, J. L. , and Imbens, G. W.   2001 . Implementing matching estimators for average treatment effects in Stata.   Stata Journal , 1: 1–18.

Google Scholar

Abbott, A.   2001 . Time Matters: On Theory and Method . Chicago: University of Chicago Press.

Google Preview

——  and Tsay, A.   2000 . Sequence analysis and optimal matching methods in sociology.   Sociological Methods and Research , 29: 3–33. 10.1177/0049124100029001001

——  and Forrest, J.   1986 . Optimal matching methods for historical sequences.   Journal of Interdisciplinary History , 16: 471–94. 10.2307/204500

Achen, C. H. , and Snidal, D.   1989 . Rational deterrence theory and comparative case studies.   World Politics , 41: 143–69. 10.2307/2010405

Allen, W. S.   1965 . The Nazi Seizure of Power: The Experience of a Single German Town, 1930–1935 . New York: Watts.

Almond, G. A.   1956 . Comparative political systems.   Journal of Politics , 18: 391–409.

Amenta, E.   1991 . Making the most of a case study: theories of the welfare state and the American experience. Pp. 172–94 in Issues and Alternatives in Comparative Social Research ed. C. C. Ragin . Leiden: E. J. Brill.

Barro, R. J.   1999 . Determinants of democracy.   Journal of Political Economy , 107: 158–83. 10.1086/250107

Belsey, D. A. , Kuh, E. , and Welsch, R. E.   2004 . Regression Diagnostics: Identifying Influential Data and Sources of Collinearity . New York: Wiley.

Bennett, A. , Lepgold, J. , and Unger, D.   1994 . Burden‐sharing in the Persian Gulf War.   International Organization , 48: 39–75. 10.1017/S0020818300000813

Bentley, A. 1908/ 1967 . The Process of Government . Cambridge, Mass.: Harvard University Press.

Brady, H. E. , and Collier, D. (eds.) 2004 . Rethinking Social Inquiry: Diverse Tools, Shared Standards . Lanham, Md.: Rowman and Littlefield.

Braumoeller, B. F.   2003 . Causal complexity and the study of politics.   Political Analysis , 11: 209–33. 10.1093/pan/mpg012

Breman, A. , and Shelton, C. 2001. Structural adjustment and health: a literature review of the debate, its role‐players and presented empirical evidence. CMH Working Paper Series, Paper No. WG6: 6. WHO, Commission on Macroeconomics and Health.

Brenner, R.   1976 . Agrarian class structure and economic development in pre‐industrial Europe.   Past and Present , 70: 30–75. 10.1093/past/70.1.30

Browne, A.   1987 . When Battered Women Kill . New York: Free Press.

Buchbinder, S. , and Vittinghoff, E.   1999 . HIV‐infected long‐term nonprogressors: epidemiology, mechanisms of delayed progression, and clinical and research implications.   Microbes Infect , 1: 1113–20. 10.1016/S1286-4579(99)00204-X

Cohen, M. R. , and Nagel, E.   1934 . An Introduction to Logic and Scientific Method . New York: Harcourt, Brace and Company.

Collier, D. , and Mahoney, J.   1996 . Insights and pitfalls: selection bias in qualitative research.   World Politics , 49: 56–91. 10.1353/wp.1996.0023

Collier, R. B. , and Collier, D. 1991/ 2002 . Shaping the Political Arena: Critical Junctures, the Labor Movement, and Regime Dynamics in Latin America . Notre Dame, Ind.: University of Notre Dame Press.

Colomer, J. M.   1991 . Transitions by agreement: modeling the Spanish way.   American Political Science Review , 85: 1283–302. 10.2307/1963946

Converse, P. E. , and Dupeux, G.   1962 . Politicization of the electorate in France and the United States.   Public Opinion Quarterly , 16: 1–23. 10.1086/267067

Coppedge, M. J. 2004. The conditional impact of the economy on democracy in Latin America. Presented at the conference “Democratic Advancements and Setbacks: What Have We Learnt?”, Uppsala University, June 11–13.

De Felice, E. G.   1986 . Causal inference and comparative methods.   Comparative Political Studies , 19: 415–37. 10.1177/0010414086019003005

Desch, M. C.   2002 . Democracy and victory: why regime type hardly matters.   International Security , 27: 5–47. 10.1162/016228802760987815

Deyo, F. (ed.) 1987 . The Political Economy of the New Asian Industrialism . Ithaca, NY: Cornell University Press.

Dion, D.   1998 . Evidence and inference in the comparative case study.   Comparative Politics , 30: 127–45. 10.2307/422284

Eckstein, H.   1975 . Case studies and theory in political science. In Handbook of Political Science , vii: Political Science: Scope and Theory , ed. F. I. Greenstein and N. W. Polsby . Reading, Mass.: Addison‐Wesley.

Eggan, F.   1954 . Social anthropology and the method of controlled comparison.   American Anthropologist , 56: 743–63. 10.1525/aa.1954.56.5.02a00020

Elman, C.   2003 . Lessons from Lakatos. In Progress in International Relations Theory: Appraising the Field , ed. C. Elman and M. F. Elman . Cambridge, Mass.: MIT Press.

——  2005 . Explanatory typologies in qualitative studies of international politics.   International Organization , 59: 293–326.

Emigh, R.   1997 . The power of negative thinking: the use of negative case methodology in the development of sociological theory.   Theory and Society , 26: 649–84. 10.1023/A:1006896217647

Epstein, L. D.   1964 . A comparative study of Canadian parties.   American Political Science Review , 58: 46–59. 10.2307/1952754

Ertman, T.   1997 . Birth of the Leviathan: Building States and Regimes in Medieval and Early Modern Europe . Cambridge: Cambridge University Press.

Esping‐Andersen, G.   1990 . The Three Worlds of Welfare Capitalism . Princeton, NJ: Princeton University Press.

Flyvbjerg, B.   2004 . Five misunderstandings about case‐study research. Pp. 420–34 in Qualitative Research Practice , ed. C. Seale , G. Gobo , J. F. Gubrium , and D. Silverman . London: Sage.

Geddes, B.   1990 . How the cases you choose affect the answers you get: selection bias in comparative politics. In Political Analysis , vol. ii, ed. J. A. Stimson . Ann Arbor: University of Michigan Press.

——  2003 . Paradigms and Sand Castles: Theory Building and Research Design in Comparative Politics . Ann Arbor: University of Michigan Press.

George, A. L. , and Bennett, A.   2005 . Case Studies and Theory Development . Cambridge, Mass.: MIT Press.

——  and Smoke, R.   1974 . Deterrence in American Foreign Policy: Theory and Practice . New York: Columbia University Press.

Gerring, J.   2001 . Social Science Methodology: A Criterial Framework . Cambridge: Cambridge University Press.

——  2007 . Case Study Research: Principles and Practices . Cambridge: Cambridge University Press.

——  Thacker, S. and Moreno, C. 2005. Do neoliberal policies save lives? Unpublished manuscript.

Goertz, G. and Starr, H. (eds.) 2003 . Necessary Conditions: Theory, Methodology and Applications . New York: Rowman and Littlefield.

——  and Levy, J. (eds.) forthcoming. Causal explanations, necessary conditions, and case studies: World War I and the end of the Cold War. Manuscript.

Goodin, R. E. and Smitsman, A.   2000 . Placing welfare states: the Netherlands as a crucial test case.   Journal of Comparative Policy Analysis , 2: 39–64. 10.1080/13876980008412635

Gujarati, D. N.   2003 . Basic Econometrics , 4th edn. New York: McGraw‐Hill.

Hamilton, G. G.   1977 . Chinese consumption of foreign commodities: a comparative perspective.   American Sociological Review , 42: 877–91. 10.2307/2094574

Haynes, B. F.   Pantaleo, G. and Fauci, A. S.   1996 . Toward an understanding of the correlates of protective immunity to HIV infection.   Science , 271: 324–8. 10.1126/science.271.5247.324

Hempel, C. G.   1942 . The function of general laws in history.   Journal of Philosophy , 39: 35–48. 10.2307/2017635

Ho, D. E.   Imai, K.   King, G. and Stuart, E. A. 2004. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Manuscript.

Howard, M. M.   2003 . The Weakness of Civil Society in Post‐Communist Europe . Cambridge: Cambridge University Press.

Howson, C. and Urbach, P.   1989 . Scientific Reasoning: The Bayesian Approach . La Salle, Ill.: Open Court.

Humphreys, M.   2005 . Natural resources, conflict, and conflict resolution: uncovering the mechanisms.   Journal of Conflict Resolution , 49: 508–37. 10.1177/0022002705277545

Jenicek, M.   2001 . Clinical Case Reporting in Evidence‐Based Medicine , 2nd edn. Oxford: Oxford University Press.

Karl, T. L.   1997 . The Paradox of Plenty: Oil Booms and Petro‐states . Berkeley: University of California Press.

Kazancigil, A.   1994 . The deviant case in comparative analysis: high stateness in comparative analysis. Pp. 213–38 in Comparing Nations: Concepts, Strategies, Substance , ed. M. Dogan and A. Kazancigil . Cambridge: Blackwell.

Kemp, K. A.   1986 . Race, ethnicity, class and urban spatial conflict: Chicago as a crucial case   Urban Studies , 23: 197–208. 10.1080/00420988620080231

Kendall, P. L. and Wolf, K. M. 1949/ 1955 . The analysis of deviant cases in communications research. In Communications Research, 1948–1949 , ed. P. F. Lazarsfeld and F. N. Stanton. New York: Harper and Brothers. Reprinted as pp. 167–70 in The Language of Social Research , ed. P. F. Lazarsfeld and M. Rosenberg . New York: Free Press.

Kennedy, C. H.   2005 . Single‐case Designs for Educational Research . Boston: Allyn and Bacon.

Kennedy, P.   2003 . A Guide to Econometrics , 5th edn. Cambridge, Mass.: MIT Press.

Khong, Y. F.   1992 . Analogies at War: Korea, Munich, Dien Bien Phu, and the Vietnam Decisions of 1965 . Princeton, NJ: Princeton University Press.

King, G.   Keohane, R. O. and Verba, S.   1994 . Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton, NJ: Princeton University Press.

Lakatos, I.   1978 . The Methodology of Scientific Research Programmes . Cambridge: Cambridge University Press.

Lazarsfeld, P. F. and Barton, A. H.   1951 . Qualitative measurement in the social sciences: classification, typologies, and indices. In The Policy Sciences , ed. D. Lerner and H. D. Lass‐ well. Stanford, Calif.: Stanford University Press.

Levy, J. S.   2002 . Qualitative methods in international relations. In Evaluating Methodology in International Studies , ed. F. P. Harvey and M. Brecher. Ann Arbor: University of Michigan Press.

Lijphart, A.   1968 . The Politics of Accommodation: Pluralism and Democracy in the Netherlands . Berkeley: University of California Press.

——  1969 . Consociational democracy.   World Politics , 21: 207–25. 10.2307/2009820

——  1971 . Comparative politics and the comparative method. American Political Science Review , 65: 682–93.

——  1975 . The comparable cases strategy in comparative research.   Comparative Political Studies , 8: 158–77.

Lipset, S. M.   1959 . Some social requisites of democracy: economic development and political development.   American Political Science Review , 53: 69–105. 10.2307/1951731

——  1960/ 1963 . Political Man: The Social Bases of Politics . Garden City, NY: Anchor.

——  1968 . Agrarian Socialism: The Cooperative Commonwealth Federation in Saskatchewan. A Study in Political Sociology . Garden City, NY: Doubleday.

——  Trow, M. A. and Coleman, J. S.   1956 . Union Democracy: The Internal Politics of the International Typographical Union . New York: Free Press.

Lynd, R. S. and Lynd, H. M. 1929/ 1956 . Middletown: A Study in American Culture . New York: Harcourt, Brace.

Mahoney, J. and Goertz, G.   2004 . The possibility principle: choosing negative cases in comparative research.   American Political Science Review , 98: 653–69.

Martin, L. L.   1992 . Coercive Cooperation: Explaining Multilateral Economic Sanctions .Princeton, NJ: Princeton University Press.

Mayo, D. G.   1996 . Error and the Growth of Experimental Knowledge . Chicago: University of Chicago Press.

Meckstroth, T.   1975 . “Most different systems” and “most similar systems:” a study in the logic of comparative inquiry.   Comparative Political Studies , 8: 133–77.

Miguel, E.   2004 . Tribe or nation: nation‐building and public goods in Kenya versus Tanzania.   World Politics , 56: 327–62. 10.1353/wp.2004.0018

Mill, J. S. 1843/ 1872 . The System of Logic , 8th edn. London: Longmans, Green.

Monroe, K. R.   1996 . The Heart of Altruism: Perceptions of a Common Humanity . Princeton, NJ: Princeton University Press.

Moore, B., Jr.   1966 . Social Origins of Dictatorship and Democracy: Lord and Peasant in the Making of the Modern World . Boston: Beacon Press.

Morgan, S. L. and Harding, D. J. 2005. Matching estimators of causal effects: from stratification and weighting to practical data analysis routines. Manuscript.

Moulder, F. V.   1977 . Japan, China and the Modern World Economy: Toward a Reinterpretation of East Asian Development ca. 1600 to ca. 1918 . Cambridge: Cambridge University Press.

Munck, G. L.   2004 . Tools for qualitative research. Pp. 105–21 in Rethinking Social Inquiry: Diverse Tools, Shared Standards , ed. H. E. Brady and D. Collier . Lanham, Md. : Rowman and Littlefield.

Njolstad, O.   1990 . Learning from history? Case studies and the limits to theory‐building. Pp. 220–46 in Arms Races: Technological and Political Dynamics , ed. O. Njolstad . Thousand Oaks, Calif.: Sage.

Patton, M. Q.   2002 . Qualitative Evaluation and Research Methods . Newbury Park, Calif.: Sage.

Popper, K. 1934/ 1968 . The Logic of Scientific Discovery . New York: Harper and Row.

——  1963 . Conjectures and Refutations . London: Routledge and Kegan Paul.

Posner, D.   2004 . The political salience of cultural difference: why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi.   American Political Science Review , 98: 529–46.

Przeworski, A. and Teune, H.   1970 . The Logic of Comparative Social Inquiry . New York: John Wiley.

Queen, S.   1928 . Round table on the case study in sociological research.   Publications of the American Sociological Society, Papers and Proceedings , 22: 225–7.

Ragin, C. C.   2000 . Fuzzy‐set Social Science . Chicago: University of Chicago Press.

——  2004 . Turning the tables. Pp. 123–38 in Rethinking Social Inquiry: Diverse Tools, Shared Standards , ed. H. E. Brady and D. Collier.   Lanham, Md. : Rowman and Littlefield.

Reilly, B.   2000 –1. Democracy, ethnic fragmentation, and internal conflict: confused theories, faulty data, and the “crucial case” of Papua New Guinea.   International Security , 25: 162–85. 10.1162/016228800560552

——  and Phillpot, R.   2003 . “Making democracy work” in Papua New Guinea: social capital and provincial development in an ethnically fragmented society.   Asian Survey , 42: 906–27. 10.1525/as.2002.42.6.906

Rogowski, R.   1995 . The role of theory and anomaly in social‐scientific inference.   American Political Science Review , 89: 467–70. 10.2307/2082443

Rohlfing, I. 2004. Have you chosen the right case? Uncertainty in case selection for single case studies. Working Paper, International University, Bremen.

Rosenbaum, P. R.   2004 . Matching in observational studies. In Applied Bayesian Modeling and Causal Inference from an Incomplete‐data Perspective , ed. A. Gelman and X.‐L. Meng . New York: John Wiley.

——  and Silber, J. H.   2001 . Matching and thick description in an observational study of mortality after surgery.   Biostatistics , 2: 217–32. 10.1093/biostatistics/2.2.217

Ross, M.   2001 . Does oil hinder democracy?   World Politics , 53: 325–61. 10.1353/wp.2001.0011

Sagan, S. D.   1995 . Limits of Safety: Organizations, Accidents, and Nuclear Weapons . Princeton, NJ: Princeton University Press.

Sekhon, J. S.   2004 . Quality meets quantity: case studies, conditional probability and counter‐ factuals.   Perspectives in Politics , 2: 281–93.

Shafer, M. D.   1988 . Deadly Paradigms: The Failure of U.S. Counterinsurgency Policy . Princeton, NJ: Princeton University Press.

Skocpol, T.   1979 . States and Social Revolutions: A Comparative Analysis of France, Russia, and China . Cambridge: Cambridge University Press.

——  and Somers, M.   1980 . The uses of comparative history in macrosocial inquiry.   Comparative Studies in Society and History , 22: 147–97.

Stinchcombe, A. L.   1968 . Constructing Social Theories . New York: Harcourt, Brace.

Swank, D. H.   2002 . Global Capital, Political Institutions, and Policy Change in Developed Welfare States . Cambridge: Cambridge University Press.

Tendler, J.   1997 . Good Government in the Tropics . Baltimore: Johns Hopkins University Press.

Truman, D. B.   1951 . The Governmental Process . New York: Alfred A. Knopf.

Tsai, L.   2007 . Accountability without Democracy: How Solidary Groups Provide Public Goods in Rural China . Cambridge: Cambridge University Press.

Van Evera, S.   1997 . Guide to Methods for Students of Political Science . Ithaca, NY: Cornell University Press.

Wahlke, J. C.   1979 . Pre‐behavioralism in political science. American Political Science Review , 73: 9–31. 10.2307/1954728

Yashar, D. J.   2005 . Contesting Citizenship in Latin America: The Rise of Indigenous Movements and the Postliberal Challenge . Cambridge: Cambridge University Press.

Yin, R. K.   2004 . Case Study Anthology . Thousand Oaks, Calif.: Sage.

Gujarati (2003) ; Kennedy (2003) . Interestingly, the potential of cross‐case statistics in helping to choose cases for in‐depth analysis is recognized in some of the earliest discussions of the case‐study method (e.g. Queen 1928 , 226).

This expands on Mill (1843/1872 , 253), who wrote of scientific enquiry as twofold: “either inquiries into the cause of a given effect or into the effects or properties of a given cause.”

This method has not received much attention on the part of qualitative methodologists; hence, the absence of a generally recognized name. It bears some resemblance to J. S. Mill's Joint Method of Agreement and Difference ( Mill 1843/1872 ), which is to say a mixture of most‐similar and most‐different analysis, as discussed below. Patton (2002 , 234) employs the concept of “maximum variation (heterogeneity) sampling.”

More precisely, George and Smoke (1974 , 534, 522–36, ch. 18 ; see also discussion in Collier and Mahoney 1996 , 78) set out to investigate causal pathways and discovered, through the course of their investigation of many cases, these three causal types. Yet, for our purposes what is important is that the final sample includes at least one representative of each “type.”

For further examples see Collier and Mahoney (1996) ; Geddes (1990) ; Tendler (1997) .

Traditionally, methodologists have conceptualized cases as having “positive” or “negative” values (e.g. Emigh 1997 ; Mahoney and Goertz 2004 ; Ragin 2000 , 60; 2004 , 126).

Geddes (1990) ; King, Keohane, and Verba (1994) . See also discussion in Brady and Collier (2004) ; Collier and Mahoney (1996) ; Rogowski (1995) .

The exception would be a circumstance in which the researcher intends to disprove a deterministic argument ( Dion 1998 ).

Geddes (2003 , 131). For other examples of casework from the annals of medicine see “Clinical reports” in the Lancet , “Case studies” in Canadian Medical Association Journal , and various issues of the Journal of Obstetrics and Gynecology , often devoted to clinical cases (discussed in Jenicek 2001 , 7). For examples from the subfield of comparative politics see Kazancigil (1994) .

For a discussion of the important role of anomalies in the development of scientific theorizing see Elman (2003) ; Lakatos (1978) . For examples of deviant‐case research designs in the social sciences see Amenta (1991) ; Coppedge (2004) ; Eckstein (1975) ; Emigh (1997) ; Kendall and Wolf (1949/1955) .

For examples of the crucial‐case method see Bennett, Lepgold, and Unger (1994) ; Desch (2002) ; Goodin and Smitsman (2000) ; Kemp (1986) ; Reilly and Phillpot (2003) . For general discussion see George and Bennett (2005) ; Levy (2002) ; Stinchcombe (1968 , 24–8).

A third position, which purports to be neither Popperian or Bayesian, has been articulated by Mayo (1996 , ch. 6 ). From this perspective, the same idea is articulated as a matter of “severe tests.”

It should be noted that Tsai's conclusions do not rest solely on this crucial case. Indeed, she employs a broad range of methodological tools, encompassing case‐study and cross‐case methods.

See also the discussion in Eckstein (1975) and Lijphart (1969) . For additional examples of case studies disconfirming general propositions of a deterministic nature see Allen (1965); Lipset, Trow, and Coleman (1956) ; Njolstad (1990) ; Reilly (2000–1) ; and discussion in Dion (1998) ; Rogowski (1995) .

Granted, insofar as case‐study analysis provides a window into causal mechanisms, and causal mechanisms are integral to a given theory, a single case may be enlisted to confirm or disconfirm a proposition. However, if the case study upholds a posited pattern of X/Y covariation, and finds fault only with the stipulated causal mechanism, it would be more accurate to say that the study forces the reformulation of a given theory, rather than its confirmation or disconfirmation. See further discussion in the following section.

Sometimes, the most‐similar method is known as the “method of difference,” after its inventor ( Mill 1843/1872 ). For later treatments see Cohen and Nagel (1934) ; Eggan (1954) ; Gerring (2001 , ch. 9 ); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) .

For good introductions see Ho et al. (2004) ; Morgan and Harding (2005) ; Rosenbaum (2004) ; Rosenbaum and Silber (2001) . For a discussion of matching procedures in Stata see Abadie et al. (2001) .

The most‐different method is also sometimes referred to as the “method of agreement,” following its inventor, J. S. Mill (1843/1872) . See also De Felice (1986) ; Gerring (2001 , 212–14); Lijphart (1971 ; 1975) ; Meckstroth (1975) ; Przeworski and Teune (1970) ; Skocpol and Somers (1980) . For examples of this method see Collier and Collier (1991/2002) ; Converse and Dupeux (1962) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). However, most of these studies are described as combining most‐similar and most‐different methods.

In the following discussion I treat the terms social capital, civil society, and civic engagement interchangeably.

E.g. Collier and Collier (1991/2002) ; Karl (1997) ; Moore (1966) ; Skocpol (1979) ; Yashar (2005 , 23). Karl (1997) , which affects to be a most‐different system analysis (20), is a particularly clear example of this. Her study, focused ostensibly on petro‐states (states with large oil reserves), makes two sorts of inferences. The first concerns the (usually) obstructive role of oil in political and economic development. The second sort of inference concerns variation within the population of petro‐states, showing that some countries (e.g. Norway, Indonesia) manage to avoid the pathologies brought on elsewhere by oil resources. When attempting to explain the constraining role of oil on petro‐states, Karl usually relies on contrasts between petro‐states and nonpetro‐states (e.g. ch. 10 ). Only when attempting to explain differences among petro‐states does she restrict her sample to petro‐states. In my opinion, very little use is made of the most‐different research design.

This was recognized, at least implicitly, by Mill (1843/1872 , 258–9). Skepticism has been echoed by methodologists in the intervening years (e.g. Cohen and Nagel 1934 , 251–6; Gerring 2001 ; Skocpol and Somers 1980 ). Indeed, explicit defenses of the most‐different method are rare (but see De Felice 1986 ).

Another way of stating this is to say that X is a “nontrivial necessary condition” of Y .

Wahlke (1979 , 13) writes of the failings of the “behavioralist” mode of political science analysis: “It rarely aims at generalization; research efforts have been confined essentially to case studies of single political systems, most of them dealing …with the American system.”

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Sales CRM Terms

What is Case Study Analysis? (Explained With Examples)

Oct 11, 2023

Case Study Analysis is a widely used research method that examines in-depth information about a particular individual, group, organization, or event. It is a comprehensive investigative approach that aims to understand the intricacies and complexities of the subject under study. Through the analysis of real-life scenarios and inquiry into various data sources, Case Study Analysis provides valuable insights and knowledge that can be used to inform decision-making and problem-solving strategies.

1°) What is Case Study Analysis?

Case Study Analysis is a research methodology that involves the systematic investigation of a specific case or cases to gain a deep understanding of the subject matter. This analysis encompasses collecting and analyzing various types of data, including qualitative and quantitative information. By examining multiple aspects of the case, such as its context, background, influences, and outcomes, researchers can draw meaningful conclusions and provide valuable insights for various fields of study.

When conducting a Case Study Analysis, researchers typically begin by selecting a case or multiple cases that are relevant to their research question or area of interest. This can involve choosing a specific organization, individual, event, or phenomenon to study. Once the case is selected, researchers gather relevant data through various methods, such as interviews, observations, document analysis, and artifact examination.

The data collected during a Case Study Analysis is then carefully analyzed and interpreted. Researchers use different analytical frameworks and techniques to make sense of the information and identify patterns, themes, and relationships within the data. This process involves coding and categorizing the data, conducting comparative analysis, and drawing conclusions based on the findings.

One of the key strengths of Case Study Analysis is its ability to provide a rich and detailed understanding of a specific case. This method allows researchers to delve deep into the complexities and nuances of the subject matter, uncovering insights that may not be captured through other research methods. By examining the case in its natural context, researchers can gain a holistic perspective and explore the various factors and variables that contribute to the case.

1.1 - Definition of Case Study Analysis

Case Study Analysis can be defined as an in-depth examination and exploration of a particular case or cases to unravel relevant details and complexities associated with the subject being studied. It involves a comprehensive and detailed analysis of various factors and variables that contribute to the case, aiming to answer research questions and uncover insights that can be applied in real-world scenarios.

When conducting a Case Study Analysis, researchers employ a range of research methods and techniques to collect and analyze data. These methods can include interviews, surveys, observations, document analysis, and experiments, among others. By using multiple sources of data, researchers can triangulate their findings and ensure the validity and reliability of their analysis.

Furthermore, Case Study Analysis often involves the use of theoretical frameworks and models to guide the research process. These frameworks provide a structured approach to analyzing the case and help researchers make sense of the data collected. By applying relevant theories and concepts, researchers can gain a deeper understanding of the underlying factors and dynamics at play in the case.

1.2 - Advantages of Case Study Analysis

Case Study Analysis offers numerous advantages that make it a popular research method across different disciplines. One significant advantage is its ability to provide rich and detailed information about a specific case, allowing researchers to gain a holistic understanding of the subject matter. Additionally, Case Study Analysis enables researchers to explore complex issues and phenomena in their natural context, capturing the intricacies and nuances that may not be captured through other research methods.

Moreover, Case Study Analysis allows researchers to investigate rare or unique cases that may not be easily replicated or studied through experimental methods. This method is particularly useful when studying phenomena that are complex, multifaceted, or involve multiple variables. By examining real-world cases, researchers can gain insights that can be applied to similar situations or inform future research and practice.

Furthermore, this research method allows for the analysis of multiple sources of data, such as interviews, observations, documents, and artifacts, which can contribute to a comprehensive and well-rounded examination of the case. Case Study Analysis also facilitates the exploration and identification of patterns, trends, and relationships within the data, generating valuable insights and knowledge for future reference and application.

1.3 - Disadvantages of Case Study Analysis

While Case Study Analysis offers various advantages, it also comes with certain limitations and challenges. One major limitation is the potential for researcher bias, as the interpretation of data and findings can be influenced by preconceived notions and personal perspectives. Researchers must be aware of their own biases and take steps to minimize their impact on the analysis.

Additionally, Case Study Analysis may suffer from limited generalizability, as it focuses on specific cases and contexts, which might not be applicable or representative of broader populations or situations. The findings of a case study may not be easily generalized to other settings or individuals, and caution should be exercised when applying the results to different contexts.

Moreover, Case Study Analysis can require significant time and resources due to its in-depth nature and the need for meticulous data collection and analysis. This can pose challenges for researchers working with limited budgets or tight deadlines. However, the thoroughness and depth of the analysis often outweigh the resource constraints, as the insights gained from a well-conducted case study can be highly valuable.

Finally, ethical considerations also play a crucial role in Case Study Analysis, as researchers must ensure the protection of participant confidentiality and privacy. Researchers must obtain informed consent from participants and take measures to safeguard their identities and personal information. Ethical guidelines and protocols should be followed to ensure the rights and well-being of the individuals involved in the case study.

2°) Examples of Case Study Analysis

Real-world examples of Case Study Analysis demonstrate the method's practical application and showcase its usefulness across various fields. The following examples provide insights into different scenarios where Case Study Analysis has been employed successfully.

2.1 - Example in a Startup Context

In a startup context, a Case Study Analysis might explore the factors that contributed to the success of a particular startup company. It would involve examining the organization's background, strategies, market conditions, and key decision-making processes. This analysis could reveal valuable lessons and insights for aspiring entrepreneurs and those interested in understanding the intricacies of startup success.

2.2 - Example in a Consulting Context

In the consulting industry, Case Study Analysis is often utilized to understand and develop solutions for complex business problems. For instance, a consulting firm might conduct a Case Study Analysis on a company facing challenges in its supply chain management. This analysis would involve identifying the underlying issues, evaluating different options, and proposing recommendations based on the findings. This approach enables consultants to apply their expertise and provide practical solutions to their clients.

2.3 - Example in a Digital Marketing Agency Context

Within a digital marketing agency, Case Study Analysis can be used to examine successful marketing campaigns. By analyzing various factors such as target audience, message effectiveness, channel selection, and campaign metrics, this analysis can provide valuable insights into the strategies and tactics that contribute to successful marketing initiatives. Digital marketers can then apply these insights to optimize future campaigns and drive better results for their clients.

2.4 - Example with Analogies

Case Study Analysis can also be utilized with analogies to investigate specific scenarios and draw parallels to similar situations. For instance, a Case Study Analysis could explore the response of different countries to natural disasters and draw analogies to inform disaster management strategies in other regions. These analogies can help policymakers and researchers develop more effective approaches to mitigate the impact of disasters and protect vulnerable populations.

In conclusion, Case Study Analysis is a powerful research method that provides a comprehensive understanding of a particular individual, group, organization, or event. By analyzing real-life cases and exploring various data sources, researchers can unravel complexities, generate valuable insights, and inform decision-making processes. With its advantages and limitations, Case Study Analysis offers a unique approach to gaining in-depth knowledge and practical application across numerous fields.

About the author

Arnaud Belinga

Close deals x2 faster with

Breakcold sales crm.

SEE PRICING

*No credit card required

Related Articles

What is the 80-20 rule? (Explained With Examples)

What is the ABCD Sales Method? (Explained With Examples)

What is an Accelerated Sales Cycle? (Explained With Examples)

What is Account-Based Marketing (ABM)? (Explained With Examples)

What is an Account Manager? (Explained With Examples)

What is Account Mapping? (Explained With Examples)

What is Account-Based Selling? (Explained With Examples)

What is Ad Targeting? (Explained With Examples)

What is the Addressable Market? (Explained With Examples)

What is the Adoption Curve? (Explained With Examples)

What is an AE (Account Executive)? (Explained With Examples)

What is Affiliate Marketing? (Explained With Examples)

What is AI in Sales? (Explained With Examples)

What is an AI-Powered CRM? (Explained With Examples)

What is an Alternative Close? (Explained With Examples)

What is the Annual Contract Value? (ACV - Explained With Examples)

What are Appointments Set? (Explained With Examples)

What is an Assumptive Close? (Explained With Examples)

What is Automated Outreach? (Explained With Examples)

What is Average Revenue Per Account (ARPA)? (Explained With Examples)

What is B2B (Business-to-Business)? (Explained With Examples)

What is B2G (Business-to-Government)? (Explained With Examples)

What is B2P (Business-to-Partner)? (Explained With Examples)

What is BANT (Budget, Authority, Need, Timing)? (Explained With Examples)

What is Behavioral Economics in Sales? (Explained With Examples)

What is Benchmark Data? (Explained With Examples)

What is Benefit Selling? (Explained With Examples)

What are Benefit Statements? (Explained With Examples)

What is Beyond the Obvious? (Explained With Examples)

What is a Bootstrapped Startup? (Explained With Examples)

What is the Bottom of the Funnel (BOFU)? (Explained With Examples)

What is Bounce Rate? (Explained With Examples)

What is Brand Awareness? (Explained With Examples)

What is the Break-Even Point? (Explained With Examples)

What is a Breakup Email? (Explained With Examples)

What is Business Development? (Explained With Examples)

What are Business Insights? (Explained With Examples)

What is Business Process Automation? (Explained With Examples)

What is a Buyer Persona? (Explained With Examples)

What is the Buyer's Journey? (Explained With Examples)

What is the Buying Cycle? (Explained With Examples)

What is a Buying Signal? (Explained With Examples)

What is a Buying Team? (Explained With Examples)

What is a C-Level Executive? (Explained With Examples)

What is Call Logging? (Explained With Examples)

What is Call Recording? (Explained With Examples)

What is a Call-to-Action (CTA)? (Explained With Examples)

What is Challenger Sales? (Explained With Examples)

What is Chasing Lost Deals? (Explained With Examples)

What is Churn Prevention? (Explained With Examples)

What is Churn Rate? (Explained With Examples)

What is Click-Through Rate (CTR)? (Explained With Examples)

What is Client Acquisition? (Explained With Examples)

What is the Closing Ratio? (Explained With Examples)

What is the Ben Franklin Close? (Explained With Examples)

What is Cognitive Bias in Sales? (Explained With Examples)

What is Cognitive Dissonance in Sales? (Explained With Examples)

What is Cold Calling? (Explained With Examples)

What is Cold Outreach? (Explained With Examples)

What is a Competitive Advantage? (Explained With Examples)

What is a Competitive Analysis? (Explained With Examples)

What is Competitive Positioning? (Explained With Examples)

What is Conceptual Selling? (Explained With Examples)

What is Consultative Closing? (Explained With Examples)

What is Consultative Negotiation? (Explained With Examples)

What is Consultative Prospecting? (Explained With Examples)

What is Consultative Selling? (Explained With Examples)

What is Content Marketing? (Explained With Examples)

What is Content Syndication? (Explained With Examples)

What is a Conversion Funnel? (Explained With Examples)

What is Conversion Optimization? (Explained With Examples)

What is a Conversion Path? (Explained With Examples)

What is Conversion Rate? (Explained With Examples)

What is Cost-Per-Click (CPC)? (Explained With Examples)

What is a CRM (Customer Relationship Management)? (Explained With Examples)

What is Cross-Cultural Selling? (Explained With Examples)

What is a Cross-Sell Ratio? (Explained With Examples)

What is Cross-Selling? (Explained With Examples)

What is Customer Acquisition Cost (CAC)? (Explained With Examples)

What is Customer-Centric Marketing? (Explained With Examples)

What is Customer-Centric Selling? (Explained With Examples)

What is Customer Journey Mapping? (Explained With Examples)

What is the Customer Journey? (Explained With Examples)

What is the Customer Lifetime Value (CLV)? (Explained With Examples)

What is Customer Profiling? (Explained With Examples)

What is Customer Retention? (Explained With Examples)

What is Dark Social? (Explained With Examples)

What is Data Enrichment? (Explained With Examples)

What is Data Segmentation? (Explained With Examples)

What is Database Marketing? (Explained With Examples)

What are Decision Criteria? (Explained With Examples)

What is a Decision Maker? (Explained With Examples)

What is a Decision-Making Unit (DMU)? (Explained With Examples)

What is Demand Generation? (Explained With Examples)

What is Digital Marketing? (Explained With Examples)

What is Direct Marketing? (Explained With Examples)

What is a Discovery Call? (Explained With Examples)

What is a Discovery Meeting? (Explained With Examples)

What are Discovery Questions? (Explained With Examples)

What is Door-to-Door Sales? (Explained With Examples)

What is a Drip Campaign? (Explained With Examples)

What is Dunning? (Explained With Examples)

What is an Early Adopter? (Explained With Examples)

What is Elevator Pitch? (Explained With Examples)

What is Email Hygiene? (Explained With Examples)

What is Email Marketing? (Explained With Examples)

What is Emotional Intelligence Selling? (Explained With Examples)

What is Engagement Marketing? (Explained With Examples)

What is Engagement Rate? (Explained With Examples)

What is Engagement Strategy? (Explained With Examples)

What is Feature-Benefit Selling? (Explained With Examples)

What is Field Sales? (Explained With Examples)

What is a Follow-Up? (Explained With Examples)

What is Forecast Accuracy? (Explained With Examples)

What is a Funnel? (Explained With Examples)

What is Gamification in Sales? (Explained With Examples)

What is Gatekeeper Strategy? (Explained With Examples)

What is Gatekeeper? (Explained With Examples)

What is a Go-to Market Strategy? (Explained With Examples)

What is Growth Hacking? (Explained With Examples)

What is Growth Marketing? (Explained With Examples)

What is Guerrilla Marketing? (Explained With Examples)

What is High-Ticket Sales? (Explained With Examples)

What is Holistic Selling? (Explained With Examples)

What is Ideal Customer Profile (ICP)? (Explained With Examples)

What is Inbound Lead Generation? (Explained With Examples)

What is an Inbound Lead? (Explained With Examples)

What is Inbound Marketing? (Explained With Examples)

What is Inbound Sales? (Explained With Examples)

What is Influencer Marketing? (Explained With Examples)

What is Inside Sales Representative? (Explained With Examples)

What is Inside Sales? (Explained With Examples)

What is Insight Selling? (Explained With Examples)

What is a Key Account? (Explained With Examples)

What is a Key Performance Indicator (KPI)? (Explained With Examples)

What is a Landing Page? (Explained With Examples)

What is Lead Database? (Explained With Examples)

What is a Lead Enrichment? (Explained With Examples)

What is Lead Generation? (Explained With Examples)

What is Lead Nurturing? (Explained With Examples)

What is Lead Qualification? (Explained With Examples)

What is Lead Scoring? (Explained With Examples)

What are LinkedIn InMails? (Explained With Examples)

What is LinkedIn Sales Navigator? (Explained With Examples)

What is Lost Opportunity? (Explained With Examples)

What is Market Positioning? (Explained With Examples)

What is Market Research? (Explained With Examples)

What is Market Segmentation? (Explained With Examples)

What is MEDDIC? (Explained With Examples)

What is Middle Of The Funnel (MOFU)? (Explained With Examples)

What is Motivational Selling? (Explained With Examples)

What is a MQL (Marketing Qualified Lead)? (Explained With Examples)

What is MRR Growth? (Explained With Examples)

What is MRR (Monthly Recurring Revenue)? (Explained With Examples)

What is N.E.A.T. Selling? (Explained With Examples)

What is Neil Rackham's Sales Tactics? (Explained With Examples)

What is Networking? (Explained With Examples)

What is NLP Sales Techniques? (Explained With Examples)

What is the Net Promotion Score? (NPS - Explained With Examples)

What is Objection Handling Framework? (Explained With Examples)

What is On-Hold Messaging? (Explained With Examples)

What is Onboarding in Sales? (Explained With Examples)

What is Online Advertising? (Explained With Examples)

What is Outbound Sales? (Explained With Examples)

What is Pain Points Analysis? (Explained With Examples)

What is Permission Marketing? (Explained With Examples)

What is Personality-Based Selling? (Explained With Examples)

What is Persuasion Selling? (Explained With Examples)

What is Pipeline Management? (Explained With Examples)

What is Pipeline Velocity? (Explained With Examples)

What is Predictive Lead Scoring? (Explained With Examples)

What is Price Negotiation? (Explained With Examples)

What is Price Objection? (Explained With Examples)

What is Price Sensitivity? (Explained With Examples)

What is Problem-Solution Selling? (Explained With Examples)

What is Product Knowledge? (Explained With Examples)

What is Product-Led-Growth? (Explained With Examples)

What is Prospecting? (Explained With Examples)

What is a Qualified Lead? (Explained With Examples)

What is Question-Based Selling? (Explained With Examples)

What is Referral Marketing? (Explained With Examples)

What is Relationship Building? (Explained With Examples)

What is Revenue Forecast? (Explained With Examples)

What is a ROI? (Explained With Examples)

What is Sales Automation? (Explained With Examples)

What is a Sales Bonus Plan? (Explained With Examples)

What is a Sales Champion? (Explained With Examples)

What is a Sales Collateral? (Explained With Examples)

What is a Sales Commission Structure Plan? (Explained With Examples)

What is a Sales CRM? (Explained With Examples)

What is a Sales Cycle? (Explained With Examples)

What is a Sales Demo? (Explained With Examples)

What is Sales Enablement? (Explained With Examples)

What is a Sales Flywheel? (Explained With Examples)

What is a Sales Funnel? (Explained With Examples)

What are Sales KPIs? (Explained With Examples)

What is a Sales Meetup? (Explained With Examples)

What is a Sales Pipeline? (Explained With Examples)

What is a Sales Pitch? (Explained With Examples)

What is a Sales Playbook? (Explained With Examples)

Try breakcold now, are you ready to accelerate your sales pipeline.

Join over +1000 agencies, startups & consultants closing deals with Breakcold Sales CRM

Get Started for free

Sales CRM Features

Sales CRM Software

Sales Pipeline

Sales Lead Tracking

CRM with social media integrations

Social Selling Software

Contact Management

CRM Unified Email LinkedIn Inbox

Breakcold works for many industries

CRM for Agencies

CRM for Startups

CRM for Consultants

CRM for Small Business

CRM for LinkedIn

CRM for Coaches

Sales CRM & Sales Pipeline Tutorials

The 8 Sales Pipeline Stages

The Best CRMs for Agencies

The Best CRMs for Consultants

The Best LinkedIn CRMs

How to close deals in 2024, not in 2010

CRM automation: from 0 to PRO in 5 minutes

LinkedIn Inbox Management

LinkedIn Account-Based Marketing (2024 Tutorial with video)

Tools & more

Sales Pipeline Templates

Alternatives

Integrations

CRM integration with LinkedIn

© 2024 Breakcold

Privacy Policy

Terms of Service

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Sage Choice

Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

Partner Center

What is the Case Study Method?

Overview Dropdown up

Overview dropdown down, celebrating 100 years of the case method at hbs.

The 2021-2022 academic year marks the 100-year anniversary of the introduction of the case method at Harvard Business School. Today, the HBS case method is employed in the HBS MBA program, in Executive Education programs, and in dozens of other business schools around the world. As Dean Srikant Datar's says, the case method has withstood the test of time.

Case Discussion Preparation Details Expand All Collapse All

In self-reflection in self-reflection dropdown down, in a small group setting in a small group setting dropdown down, in the classroom in the classroom dropdown down, beyond the classroom beyond the classroom dropdown down, how the case method creates value dropdown up, how the case method creates value dropdown down, in self-reflection, in a small group setting, in the classroom, beyond the classroom.

How Cases Unfold In the Classroom

How cases unfold in the classroom dropdown up, how cases unfold in the classroom dropdown down, preparation guidelines expand all collapse all, read the professor's assignment or discussion questions read the professor's assignment or discussion questions dropdown down, read the first few paragraphs and then skim the case read the first few paragraphs and then skim the case dropdown down, reread the case, underline text, and make margin notes reread the case, underline text, and make margin notes dropdown down, note the key problems on a pad of paper and go through the case again note the key problems on a pad of paper and go through the case again dropdown down, how to prepare for case discussions dropdown up, how to prepare for case discussions dropdown down, read the professor's assignment or discussion questions, read the first few paragraphs and then skim the case, reread the case, underline text, and make margin notes, note the key problems on a pad of paper and go through the case again, case study best practices expand all collapse all, prepare prepare dropdown down, discuss discuss dropdown down, participate participate dropdown down, relate relate dropdown down, apply apply dropdown down, note note dropdown down, understand understand dropdown down, case study best practices dropdown up, case study best practices dropdown down, participate, what can i expect on the first day dropdown down.

Most programs begin with registration, followed by an opening session and a dinner. If your travel plans necessitate late arrival, please be sure to notify us so that alternate registration arrangements can be made for you. Please note the following about registration:

HBS campus programs – Registration takes place in the Chao Center.

India programs – Registration takes place outside the classroom.

Other off-campus programs – Registration takes place in the designated facility.

What happens in class if nobody talks? Dropdown down

Professors are here to push everyone to learn, but not to embarrass anyone. If the class is quiet, they'll often ask a participant with experience in the industry in which the case is set to speak first. This is done well in advance so that person can come to class prepared to share. Trust the process. The more open you are, the more willing you’ll be to engage, and the more alive the classroom will become.

Does everyone take part in "role-playing"? Dropdown down

Professors often encourage participants to take opposing sides and then debate the issues, often taking the perspective of the case protagonists or key decision makers in the case.

View Frequently Asked Questions

Subscribe to Our Emails

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading Change and Organizational Renewal
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

While several factors make HBS Online unique —including a global Community and real-world outcomes —active learning through the case study method rises to the top.

In a 2023 City Square Associates survey, 74 percent of HBS Online learners who also took a course from another provider said HBS Online’s case method and real-world examples were better by comparison.

Here’s a primer on the case method, five benefits you could gain, and how to experience it for yourself.

Access your free e-book today.

What Is the Harvard Business School Case Study Method?

The case study method , or case method , is a learning technique in which you’re presented with a real-world business challenge and asked how you’d solve it. After working through it yourself and with peers, you’re told how the scenario played out.

HBS pioneered the case method in 1922. Shortly before, in 1921, the first case was written.

“How do you go into an ambiguous situation and get to the bottom of it?” says HBS Professor Jan Rivkin, former senior associate dean and chair of HBS's master of business administration (MBA) program, in a video about the case method . “That skill—the skill of figuring out a course of inquiry to choose a course of action—that skill is as relevant today as it was in 1921.”

Originally developed for the in-person MBA classroom, HBS Online adapted the case method into an engaging, interactive online learning experience in 2014.

In HBS Online courses , you learn about each case from the business professional who experienced it. After reviewing their videos, you’re prompted to take their perspective and explain how you’d handle their situation.

You then get to read peers’ responses, “star” them, and comment to further the discussion. Afterward, you learn how the professional handled it and their key takeaways.

HBS Online’s adaptation of the case method incorporates the famed HBS “cold call,” in which you’re called on at random to make a decision without time to prepare.

“Learning came to life!” said Sheneka Balogun , chief administration officer and chief of staff at LeMoyne-Owen College, of her experience taking the Credential of Readiness (CORe) program . “The videos from the professors, the interactive cold calls where you were randomly selected to participate, and the case studies that enhanced and often captured the essence of objectives and learning goals were all embedded in each module. This made learning fun, engaging, and student-friendly.”

If you’re considering taking a course that leverages the case study method, here are five benefits you could experience.

5 Benefits of Learning Through Case Studies

1. take new perspectives.

The case method prompts you to consider a scenario from another person’s perspective. To work through the situation and come up with a solution, you must consider their circumstances, limitations, risk tolerance, stakeholders, resources, and potential consequences to assess how to respond.

Taking on new perspectives not only can help you navigate your own challenges but also others’. Putting yourself in someone else’s situation to understand their motivations and needs can go a long way when collaborating with stakeholders.

2. Hone Your Decision-Making Skills

Another skill you can build is the ability to make decisions effectively . The case study method forces you to use limited information to decide how to handle a problem—just like in the real world.

Throughout your career, you’ll need to make difficult decisions with incomplete or imperfect information—and sometimes, you won’t feel qualified to do so. Learning through the case method allows you to practice this skill in a low-stakes environment. When facing a real challenge, you’ll be better prepared to think quickly, collaborate with others, and present and defend your solution.

3. Become More Open-Minded

As you collaborate with peers on responses, it becomes clear that not everyone solves problems the same way. Exposing yourself to various approaches and perspectives can help you become a more open-minded professional.

When you’re part of a diverse group of learners from around the world, your experiences, cultures, and backgrounds contribute to a range of opinions on each case.

On the HBS Online course platform, you’re prompted to view and comment on others’ responses, and discussion is encouraged. This practice of considering others’ perspectives can make you more receptive in your career.

“You’d be surprised at how much you can learn from your peers,” said Ratnaditya Jonnalagadda , a software engineer who took CORe.

In addition to interacting with peers in the course platform, Jonnalagadda was part of the HBS Online Community , where he networked with other professionals and continued discussions sparked by course content.

“You get to understand your peers better, and students share examples of businesses implementing a concept from a module you just learned,” Jonnalagadda said. “It’s a very good way to cement the concepts in one's mind.”

4. Enhance Your Curiosity

One byproduct of taking on different perspectives is that it enables you to picture yourself in various roles, industries, and business functions.

“Each case offers an opportunity for students to see what resonates with them, what excites them, what bores them, which role they could imagine inhabiting in their careers,” says former HBS Dean Nitin Nohria in the Harvard Business Review . “Cases stimulate curiosity about the range of opportunities in the world and the many ways that students can make a difference as leaders.”

Through the case method, you can “try on” roles you may not have considered and feel more prepared to change or advance your career .

5. Build Your Self-Confidence

Finally, learning through the case study method can build your confidence. Each time you assume a business leader’s perspective, aim to solve a new challenge, and express and defend your opinions and decisions to peers, you prepare to do the same in your career.

According to a 2022 City Square Associates survey , 84 percent of HBS Online learners report feeling more confident making business decisions after taking a course.

“Self-confidence is difficult to teach or coach, but the case study method seems to instill it in people,” Nohria says in the Harvard Business Review . “There may well be other ways of learning these meta-skills, such as the repeated experience gained through practice or guidance from a gifted coach. However, under the direction of a masterful teacher, the case method can engage students and help them develop powerful meta-skills like no other form of teaching.”

How to Experience the Case Study Method

If the case method seems like a good fit for your learning style, experience it for yourself by taking an HBS Online course. Offerings span seven subject areas, including:

• Business essentials
• Leadership and management
• Entrepreneurship and innovation
• Finance and accounting
• Business in society

No matter which course or credential program you choose, you’ll examine case studies from real business professionals, work through their challenges alongside peers, and gain valuable insights to apply to your career.

Are you interested in discovering how HBS Online can help advance your career? Explore our course catalog and download our free guide —complete with interactive workbook sections—to determine if online learning is right for you and which course to take.

About the Author

8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

Recent Expert Contributors Articles

• Open access
• Published: 29 May 2024

Cannabis use in a Canadian long-term care facility: a case study

• Lynda G. Balneaves 1 , 4 ,
• Abeer A. Alraja 1 ,
• Genevieve Thompson 1 ,
• Jamie L. Penner 1 ,
• Philip St. John 2 ,
• Daniella Scerbo 1 &
• Joanne van Dyck 3  

BMC Geriatrics volume  24 , Article number:  467 ( 2024 ) Cite this article

300 Accesses

2 Altmetric

Metrics details

Following the legalization of cannabis in Canada in 2018, people aged 65 + years reported a significant increase in cannabis consumption. Despite limited research with older adults regarding the therapeutic benefits of cannabis, there is increasing interest and use among this population, particularly for those who have chronic illnesses or are at end of life. Long-term Care (LTC) facilities are required to reflect on their care and policies related to the use of cannabis, and how to address residents’ cannabis use within what they consider to be their home.

Using an exploratory case study design, this study aimed to understand how one LTC facility in western Canada addressed the major policy shift related to medical and non-medical cannabis. The case study, conducted November 2021 to August 2022, included an environmental scan of existing policies and procedures related to cannabis use at the LTC facility, a quantitative survey of Healthcare Providers’ (HCP) knowledge, attitudes, and practices related to cannabis, and qualitative interviews with HCPs and administrators. Quantitative survey data were analyzed using descriptive statistics and content analysis was used to analyze the qualitative data.

A total of 71 HCPs completed the survey and 12 HCPs, including those who functioned as administrators, participated in the interview. The largest knowledge gaps were related to dosing and creating effective treatment plans for residents using cannabis. About half of HCPs reported providing care in the past month to a resident who was taking medical cannabis (54.9%) and a quarter (25.4%) to a resident that was taking non-medical cannabis. The majority of respondents (81.7%) reported that lack of knowledge, education or information about medical cannabis were barriers to medical cannabis use in LTC. From the qualitative data, we identified four key findings regarding HCPs’ attitudes, cannabis access and use, barriers to cannabis use, and non-medical cannabis use.

Conclusions

With the legalization of medical and non-medical cannabis in jurisdictions around the world, LTC facilities will be obligated to develop policies, procedures and healthcare services that are able to accommodate residents’ use of cannabis in a respectful and evidence-informed manner.

Peer Review reports

In October 2018, Canada became the second country to legalize non-medical cannabis [ 1 ]. Despite the increasing interest in cannabis among Canadians of all ages [ 2 ], the percentage of individuals over the age of 15 years reporting cannabis use a year following legalization remained relatively unchanged at 18% [3]. The only age group to report a significant increase in cannabis consumption was those aged 65 + years, with 7.6% reporting cannabis use in the past 3 months [ 3 ] in 2019 compared to 4% in 2018. This upward trend in cannabis use among Canadians 65 years or older was also observed in 2021 [ 4 ].

This increase may reflect a growing acceptance of cannabis among older populations who were previously dissuaded from taking cannabis due to its illegal status as well as limited accessibility through legal means. In addition, the rise in cannabis use among older adults may reflect a harm reduction approach, substituting cannabis for other recreational substances with substantial health risks, such as alcohol [ 5 ]. Moreover, the belief in the potential therapeutic benefits of cannabis [ 6 , 7 , 8 ], such as the management of pain and sleep issues, is becoming increasingly prevalent among older adults. There has been limited research, however, among older adults in Canada to understand this progressive trend in cannabis use and the influencing factors [ 9 ].

Canada has been a world leader in cannabis legalization, launching a federal medical cannabis program in 2001. Since this time, the medical cannabis program has undergone numerous revisions, including how authorization is obtained, what types of products are available, and where cannabis is purchased. Currently, Canadians can seek medical authorization from either a physician or a nurse practitioner, and access a variety of cannabis products, including dried flower, capsules, and oils, which are purchased online through a licensed producer (LP). Some individuals also apply for a personal or designated grow license to produce their own supply of dried cannabis. Outside of the medical authorization program, individuals can access non-medical cannabis through an authorized storefront. It is estimated that over 1 million Canadians are using cannabis for therapeutic purposes [4], with 247,548 individuals officially registered as of March 2022 [ 10 ]. Among the 479,400 individuals over the age of 65 who reported cannabis use in the third quarter of 2019, 52% utilized cannabis exclusively for medical reasons, and another 24% reported using cannabis for both recreational and medical purposes [ 3 ].

Despite the growing interest in cannabis as a therapeutic agent, there has been limited human research due to its illegal status in many countries, as well as the challenges posed by the complexity of the cannabis plant compared to single agent, pharmaceutical forms of cannabis (e.g., nabilone) [ 11 , 12 ]. Notwithstanding these challenges, there is emergent research on the potential role of cannabis-based medicines in the management of health conditions common among older adults, including osteoarthritis [ 13 ], sleep disorders [ 14 ], dementia [ 15 ], and Parkinson’s [ 16 , 17 ], which are also prevalent among individuals residing at long-term care (LTC) facilities. For example, several studies have found cannabis-based medicines to significantly reduce neuropsychiatric symptoms and improve quality of life among people living with Alzheimer’s Disease [ 18 , 19 , 20 ]. Cannabis may also play a significant role at end of life in not only alleviating physical symptoms, such as pain, nausea and vomiting, and appetite loss, but also addressing the emotional and existential issues that may arise [ 21 ]. It has also been proposed that cannabis may have a therapeutic role among rehabilitative populations who often reside in LTC settings, including those with spinal cord injuries [ 22 , 23 ] and traumatic brain injury [ 24 ]. The evidence base surrounding cannabis as a therapeutic agent, however, remains limited with few large randomized clinical trials conducted to date.

Cannabis is not a benign substance and may pose risk to older adults, especially those living with frailty or cognitive impairment. Given the known cognitive effects of tetrahydrocannabinol (THC), a cannabinoid found in many forms of cannabis, adults living in long-term and rehabilitative care settings may experience somnolence, confusion, and fatigue [ 25 ]. Cannabis high in THC may also negatively impact motor coordination and increase the risk of falls, especially among those with impaired balance and walking ability [ 25 ]. As research advances on cannabis, there has been growing awareness of its negative interactions with certain medications [ 26 ], which can pose a significant issue among older clients prone to polypharmacy. Lastly, numerous health conditions are contraindicated with cannabis use, including heart disease, and a personal or family history of psychosis, schizophrenia, or bipolar disorder [ 27 ].

Despite limited research with older adults regarding the therapeutic benefits of cannabis, there is increasing interest and use among this population, particularly for those who have chronic illnesses. As adults age, they are more likely to experience multimorbidity, and a significant number of older adults spend their last years of life residing in a LTC facility [ 28 , 29 ]. LTC facilities are, thus, placed in a unique position. While these facilities are considered medical institutions that provide evidence-informed supportive health care, they have also become home for individuals who are no longer able to reside safely in the community. Increasingly, these types of facilities are challenged to create home-like environments and offer residents the opportunity and autonomy to engage in potentially risky health behaviours [ 30 ]; behaviours that individuals in the community have the independence and legal right to choose, such as alcohol or tobacco consumption. With the legalization of non-medical cannabis and the growing interest in the potential of cannabis to manage challenging health conditions, it behooves LTC facilities to reflect on their care and policies related to the use of legal substances, such as cannabis, and how to address residents’ cannabis use within what they consider to be their home.

The overarching aim of this case study was to understand how one LTC facility, and its healthcare professionals (HCPs) and administrators, addressed the major policy shift in Canada related to medical and non-medical cannabis. Specific research questions included: (1) What are the experiences and perceptions of HCPs and administrators regarding the use of medical and non-medical cannabis at LTC settings?; (2) What are the perceived barriers/facilitators to medical and non-medical cannabis use at LTC facilities from the perspective of HCPs and administrators?; and (3) What are the educational needs, attitudes, and practices of HCPs at LTC facilities related to medical and non-medical cannabis?

Research design and setting

An exploratory case study design was utilized in this study. This type of case study is used to explore those situations in which the phenomenon being evaluated has no clear or single set of outcomes [ 31 ]. The case selected for this study was a large LTC facility in Western Canada. This 387-bed residential facility provides 24/7 care to a diverse population, including older adults with cognitive and physical disabilities, individuals recovering from stroke and traumatic brain injury, and those requiring end-of-life care. Individuals with these various conditions may reside in several units, including palliative care, rehabilitation, personal care home, and complex chronic care. The case study included an environmental scan of existing policies and procedures related to medical and non-medical cannabis use at the LTC facility, a quantitative survey of HCPs’ knowledge, attitudes, and practices related to medical and non-medical cannabis, and qualitative interviews with HCPs and administrators. The qualitative interviews were informed by qualitative descriptive methodology [ 32 ] and explored HCPs’ and administrators’ experiences, beliefs, perceptions regarding cannabis use in LTC, and the related barriers and facilitators.

Sample and recruitment

For the survey, a convenience sample was drawn from the entire population of accredited HCPs working in the selected facility. Eligibility criteria included being 18 + years, able to read/speak English, currently employed and providing care at the LTC facility, and able to provide informed written consent. Study participants were recruited through an emailed letter of invitation, posters placed in staff areas, and in-person presentations by a research assistant. From participants who took part in the survey, a subsample of HCPs, including administrators, who expressed interest in taking part in an interview was selected. The data collection period was from November 2021 and August 2022.

Data collection

For the environmental scan, facility administrators were approached via an emailed letter and asked to identify relevant policies and procedures related to cannabis use within their LTC facility. Policies relevant to both residents’ use of cannabis and HCPs’ practice related to medical and non-medical cannabis were requested. Provincial and federal cannabis policies were also collected.

The survey was modified from a questionnaire utilized in two national studies that examined Canadian physicians’ and nurse practitioners’ knowledge, attitudes, and perceptions of the associated barriers and facilitators related to medical cannabis use, as well as their preferences regarding medical cannabis education [ 33 , 34 ]. This survey has been found to be internally consistent, with Cronbach’s alphas of 0.70 to 0.92 reported across subscales [ 33 , 34 ]. Slight word changes were made to reflect the fact that individuals living in LTC facility are referred to as residents, not patients, and the name of the facility was used to orientate the questions towards HCPs’ attitudes and practices related to cannabis use within the LTC setting.

Survey items were added that assessed HCPs’ practices related to addressing residents’ and family members’ questions about cannabis, as well as requests for medical cannabis authorization and follow-up care. A demographic survey that assessed gender, age, professional designation, years in practice, area(s) of practice, and education related to medical cannabis was included. The survey was available in hard copy (Supplementary Material 1 ) as well as online through the software program, Qualtrics®.

An interview guide was developed by the research team, which included a facility administrator and HCP, and was informed by the literature and previous cannabis research conducted by members of the research team [ 35 ] (Supplementary Material 2 ). Due to the COVID-19 pandemic, all but one interview was conducted by the project coordinator (AAA) via Zoom, with one interview occurring over the phone. The interviews were 20–30 min in length and were digitally recorded and transcribed verbatim. Both the survey and interview were completed at times preferred by the respondents, including within and outside work time. No honoraria were provided for study participants.

Data analysis

The policies identified through the environmental scan were reviewed and summarized in table format, with similarities, contradictions and gaps identified.

Quantitative survey data was uploaded into the statistical program, SPSS® v.25. Descriptive statistics were used to summarize demographic information, knowledge about medical cannabis and related attitudes, perceived barriers and facilitators, practice experiences, and preferred educational approaches.

Perceived knowledge gap was calculated by computing the difference between perceived current and desired knowledge levels (i.e., “the level of knowledge you desire” about medical cannabis). Rather than using averages, the knowledge gap was calculated based on how much greater an individual’s desired knowledge level was compared to their current knowledge level [ 36 ]. Only response pairs (i.e., current and desired knowledge) were used, and responses where the desired level was lower than the current level were excluded. To further elucidate, the knowledge gap was calculated by having each respondent’s current knowledge level response subtracted from their desired knowledge level response.

Prior to the onset of qualitative data analysis, the accuracy of the transcripts was checked by listening to the digital recordings. Content analysis was used to analyze the qualitative data [ 37 ], with two team members (AAA and LGB) independently reading the transcripts and developing a preliminary coding scheme. Constant comparison of new and existing data ensured consistency, relevance, and comprehensiveness of emerging codes. Several strategies were applied to ensure rigour in the qualitative analysis. To increase credibility, a team member with expertise in qualitative inquiry (LGB) monitored the qualitative data and its analysis. Confirmability was addressed by using the participants’ own words throughout the process of data analysis, interpretation, and description. An audit trail was kept documenting the activities of the study, including data analysis decisions.

Environmental scan of cannabis-related policies

Administrators at the LTC facility provided the research team with the policies and procedures that addressed the management and use of medical and non-medical cannabis within the facility. The guiding policy adopted by the LTC facility was a generic policy applicable to all sites and facilities governed by a regional health authority. This policy, entitled “Patient Use of Medical Cannabis (Marijuana)” was issued in June 2020. The policy, which aimed to provide individuals with “reasonable access to medical cannabis”, outlined numerous issues that might arise with institutional cannabis use, including “ordering, labeling, packaging, storage, security, administration, documentation and monitoring requirements for the use of medical cannabis”. Key aspects of the policy are summarised in a table found in the Supplementary Material section (Supplementary Material 3 ).

Other relevant policies that were reviewed included the standards of practice issued by the provincial college of nurses and the college of physicians and surgeons [ 38 , 39 , 40 ], which provided direction to HCPs working in LTC about their scope of practice regarding medical and non-medical cannabis. The regional health authority’s smoke-free policy [ 41 ] also informed how inhaled forms of medical and non-medical cannabis were addressed, requiring residents to leave the facility grounds to smoke or vape cannabis. Lastly, the overarching federal Cannabis Act and Regulations provided guidance to both administrators and HCPs regarding the Canadian regulations specific to medical and non-medical cannabis [ 1 , 42 ]. Together, existing facility, regional, and national policies created a context in which cannabis was framed as neither a medicine nor a controlled substance, but something unique and complex that must be navigated by residents, family members and staff in LTC settings.

Quantitative survey

Demographic characteristics.

From the approximately 318 eligible HCPs employed at the LTC facility, a total of 71 participants consented and completed the survey, yielding a response rate of 22.3%. With regards to response rate by profession, pharmacists (50.0%) and social workers (42.9%) were best represented, followed by physicians (23.1%), nurses (21.0%), and PT/OT (11.4%).

Most respondents were women (71.8%), registered nurses (62.0%) and worked within the palliative care unit (76.1%) at the facility. The average age of the sample was 40.9 years and the largest proportion of the sample had worked in the LTC facility for 5 or less years. See Table  1 for additional details.

Knowledge about medical cannabis

HCPs reported being most knowledgeable about the therapeutic potential of cannabis (3.1/5.0), potential risks of medical cannabis (2.9/5.0), and the different ways to administer medical cannabis (2.9/5.0). They reported being least knowledgeable about the dosing of medical cannabis (2.0/5.0), how to create effective treatment plans related to medical cannabis (2.1/5.0), and the similarities and differences between different forms of cannabis products and prescription cannabinoid medications (2.2/5.0). The top three ranked knowledge gaps mirrored the items ranked lowest with regards to knowledge (see Table  2 ). Overall, there was high interest in gaining more medical cannabis knowledge, with all knowledge items scoring greater than 4 on desired knowledge level.

Practice experiences with medical cannabis

About half of HCPs reported providing care in the past month to a resident who was taking medical cannabis (54.9%) and a quarter (25.4%) to a resident that was taking non-medical cannabis. Over 60% had been approached by a resident and/or a family member to discuss the potential use of medical cannabis; however, few HCPs reported initiating these conversations. Moreover, when asked if they felt comfortable discussing medical cannabis, 32.4% of HCPs disagreed (data not shown). Less than 20% reported helping residents, either directly or indirectly, to use medical cannabis and a very small proportion (1.3–2.8%) reported assisting residents’ consumption of non-medical cannabis. With regards to authorizing the use of medical cannabis or prescribing cannabinoid medication, which in Canada can be done by either a physician or nurse practitioner, just over half of physicians reported supporting residents’ access to these types of treatment. See Table  3 for additional details.

Barriers to medical cannabis use in long-term care

Lack of knowledge, education or information about medical cannabis were reported to be barriers to medical cannabis use in LTC by most HCPs (81.7%). Moreover, the uncertain risks and benefits of medical cannabis and the lack of clinical guidelines were also perceived as barriers by 66.2% and 63.4% of HCPs, respectively. The complete list of barriers is presented in Table  4 .

Education about medical cannabis

Most of the HCPs agreed that additional education on medical cannabis would increase their comfort with discussing this treatment option with residents and family members (87.4%; data not shown). With regards to indirectly or directly administering medical cannabis to a resident, most HCPs for which this fell within their scope of practice also reported they would feel more comfortable if they had further education (59.2% and 56.4%, respectively; data not shown).

Over half of HCPs had not received any prior education related to medical cannabis (54.9%). Those that had, received it from conferences or workshops (65.6%), books or journal articles (43.8%) or through a colleague (37.5%). While almost half the sample (49.3%) reported receiving information from peer-reviewed sources, nearly a quarter received information about medical cannabis from a non-peer reviewed source or from a resident or family member. Some participants also received information from a cannabis industry source. Table  5 provides additional details.

The preferred sources of medical cannabis education were online learning programs (i.e., continuing education) (74.6%), monographs (66.2%), and topic-specific one-pagers (64.8%). See Fig.  1 for further details.

Percentage of respondents indicating prefered method of cannabis education*

Qualitative findings

A total of 12 HCPs were interviewed regarding their perceptions and experiences related to medical and non-medical cannabis in the LTC facility. This included 3 HCPs who were administrators, 6 nurses, 1 physician, 1 social worker and 1 pharmacist. Four main themes were identified.

Attitudes regarding medical cannabis: cautious support

There were mixed attitudes regarding the potential role of medical cannabis in general and in LTC populations. While some HCPs felt medical cannabis was a “good idea” for which there was beginning research regarding its health benefits, other HCPs believed additional high-quality evidence was needed prior to medical cannabis becoming a therapeutic option.

I think it’s [medical cannabis] the fair option, it helps some people, but it doesn’t help others. So, I think we need a bit more evidence and a bit more research and having it available sort of allows for that research to occur (Physician; PC07).

There appeared to be greater acceptance for medical cannabis use by individuals at end of life compared to those not considered immediately palliative (i.e., living with dementia, stroke, or traumatic brain injury), the latter of which comprise the majority of the people living in LTC settings. For individuals receiving palliative care, some HCPs perceived medical cannabis to be beneficial in managing pain, nausea, and anxiety, as well as reducing the use of other medications that may be problematic (e.g., opioids) due to their side effects. The potential value of medical cannabis in “adding quality of life and living” at the end of life was also mentioned.

I’m working on the palliative care unit right now. A lot of patients that I’ve seen use it [medical cannabis] for anxiety purposes, or for nausea… some people find beneficial. So, I’ve seen it – people find it helpful for those reasons, and then they have to take less of their other medications. So, if it’s worked well for them and that’s what they prefer to do, then I think it should be an option for people, especially if some people find it beneficial. (Registered nurse; PC03)

Within the context of LTC, several HCPs also spoke of the importance of respecting residents’ autonomy and previous experiences taking medical cannabis. The reality of a LTC facility being a resident’s “home” was particularly influential in HCPs’ support of medical cannabis being included as part of a holistic approach to care.

I guess because people live at [LTC facility’s name], that is their home and if they were at home in the community, they would be able to access it [medical cannabis]. (Registered nurse, PC02)

I think it’s a part of people’s lives. And I think if we’re allowing people to have certain things and keeping it as part of their treatment because if you look at a holistic view, preventing somebody from doing something that they’ve been doing for many years is not going to help them be accepting of other types of therapies. (Pharmacist, PC09)

Some HCPs also perceived medical cannabis as offering an alternative to medical treatments that were not consistently effective in managing challenging health conditions, such as dementia and agitation.

HCPs’ attitudes towards medical cannabis varied across different products and routes of administration. Given the existing smoke-free policy at the facility, HCPs were more supportive of edibles, oils, oral sprays or topical creams and lotions than any form of inhaled medical cannabis (i.e., smoking and vaping). They were concerned not only about lung health, environmental exposure, and maintaining a scent-free facility, but also about how to safely manage vulnerable residents travelling off the facility’s property to smoke or vape.

Medical cannabis access and use: concern, confusion, and limited conversations

According to HCPs interviewed, most residents using medical cannabis obtained their authorization prior to moving to LTC. Individuals who sought authorization after arriving at the facility struggled to have their requests acknowledged or addressed by the health care team. As one nurse shared:

I do remember I had a resident that did ask about it [medical cannabis]. And whenever it was kind of brought up, it didn’t seem to be acknowledged all the time. Or there were people who didn’t like the idea of having a resident on it. (Registered nurse; PC06)

Conversations about medical cannabis were perceived to be severely limited by the culture surrounding medical cannabis at the LTC facility. The lack of open discussion about medical cannabis was seen by some to create conflict and negatively impact the development of trust between residents, family members, and the health care team: “ Without that discussion, it does create conflict within the team and between the physician and family, and perhaps that could impact the trusting relationship” (Administrator; PA03). Further, several HCPs expressed the belief that conversations about non-pharmacological forms of medical cannabis could not be initiated by them due to policy issues; residents who expressed interest but did not have prior authorization were instead directed towards pharmaceutical forms of cannabis.

There have been residents who have asked about using cannabis. And as I said, you can’t initiate it, if they’re going to get it on their own, fair enough. That’s pretty much been the experience I’ve had with residents with just non-pharmaceutical medical cannabis . (Physician, PC07)

The only HCP-initiated conversations about medical cannabis mentioned were those occurring between pharmacists and residents, which focused on the potential side effects, benefits, and “red flags” to watch out for, such as allergic reactions.

HCPs shared that for those residents with authorization, they or a support person were responsible for ordering the medical cannabis product from an LP, which would then send the product to either the resident at the LTC facility or to their support person’s home. The cannabis product was then stored in a locked drawer in the resident’s room if they were self-administering or in a medication room if nursing staff were assisting with administration. According to one pharmacist, the pharmacy department was not permitted, due to existing federal regulations, to either directly order or dispense medical cannabis:

No, we don’t dispense any cannabis. It’s considered resident’s own. So, we don’t acquire it for them. They have to directly be the holders of it and have it provided to them directly. And I think that has more to do with the regulations within Canada, the resident has to have certain type of documentation in order to have medical cannabis. So, it’s directly to them, we’re not able to order it for them or anything like that on their behalf. (Pharmacist; PC09)

With regards to the type of medical cannabis products permitted in the facility, due to non-smoking policies and concerns about safety issues and the “smell”, combustible forms and inhaled routes of administration (i.e., joints, vaporizers, vape pens) were not allowed; instead, ingestible forms were mentioned most frequently by HCPs.

There was some confusion and concerns expressed regarding the storage and disposal of medical cannabis, which may have reflected changes in facility policies over time. Some HCPs expressed concerns about the storage of cannabis in residents’ rooms and the lack of “safeguards” to limit potential diversion and allow an accurate “count” of medical cannabis.

We have to go into our Pyxis machine to retrieve a key to open that drawer. So, by going by that you’re able to know who’s actually accessed the key, but once the key is out you have no idea how many people have used that key and accessed that drawer before it’s gone back. You have no way of knowing how much cannabis has been taken out [of the drawer] or used, because you know there’s no way to measure it. So that’s a huge problem, I find. (Registered nurse; PC01)

This nurse was particularly concerned about the potential risk of being accused of diversion:

I’m not worried about people abusing it, it’s more the worry of being accused. You know, like, if a resident says, ‘why is my cannabis running out already, I thought I had enough for a few more weeks?’ and we’re like, ‘I don’t know’, right? There’s the potential for that sort of thing to happen. (Registered nurse; PC01)

There was also a perception that there was a lack of direction from the facility regarding the appropriate disposal of medical cannabis. Most believed residents or family members were expected to remove any unused product once the resident was no longer at the facility. When such disposal was not possible, the policy was to destroy the cannabis product in a manner similar to narcotics or other controlled substances. However, variations in practice occurred with some HCPs described “throwing it in the trash” or using a medical waste disposal bin with or without a witness.

Barriers to medical cannabis use: safety, stigma and lack of knowledge

Numerous barriers to the use of medical cannabis by LTC residents were identified by HCPs. Foremost, the policies related to how cannabis products were ordered, accessed, stored, and administered were perceived to be complicated and created barriers to residents wanting to take medical cannabis, particularly those without family support. The inability of the LTC facility to order medical cannabis on behalf of a resident was perceived to be especially problematic, as described by one registered nurse:

I know when it became legal, there were a few residents who have inquired about it, but they didn’t have the family resources in place to be able to get it because I believe there’s some hoops that you have to go through to be able to have it medically prescribed in getting it on to the unit. And so, the ones who were interested in it didn’t have those supports in place, so they weren’t able to get it prescribed for them. (Registered nurse; PC05)

The lack of awareness and understanding of the regional policies related to medical cannabis by some of the clinical staff was also seen as being problematic. As one registered nurse shared:

My only concern is that there’s a lot of rules around being able to administer and how it’s [medical cannabis] administered, which can again make things a bit complicated. I would say that’s probably my biggest concern is just it’s hard to remember everything that you have to do when you’re trying to administer it or helping a resident. So, you don’t get involved. (Registered nurse; PC06)

Several HCPs attributed the lack of awareness about cannabis policy to the onset of the COVID-19 pandemic, which overshadowed all other health issues within their facility: “ Everybody’s been so focused on COVID for a year and a half that there hasn’t been really time to really think about or educate on other things. ” (Registered nurse; PC01).

HCPs suggested that more “straight forward” and tailored policies were needed that simplified how medical cannabis was managed. Having facility-specific policies would acknowledge the uniqueness of the LTC population, who may have cognitive impairment, limited social support, and complex healthcare plans. As one nurse shared: “ If it’s a dementia patient, they can’t really administer it on their own. So how do we follow the policy to help the patient take the cannabis? How would we know when they would want to take it PRN?” (Registered nurse; PC03). It was also recommended that the policy that prevented the facility from directly ordering and supplying medical cannabis required revision so that LTC residents were not reliant on family members to gain access. Lastly, several HCPs suggested that medical cannabis policies need to be well advertised and additional training developed for clinical staff to enhance their awareness and comfort level in providing appropriate and supportive care.

There needs to be a training session… staff have to read through them [cannabis policies] and get instructions about them, sort of like a self-learning activity. But that is not part of what we do when orienting. (Registered nurse; PC02)

Another perceived barrier frequently mentioned by HCPs was their lack of knowledge regarding the potential risks and benefits of medical cannabis. There was limited understanding about the effects of medical cannabis, how it may interact with other medications and health conditions, what side effects could arise, as well as basic information about starting dose, titration, and difference between THC and cannabidiol (CBD). Without such information, HCPs were perceived to be very hesitant about recommending or supporting medical cannabis as a treatment alternative for LTC residents:

There’s lots of unknown, that’s the problem. If there were more specifics about the recreational and the medical use of cannabis, then I think health care professionals would be more likely to want to provide it to the residents. But if not, then that’s kind of what’s hindering health care professionals to provide it. (Registered nurse; PC08)

There was also substantial discussion by HCPs regarding the “stigma” that they perceived to exist within the facility regarding medical cannabis. As described by one pharmacist: “ I think the understanding of cannabis, regardless of if it’s medical or anything, it’s still considered in many people’s minds as an illicit drug. It hasn’t shaken that. And I think there’s a lot of stereotypes around the type of people that use cannabis” (Pharmacist; PC09). The stigmatization of medical cannabis was perceived to be particularly pronounced among the medical staff, which led to what was described as a “hands-off approach” with regards to authorizing medical cannabis.

Almost all HCPs and administrators interviewed recommended that education programming and resources for HCPs be developed to address the lingering stigma associated with cannabis and the knowledge gaps that exist about medical cannabis and associated policies. Several participants recommended that education initiatives should first target physicians, who were responsible for authorizing medical cannabis in the facility. Physicians were perceived to need education on when and for whom medical cannabis would be appropriate, the latest evidence regarding efficacy and safety (i.e., drug interactions), and what their obligations and responsibilities were as the authorizing HCP. Participants also thought that all HCPs could benefit from additional training regarding medical cannabis, including the different types of cannabinoids and products, the process of titration, and dosing. Some of the nurses interviewed also expressed the need for education about the legal implications of medical cannabis and their role regarding provision and administration:

I think the legal implications of cannabis use, I think that would be a good focus for the nursing group – so that they understood what their obligations were, what they could be held accountable for, those kinds of things. (Administrator; PA02)

Finally, numerous HCPs spoke of the need for “safeguards” and clear policies and procedures to ensure that clinical staff were aware of what type of medical cannabis products residents were taking, what was the “right dose”, and the possibility of cannabis interacting with other medications. As shared by one pharmacist:

So that we know that this patient is on it because there are potential drug interactions with other things that patients are taking. So, we just have to be cautious and aware that patients are doing this. Because especially right now with studies, there haven’t been a lot of great studies on drug interactions. (Pharmacist; PC09)

Non-medical cannabis use: balancing autonomy and safety

HCPs were asked about their attitudes and experiences about residents’ use of non-medical cannabis in the facility. Two disparate points of view became apparent – those that perceived non-medical cannabis as a legal substance that should be available to LTC residents given the facility was their home and those that saw non-medical cannabis as a stigmatized substance that could lead to problematic use and disruptions in the care environment.

Because it is somebody’s home and so you’re trying to honour and match what their lifestyle and aspects of their life at home were and matching that here [LTC facility]. The bad is, while it is somebody’s home, it’s the next person’s home too, and so it’s trying to balance that, right? In an institutional setting, trying to make it as home-like as possible but, at the same time, you know, monitoring and matching for what everyone’s needs are. (Registered nurse; PA01)

Professionally, I think that it creates issues in terms of trying to police the use of recreational cannabis. In terms of smoking cigarette tobacco, that’s an issue in itself. We’re a non-smoking facility. So, adding cannabis to the mix creates issues…having staff perhaps exposed or other people exposed if people are using cannabis indoors or where they’re not supposed. Or if they want to access and use cannabis outside, who’s going to take them for that? Because that creates exposure too for staff or others who may have to escort them. (Registered nurse; PA03)

HCPs frequently mentioned the complexity of managing residents’ non-medical use of cannabis given the facility’s non-smoking policy that required residents to leave the facility grounds to use inhaled forms of cannabis. With staff unable to transport residents outside, concerns were raised regarding the safety of residents, particularly in the winter months, and who would be responsible for their transfer in and out of the facility as well as monitoring how much cannabis was consumed. In addition, residents’ access to non-medical cannabis was again dependent on having a support person that was able and willing to transport the product to the facility, posing a potential equity issue for some residents:

If someone’s wanting to go smoke outside, then mobility might be an issue. If they don’t have the right wheelchair or family to take them outside for that. If they have the access. Like, if they need family to go and buy it and bring it to them, that could be more of an access issue depending on their family support. (Registered nurse; PC03)

There was specific concern expressed for individuals in the rehabilitation units who may have pre-existing substance use issues. For these individuals, HCPs were concerned that allowing access to non-medical cannabis could add to an already complex care plan. In addition, with many vulnerable residents living at the facility, concerns were raised regarding them being “incredibly suggestible” to others encouraging their consumption of cannabis:

These people – they have an addiction. For sure they’re making choices, but those choices are influenced by physical withdrawal or influenced by stress; they’re influenced by lots of things. So, I would hate to put residents in a position where that was one other [non-medical cannabis] thing they had to contend with during the rehab stay. (Administrator, PA02)

The use of cannabis for therapeutic and recreational purposes is becoming more prevalent within older adult populations, both in the community as well as within healthcare institutions. There has also been growing interest in the possible role of medical cannabis for select chronic, rehabilitative, and palliative health conditions, frequently found among individuals residing within LTC settings. LTC facilities, thus, face the complex practice and policy implications associated with a substance that has been surrounded in controversy for close to a century. This case study is among the first to explore in one LTC facility in Western Canada how cannabis use is being addressed following the legalization of non-medical cannabis products, and what challenges exist. It provides an important snapshot of the complexities surrounding cannabis use in LTC and a foundation for future research.

Cannabis use in LTC settings: a clash of cultures

One challenge experienced by people residing in LTC facilities is the tension that exists between social and medical models of care that most facilities are founded on. Historically, LTC facilities have operated as what Goffman [ 43 ] termed “total institutions”, places where every aspect of a person’s life was controlled by others, paternalism dominated, and the medical needs of people were what drove care practices. Aspects of the total institution still exist, as noted in this case study, whereby cannabis use is in the control of the HCPs; it is dispensed during medication administration times rather than being freely available for use by the resident when they so desire as would be in a person’s home. In trying to create more home-like environments and meet the broad range of social and emotional needs of residents, resident-centred care practices and relational models of care have emerged [ 44 ]. Within this milieu, resident autonomy and choice are at the forefront and HCPs are there to assist, rather than take control of residents’ daily lives. In the most ideal settings, behaviours that are considered ‘risky’, like alcohol consumption, are treated as social experiences, not care tasks to be managed [ 45 ]. The tension arises, however, that despite the desire to be resident-centred, most LTC facilities are highly regulated by governments, putting limits to resident choice and, therefore, their autonomy [ 45 ]. While HCPs in our study acknowledged that residents should have the right to use medical or non-medical cannabis, the regional and institutional policies surrounding safety and the rights of other residents and staff to not be exposed to potentially risky behaviour underscored many of their views. LTC facilities would be wise to consider the principles of dignity of risk [ 46 ] with relation to cannabis consumption/use along the frail elderly population that reside in the home.

Cannabis policies and LTC: one size doesn’t fit all

The cannabis policies developed at the advent of legalization, without consideration of the unique populations and healthcare challenges that exist within LTC facilities, created numerous barriers to residents accessing and using cannabis, as well as for HCPs attempting to provide appropriate care. One of the most significant challenges experienced by LTC residents in our study was the inability to obtain a medical cannabis authorization from a physician working in the facility. Another significant challenge was the regional policy that medical cannabis could not be couriered directly to the LTC pharmacy; instead, the resident or their support persons were responsible for ordering and bringing cannabis products into the facility. Both challenges created enormous inequity in which residents that lacked the physical and cognitive ability to obtain authorization and order medical cannabis from an LP or were without a support person willing and able to obtain medical cannabis on their behalf, were unable to access medical cannabis. Given the nature of LTC populations, these policies led to only a few residents being able to access and use medical cannabis as part of their care.

Another policy that had substantial safety implications for residents wanting to use inhaled forms of cannabis was the regional and institutional no smoking policies that prevented both tobacco and cannabis products from being consumed within the centre as well as on the grounds. As a result, residents had to make their own way, or be accompanied by a support person, to walk approximately 300 m to the public sidewalk where they were allowed to smoke or vape cannabis. With the LTC facility located in a region where winter temperatures can reach − 35 Celsius and sidewalks are covered in snow and ice, this poses significant risk for residents who may be at heightened risk of falls and utilizing assisted walking devices. Similar safety implications of smoke-free policies have been identified in previous research [ 47 ].

Lastly, the policies surrounding the storage and self-administration of medical cannabis for those residents with the physical and emotional capacity (or with a support person willing to administer) may pose potential safety and liability risks and contribute to the concerns held by some HCPs about the use of cannabis in LTC. While residents’ autonomy must be respected, as well as their own expertise with regards to medical cannabis use, the value of standardized medication protocols to ensure the safety of residents as well as to inform care decisions must be acknowledged. The tension experienced in balancing LTC residents’ autonomy with health and safety concerns in the context of substance use has been cited in a recent scoping review [ 48 ] as well as prior research that has examined the use of tobacco in residential care settings [ 49 ].

The policy-related challenges identified by study participants suggest that consultations with LTC residents, families and HCPs are urgently needed to develop and refine cannabis policies that address the needs and reality of individuals living and receiving care in LTC. Future policy reviews must balance LTC residents’ autonomy with the safety issues associated with cannabis use (i.e., dignity of risk), particularly among older adults and those with cognitive and physical impairments. Approaching cannabis policies and procedures in LTC from a harm reduction perspective [ 50 ] with regards to supporting safer consumption of medical cannabis (e.g., route of administration, designated consumption areas) may also be important. Further, the unique context of LTC must also be acknowledged in that for many residents, a LTC facility is their home, and will continue to be so until the end of their lives. But the shared nature of a LTC setting requires that some boundaries be established to protect all residents, as well as those working within LTC. From a staff perspective, a review of policies related to the administration and documentation of cannabis use is needed to protect them from claims of diversion as well as other medicolegal challenges.

Cannabis knowledge gap and stigma in LTC

Across both the quantitative and qualitative data, the gap in knowledge regarding cannabis and the need for continuing education for HCPs working in LTC were readily apparent. When HCPs are unfamiliar about the various forms of medical cannabis, appropriate dosing and titration schedules, and routes of administration, they are hindered in their ability to engage in shared decision making with LTC residents as well as provide high-quality care [ 51 , 52 , 53 , 54 ]. Education is particularly needed that is tailored to the unique risks and benefits of medical cannabis use among LTC populations, including those living with physical and cognitive impairment. Older adults may be more sensitive to the side effects of cannabis due to changes in how medications and drugs are metabolised, and the predominance of polypharmacy among those residing in LTC may further complicate how individuals respond to cannabis [ 55 ]. Therefore, HCPs working in LTC must be aware of how cannabis use may impact individuals’ mobility, memory, and behaviour, as well as the potential for dependency, particularly among those who have experienced substance use issues in the past.

Beyond basic education regarding cannabis and its effects, HCPs must also become aware and informed about existing federal, regional, and institutional policies as well as professional practice standards regarding both medical and non-medical cannabis. The study findings highlighted the uncertainty many HCPs experienced regarding how medical and non-medical cannabis was to be accessed, authorized, administered, stored, and disposed within the LTC facility and what was within their professional scope of practice. Legal concerns about liability, workplace safety, and diversion were also raised.

It is important that future cannabis education programs targeting LTC settings also address the underlying stigma and stereotypes that still surround cannabis use [ 56 , 57 ], despite the existence of a medical cannabis program in Canada for over 20 years and the recent legalization of non-medical cannabis. Experiential training that promotes non-judgmental communication that avoids stigmatizing language (e.g., user, addict, marijuana) and considers both the risks and benefits of cannabis use, particularly within the context of end-of-life care, will help address the stigma that HCPs and LTC residents and families may hold towards cannabis.

With the legalization of cannabis in many regions around the world, it is imperative that undergraduate health professional training programs include information about both medical and non-medical cannabis. Currently, there is a knowledge gap among HCPs due to the lack of standardized curriculum for medical cannabis across nursing or medical schools [ 35 , 58 ]. Understanding such foundational knowledge such as the endocannabinoid system, the different forms and types of cannabis, and the potential health effects will enable physicians, nurses, pharmacists and other HCPs to engage in informed conversations with individuals and families both within and beyond LTC [ 33 ]. In addition, the development of continuing education programs focused on cannabis will ensure practicing HCPs have current knowledge about cannabis, including existing policies and programs relevant to medical and non-medical cannabis. For example, the Canadian Coalition for Seniors’ Mental Health created asynchronous e-learning modules to provide evidence-based knowledge for various clinicians [ 59 ].

Non-medical cannabis use in LTC: it’s legal but…

Despite non-medical cannabis being a legal substance for over three years in Canada at the time of the case study, the use of non-medical cannabis by LTC residents was considered controversial amongst the HCPs interviewed. Not only were HCPs limited in their ability to support the use of non-medical cannabis due to regional policies that prohibited non-medical cannabis consumption at any healthcare facility and surrounding grounds but concerns about potential safety risks and disruptions to the care environment made some HCPs hesitant about supporting residents’ use of non-medical cannabis.

Notwithstanding these challenges, at least a quarter of HCPs surveyed reported providing care to a LTC resident who used non-medical cannabis, which suggests that regulatory and policy changes are required to ensure there is equity across LTC residents who may express interest in non-medical cannabis, as well as to address the unique safety and care issues associated with recreational cannabis use in LTC populations. Similar to medical cannabis, LTC residents’ autonomy must be considered in future policy changes related to non-medical cannabis to facilitate care that is free from stigma and bias, respects residents’ rights to make informed decisions and to live with risk, and to create a home-like environment where residents can engage in activities that were an important part of their lives before entering LTC.

Lessons can be drawn from literature that has examined the use of other legal substances, such as alcohol and tobacco in LTC [ 48 , 60 ], and the need to develop person-centered care plans that ensure the safety of the individual, fellow residents, and the healthcare team.

Limitations

Like all case studies, the findings cannot be extrapolated to other LTC settings and populations. Given that this study was undertaken in Canada, which has a socialized healthcare system and legalized both medical and non-medical cannabis, the experiences and attitudes of HCPs who participated may be unique and limit the generalizability of the findings. However, there are lessons to be learned regarding the challenges that residents in LTC facilities face in using medical and non-medical cannabis, as well as the potential need for both education and policy reform to better support HCPs in providing appropriate, safe, and person-centred care of LTC residents. In addition, the collection of both quantitative and qualitative data allowed triangulation during the data analysis and helped improved the rigor of the findings [ 61 ]. Recruitment and data collection for this study also occurred during the height of the COVID-19 pandemic. Therefore, the response rate was lower than desired and there was limited diversity among study participants with regards health profession designation. However, the proportion of physicians, nurses, pharmacists, and other allied health professions reflected the overall staff composition of the LTC facility.

Implications for future research

Beyond the policy and practice implications discussed earlier, the study findings also point to the urgent need for research focused on cannabis use among populations commonly found within LTC settings. The lack of evidence regarding the potential health effects of cannabis in the management of diseases such as dementia, arthritis, Parkinson’s, traumatic brain injury, and multiple sclerosis led many of the HCPs interviewed to be hesitant about authorizing and supervising cannabis use for LTC residents living with these conditions. While there is a growing number of studies being undertaken focused on medical cannabis, many are limited by their sample size and study design. It is only through high-quality clinical trials that evaluate the efficacy and safety of medical cannabis that a change in practice will occur.

Future medical cannabis research must also be developed in a manner that is inclusive of older adults and those living in LTC. The exclusion of such populations from clinical research has been previously identified as problematic [ 62 ], resulting in research findings that lack generalizability and pose challenges in determining the applicability of research to older adults who may be living with numerous co-morbidities and using multiple medications. While the inclusion of older adults in medical cannabis clinical trials may be more methodologically and ethically challenging, it will lead to evidence that will inform both future policies and practices.

Lastly, our case study offers insight into the reality and challenges of cannabis use by residents of one LTC facility. Additional research across different jurisdictions is needed to explore how LTC settings are addressing cannabis use and to learn from their experiences. We encourage the continued use of mixed methods study designs to ensure the experiences and perspectives of residents, family members and HCPs are captured alongside administrative data related to medical and non-medical cannabis use.

With the legalization of medical and non-medical cannabis in jurisdictions around the world, LTC facilities will be obligated to develop policies, procedures and healthcare services that are able to accommodate residents’ use of cannabis in a respectful and evidence-informed manner. Balancing the safety concerns against the potential therapeutic value of cannabis, as well as considering residents’ autonomy and the home-like environment of LTC, will be important considerations in how cannabis use is addressed and regulated. Our case study highlights the lack of knowledge, inequities, and stigma that continue to surround cannabis in LTC. There is an urgent need for research that not only explores the potential risks and benefits of cannabis, but also informs the development of more nuanced and equitable policies and education resources that will support reasonable and informed access to medical and non-medical cannabis for older adults and others living in LTC.

Data availability

The datasets generated and analysed during the current study are not publicly available due to the small sample size drawn from one health care facility but are available from the corresponding author on reasonable request.

Abbreviations

Cannabidiol

Healthcare provider

Long–term care

Tetrahydrocannabinol

Licensed Producer

Government of Canada. Cannabis Act (S.C. 2018, c.16). 2018. http://canlii.ca/t/5392s retrieved on 2019-10-07.

Sandhu HS, Anderson LN, Busse JW. Characteristics of canadians likely to try or increase cannabis use following legalization for nonmedical purposes: a cross-sectional study. CMAJ Open. 2019;7(2):E399–404.

Article   PubMed   PubMed Central   Google Scholar  

Statistics Canada. National Cannabis Survey, third quarter 2019. https://www150.statcan.gc.ca/n1/daily-quotidien/191030/dq191030a-eng.htm

Statistics Canada. Research to Insights: Cannabis in Canada. 2023. https://www150.statcan.gc.ca/n1/pub/11-631-x/11-631-x2023006-eng.htm

Lau N, Sales P, Averill S, Murphy F, Sato SO, Murphy S. A safer alternative: cannabis substitution as harm reduction. Drug Alcohol Rev. 2015;34(6):654–9.

Bobitt J, Qualls SH, Schuchman M, Wickersham R, Lum HD, Arora K, et al. Qualitative analysis of cannabis use among older adults in Colorado. Drugs Aging. 2019;36(7):655–66.

Article   PubMed   Google Scholar  

Reynolds IR, Fixen DR, Parnes BL, Lum HD, Shanbhag P, Church S, et al. Characteristics and patterns of marijuana use in community-dwelling older adults. J Am Geriatr Soc. 2018;66(11):2167–71.

Baumbusch J, Sloan Yip I. Exploring new use of cannabis among older adults. Clin Gerontol. 2021;44(1):25–31.

Tumati S, Lanctôt KL, Wang RD, Li A, Davis A, Herrmann N. Medical cannabis use among older adults in Canada: self-reported data on types and amount used, and perceived effects. Drugs Aging. 2022;39(2):153–63.

Government of Canada. Data on cannabis for medical purposes. 2022. https://www.canada.ca/en/health-canada/services/drugs-medication/cannabis/research-data/medical-purpose.html

Cooper ZD, Abrams DI, Gust S, Salicrup A, Throckmorton DC. Challenges for clinical cannabis and cannabinoid research in the United States. J Natl Cancer Inst Monogr. 2021;2021(58):114–22.

Piomelli D, Solomon R, Abrams D, Balla A, Grant I, Marcotte T, et al. Regulatory barriers to research on cannabis and cannabinoids: a proposed path forward. Cannabis Cannabinoid Res. 2019;4(1):21–32.

O’Brien M, McDougall JJ. Cannabis and joints: scientific evidence for the alleviation of osteoarthritis pain by cannabinoids. Curr Opin Pharmacol. 2018;40:104–9.

Babson KA, Sottile J, Morabito D. Cannabis, cannabinoids, and sleep: a review of the literature. Curr Psychiatry Rep. 2017;19(4):23.

Hillen JB, Soulsby N, Alderman C, Caughey GE. Safety and effectiveness of cannabinoids for the treatment of neuropsychiatric symptoms in dementia: a systematic review. Ther Adv Drug Saf. 2019;10:2042098619846993.

Chagas MHN, Zuardi AW, Tumas V, Pena-Pereira MA, Sobreira ET, Bergamaschi MM, et al. Effects of cannabidiol in the treatment of patients with Parkinson’s disease: an exploratory double-blind trial. J Psychopharmacol. 2014;28(11):1088–98.

Shohet A, Khlebtovsky A, Roizen N, Roditi Y, Djaldetti R. Effect of medical cannabis on thermal quantitative measurements of pain in patients with Parkinson’s disease. Eur J Pain. 2017;21(3):486–93.

Article   CAS   PubMed   Google Scholar  

Herrmann N, Ruthirakuhan M, Gallagher D, Verhoeff NPLG, Kiss A, Black SE, et al. Randomized placebo-controlled trial of nabilone for agitation in Alzheimer’s disease. Am J Geriatr Psychiatry. 2019;27(11):1161–73.

Shelef A, Barak Y, Berger U, Paleacu D, Tadger S, Plopsky I, et al. Safety and efficacy of medical cannabis oil for behavioral and psychological symptoms of dementia: an open label, add-on, pilot study. J Alzheimer’s Dis. 2016;51(1):15–9.

Article   CAS   Google Scholar  

Volicer L, Stelly M, Morris J, Mclaughlin J, Volicer BJ. Effects of dronabinol on anorexia and disturbed behavior in patients with Alzheimer’s disease. Int J Geriatr Psychiatry. 1997;12(9):913–9.

Cyr C, Arboleda MF, Aggarwal SK, Balneaves LG, Daeninck P, Néron A, et al. Cannabis in palliative care: current challenges and practical recommendations. Ann Palliat Med. 2018;7(4):463–77.

Pooyania S, Ethans K, Szturm T, Casey A, Perry D. A randomized, double-blinded, crossover pilot study assessing the effect of nabilone on spasticity in persons with spinal cord injury. Arch Phys Med Rehabil. 2010;91(5):703–7.

Wilsey B, Marcotte TD, Deutsch R, Zhao H, Prasad H, Phan A. An exploratory human laboratory experiment evaluating vaporized cannabis in the treatment of neuropathic pain from spinal cord injury and disease. J Pain. 2016;17(9):982–1000.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Grenier K, Ponnambalam F, Lee D, Lauwers R, Bhalerao S. Cannabis in the treatment of traumatic brain injury: a primer for clinicians. Can J Neurol Sci. 2019;47(1):11–7.

Minerbi A, Hauser W, Fitzcharles MA. Medical cannabis for older patients. Drugs Aging. 2019;36(1):39–51.

MacCallum CA, Russo EB. Practical considerations in medical cannabis administration and dosing. Eur J Int Med. 2018;49:12–9.

Bertram JR, Porath A, Seitz D, Kalant H, Krishnamoorthy A, Nickerson J, et al. Canadian guidelines on cannabis use disorder among older adults. Can Geriatr J. 2020;23(1):135–42.

Banerjee S. Multimorbidity–older adults need health care that can count past one. Lancet. 2015;385(9968):587–9.

Bom J, Bakx P, Rellstab S. Well-being right before and after a permanent nursing home admission. Health Econ. 2022;31(12):2558–74.

Calkins M, Brush J. Honoring individual choice in long-term residential communities when it involves risk: a person-centered approach. J Gerontol Nurs. 2016;42(8):12–7.

Yin R. Case study research and applications: design and methods. 6th ed. Thousand Oaks, CA: Sage; 2018.

Google Scholar  

Sandelowski M. Whatever happened to qualitative description? Res Nurs Health. 2000;23(4):334–40.

Balneaves LG, Alraja A, Ziemianski D, McCuaig F, Ware M. A national needs assessment of Canadian nurse practitioners regarding cannabis for therapeutic purposes. Cannabis Cannabinoid Res. 2018;3(1):66–73.

Ziemianski D, Capler R, Tekanoff R, Lacasse A, Luconi F, Ware MA. Cannabis in medicine: a national educational needs assessment among Canadian physicians. BMC Med Educ. 2015;15:50–2.

Article   Google Scholar  

Balneaves LG, Alraja AA. Guarding their practice: a descriptive study of Canadian nursing policies and education related to medical cannabis. BMC Nurs. 2019;18(1):66.

Fox RD. Revisiting discrepancy analysis in continuing medical education: a conceptual model 1983. J Contin Health Prof. 2011;31(1):71–6.

Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

College of Registered Nurses of Manitoba. Medical cannabis: Guidelines for Manitoba nurses. 2019. https://www.crnm.mb.ca/uploads/document/document_file_247.pdf?t=1549645011

College of Registered Nurses of Manitoba. Practice direction: RN(NP) authorization of medical cannabis. 2018. https://www.crnm.mb.ca/uploads/document/document_file_252.pdf?t=1538755351

The College of Physicians & Surgeons of Manitoba. Standard of practice: Cannabis for medical purpose. 2020. https://cpsm.mb.ca/assets/Standards of Practice/Standard of Practice Authorizing Cannabis for Medical Purposes.pdf.

Winnipeg Regional Health Authority. Smoke-Free Policy. 2014. https://wrha.mb.ca/files/wrha-policy-10-00-010.pdf

Government of Canada. Cannabis regulations (SOR/2018 – 144) [. 2018. https://laws-lois.justice.gc.ca/eng/Regulations/SOR-2018-144/index.html

Goffman E. Asylums: essays on the social situations of mental patients and other inmates. Oxford, England: Doubleday (Anchor); 1961.

White-Chu EF, Graves WJ, Godfrey SM, Bonner A, Sloane P. Beyond the medical model: the culture change revolution in long-term care. J Am Med Dir Assoc. 2009;10(6):370–8.

Chivers S. Make mine a double. In: Armstrong P, Daly T, editors. Exercising choice in long-term residential care. Montreal, QC: RR Donnelley; 2017. pp. 55–8.

Woolford MH, de Lacy-Vawdon C, Bugeja L, Weller C, Ibrahim JE. Applying dignity of risk principles to improve quality of life for vulnerable persons. Int J Geriatr Psychiatry. 2020;35(1):122–30.

Schultz ASH, Finegan B, Nykiforuk CIJ, Kvern MA. A qualitative investigation of smoke-free policies on hospital property. CMAJ. 2011;183(18):2085–6.

De Graaf L, Janssen M, Roelofs T, Luijkx K. Substance use and misuse of older adults living in residential care facilities: a scoping review from a person-centred care approach. Ageing Soc. 2021;43(6):1–27.

Adler G, Greeman M, Parker H, Kuskowski M. Self-determination and residents who smoke. J Social Work Long-Term Care. 2002;1(4):19–30.

Lau N, Sales P, Averill S, Murphy F, Sato SO, Murphy S. Responsible and controlled use: older cannabis users and harm reduction. Int J Drug Policy. 2015;26(8):709–18.

Clobes TA, Palmier LA, Gagnon M, Klaiman C, Arellano M. The impact of education on attitudes toward medical cannabis. PEC Innov. 2022;1:100009.

Croker JA, Bobitt J, Sanders S, Arora K, Mueller K, Kaskie B. Cannabis and end-of-life care: a snapshot of hospice planning and experiences among Illinois medical cannabis patients with a terminal diagnosis. Am J Hosp Palliat Med. 2022;39(3):345–52.

Hachem Y, Abdallah SJ, Rueda S, Wiese JL, Mehra K, Rup J, et al. Healthcare practitioner perceptions on barriers impacting cannabis prescribing practices. BMC Complement Med Ther. 2022;22(1):1–12.

Hope J, Schoonhoven L, Griffiths P, Gould L, Bridges J. I’ll put up with things for a long time before I need to call anybody: face work, the Total Institution and the perpetuation of care inequalities. Sociol Health Illn. 2022;44(2):469–87.

Williams AR. Ethical dilemmas in counseling older adults on cannabis use: less science, more quandaries. Generations J. 2020;44(4):1–5.

Skliamis K, Benschop A, Korf DJ. Cannabis users and stigma: a comparison of users from European countries with different cannabis policies. Eur J Crimin. 2022;19(6):1483–500.

Troup LJ, Erridge S, Ciesluk B, Sodergren MH. Perceived stigma of patients undergoing treatment with cannabis-based medicinal products. Int J Env Res Public Health. 2022;19(12).

Evanoff AB, Quan T, Dufault C, Awad M, Bierut LJ. Physicians-in-training are not prepared to prescribe medical marijuana. Drug Alcohol Depend. 2017;180:151–5.

Canadian Coalition for Seniors’ Mental Health. Cannabis and older adults. https://ccsmh.ca/cannabis-and-older-adults-project/

Grossi A, Holmes A, Ibrahim JE. Use of alcohol in long term care settings: a comparative analysis of personal choice, public health advice and the law. J Am Med Dir Assoc. 2021;22(1):9–e141.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Med Res Methodol. 2011;11:100.

Crome P, Lally F, Cherubini A, Oristrell J, Beswick AD, Clarfield AM, et al. Exclusion of older people from clinical trials: professional views from nine European countries participating in the PREDICT study. Drugs Aging. 2011;28(8):667–77.

Download references

Acknowledgements

The authors would like to thank the healthcare professionals that graciously took the time to share their thoughts about cannabis use in long-term care settings. In addition, Ms. Sina Barkman, Chief Human Resources Officer, Riverview Health Centre, helped the research team navigate the complexity of conducting research in long-term care settings during the COVID-19 pandemic.

Funding for this study was received from the Riverview Health Centre Foundation.

Author information

Authors and affiliations.

College of Nursing, University of Manitoba, Winnipeg, MB, Canada

Lynda G. Balneaves, Abeer A. Alraja, Genevieve Thompson, Jamie L. Penner & Daniella Scerbo

Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada

Philip St. John

Riverview Health Centre, Winnipeg, MB, Canada

Joanne van Dyck

College of Nursing, Rady Faculty of Health Sciences, University of Manitoba, 89 Curry Place, Winnipeg, MB, R3T 2N2, Canada

Lynda G. Balneaves

You can also search for this author in PubMed   Google Scholar

Contributions

“L.G.B, G.T, J.P and P.StJ. conceptualised the study. A.A.A. and D.S. engaged in recruitment and data collection activities. L.G.B. and A.A.A. analysed and interpreted the quantitative and qualitative data and developed a first draft of the manuscript, with assistance from G.T. All authors read and approved the final manuscript.”

Corresponding author

Correspondence to Lynda G. Balneaves .

Ethics declarations

Ethics approval and consent to participate.

Ethical approval for the study was obtained from the University of Manitoba Research Ethics Board (R1-2021:011 (HS24693)) and was approved by the Riverview Health Centre Research Committee. Implied consent was received from participants who completed the survey and written informed consent was obtained from all participants who completed an interview. We confirm that all methods were performed in accordance with the relevant ethical guidelines and regulations, (i.e., Tri-Council Policy Statement).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Balneaves, L.G., Alraja, A.A., Thompson, G. et al. Cannabis use in a Canadian long-term care facility: a case study. BMC Geriatr 24 , 467 (2024). https://doi.org/10.1186/s12877-024-05074-2

Download citation

Received : 11 July 2023

Accepted : 13 May 2024

Published : 29 May 2024

DOI : https://doi.org/10.1186/s12877-024-05074-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical cannabis
• Non-medical cannabis
• Long-term care
• Residential care
• Older adults

BMC Geriatrics

ISSN: 1471-2318

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 30 May 2024

Exploring the tradeoff between data privacy and utility with a clinical data analysis use case

• Eunyoung Im 1 , 2 ,
• Hyeoneui Kim 1 , 2 , 3 ,
• Hyungbok Lee 1 , 5 ,
• Xiaoqian Jiang 4 &
• Ju Han Kim 5 , 6  

BMC Medical Informatics and Decision Making volume  24 , Article number:  147 ( 2024 ) Cite this article

161 Accesses

Metrics details

Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset’s utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset’s utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two.

All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores.

Conclusions

As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data’s intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

Peer Review reports

Clinical data gathered through Electronic Health Records (EHR) is an invaluable asset for producing meaningful insights into patient care and healthcare service management. However, as this data includes sensitive personal information, there is a heightened risk of financial or social damage to individuals if their health data is improperly disclosed [ 1 , 2 ]. To address these concerns, many countries have implemented stringent regulations to safeguard patient privacy while still enabling the efficient use of data for health advancements [ 3 ]. In the United States, for example, the Health Insurance Portability and Accountability Act (HIPAA) sets forth provisions for data protection and usage [ 4 ]. Similarly, the General Data Protection Regulation (GDPR) offers a comprehensive data privacy framework within the European Union [ 5 ]. Additionally, South Korea’s Personal Information Protection Act delineates the guidelines for secure and permissible data handling [ 6 ].

The growing imperative for data privacy has spurred significant progress in privacy-preserving technologies. Differential Privacy (DP) safeguards data by integrating controlled random noise, thus ensuring individual data points remain confidential while aggregate analysis remains accurate [ 7 ]. In the biomedical field, DP is extensively employed in data query systems; the noise integrated into query responses helps protect sensitive inquiries pertaining to uncommon cases [ 8 , 9 ]. Current research in DP focuses on solving complex problems such as determining optimal privacy budgets and noise levels to balance confidentiality with data utility [ 8 , 10 , 11 ].

Homomorphic Encryption (HE) represents a breakthrough in cryptography for preserving privacy, enabling computations on encrypted data without altering the original values [ 12 ]. Recent research has validated the practicality of performing data analysis using HE [ 13 , 14 , 15 ]. Nonetheless, HE has not become mainstream in healthcare applications, primarily due to its substantial computational demands, intricate implementation, and the limited range of analytics that can be performed on data in its encrypted form [ 12 , 16 ].

Blockchain technology, recognized for its immutable, decentralized, and transparent nature [ 17 ], is gaining attention as an innovative approach for data privacy [ 18 , 19 , 20 ]. Despite this interest, the real-world application of blockchain is contingent upon enhancements in its capacity to process substantial data volumes, simplification of its implementation, and resolution of related regulatory challenges [ 21 , 22 , 23 , 24 ].

When preparing datasets with personal health information for secondary analysis, the prevailing practice is to mitigate the risk of re-identification of the subjects in the dataset by employing stringent de-identification procedures [ 25 , 26 ]. This involves the removal of direct identifiers that can uniquely pinpoint individual subjects within the dataset and altering quasi-identifiers, which alone do not identify subjects but could do so when merged with other data sources. Furthermore, the process considers sensitive information that, despite not directly identifying subjects, could have detrimental effects if disclosed, ensuring such data is also considered during the de-identification process.

The leading method for data de-identification employs strategies like K-anonymity, L-diversity, and T-closeness to modify data. K-anonymity safeguards against linkage attacks by ensuring that there are at least K identical records for any set of quasi-identifiers within a dataset, making it impossible to distinguish one individual from K-1 others [ 27 ]. In line with this, South Korea’s data publishing guidelines recommend adhering to a minimum of ‘K = 3’ for K-anonymity [ 28 , 29 ]. Additionally, L-diversity mandates a sensitive variable must have at least L distinct values, thereby offering protection against homogeneity attacks [ 30 ]. T-closeness, on the other hand, ensures that the distribution of a sensitive variable within any subset of the dataset closely approximates the distribution of that variable of the entire dataset, adhering to a specified threshold [ 31 ]. T-closeness prevents the likelihood that knowledge of the variable’s distribution could be exploited to reveal an individual’s identity [ 31 ]. The process of de-identification, which often involves masking or altering certain data values, can result in information loss and potentially reduce the utility of the dataset [ 32 ].

Determining the optimal threshold between data privacy and utility remains a complex challenge. Several studies have investigated how various de-identification strategies, specifically K-anonymity, L-diversity, and T-closeness, influence data utility. This is typically assessed by comparing the analytical results of de-identified datasets with those derived from the original dataset. Some researchers advocate that the privacy enhancements are overshadowed by a substantial reduction in data utility [ 33 , 34 ], while others argue that such utility loss might not be as severe as some studies imply [ 35 ]. However, these studies evaluated each de-identification technique in isolation, often resorting to simplified models that fail to fully capture the complexities of real-world data use, and led to mixed conclusions [ 34 , 35 ].

Moreover, the insights offered by such research into the tangible effects of data de-identification on actual data analysis tasks are somewhat restricted. This is because the analyses were either performed using overly simplistic examples [ 28 , 34 ] or on public datasets that have already undergone some form of de-identification [ 35 , 36 ], or focusing on theoretical aspects [ 37 ]. Therefore, there is a need for more intricate research that closely mirrors the complexities of real-life data analytics tasks and considers the multifaceted nature of data utility and privacy in actual applications.

This study explores the effects of different de-identification strategies on clinical datasets prepared for secondary analysis, with a focus on their implications for practical data analysis tasks. The aims of this study are twofold: firstly, to assess the effects of de-identification on both the dataset’s integrity and the outcomes of data analyses; and secondly, to ascertain if discernible trends emerge from the application of various de-identification techniques that could guide the establishment of a feasible balance between data privacy and data utility.

Data analysis use case

This study explores the impact of various de-identification techniques on datasets and their subsequent analysis results using a data analytic use case. The analytic use case involved predicting the Length of Stay (LOS) of high-acuity patients transferred to the emergency department (ED) of an academic medical center located in Seoul, South Korea. LOS in the ED serves as a crucial quality metric for ED services [ 38 , 39 , 40 ]. In Korea, an ED LOS under six hours is considered optimal [ 41 ]. Nonetheless, the overcrowding issues prevalent in tertiary hospital EDs elevate the risk of prolonged ED stays for patients transferred from other facilities for specialized care [ 42 , 43 ]. Understanding the factors affecting the ED LOS of transferred high-acuity patients is essential to providing timely care. The authors, HK and HL, previously developed a model to predict ED LOS using logistic regression, Random Forest, and Naïve Bayes techniques [ 44 ]. Building on insights from this earlier research, the current use case was crafted to develop a logistic regression model to predict ED LOS based on variables including the patient’s sex, age, medical conditions, the type and location of the transferring hospital, and the treatment outcomes.

The prediction model for ED LOS was developed using data from 1,155 patients who were transferred to the study site’s ED between January 2019 and December 2019. Patient demographics, clinical details, and transfer-related information were extracted from the study site’s Clinical Data Warehouse (CDW). The variables collected for this study are listed in Table  1 .

De-identification of the datasets

Developing de-identification scenarios.

Identifiers such as patient names and medical record numbers were removed. Quasi-identifiers play a critical role in de-identification as they form the foundation for assessing the adequacy of de-identification efforts and undergo most data transformations. To select the variables to test as quasi-identifiers, we first examined the extent to which each variable could uniquely link to individual subjects within the dataset, potentially identifying them. Table  2 displays the percentage of subjects in the dataset uniquely linked to either a single variable or a combination of variables. For instance, the sending hospital and primary diagnosis were uniquely linked to 27.71% and 17.75% of the subjects, respectively, and their combination linked up to 94% of the subjects. Consequently, information regarding the sending hospital and the primary diagnosis , coded using the International Classification of Disease (ICD) [ 45 ], were utilized as quasi-identifiers, along with sex and age , which are commonly considered quasi-identifiers in various de-identification efforts [ 4 , 46 ]. Treatment outcomes were identified as sensitive information. We developed 19 de-identification scenarios by varying the quasi-identifiers and sensitive information, and applying diverse configurations of privacy-preserving techniques such as K-anonymity, L-diversity, and T-closeness to each scenario.

Data transformation for de-identification

De-identification was performed using ARX, a publicly accessible and well-validated data anonymization tool that supports various de-identification methods [ 47 , 48 , 49 ]. We employed generalization and micro-aggregation techniques to modify the quasi-identifiers, both aimed at reducing the risk of re-identification by transforming original data into more general values. Generalization involves building a hierarchy for the given values by specifying minimum and maximum generalization levels. Generalization involves creating a hierarchy of values by specifying minimum and maximum levels, which can be adjusted based on criteria such as the number of digits masked in zip codes, size of intervals for age , condensation of 5-point Likert scores to 3-point scales, and generalization of full dates to broader time units such as week, month, or year [ 50 ]. Micro-aggregation, on the other hand, assigns representative values for alphanumeric data, such as using the mode for sex and the mean for age [ 50 ].

In our de-identification process, quasi-identifiers such as the sending hospital and primary diagnosis were transformed using generalization, while sex was modified through micro-aggregation. Age was subjected to both generalization and micro-aggregation. The generalization hierarchy for age included three levels with intervals of 5, 10, and 30 years respectively. For micro-aggregation, mean age values were used. The primary diagnosis was generalized into two levels based on higher-level ICD codes. For instance, a primary diagnosis with the ICD code I20.0, representing unstable angina , was generalized to I20 (i.e., angina pectoris ) at level 1, and further to I20-I25 (i.e., ischemic heart diseases ) at level 2. Generalization of the sending hospital also included two levels, where a specific facility such as “Hanmaeum Clinic in Jongno-gu, Seoul city” was generalized to the county level as “facility in Jongno-gu” at level 1 and then to the city level as “facility in Seoul” at level 2. For sex , micro-aggregation was employed, setting the mode as the representative value.

K-anonymity, L-diversity, and T-closeness were employed concurrently with specific parameters set for each: K and L were both set at 3, and T was set at 0.5. K-anonymity was specifically applied to quasi-identifiers to ensure that each individual is indistinguishable from at least two others. L-diversity and T-closeness, on the other hand, were applied to the variable designated as sensitive, ensuring that sensitive information is both sufficiently diverse and closely aligned with the overall distribution of the dataset. Table  3 details these 19 de-identification scenarios.

Data transformation was carried out in ARX according to the de-identification scenarios outlined in Table  3 . ARX provides options to adjust additional transformation parameters: the suppression limit , which sets the maximum proportion of records that can be omitted from the original dataset; approximation , which prioritizes solutions with shorter execution times; and precomputation , which determines the threshold for the fraction of unique data values in the dataset [ 50 ]. For this study, we utilized the default settings in ARX, where the suppression limit was set to 100%, and both approximation and precomputation features were disabled.

During execution, ARX evaluated various combinations of generalization and micro-aggregation levels to meet the requirements for K-anonymity, L-diversity, and T-closeness, ultimately recommending an optimal solution based on the balance between minimizing re-identification risk and preserving data utility. Figure  1 displays a screenshot of the data transformation solutions for the scenario where age , primary diagnosis , and sending hospital were designated as quasi-identifiers. Ultimately, we produced 19 versions of de-identified datasets, each based on the transformation solution that ARX identified as optimal.

The data transformation solutions suggested by ARX

Examination of the de-identified datasets

We reviewed the reduction in re-identification risk and the data utility scores that ARX estimated for the 19 de-identified datasets. To assess the similarity between each de-identified dataset and the original dataset, we employed Earth Mover’s Distance (EMD) [ 51 ]. Additionally, we calculated the dataset retention ratio. This metric is derived by dividing the number of data points in the transformed dataset by the number of data points in the original dataset. EMD and dataset retention ratio quantitatively evaluate the dissimilarity between the original dataset and the de-identified datasets, offering insights into how much the data has been altered through de-identification.

Testing the effects of de-identification on ED LOS prediction

Variable creation for predictive modeling.

To construct a logistic regression model for predicting ED LOS, we defined outcome and predictor variables. ED LOS, the outcome variable, was dichotomized into two categories: 6 h or less, and more than 6 h. We identified 13 predictors, including patient sex, age, medical conditions, treatment outcome, and the sending hospital type. Age , sending hospital location , and treatment outcome were dichotomized. Five dummy variables were created from primary diagnosis to represent high priority disease , neoplastic disease , circulatory disease , respiratory disease , and injury-related visits . The sending hospital type was derived from the sending hospital information . These variables, detailed in Table  4 , were consistently defined across all 19 de-identified datasets as well as the original dataset to facilitate comparative analyses.

Data analysis

After defining the outcome and predictor variables for logistic regression, we examined their distributions across the 19 de-identified datasets and the original dataset. To assess the differences in variable distributions, we utilized the proportion test [ 52 ]. Subsequently, logistic regression analysis was conducted using both the de-identified and original dataset. The predictive performance of these models was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. We compared the AUC scores (AUROC) of the logistic regression models derived from the 19 de-identified datasets to that from the original dataset, employing the DeLong test [ 53 ]. Additionally, we analyzed the differences in the odds ratios of the predictors and their statistical significance to assess any impact the de-identification process might have had on the predictive capability of the models. All analyses were performed using R (version 4.0.4) [ 54 ].

Data transformation configurations applied for the de-identification of the datasets

Table  5 displays the optimal configurations for data transformation used in the 19 de-identified datasets. Variables subjected to generalization or micro-aggregation were designated as quasi-identifiers. Sensitive information is identified as ‘SI’ within the table. It is important to note that empty cells signify that the corresponding variable was treated as non-sensitive information in the specific dataset.

The de-identified datasets

Table  6 displays the re-identification reduction rates, ARX utility scores, EMD scores, and dataset retention ratios for the 19 transformed datasets. Additionally, the table presents the number of records retained post-transformation and the number of predictor variables generated. The ARX utility score reflects the extent of information loss, with a higher score indicating lower utility. It is important to note that the baseline re-identification risk varied among the datasets due to differences in the configuration of quasi-identifiers.

Overall, all 19 de-identification scenarios significantly reduced re-identification risk. However, the data transformation processes involved in de-identification led to record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. Notably, except for three datasets (13, 15, 16), which used only sex and age as quasi-identifiers, there was a loss of one or more predictor variables. Datasets 13, 15, and 16 demonstrated the highest retention ratios and the lowest ARX utility and EMD scores, indicating minimal information loss and the highest similarity to the original dataset, thus reflecting superior dataset utility. They also exhibited the lowest baseline and post-transformation re-identification risks.

Datasets 7 and 8 underwent a transformation under the most complex de-identification scenarios, employing three quasi-identifiers and applying both L-diversity and T-closeness to two sensitive variables. Although these datasets achieved complete re-identification risk reduction, the extensive data transformation allowed only seven predictor variables to be generated. The de-identification scenarios 1 and 3, 2 and 4, and 13, 15, and 16 shared identical configurations of quasi-identifiers but varied in the L-diversity and T-closeness conditions applied to sensitive information, resulting in identical de-identified datasets (see Table 3 ).

Table  7 details the differences in variable distribution between each transformed dataset and the original dataset. As expected, variables designated as quasi-identifiers underwent the most transformation, leading to significant changes. Variables derived from these quasi-identifiers, such as sending hospital type, circulatory disease , and high priority disease , also exhibited notable distributional changes.

The prediction results

Logistic regression models were developed using both the original dataset and 19 de-identified datasets. The complete masking of variables classified as quasi-identifiers in some de-identified datasets resulted in differences in the number and types of predictors available for constructing the logistic regression models. Additionally, the number of records included in the regression analysis varied due to record suppression associated with the de-identification process. Figure  2 illustrates the ROC curves and the AUC values for all 20 datasets. The AUC values ranged from 0.695 to 0.787. The models generated from datasets 7 and 8, which only retained seven predictors due to extensive data masking, exhibited a statistically significant difference in AUC when compared to the original dataset, with a p-value of 0.002. For the models derived from the other datasets, no significant differences in AUC values were observed.

The number of records and predictors included in each model and the model performance

Figure  3 displays the Odds Ratios (OR) for predictors from selected datasets. Datasets 13, 15, and 16 were chosen because they retained all 13 predictor variables (Fig.  3 (a)). Dataset 9 was selected for having the next highest number of predictors ( N  = 12) and for utilizing three quasi-identifiers: the sending hospital , which is identified as the most revealing variable in Table  2 , along with sex and age , which are commonly used as quasi-identifiers (Fig.  3 (b)). Dataset 19 was also included because it was configured using only the sending hospital and primary diagnosis as quasi-identifiers (Fig.  3 (c)). The ORs for all 19 datasets are detailed in Additional file 1: Figure S1 .

As depicted in Fig.  3 (a), the original dataset and de-identified datasets 13, 15, and 16 showed comparable prediction outcomes, with sex being the only predictor that displayed an OR notably different from the original dataset; however, it was not statistically significant in either model. Figure  3 (b) indicates that the ORs of the 12 predictors in dataset 9 were similar to those in the original dataset, although the OR for injury-related visits became insignificant. In contrast, dataset 19, which excluded two predictors, showed more pronounced differences in the ORs of the 11 remaining predictors (Fig.  3 (c)). Additionally, neoplastic disease and respiratory disease , significant predictors in the original dataset, became insignificant in dataset 9, while injury-related visits , previously insignificant, became significant (Fig.  3 (c)).

The Odds-Ratios of the predictors from the original dataset and the selected de-identified datasets

Data utility vs. data privacy

Figure  4 presents the correlations between re-identification risk reduction rates, ARX utility scores, EMD, and dataset retention ratios. There is a significant correlation between the re-identification reduction rate and the ARX utility score, indicating that greater reductions in re-identification risk are typically accompanied by larger losses of information. Conversely, the re-identification reduction rate exhibits a slight negative correlation with both EMD and dataset retention ratio; however, these correlations are not statistically significant.

The correlations between re-identification risk reduction and features of the de-identified datasets

This study tested various de-identification strategies on a clinical dataset, adjusting the number and types of quasi-identifiers and sensitive information, and configuring K-anonymity, L-diversity, and T-closeness in diverse ways. It aimed to address gaps left by earlier studies that utilized simplistic data use cases and de-identification configurations [ 28 , 34 , 35 ].

The results indicated that de-identification led to the suppression of records and variables, precluding the replication of analyses performed on the original dataset. Consequently, logistic regression models for predicting ED LOS yielded differing conclusions based on the de-identification approach, as illustrated in Fig.  3 . This highlights the need for the evolution of privacy technologies that maintain data integrity. Additionally, it cautions data users about potential biases introduced when working with de-identified datasets.

The study found optimal data utility when only sex and age were classified as quasi-identifiers, maintaining all variables and losing only six records. This configuration also significantly reduced the baseline re-identification risk, albeit sex and age by themselves did not strongly individualize records. However, this configuration did not account for the additional re-identification risk posed by the sending hospital and primary diagnosis , both of which were considered the most identifying variables in the dataset (Table  2 ). To eliminate any alterations to sex and age —key variables for clinical research—we examined the impact of designating only the sending hospital and primary diagnosis as quasi-identifiers (dataset 19). This strategy greatly reduced the chance of re-identification but at a considerable cost to data utility, resulting in the loss of over half the dataset and two predictor variables: the sending hospital type and high priority disease .

Seeking a compromise, datasets 5–12 incorporated sex , age , and either sending hospital or primary diagnosis as quasi-identifiers. In this series, datasets 7 and 8 achieved zero re-identification risk post-de-identification but sacrificed nearly half of the predictor variables. Datasets 11 and 12, while managing to retain all records, were considered less favorable due to the loss of four predictor variables. Datasets 5 and 6 struck a more acceptable balance, offering substantial re-identification risk reduction, retaining over 78% of records, and sacrificing only one predictor variable. Although dataset 5 had marginally better scores for risk reduction and data utility, dataset 6 was preferred because it retained information on high priority disease , a key predictor of ED LOS.

In this study, three different data utility metrics were examined, but only the ARX utility score exhibited a statistically significant correlation with the re-identification risk reduction rate. The EMD and dataset retention ratio both showed minor negative correlations with re-identification risk reduction; however, these were not statistically significant. This could suggest that the structural aspects of a dataset may not alone be adequate for assessing its utility, although further studies with a broader array of datasets would be required to substantiate this preliminary indication.

The scope of this research was limited to a single use case, analyzing data obtained from one hospital. Moreover, the range of de-identification scenarios tested did not encompass the full spectrum of complex configurations that could be employed. Despite these constraints, the research offers valuable insights into the nuanced interplay between data de-identification processes and data utility. It contributes to the ongoing conversation about how to approach data privacy in a way that still enables effective data usage.

As health data analysis grows more critical, so does the imperative to devise effective methods for ensuring data privacy. While established guidelines [ 47 ] offer a foundation for the de-identification of datasets, crafting a dataset that maintains a high level of privacy without unduly compromising its utility remains a nuanced challenge. It demands a thorough grasp of the data’s intended application. Incorporating input from data users during the de-identification process and considering the variety of potential data use cases could prove beneficial in finding a workable tradeoff between data privacy and utility.

Data availability

The clinical dataset used in this study is not made available due to the sensitive nature of clinical data. However, de-identified analytic datasets are available upon reasonable request from the corresponding author and with permission of Seoul National University Hospital.

Abbreviations

Area Under the receiver operating characteristic (ROC) Curve

Differential Privacy

Emergency Department

Earth Mover’s Distance

Homomorphic Encryption

International Classification of Disease

Length Of Stay

Receiver Operating Characteristic

Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37–43.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Gostin LO, Halabi SF, Wilson K. Health data and privacy in the digital era. JAMA. 2018;320(3):233–4.

Article   PubMed   Google Scholar  

Data Protection and Privacy Legislation Worldwide | UNCTAD. https://unctad.org/page/data-protection-and-privacy-legislation-worldwide . Accessed 6 Oct 2022.

Health and Human Services. Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html#coveredentities (2022). Accessed 28 Mar 2024.

General Data Protection Regulation (GDPR). Article 32 GDPR( https://gdprhub.eu/index.php?title=Article_32_GDPR (2023). Accessed 4 Apr 2024.

Personal Information Protection Commission. Pseudonymization Guidelines. Korea;2024.

Thapa C, Camtepe S. Precision health data: requirements, challenges and existing techniques for data security and privacy. Comput Biol Med. 2021;129:104130.

Cho H, Simmons S, Kim R, Berger B. Privacy-preserving biomedical database queries with optimal privacy-utility trade-offs. Cell Syst. 2020;10(5):408–16. e9.

Article   CAS   PubMed   Google Scholar  

Deldar F, Abadi M. Differentially private count queries over personalized-location trajectory databases. Data Brief. 2018;20:1510–4.

Article   PubMed   PubMed Central   Google Scholar  

Venkatesaramani R, Wan Z, Malin BA, Vorobeychik Y. Enabling tradeoffs in privacy and utility in genomic data beacons and summary statistics. Genome Res. 2023;33(7):1113–23.

PubMed   PubMed Central   Google Scholar  

Xiong L, Post A, Jiang X, Ohno-Mochado L. New Methods to Protect Privacy When Using Patient Health Data to Compare Treatments. 2021.

Scheibner J, Raisaro JL, Troncoso-Pastoriza JR, Ienca M, Fellay J, Vayena E, et al. Revolutionizing medical data sharing using advanced privacy-enhancing technologies: technical, legal, and ethical synthesis. J Med Internet Res. 2021;23(2):e25120.

Bataa M, Song S, Park K, Kim M, Cheon JH, Kim S. Finding highly similar regions of genomic sequences through homomorphic encryption. J Comput Biol. 2024;31(3):197–212.

Kim D, Son Y, Kim D, Kim A, Hong S, Cheon JH. Privacy-preserving approximate GWAS computation based on homomorphic encryption. BMC Med Genom. 2020;13:1–12.

Article   Google Scholar  

Rovida L, Leporati A. Encrypted image classification with low memory footprint using fully homomorphic encryption. Cryptology ePrint Archive; 2024.

Acar A, Aksu H, Uluagac AS, Conti M. A survey on homomorphic encryption schemes: theory and implementation. ACM Comput Surv (Csur). 2018;51(4):1–35.

Kuo T-T, Kim H-E, Ohno-Machado L. Blockchain distributed ledger technologies for biomedical and health care applications. J Am Med Inform Assoc. 2017;24(6):1211–20.

Zhang F, Zhang Y, Ji S, Han Z. Secure and decentralized Federated Learning Framework with Non-IID Data based on Blockchain. Heliyon. 2024.

Wu C, Tang YM, Kuo WT, Yip HT, Chau KY. Healthcare 5.0: a secure and distributed network for system informatics in medical surgery. Int J Med Informatics. 2024:105415.

Ali A, Al-Rimy BAS, Tin TT, Altamimi SN, Qasem SN, Saeed F. Empowering Precision Medicine: Unlocking Revolutionary insights through Blockchain-enabled Federated Learning and Electronic Medical Records. Sensors. 2023;23(17):7476.

Chukwu E, Garg L. A systematic review of blockchain in healthcare: frameworks, prototypes, and implementations. Ieee Access. 2020;8:21196–214.

Fan C, Ghaemi S, Khazaei H, Musilek P. Performance evaluation of blockchain systems: a systematic survey. IEEE Access. 2020;8:126927–50.

Thantilage RD, Le-Khac N-A, Kechadi M-T. Healthcare data security and privacy in Data Warehouse architectures. Inf Med Unlocked. 2023:101270.

Tandon A, Dhir A, Islam AN, Mäntymäki M. Blockchain in healthcare: a systematic literature review, synthesizing framework and future research agenda. Comput Ind. 2020;122:103290.

Ahmed T, Aziz MMA, Mohammed N. De-identification of electronic health record using neural network. Sci Rep. 2020;10(1):18600.

Ahmed T, Aziz MMA, Mohammed N, Jiang X, editors. Privacy preserving neural networks for electronic health records de-identification. Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics; 2021.

Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain Fuzziness Knowledge-Based Syst. 2002;10(05):571–88.

Jeon S, Seo J, Kim S, Lee J, Kim J-H, Sohn JW, et al. Proposal and assessment of a de-identification strategy to enhance anonymity of the observational medical outcomes partnership common data model (OMOP-CDM) in a public cloud-computing environment: anonymization of medical data using privacy models. J Med Internet Res. 2020;22(11):e19597.

Personal Information Protection Commission. uidelines for Personal Information De-identification Measures. 2016.

Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M. l-diversity: privacy beyond k-anonymity. Acm Trans Knowl Discovery data (tkdd). 2007;1(1):3–es.

Li N, Li T, Venkatasubramanian S, editors. t-closeness: Privacy beyond k-anonymity and l-diversity. 2007 IEEE 23rd international conference on data engineering; 2006: IEEE.

Tomashchuk O, Van Landuyt D, Pletea D, Wuyts K, Joosen W, editors. A data utility-driven benchmark for de-identification methods. Trust, Privacy and Security in Digital Business: 16th International Conference, TrustBus 2019, Linz, Austria, August 26–29, 2019, Proceedings 16; 2019: Springer.

Brickell J, Shmatikov V, editors. The cost of privacy: destruction of data-mining utility in anonymized data publishing. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining; 2008.

Wu L, He H, Zaïane OR, editors. Utility of privacy preservation for health data publishing. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems; 2013: IEEE.

Li T, Li N, editors. On the tradeoff between privacy and utility in data publishing. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining; 2009.

Karagiannis S, Ntantogian C, Magkos E, Tsohou A, Ribeiro LL. Mastering data privacy: leveraging K-anonymity for robust health data sharing. Int J Inf Secur. 2024:1–13.

Zamani A, Oechtering TJ, Skoglund M. On the privacy-utility trade-off with and without direct access to the private data. IEEE Trans Inf Theory. 2023.

Baek S-M, Seo D-W, Kim Y-J, Jeong J, Kang H, Han KS, et al. Analysis of emergency department length of stay in patient with severe illness code. J Korean Soc Emerg Med. 2020;31(5):518–25.

Google Scholar  

Laam LA, Wary AA, Strony RS, Fitzpatrick MH, Kraus CK. Quantifying the impact of patient boarding on emergency department length of stay: all admitted patients are negatively affected by boarding. J Am Coll Emerg Physicians Open. 2021;2(2):e12401.

Otto R, Blaschke S, Schirrmeister W, Drynda S, Walcher F, Greiner F. Length of stay as quality indicator in emergency departments: analysis of determinants in the German Emergency Department Data Registry (AKTIN registry). Intern Emerg Med. 2022;17(4):1199–209.

National Emergency Medical Center: Statistical yearbook of National Emergency Department Information System. https://www.e-gen.or.kr/nemc/statistics_annual_report.do?%20brdclscd=02 (2022). Accessed 7 Oct 2022.

Chang Y-H, Shih H-M, Chen C-Y, Chen W-K, Huang F-W, Muo C-H. Association of sudden in-hospital cardiac arrest with emergency department crowding. Resuscitation. 2019;138:106–9.

Kim J-s, Bae H-J, Sohn CH, Cho S-E, Hwang J, Kim WY, et al. Maximum emergency department overcrowding is correlated with occurrence of unexpected cardiac arrest. Crit Care. 2020;24:1–8.

Lee H, Lee S, Kim H. Factors affecting the length of stay in the emergency department for critically ill patients transferred to regional emergency medical center. Nurs Open. 2023;10(5):3220–31.

World Health Organization(WHO). International Statistical Classification of Diseases and Related Health Problems(ICD). https://www.who.int/standards/classifications/classification-of-diseases/1 (2019). Accessed 11 Oct, 2022.

Eicher J, Kuhn KA, Prasser F. An experimental comparison of quality models for health data de-identification. MEDINFO 2017: Precision Healthcare through Informatics: IOS; 2017. p. 704–8.

Jakob CE, Kohlmayer F, Meurers T, Vehreschild JJ, Prasser F. Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19. Sci data. 2020;7(1):435.

Meurers T, Bild R, Do K-M, Prasser F. A scalable software solution for anonymizing high-dimensional biomedical data. GigaScience. 2021;10(10):giab068.

Prasser F, Kohlmayer F, Lautenschläger R, Kuhn KA, editors. Arx-a comprehensive tool for anonymizing biomedical data. AMIA Annual Symposium Proceedings; 2014: American Medical Informatics Association.

ARX Configuration. n.d. https://arx.deidentifier.org/anonymization-tool/configuration/ . Accessed 4 Apr 2024.

Pele O, Werman M, editors. Fast and robust earth mover’s distances. 2009 IEEE 12th international conference on computer vision; 2009: IEEE.

Gart JJ. The comparison of proportions: a review of significance tests, confidence intervals and adjustments for stratification. Revue de l’Institut International de Statistique; 1971. pp. 148–69.

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988:837–45.

R Core Team. R: a language and environment for statistical. Version 4.0.4. Vienna. Austria: R Foundation for Statistical Computing; 2021.

Download references

Acknowledgements

EI received a scholarship from the BK21 education program (Center for World-leading Human-care Nurse Leaders for the Future).

This study was supported in part by a research grant from the Korean Healthcare Bigdata showcase Project by the Korea Disease Control and Prevention Agency in the Republic of Korea (no.4800-4848-501). The funding body played no role in the design of the study and collection, analysis, interpretation of data, and writing the manuscript.

Author information

Authors and affiliations.

College of Nursing, Seoul National University, Seoul, South Korea

Eunyoung Im, Hyeoneui Kim & Hyungbok Lee

Center for World-leading Human-care Nurse Leaders for the Future by Brain Korea 21 (BK 21) four project, College of Nursing, Seoul National University, Seoul, South Korea

Eunyoung Im & Hyeoneui Kim

The Research Institute of Nursing Science, Seoul National University, Seoul, South Korea

Hyeoneui Kim

School of Biomedical Informatics, UTHealth, Houston, TX, USA

Xiaoqian Jiang

Seoul National University Hospital, Seoul, South Korea

Hyungbok Lee & Ju Han Kim

College of Medicine, Seoul National University, Seoul, South Korea

You can also search for this author in PubMed   Google Scholar

Contributions

EI conducted data de-identification and data analysis. HK conceived the initial project idea and interpreted the results. EI and HK designed the study and wrote the manuscript. HL prepared the clinical data and analyzed the utility of the de-identified dataset. XJ and JK interpreted the analysis results and provided critical insights into the data de-identification approaches. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hyeoneui Kim .

Ethics declarations

Ethics approval and consent to participate.

This study utilized retrospective EHR data and was approved by the Institutional Review Board of the Seoul National University Hospital Biomedical Research Institute (IRB approval No: H-2009-156-1159). In accordance with Article 16 of the Korean Bioethics Law, informed consent was waived by the IRB. All experiments were performed in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Im, E., Kim, H., Lee, H. et al. Exploring the tradeoff between data privacy and utility with a clinical data analysis use case. BMC Med Inform Decis Mak 24 , 147 (2024). https://doi.org/10.1186/s12911-024-02545-9

Download citation

Received : 01 June 2023

Accepted : 21 May 2024

Published : 30 May 2024

DOI : https://doi.org/10.1186/s12911-024-02545-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Data privacy
• Data utility
• Data de-identification
• Clinical data analysis

BMC Medical Informatics and Decision Making

ISSN: 1472-6947

• General enquiries: [email protected]

• Open access
• Published: 06 June 2024

Multi-stage optimization strategy based on contextual analysis to create M-health components for case management model in breast cancer transitional care: the CMBM study as an example

• Hong Chengang 1 ,
• Wang Liping 1 ,
• Wang Shujin 1 ,
• Chen Chen 1 ,
• Yang Jiayue 1 ,
• Lu Jingjing 1 ,
• Hua Shujie 1 ,
• Wu Jieming 1 ,
• Yao Liyan 1 ,
• Zeng Ni 1 ,
• Chu Jinhui 1 &
• Sun Jiaqi 1  

BMC Nursing volume  23 , Article number:  385 ( 2024 ) Cite this article

Metrics details

None of the early M-Health applications are designed for case management care services. This study aims to describe the process of developing a M-health component for the case management model in breast cancer transitional care and to highlight methods for solving the common obstacles faced during the application of M-health nursing service.

We followed a four-step process: (a) Forming a cross-functional interdisciplinary development team containing two sub-teams, one for content development and the other for software development. (b) Applying self-management theory as the theoretical framework to develop the M-health application, using contextual analysis to gain a comprehensive understanding of the case management needs of oncology nursing specialists and the supportive care needs of out-of-hospital breast cancer patients. We validated the preliminary concepts of the framework and functionality of the M-health application through multiple interdisciplinary team discussions. (c) Adopting a multi-stage optimization strategy consisting of three progressive stages: screening, refining, and confirmation to develop and continually improve the WeChat mini-programs. (d) Following the user-centered principle throughout the development process and involving oncology nursing specialists and breast cancer patients at every stage.

Through a continuous, iterative development process and rigorous testing, we have developed patient-end and nurse-end program for breast cancer case management. The patient-end program contains four functional modules: “Information”, “Interaction”, “Management”, and “My”, while the nurse-end program includes three functional modules: “Consultation”, “Management”, and “My”. The patient-end program scored 78.75 on the System Usability Scale and showed a 100% task passing rate, indicating that the programs were easy to use.

Conclusions

Based on the contextual analysis, multi-stage optimization strategy, and interdisciplinary team work, a WeChat mini-program has been developed tailored to the requirements of the nurses and patients. This approach leverages the expertise of professionals from multiple disciplines to create effective and evidence-based solutions that can improve patient outcomes and quality of care.

Peer Review reports

Female breast cancer is the second leading cause of global cancer incidence in 2022, with an estimated 2.3 million new cases, representing 11.6% of all cancer cases [ 1 ]. Due to surgical trauma, side effects of drugs, fear of the recurrence or metastasis of breast cancer, changes in female characteristics, and lack of knowledge, patients with breast cancer frequently experience a series of physical and psychological health problems [ 2 , 3 , 4 , 5 , 6 ]. These health problems seriously affected patients’ life and work [ 7 , 8 ]. At present, community nursing in China is still in the developing stage, and the oncology specialty nursing service capacity of community nurses is not enough to deal with the health problems of breast cancer patients. It made continuous care for out-of-hospital breast cancer patients a weak link in the Chinese oncology nursing service system.

Nowadays, case management is employed to manage health problems for out-of-hospital breast cancer patients worldwide [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Case management involves regular telephone follow-ups and home visits by case management nurses to provide educational support to patients, thereby ensuring uninterrupted continuity of care [ 16 , 17 ]. The home visits and organization of patient information required for case management tasks consume a significant amount of time, manpower, and material resources [ 17 ]. In China, case management services are primarily undertaken by oncology nursing specialists from tertiary hospitals in their spare time [ 18 ]. However, the shortage of nurses has consistently been one of the major challenges facing the nursing industry in China, especially in tertiary hospitals [ 19 ]. Consequently, the implementation and promotion of case management in China also face great difficulties in reality [ 20 ].

The Global Observatory for eHealth (GOe) of the World Health Organization (WHO) defines mobile health (M-Health) as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” [ 21 , 22 ]. With the development of digital technology and the COVID-19 pandemic in 2019, M-Health applications were further integrated into healthcare services, which increased the demand for M-Health applications in turn [ 23 , 24 ]. Compared with the traditional health service model, M-Health service model has the advantages of high-level informatization, fast response speed, freedom from time and location constraints, and resource-saving, etc. In the context of limited nursing human resources, M-Health service provides a new solution for the case management of out-of-hospital breast cancer patients [ 23 , 25 , 26 ].

Researchers have developed a range of M-Health applications targeting breast cancer patients. To our knowledge, none of these developed M-Health applications are designed for case management nursing services.

Early M-Health applications were mostly designed for single interventional goals, such as health education, medication compliance, self-monitoring, etc. Larsen et al. applied a M-Health application to monitor and adjust the dosage of oral chemotherapy drugs in breast cancer patients, and the results suggested that the treatment adherence was effectively improved [ 27 ]. Heo and his team successfully promoted self-breast-examination behavior in women under 30 years old using a M-Health application [ 28 ]. Mccarrol carried out a M-Health diet and exercise intervention in overweight breast cancer patients and found that the weight, BMI, and waist circumference of the intervention group decreased after one month [ 29 ]. Smith’s team found that their application promoted the adoption of healthy diet and exercise behaviors among breast cancer patients [ 30 ]. The application designed by Eden et al. enhanced the ability of breast cancer patients receiving chemotherapy to recognize adverse drug reactions [ 31 ]. Keohane and colleagues designed a health educational application based on the best practices and it proved effective in improving breast cancer-related knowledge [ 32 ]. The guideline-based M-Health application developed by Eden et al. optimized breast cancer patients’ individualized health decision-making regarding mammography [ 33 ].

With the progress of computer technology and the emphasis on physical and mental rehabilitation of breast cancer patients, some universities [ 34 , 35 ] in China have separately developed M-Health applications for comprehensive health management, which provide access to online communication, health education, and expert consultation.

Analyzing these developed applications deeply, three factors could be found that hindered the promotion of applications in real life. Firstly, the developing procedure usually lacks contextual analysis based on the actual usage context during the design phase. Secondly, there is a lack of consistent and long-term monitoring and operation staff in the subsequent program implementation. These factors may be the main reasons why many M-Health applications face difficulties in promotion and continuous operation after the research phase. Furthermore, as applications need to be installed on patients’ smartphones, certain hardware requirements, such as memory, may also pose restrict the adoption of M-Health applications to some extent.

In order to meet the needs of supportive care for out-of-hospital breast cancer patients and the needs of case management for oncology nurse specialists, we formed a multidisciplinary research team and collaboratively developed a WeChat mini-program for breast cancer case management in the CMBM (M-health for case management model in breast cancer transitional care) project. WeChat is chosen as the program development platform based on the following considerations. Firstly, WeChat is the most popular and widely used social software in China. As of December 31, 2020, the monthly active users of WeChat have exceeded 1.2 billion, and the daily active users of WeChat mini-programs exceeded 450 million [ 36 ]. Secondly, users can access and use the services of the mini-program directly within the WeChat platform, without the need to download or install additional mobile applications. This reduces the hardware requirements for software applications. The above two factors allow for a positive user experience and a realistic foundation for software promotion.

The purpose of this study is to describe the process of developing a tailored M-health component for the case management model in breast cancer transitional care and to highlight methods for solving the common obstacles faced during the application of M-health nursing service.

Methods and results

The development process was conducted in four steps: (a) An interdisciplinary development team was formed, consisting of two sub-teams dedicated to content and software development. (b) Using the self-management theory as the theoretical framework, contextual analysis was used to understand the case management needs of oncology nursing specialists and the supportive care needs of out-of-hospital breast cancer patients. Through iterative discussion within the interdisciplinary team, the preliminary conception of the application framework and function was formed. (c) A multi-stage optimization strategy was adopted to develop and regularly update the WeChat mini-programs, including three stages (screening, refining, and confirming). (d) During the entire development process, a user-centered principle was followed with the involvement of oncology nursing specialists and breast cancer patients, including development, testing, and iterative development phases.

The interdisciplinary team

An important prerequisite for developing M-health applications is the formation of an interdisciplinary development team. We built a multidisciplinary team consisting of researchers, oncology nursing specialists, and software developers. Each team member brought their expertise from their respective fields, and all individuals were considered members of the same team rather than separate participants with a common goal.

Two sub-teams were established, one responsible for content development, and the other for software development. The content development team consisted of researchers and six senior breast oncology nursing specialists with bachelor’s degrees and over 10 years of clinical experience. Their work included contextual analysis, functional framework design, and content review of the “Information” module. The software development team included researchers and experienced software developers. Their tasks involved developing the mini-program based on the functional framework and requirements designed by the content development team.

The development team used contextual analysis to identify the actual usage needs of two target groups for the mini-program: oncologist nurse specialists and out-of-hospital breast cancer patients.

Involvement of oncology nursing specialists and breast cancer patients following user-centered design principle

Since the oncology nursing specialists and breast cancer patients are targeted users of the mini-program, the two groups fully participated in the development according to the user-centered principle. Nursing specialists who in charge of case management were interviewed about the preliminary functional framework of the mini-program. The interview results are presented in the section “Driving the Development Process via the Contextual Analysis Findings.” Semi-structured in-depth interviews were conducted in the testing and iteration stage to gain user feedback from nursing specialists to improve the applicability and usability of the mini-program. The interview guide can be found in the supplementary material.

Breast cancer patients fully engaged in the three developing phases (Screening, Refining, and Confirming). In the Screening Phase, since the self-management theory was selected as the theoretical framework, the supportive care needs of out-of-hospital breast cancer patients were explored, and the functional framework of the mini-program was constructed accordingly. In the Refining Phase, patients were invited to evaluate the usability and practicality of the mini-program through system tests and semi-structured in-depth interviews. The results of the system test are presented in the Results of System Test section. The feedback from interviews and corresponding iterative updates are listed in Table  1 . In the Confirming Phase, our research team is conducting clinical trials in out-of-hospital breast cancer patients to find out the actual effect of the mini-program on recovery.

The theory framework of the mini-program

This study applied the self-management theory [ 37 ] as the theoretical framework. The self-management theory explains how individual factors and environmental factors influence an individual’s self-efficacy, which ultimately affects the generation and development of individual behaviors. Self-efficacy is influenced by direct experience, indirect learning, verbal persuasion, and psychological arousal. By providing individuals with sufficient knowledge, healthy beliefs, skills, and support, their self-efficacy is increased, and they are likely to engage in beneficial health behaviors and self-management. Individuals who are confident in their abilities to apply self-management behaviors and overcome obstacles by improving their self-management skills and persevere in their efforts to manage their health [ 37 ]. Self-efficacy is directly and linearly positively related to the active adoption of health management behaviors [ 38 ]. The functions of the various parts of the mini-program designed using self-management theory can broaden the pathways and levels of efficacy information generation in four ways: direct experience, indirect learning, verbal persuasion, and mental arousal. Patients with high self-efficacy will take positive steps to achieve desired goals and possess disease-adapted behaviors. The form of the mini-application function block diagram is shown in Fig.  1 .

Driving the development process via the contextual analysis findings

Contextual analysis [ 39 ] is a method of discerning the profound significance and influence of language, behavior, events, and so forth, by examining them within a particular environment or background. Rather than being an afterthought, contextual analysis sheds light on the meaning and inner dynamics of our primary subject of interest. Through contextual analysis, we can gain a deeper understanding of the user’s usage scenarios, including their motivations, goals, environment, and behavior. This helps us better understand user needs, as well as the problems and challenges they may encounter when using the software.

In this paper, we adopted contextual analysis to gain a detailed understanding of the needs of oncology nurse specialists and out-of-hospital breast cancer patients. The research team adopted a mixed research strategy to achieve contextual analysis of the target users. A cross-sectional study was conducted among 286 patients and qualitative semi-structured in-depth interviews were applied in 12 patients to find out the supportive care needs of out-of-hospital breast cancer patients. According to the contextual analysis results from patients, the functional framework of the mini-program was constructed. See Fig.  2 for details.

Supportive care needs of out-of-hospital breast cancer patients

Contextual analysis of breast cancer case management nurses was conducted through focus group interview. The interview results were listed as three themes: health information, personal self-management, and case management needs. Health information included breast cancer-related knowledge, the side effects of chemotherapy drugs, and symptom management measures. The key task of personal self-management contained temperature monitoring, weight management, functional exercise, and symptom management. Case management needs involved storage and management of patients’ medical records and development of a nurse-end program.

Based on the contextual analysis results of out-of-hospital breast cancer patients and the oncology case management nurses, the framework and functional block of the mini-program were formed. An overview of the CMBM Software development process is listed in Fig.  3 .

Overview of the CMBM software development process

Patient-end program functional modules

Using the results of the contextual analysis, we design the functional modules of the patient-end program based on the patient’s supportive care needs. For example, the “Information” section is designed to meet the “Information need” of breast cancer patients; the “social needs” and “spiritual needs” of patients suggest that breast cancer patients lack peer support, and for this reason, the"Interaction” section for patients has been added to the app to provide a communication platform for patients.

The patient-end program include four functional modules: “Information”, “Interaction”, “Management” and “My”. In the “Information” module, information about breast cancer treatment and health management are compiled based on clinical guildlines. The “Interaction” module allows patients to interact with fellow patients and consult an case management nurse. In the “Management” module, patients can record and review their self-management-related health status, including three medical parameters (temperature, blood pressure, weight) and three behavioral parameters (daily steps, medication, mindfulness excersice). The “My” module enables patients to input and edit their basic personal information and medical history. The main structure and information support module contents are listed in Fig.  4 .

The main menu of patient-end program

Nurse-end program functional modules

The design of the functional modules of the nurse-end program was also derived from the results of contextual analyses. The nurse-end program includes three functional modules: “Consultation”, “Management”, and “My”. The “Consultation” module is mainly used for online communication between case management nurses and patients. Nurses can enter the patient’s name in the search box to open a dialog box, and communicate with each other by sending text, voice and pictures. In the “Management” module, nurses can effortlessly search for patients by entering their name, WeChat nickname, or mobile phone number in the search box. This initiates a seamless dialogue, and with a simple click of the “+” button, patients can be promptly added to the “My Concerns” list. They can view the medical record information on its homepage, and add the postoperative treatment plan for the patient. The “self-management report” feature empowers nurses to stay up-to-date with patients’ recent well-being. By monitoring vital indicators like temperature, weight, and incidents of nausea or vomiting following chemotherapy, nurses can proactively ensure patients’ safety. The “clock in record” feature meticulously logs various patient activities including weight variations, exercise regimens, and medication adherence, providing a holistic view of their health journey. “Treatment monitoring Schedule” enables nurses to create customized chemotherapy schedules. With the first postoperative chemotherapy session scheduled in the calendar, the system seamlessly computes subsequent chemotherapy sessions and associated assessments. This transition to an online system marks a significant advancement from the traditional paper-based chemotherapy planning. Its automated scheduling and data tracking functions serve to alleviate the clinical nursing workload, enhancing efficiency and freeing up valuable time for focused patient care. The “My” module offers nurses the convenience of adding patients of interest or relevant content to their “My Favorites” section, enabling streamlined one-click access for viewing and management. The core structure and informational components of this module are outlined in Fig.  5 .

The main menu of nurse-end program

Driving the development process via the multi-stage optimization strategy

We adopted a multi-phase optimization strategy to drive the software development process. This strategy was proposed by Collins in 2005 and has become an important guiding theory for the development and evaluation of M-health interventions in recent years [ 40 ]. The strategy consists of three phases: Screening Phase, Refining Phase, and Confirming Phase. The Screening Phase need theories to identify and incorporate intervention elements. In this study, the initial version (1.0) development was based on self-management theory. Focusing on self-management, the results of contextual analysis, literature review and expert consultation were combined to design the mini-program version (1.0). The Refining Phase involves iterative adjustments to the previously version. In this study, the development team iteratively adjusted the mini-program version (1.0) according to users’ suggestions and test results. The Confirming Phase includes planning for clinical trials to test effect of the mini-program version (2.0) on self-management and recovery outcomes in out-of-hospital breast cancer patients.

Results of system test

Eight out-of-hospital breast cancer patients were recruited for system tests. The patient’s general information is listed in Table  2 .

The 10-item System Availability Scale (SUS)developed by Brooke was used [ 41 ]. The scale is a widely used method for quantitatively assessing user satisfaction with software systems. SUS is a Likert-5 and 10-item questionnaire (4 = strongly agree, 0 = strongly disagree), with Cronbach Alpha of 0.91. Generally, a system score above 60 on the SUS scale could be considered to be easy and simple to use, and the average score of SUS in our research is 78.75. The SUS scores of the mini-program system are presented in Fig.  6 .

System availability scale (SUS) score of patients

The research team designed the core task tests based on the typical and necessary self-management tasks of out-of-hospital patients. The core task of the “Information” module was listed as an example (Table  3 ). Functional tests include the passing rate for each task, and performance tests include the completion time of each task. More details can be found in Table  4 .

In this article, we demonstrated how to create a customized software solution for breast cancer case management practices based on a multi-stage optimization strategy, applied the contextual analysis method, and followed the user-centered principle. Preliminary test results showed satisfaction and acceptance of the WeChat mini-program among both out-of-hospital breast cancer patients and oncology nursing specialists.

Team effort

There were two typical patterns for developing M-health applications in the past. One was led by software developers, while the other was led by medical professionals. Each of these patterns has its own advantages and disadvantages. To overcome these shortcomings, some projects [ 42 ] developing M-health applications are now utilizing interdisciplinary team collaborations. This approach not only ensures the quality of the software but also makes sure that applications meet the actual needs.

In order to develop a customized software solution, our research team consisted of researchers, oncology nursing specialists, and software developers. The interdisciplinary team work dedicated to customizing software solutions together. Our team members each played to their strengths and held regular meetings to discuss and enhance our understanding and resolution of issues encountered during the software development process. Our team also included informal members: breast cancer patients, whose suggestions contributed to the practicality of the program.

Contextual analysis and user-centered design

Contextual analysis is a valuable tool that enables developers to design systems that are more relevant and user-friendly. And it allows us to understand any context-specific characteristics, practice patterns, and the openness of the target setting’s nurses and patients towards technology [ 42 ]. User-centered design can significantly reduce the cost of program iteration. More importantly, it has a profound influence on various aspects of a program including its design, functionality, information architecture, and interactive elements [ 43 ]. By analyzing different contexts, not only did we design features that better meet user needs, but we also predicted and addressed potential issues that users may encounter when using the mini-program in advance, thereby enhancing the user experience. In the iterative development stage, we discovered and improved some deficiencies in the design through core task testing and usability testing. Notably, the completion rate of the core task test reached 100%, indicating that our application is user-friendly and easy to operate.

• Multi-stage optimization strategy

In several priority areas of public health, researchers have successfully applied multi-stage optimization strategies to enhance their work, including software development and intervention programs [ 44 , 45 , 46 ]. In this study, we also apply this strategy to software development. While the multi-stage optimization strategy provides an optimization framework, it is important to note that our optimization objectives (such as software functionality and content requirements) are determined by key users involved in the research (out-of-hospital breast cancer patients and oncology nurse spescialists). This project adopts a multi-stage optimization strategy, iteratively improving the development of the mini-program through screening, refinement, and confirmation stages. Each stage aims to optimize our program.

The research team plans to explore the feasibility of mini program development program through preliminary experiment, and verify the intervention effect of mini program on self-management behavior, self-efficacy and quality of life and other indicators through formal experiment. A randomized controlled trial (IRB-2020-408) was initiated in August 2022 at a Class III hospital in Zhejiang, China, and is currently in the data collection phase.

There is no doubt that M-health will play a core role in the future of health care. However, to successfully implement and promote M-health applications in clinical setting, it is essential to analyze the needs of the target population. Additionally, it is crucial to determine who will be the driving force behind the implementation of the entire M-health project. This study demonstrates how to integrate M-health components into existing breast cancer case management care practices. In addition to providing a reference for other teams interested in developing and integrating M-health components into case management care models, this study also provides a reference for building M-health-featured care work models in practices.

In this study, the collaborative work of an interdisciplinary team with backgrounds in nursing and computer science, along with the active involvement of patients, not only facilitated the planning, developing, updating, and testing of M-health components based on the actual needs of the target population, but also increased the chances of acceptance and long-term implementation of the M-health program in practice.

This study demonstrates how to integrate M-health components into existing breast cancer case management practices. It provides insights for other reserch teams interested in developing and integrating M-health components into daily nursingt practice.

In the context of the digital age, M-health applications are rapidly becoming information sources and decision support tools for healthcare professionals and patients. However, it is crucial not to overlook the issues of information security and digital barriers for older adults.

Through interviews with outpatients with breast cancer and oncology nurses, we have gained insights into their concerns regarding information security. Some interviewees expressed concerns about information security and were worried about the risk of their personal information being leaked during app usage. Such concerns, to some extent, hinder the widespread adoption of M-health applications. Additionally, some interviewees mentioned that older patients, in general, find it challenging to learn and use the various functions of WeChat mini-programs, making it difficult to promote and apply M-health applications among the elderly population.

Solving these issues effectively is not only vital for the patients’ rights and interests but also crucial for the comprehensive implementation of M-health in practice. It is a matter that requires careful consideration in future development of M-health applications.

Data availability

The datasets generated and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Bray F, Laversanne M, Sung H et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024: 1–35.

Wu J, Zeng N, Wang L, Yao L. The stigma in patients with breast cancer: a concept analysis. Asia Pac J Oncol Nurs. 2023;10(10):100293.

Article   PubMed   PubMed Central   Google Scholar  

Heidkamp P, Hiltrop K, Breidenbach C, Kowalski C, Pfaff H, Geiser F, Ernstmann N. Coping with breast cancer during medical and occupational rehabilitation: a qualitative study of strategies and contextual factors. BMC Womens Health. 2024;24(1):183.

Zhao H, Li X, Zhou C, Wu Y, Li W, Chen L. Psychological distress among Chinese patients with breast cancer undergoing chemotherapy: concordance between patient and family caregiver reports. J Adv Nurs. 2022;78(3):750–64.

Article   PubMed   Google Scholar  

Jang Y, Seong M, Sok S. Influence of body image on quality of life in breast cancer patients undergoing breast reconstruction: Mediating of self-esteem. J Clin Nurs. 2023;32(17–18):6366–73.

Oh PJ, Cho JR. Changes in fatigue, psychological distress, and Quality of Life after Chemotherapy in women with breast Cancer: a prospective study. Cancer Nurs. 2020 Jan/Feb;43(1):E54–60.

Maass SWMC, Boerman LM, Verhaak PFM, Du J, de Bock GH, Berendsen AJ. Long-term psychological distress in breast cancer survivors and their matched controls: a cross-sectional study. Maturitas. 2019;130:6–12.

Article   CAS   PubMed   Google Scholar  

De Vrieze T, Nevelsteen I, Thomis S, De Groef A, Tjalma WAA, Gebruers N, Devoogdt N. What are the economic burden and costs associated with the treatment of breast cancer-related lymphoedema? A systematic review. Support Care Cancer. 2020;28(2):439–49.

Liang Y, Gao Y, Yin G, Chen W, Gan X. Development of a breast cancer case management information platform (BC-CMIP) module based on patient-perceived value. Front Oncol. 2022;12:1034171.

Jin L, Zhao Y, Wang P, Zhu R, Bai J, Li J, Jia X, Wang Z. Efficacy of the whole-course case management model on compliance and satisfaction of breast Cancer patients with whole-course standardized treatment. J Oncol. 2022;2022:2003324.

Scherz N, Bachmann-Mettler I, Chmiel C, Senn O, Boss N, Bardheci K, Rosemann T. Case management to increase quality of life after cancer treatment: a randomized controlled trial. BMC Cancer. 2017;17(1):223.

Yamei Y, Yongfang Zh J, Sh, Xixi C, Dehong Z, Chuner J, Jianfen N. The influence of the whole course professional nursing case management model on the disease uncertainty for breast cancer patients with chemotherapy. J Nur Train. 2018;(02), 99–111.

Cuie P. The whole case management model for breast cancer patients to study the effect of quality of life and psychological society. Hunan Normal University; 2015.

Bleich C, Büscher C, Melchior H, Grochocka A, Thorenz A, Schulz H, Koch U, Watzke B. Effectiveness of case management as a cross-sectoral healthcare provision for women with breast cancer. Psycho Oncol. 2017;26(3):354–60.

Article   Google Scholar  

Huiting Zh J, Zh, Xiaodan W, Lijuan Zh, Wenhao H, Huiying Q. Exploration of case management model for breast cancer patients. J Nurs Sci. 2017;(14), 19–21.

Woodward J, Rice E. Case management. Nurs Clin North Am. 2015;50(1):109–21.

Luo X, Chen Y, Chen J, Zhang Y, Li M, Xiong C, Yan J. Effectiveness of mobile health-based self-management interventions in breast cancer patients: a meta-analysis. Support Care Cancer. 2022;30(3):2853–76.

Meiqin X, Lingjuan Z. The delivery and inspiration of case management model in China. Chin J Nurs. 2014;(03), 367–71.

Huanhuan L, Zhuangjie X, Yuan L, Ying L, ShouQi W, MeiLing Z, Jie Y, Pengcheng L, Huanhuan Zh, Jiao S. A review of the interventions of nurses’ intent to stay. Chin J Nurs. 2017;(08), 1007–9.

Dan W, Shanling L, Yulin X. Research status of continuous nursing at home and abroad. Nurs Res 2016;(20), 2436–8.

Mariani AW, Pêgo-Fernandes PM. Telemedicine: a technological revolution. Sao Paulo Med J. 2012;130(5):277–8.

Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17(2):e52.

Mahmood S, Hasan K, Colder Carras M, Labrique A. Global preparedness against COVID-19: we must leverage the Power of Digital Health. JMIR Public Health Surveill. 2020;6(2):e18980.

Petracca F, Ciani O, Cucciniello M, Tarricone R. Harnessing Digital Health Technologies during and after the COVID-19 pandemic: context matters. J Med Internet Res. 2020;22(12):e21815.

Cong H, Yongyi C, Xiangyu C, Xuying L. Rehabilitation Effect of Chemotherapy-based adverse reactions to breast Cancer patients based on continuous care platform. Oncol Pharma. 2020;(02), 244–51.

Cong A, Liping W. Application progress of mobile health in transitional care of patients with hypertension. Chin J Mod Nurs. 2021;27(4):539–42.

Google Scholar  

Larsen ME, Farmer A, Weaver A, Young A, Tarassenko L. Mobile health for drug dose optimisation. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1540–3.

PubMed   Google Scholar  

Heo J, Chun M, Lee KY, Oh YT, Noh OK, Park RW. Effects of a smartphone application on breast self-examination: a feasibility study. Healthc Inf Res. 2013;19(4):250–60.

McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, Roulette GD, Andrews SJ, von Gruenigen VE. Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. Gynecol Oncol. 2015;137(3):508–15.

Smith SA, Whitehead MS, Sheats J, Mastromonico J, Yoo W, Coughlin SS. A Community-Engaged Approach to developing a Mobile Cancer Prevention App: the mCPA Study Protocol. JMIR Res Protoc. 2016;5(1):e34.

Eden KB, Ivlev I, Bensching KL, Franta G, Hersh AR, Case J, Fu R, Nelson HD. Use of an online breast Cancer Risk Assessment and patient decision aid in Primary Care practices. J Womens Health (Larchmt). 2020;29(6):763–9.

Keohane D, Lehane E, Rutherford E, Livingstone V, Kelly L, Kaimkhani S, O’Connell F, Redmond HP, Corrigan MA. Can an educational application increase risk perception accuracy amongst patients attending a high-risk breast cancer clinic? Breast. 2017;32:192–8.

Eden KB, Scariati P, Klein K, Watson L, Remiker M, Hribar M, Forro V, Michaels L, Nelson HD. Mammography decision aid reduces Decisional Conflict for women in their forties considering screening. J Womens Health (Larchmt). 2015;24(12):1013–20.

Zhu J, Ebert L, Liu X, Chan SW. A mobile application of breast cancer e-support program versus routine care in the treatment of Chinese women with breast cancer undergoing chemotherapy: study protocol for a randomized controlled trial. BMC Cancer. 2017;17(1):291.

Ying L. Construction of an M-Health based information support program for women with breast Cancer during diagnosis and treatment process. The Second Military Medical University; 2017.

Pengfei X, Bo Y, Yue H, Jingyun H. An empirical research on the interactive behavior of WeChat subscription number users from the perspective of the theory of interactive ritual chain. Chin J Inf Syst. 2023;(01), 69–83.

Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191Y215.

Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143Y164.

George A, Scott K, Garimella S, Mondal S, Ved R, Sheikh K. Anchoring contextual analysis in health policy and systems research: a narrative review of contextual factors influencing health committees in low and middle income countries. Soc Sci Med. 2015;133:159–67.

Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5 Suppl):S112–8.

Brooke JB. SUS: a quick and dirty usability scale. Usability Evaluation Ind. 1996;189(194):4–7.

Leppla L, Hobelsberger S, Rockstein D, Werlitz V, Pschenitza S, Heidegger P, De Geest S, Valenta S, Teynor A. SMILe study team. Implementation Science meets Software Development to create eHealth Components for an Integrated Care Model for allogeneic stem cell transplantation facilitated by eHealth: the SMILe Study as an Example. J Nurs Scholarsh. 2021;53(1):35–45.

Luna D, Quispe M, Gonzalez Z, Alemrares A, Risk M, Garcia Aurelio M, Otero C. User-centered design to develop clinical applications. Literature review. Stud Health Technol Inf. 2015;216:967.

Piper ME, Fiore MC, Smith SS, Fraser D, Bolt DM, Collins LM, Mermelstein R, Schlam TR, Cook JW, Jorenby DE, Loh WY, Baker TB. Identifying effective intervention components for smoking cessation: a factorial screening experiment. Addiction. 2016;111(1):129–41.

Spring B, Pfammatter AF, Marchese SH, Stump T, Pellegrini C, McFadden HG, Hedeker D, Siddique J, Jordan N, Collins LM. A factorial experiment to optimize remotely delivered behavioral treatment for obesity: results of the Opt-IN Study. Obes (Silver Spring). 2020;28(9):1652–62.

O’Hara KL, Knowles LM, Guastaferro K, Lyon AR. Human-centered design methods to achieve preparation phase goals in the multiphase optimization strategy framework. Implement Res Pract. 2022;3:26334895221131052.

PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

The authors would like to express our sincere gratitude to all the breast cancer patients who participated in this research.

This study was supported by the Zhejiang Provincial Natural Science Foundation of China (LY18H160061) and Funding for innovation and entrepreneurship of high-level overseas students in Hangzhou.

Author information

Authors and affiliations.

School of Nursing, Hangzhou Normal University, Hangzhou City, Zhejiang Province, 311100, China

Hong Chengang, Wang Liping, Wang Shujin, Chen Chen, Yang Jiayue, Lu Jingjing, Hua Shujie, Wu Jieming, Yao Liyan, Zeng Ni, Chu Jinhui & Sun Jiaqi

You can also search for this author in PubMed   Google Scholar

Contributions

HCG conceived the entire paper framework and was responsible for writing the paper. WSJ and CC conducted all interviews and managed the mini-programs. YJY, LJJ and HSJ were responsible for the collection of clinical nurse data. CJH and SJQ were responsible for patient data collection. Data analysis was conducted by WJM, YLY and ZN. WLP was responsible for the revision, editing and approval of manuscripts. All authors have rigorously revised and edited successive drafts of the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Wang Liping .

Ethics declarations

Ethics approval and consent to participate.

The study was reviewed and approved by the Ethics Committee of ZheJiang Cancer Hospital (Ethic ID: ZJZLYY IRB-2020-408). All the participants signed written informed consent forms. This study was conducted in accordance with the 1964 Declaration of Helsinki guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Chengang, H., Liping, W., Shujin, W. et al. Multi-stage optimization strategy based on contextual analysis to create M-health components for case management model in breast cancer transitional care: the CMBM study as an example. BMC Nurs 23 , 385 (2024). https://doi.org/10.1186/s12912-024-02049-x

Download citation

Received : 22 November 2023

Accepted : 29 May 2024

Published : 06 June 2024

DOI : https://doi.org/10.1186/s12912-024-02049-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• User-centered design
• Contextual analysis
• Breast cancer patients
• Case management
• Transitional care

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]