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Case Study – Methods, Examples and Guide

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Prevent plagiarism. Run a free check.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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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
• Research Process Flow Chart
• Research Alignment 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
• 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.

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• Last Updated: Apr 2, 2024 6:35 PM
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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

• 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

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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.

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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.

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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.

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Educational interventions targeting pregnant women to optimise the use of caesarean section: What are the essential elements? A qualitative comparative analysis

• Rana Islamiah Zahroh   ORCID: orcid.org/0000-0001-7831-2336 1 ,
• Katy Sutcliffe   ORCID: orcid.org/0000-0002-5469-8649 2 ,
• Dylan Kneale   ORCID: orcid.org/0000-0002-7016-978X 2 ,
• Martha Vazquez Corona   ORCID: orcid.org/0000-0003-2061-9540 1 ,
• Ana Pilar Betrán   ORCID: orcid.org/0000-0002-5631-5883 3 ,
• Newton Opiyo   ORCID: orcid.org/0000-0003-2709-3609 3 ,
• Caroline S. E. Homer   ORCID: orcid.org/0000-0002-7454-3011 4 &
• Meghan A. Bohren   ORCID: orcid.org/0000-0002-4179-4682 1  

BMC Public Health volume  23 , Article number:  1851 ( 2023 ) Cite this article

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Caesarean section (CS) rates are increasing globally, posing risks to women and babies. To reduce CS, educational interventions targeting pregnant women have been implemented globally, however, their effectiveness is varied. To optimise benefits of these interventions, it is important to understand which intervention components influence success. In this study, we aimed to identify essential intervention components that lead to successful implementation of interventions focusing on pregnant women to optimise CS use.

We re-analysed existing systematic reviews that were used to develop and update WHO guidelines on non-clinical interventions to optimise CS. To identify if certain combinations of intervention components (e.g., how the intervention was delivered, and contextual characteristics) are associated with successful implementation, we conducted a Qualitative Comparative Analysis (QCA). We defined successful interventions as interventions that were able to reduce CS rates. We included 36 papers, comprising 17 CS intervention studies and an additional 19 sibling studies (e.g., secondary analyses, process evaluations) reporting on these interventions to identify intervention components. We conducted QCA in six stages: 1) Identifying conditions and calibrating the data; 2) Constructing truth tables, 3) Checking quality of truth tables; 4) Identifying parsimonious configurations through Boolean minimization; 5) Checking quality of the solution; 6) Interpretation of solutions. We used existing published qualitative evidence synthesis to develop potential theories driving intervention success.

We found successful interventions were those that leveraged social or peer support through group-based intervention delivery, provided communication materials to women, encouraged emotional support by partner or family participation, and gave women opportunities to interact with health providers. Unsuccessful interventions were characterised by the absence of at least two of these components.

We identified four key essential intervention components which can lead to successful interventions targeting women to reduce CS. These four components are 1) group-based delivery, 2) provision of IEC materials, 3) partner or family member involvement, and 4) opportunity for women to interact with health providers. Maternal health services and hospitals aiming to better prepare women for vaginal birth and reduce CS can consider including the identified components to optimise health and well-being benefits for the woman and baby.

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Introduction

In recent years, caesarean section (CS) rates have increased globally [ 1 , 2 , 3 , 4 ]. CS can be a life-saving procedure when vaginal birth is not possible; however, it comes with higher risks both in the short- and long-term for women and babies [ 1 , 5 ]. Women with CS have increased risks of surgical complications, complications in future pregnancies, subfertility, bowel obstruction, and chronic pain [ 5 , 6 , 7 , 8 ]. Similarly, babies born through CS have increased risks of hypoglycaemia, respiratory problems, allergies and altered immunity [ 9 , 10 , 11 ]. At a population level, CS rates exceeding 15% are unlikely to reduce mortality rates [ 1 , 12 ]. Despite these risks, an analysis across 154 countries reported a global average CS rate of 21.1% in 2018, projected to increase to 28.5% by 2030 [ 3 ].

There are many reasons for the increasing CS rates, and these vary between and within countries. Increasingly, non-clinical factors across different societal dimensions and stakeholders (e.g. women and communities, health providers, and health systems) are contributing to this increase [ 13 , 14 , 15 , 16 , 17 ]. Women may prefer CS over vaginal birth due to fear of labour or vaginal birth, previous negative experience of childbirth, perceived increased risks of vaginal birth, beliefs about an auspicious or convenient day of birth, or beliefs that caesarean section is safer, quick, and painless compared to vaginal birth [ 13 , 14 , 15 ].

Interventions targeting pregnant women to reduce CS have been implemented globally. A Cochrane intervention review synthesized evidence from non-clinical interventions targeting pregnant women and family, providers, and health systems to reduce unnecessary CS, and identified 15 interventions targeting women [ 18 ]. Interventions targeting women primarily focused on improving women’s knowledge around birth, improving women’s ability to cope during labour, and decreasing women’s stress related to labour through childbirth education, and decision aids for women with previous CS [ 18 ]. These types of interventions aim to reduce the concerns of pregnant women and their partners around childbirth, and prepare them for vaginal birth.

The effectiveness of interventions targeting women in reducing CS is mixed [ 18 , 19 ]. Plausible explanations for this limited success include the multifactorial nature of the factors driving increases in CS, as well as the contextual characteristics of the interventions, which may include the study environment, participant characteristics, intensity of exposure to the intervention and method of implementation. Understanding which intervention components are essential influencers of the success of the interventions is conducive to optimising benefits. This study used a Qualitative Comparative Analysis (QCA) approach to re-analyse evidence from existing systematic reviews to identify essential intervention components that lead to the successful implementation of non-clinical interventions focusing on pregnant women to optimise the use of CS. Updating and re-analysing existing systematic reviews using new analytical frameworks may help to explore the heterogeneity in effects and ascertain why some studies appear to be effective while others are not.

Data sources, case selection, and defining outcomes

Developing a logic model.

We developed a logic model to guide our understanding of different pathways and intervention components potentially leading to successful implementation (Additional file 1 ). The logic model was developed based on published qualitative evidence syntheses and systematic reviews [ 18 , 20 , 21 , 22 , 23 , 24 ]. The logic model depicts the desired outcome of reduced CS rates in low-risk women (at the time of admission for birth, these women are typically represented by Robson groups 1–4 [ 25 ] and are women with term, cephalic, singleton pregnancies without a previous CS) and works backwards to understand what inputs and processes are needed to achieve the desired outcome. Our logic model shows multiple pathways to success and highlights the interactions between different levels of factors (women, providers, societal, health system) (Additional file 1 ). Based on the logic model, we have separated our QCA into two clusters of interventions: 1) interventions targeting women, and 2) interventions targeting health providers. The results of analysis on interventions targeting health providers have been published elsewhere [ 26 ]. The logic model was also used to inform the potential important components that influence success.

Identifying data sources and selecting cases

We re-analysed the systematic reviews which were used to inform the development and update of World Health Organization (WHO) guidelines. In 2018, WHO issued global guidance on non-clinical interventions to reduce unnecessary CS, with interventions designed to target three different levels or stakeholders: women, health providers, and health systems [ 27 ]. As part of the guideline recommendations, a series of systematic reviews about CS interventions were conducted: 1) a Cochrane intervention review of effectiveness by Chen et al. (2018) [ 18 ] and 2) three qualitative evidence syntheses exploring key stakeholder perspectives and experiences of interventions focusing on women and communities, health professionals, and health organisations, facilities and systems by Kingdon et al. (2018) [ 20 , 21 , 22 ]. Later on, Opiyo and colleagues (2020) published a scoping review of financial and regulatory interventions to optimise the use of CS [ 23 ].

Therefore, the primary data sources of this QCA are the intervention studies included in Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ]. We used these two systematic reviews as not only they are comprehensive, but they were also used to inform the WHO guidelines development. A single intervention study is referred to as a “case”. Eligible cases were intervention studies focusing on pregnant women and aimed to reduce or optimise the use of CS. No restrictions on study design were imposed in the QCA. Therefore, we also assessed the eligibility of intervention studies excluded from Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ] due to ineligible study designs (such as cohort study, uncontrolled before and after study, interrupted time series with fewer than three data points), as these studies could potentially show other pathways to successful implementation. We complemented these intervention studies with additional intervention studies published since the last review updates in 2018 and 2020, to include intervention studies that are likely to meet the review inclusion criteria for future review updates. No further search was conducted as QCA is suitable for medium-N cases, approximately around 10–50 cases, and inclusion of more studies may threaten study rigour [ 28 ].

Once eligible studies were selected, we searched for their ‘sibling studies’. Sibling studies are studies linked to the included intervention studies, such as formative research or process evaluations which may have been published separately. Sibling studies can provide valuable additional information about study context, intervention components, and implementation outcomes (e.g. acceptability, fidelity, adherence, dosage), which may not be well described in a single article about intervention effectiveness. We searched for sibling studies using the following steps: 1) reference list search of the intervention studies included in Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ], 2) reference list search of the qualitative studies included in Kingdon et al. (2018) reviews [ 20 , 21 , 22 ]; and 3) forward reference search of the intervention studies (through “Cited by” function) in Scopus and Web of Science. Sibling studies were included if they included any information on intervention components or implementation outcomes, regardless of the methodology used. One author conducted the study screening independently (RIZ), and 10% of the screening was double-checked by a second author (MAB). Disagreements during screening were discussed until consensus, and with the rest of the author team if needed.

Defining outcomes

We assessed all outcomes related to the mode of birth in the studies included in the Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ] reviews. Based on the consistency of outcome reporting, we selected “overall CS rate” as the primary outcome of interest due to its presence across studies. We planned to rank the rate ratio across these studies to select the 10 most successful and unsuccessful intervention studies. However, due to heterogeneity in how CS outcomes were reported across studies (e.g. odds ratios, rate ratios, percentages across different intervention stages), the final categorisation of successful or unsuccessful interventions is based on whether the CS rate decreased, based on the precision of the confidence interval or p-value (successful, coded as 1), or CS rate increased or did not change (unsuccessful, coded as 0).

Assessing risk of bias in intervention studies

All intervention studies eligible for inclusion were assessed for risk of bias. All studies included in Chen et al. (2018) and Opiyo et al. (2020) already had risk of bias assessed and reported [ 18 , 23 ], and we used these assessments. Additional intervention studies outside the included studies on these reviews were assessed using the same tools depending on the type of evidence (two randomized controlled trials and one uncontrolled before and after study), and details of the risk of bias assessment results can be found in Additional file 2 . We excluded studies with a high risk of bias to ensure that the analysis was based on high-quality studies and to enhance the ability of researchers to develop deep case knowledge by limiting the overall number of studies.

Qualitative comparative analysis (QCA)

QCA was first developed and used in political sciences and has since been extended to systematic reviews of complex health interventions [ 24 , 29 , 30 , 31 ]. Despite the term “qualitative”, QCA is not a typical qualitative analysis, and is often conceptualised as a methodology that bridges qualitative and quantitative methodologies based on its process, data used and theoretical standpoint [ 24 ]. Here, QCA is used to identify if certain configurations or combinations of intervention components (e.g. participants, types of interventions, contextual characteristics, and intervention delivery) are associated with the desired outcome [ 31 ]. These intervention components are referred to as “conditions” in the QCA methodology. Whilst statistical synthesis methods may be used to examine intervention heterogeneity in systematic reviews, such as meta-regression, QCA is a particularly suitable method to understand complex interventions like those aiming to optimise CS, as it allows for multiple overlapping pathways to causality [ 31 ]. Moreover, QCA allows the exploration of different combinations of conditions, rather than relying on a single condition leading to intervention effectiveness [ 31 ]. Although meta-regression allows for the assessment of multiple conditions, a sufficient number of studies may not be available to conduct the analysis. In complex interventions, such as interventions aiming to optimise the use of CS, single condition or standard meta-analysis may be less likely to yield usable and nuanced information about what intervention components are more or less likely to yield success [ 31 ].

QCA uses ‘set theory’ to systematically compare characteristics of the cases (e.g. intervention in the case of systematic reviews) in relation to the outcomes [ 31 , 32 ]. This means QCA compares the characteristics of the successful ‘cases’ (e.g. interventions that are effective) to those unsuccessful ‘cases’ (e.g. interventions that are not effective). The comparison is conducted using a scoring system based on ‘set membership’ [ 31 , 32 ]. In this scoring, conditions and outcomes are coded based on the extent to which a certain feature is present or absent to form set membership scores [ 31 , 32 ]. There are two scoring systems in QCA: 1) crisp set QCA (csQCA) and 2) fuzzy set QCA (fsQCA). csQCA assigns binary scores of 0 (“fully out” to set membership for cases with certain conditions) and 1 (“fully in” to set membership for cases with certain conditions), while fsQCA assigns ordinal scoring of conditions and outcomes, permitting partial membership scores between 0 and 1 [ 31 , 32 ]. For example, using fsQCA we may assign a five-level scoring system (0, 0.33, 0.5, 0.67, 1), where 0.33 would indicate “more out” than “in” to the set of membership, and 0.67 would indicate “more in” than “out”, and 0.5 would indicate ambiguity (i.e. a lack of information about whether a case was “in” or “out”) [ 31 , 32 ]. In our analysis, we used the combination of both csQCA and fsQCA to calibrate our data. This approach was necessary because some conditions were better suited to binary options using csQCA, while others were more complex, depending on the distribution of cases, and required fsQCA to capture the necessary information. In our final analysis, however, the conditions run on the final analysis were all using the csQCA scoring system.

Two relationships can be investigated using QCA [ 24 , 31 ]. First, if all instances of successful interventions share the same condition(s), this suggests these features are ‘necessary’ to trigger successful outcomes [ 24 , 31 ]. Second, if all instances of a particular condition are associated with successful interventions, this suggests these conditions are ‘sufficient’ for triggering successful outcomes [ 24 , 31 ]. In this QCA, we were interested to explore the relationship of sufficiency: that is, to assess the various combinations of intervention components that can trigger successful outcomes. We were interested in sufficiency because our logic model (explained further below) highlighted the multiple pathways that can lead to a CS and different interventions that may optimise the use of CS along those pathways, which suggested that it would be unlikely for all successful interventions to share the same conditions. We calculated the degree of sufficiency using consistency measures, which evaluate the frequency in which conditions are present when the desired outcome is achieved [ 31 , 32 ]. The conditions with a consistency score of at least 0.8 were considered sufficient in triggering successful interventions [ 31 , 32 ]. At present, there is no tool available for reporting guidelines in the re-analysis of systematic reviews using QCA, however, CARU-QCA is currently being developed for this purpose [ 33 ]. QCA was conducted using R programming software with a package developed by Thiem & Duşa (2013) and QCA with R guidebook [ 32 ]. QCA was conducted in six stages based on Thomas et al. (2014) [ 31 ] and explained below.

QCA stage 1: Identifying conditions, building data tables and calibration

We used a deductive and inductive process to determine the potential conditions (intervention components) that may trigger successful implementation. Conditions were first derived deductively using the developed logic model (Additional file 1 ). We then added additional conditions inductively using Intervention Component Analysis from the intervention studies [ 34 ], and qualitative evidence (“view”) synthesis [ 22 ] using Melendez-Torres’s (2018) approach [ 35 ]. Intervention Component Analysis is a methodological approach that examines factors affecting implementation through reflections from the trialist, which is typically presented in the discussion section of a published trial [ 34 ]. Examples of conditions identified in the Intervention Component Analysis include using an individualised approach, interaction with health providers, policies that encourage CS and acknowledgement of women’s previous birth experiences. After consolidating or merging similar conditions, a total of 52 conditions were selected and extracted from each included intervention and analysed in this QCA (Details of conditions and definitions generated for this study can be found in Additional files 3 and 4 ). We adapted the coding framework from Harris et al. (2019) [ 24 ] by adapting coding rules and six domains that were used, to organize the 52 conditions and make more sense of the data. These six domains are broadly classified as 1) context and participants, 2) intervention design, 3) program content, 4) method of engagement, 5) health system factors, and 6) process outcomes.

One author (RIZ) extracted data relevant to the conditions for each included study into a data table, which was then double-reviewed by two other authors (MVC, MAB). The data table is a matrix in which each case is represented in a row, and columns are used to represent the conditions. Following data extraction, calibration rules using either csQCA or fsQCA (e.g. group-based intervention delivery condition: yes = 1 (present), no = 0 (absent)) were developed through consultation with all authors. We developed a table listing the conditions and rules of coding the conditions, by either direct or transformational assignment of quantitative and qualitative data [ 24 , 32 ] (Additional file 3 depicts the calibration rules). The data tables were then calibrated by applying scores, to explore the extent to which interventions have ‘set membership’ with the outcome or conditions of interest. During this iterative process, the calibration criteria were explicitly defined, emerging from the literature and the cases themselves. It is important to note, that maximum ambiguity is typically scored as 0.5 in QCA, however, we decided it would be more appropriate to assume that if a condition was not reported it was unlikely to be a feature of the intervention, so we treated not reported as “absence” that is we coded it 0.

QCA stage 2: Constructing truth tables

Truth tables are an analytical tool used in QCA to analyse associations between configurations of conditions and outcomes. Whereas the data table represents individual cases (rows) and individual conditions (columns) – the truth table synthesises this data to examine configurations – with each row representing a different configuration of the conditions. The columns indicate a) which conditions are featured in the configuration in that row, b) how many of the cases are represented by that configuration, and c) their association with the outcome.

We first constructed the truth tables based on context and participants, intervention designs, program content, and method of engagement; however, no configurations to trigger successful interventions were observed. Instead, we observed limited diversity, meaning there were many instances in which the configurations were unsupported by cases, likely due to the presence of too many conditions in the truth tables. We used the learning from these truth tables to return to the literature to explore potential explanatory theories about what conditions are important from the perspectives of participants and trialists to trigger successful interventions (adhering to the ‘utilisation of view’ perspective [ 35 ]). Through this process, we found that women and communities liked to learn new information about childbirth, and desired emotional support from partners and health providers while learning [ 22 ]. They also appreciated educational interventions that provide opportunities for discussion and dialogue with health providers and align with current clinical practice and advice from health providers [ 22 ]. Therefore, three models of truth tables were iteratively constructed and developed based on three important hypothesised theories about how the interventions should be delivered: 1) how birth information was provided to women, 2) emotional support was provided to women (including interactions between women and providers), and 3) a consolidated model examining the interactions of important conditions identified from model 1 and 2. We also conducted a sub-analysis of interventions targeting both women and health providers or systems (‘multi-target interventions’). This sub-analysis was conducted to explore if similar conditions were observed in triggering successful interventions in multi-target interventions, among the components for women only. Table 1 presents the list of truth tables that were iteratively constructed and refined.

QCA stage 3: Checking quality of truth tables

We iteratively developed and improved the quality of truth tables by checking the configurations of successful and unsuccessful interventions, as recommended by Thomas et al. (2014) [ 31 ]. This includes by assessing the number of studies clustering to each configuration, and exploring the presence of any contradictory results between successful and unsuccessful interventions. We found contradictory configurations across the five truth tables, which were resolved by considering the theoretical perspectives and iteratively refining the truth tables.

QCA stage 4: Identifying parsimonious configurations through Boolean minimization

Once we determined that the truth tables were suitable for further analysis, we used Boolean minimisation to explore pathways resulting in successful intervention through the configurations of different conditions [ 31 ]. We simplified the “complex solution” of the pathways to a “parsimonious solution” and an “intermediate solution” by incorporating logical remainders (configurations where no cases were observed) [ 36 ].

QCA stage 5: Checking the quality of the solution

We presented the intermediate solution as the final solution instead of the most parsimonious solution, as it is most closely aligned with the underlying theory. We checked consistency and coverage scores to assess if the pathways identified were sufficient to trigger success. We also checked the intermediate solution by negating the outcome to see if it predicts the observed solutions.

QCA stage 6: Interpretation of solutions

We iteratively interpreted the results of the findings through discussions among the QCA team. This reflexive approach ensured that the results of the analysis considered the perspectives from the literature discourse, methodological approach, and that the results were coherent with the current understanding of the phenomenon.

Overview of included studies

Out of 79 intervention studies assessed by Chen et al. (2018) [ 18 ] and Opiyo et al. (2020) [ 23 ], 17 intervention studies targeted women and are included, comprising 11 interventions targeting only women [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ] and six interventions targeting both women and health providers or systems [ 44 , 45 , 46 , 47 , 48 , 49 ]. From 17 included studies, 19 sibling studies were identified [ 43 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ]. Thus, a total of 36 papers from 17 intervention studies are included in this QCA (See Fig.  1 : PRISMA Flowchart).

PRISMA flowchart. *Sibling studies: studies that were conducted in the same settings, participants, and timeframe; **Intervention components: information on intervention input, activities, and outputs, including intervention context and other characteristics

The 11 interventions targeting women comprised of five successful interventions [ 37 , 68 , 69 , 70 , 71 ] and six unsuccessful interventions [ 37 , 38 , 39 , 40 , 41 , 42 , 43 ] in reducing CS. Sixteen sibling studies were identified, from five out of 11 included interventions [ 37 , 41 , 43 , 70 , 71 ]. Included studies were conducted in six countries across North America (2 from Canada [ 38 ] and 1 from United States of America [ 71 ]), Asia–Pacific (1 from Australia [ 41 ]), 5 from Iran [ 39 , 40 , 68 , 69 , 70 ]), Europe (2 from Finland [ 37 , 42 ], 1 from United Kingdom [ 43 ]). Six studies were conducted in high-income countries, while five studies were conducted in upper-middle-income countries (all from Iran). All 11 studies targeted women, with three studies also explicitly targeting women’s partners [ 68 , 69 , 71 ]. One study delivering psychoeducation allowed women to bring any family members to accompany them during the intervention but did not specifically target partners [ 37 ]. All 11 studies delivered childbirth education, with four delivering general antenatal education [ 38 , 40 , 68 , 69 ], six delivering psychoeducation [ 37 , 39 , 41 , 42 , 70 , 71 ], and one implementing decision aids [ 43 ]. All studies were included in Chen et al. (2018), and some risks of bias were identified [ 18 ] (Additional file 2).

The multi-target interventions consisted of five successful interventions [ 44 , 45 , 46 , 47 , 48 ] and one unsuccessful intervention [ 49 ]. Sibling studies were only identified from one study [ 48 ]. The interventions were delivered in five countries across: South America (1 from Brazil [ 46 ]), Asia–Pacific (4 from China [ 44 , 45 , 47 , 49 ]), Europe (1 from Italy [ 48 ], 1 from Ireland [ 48 ], and 1 from Germany [ 48 ]). Three studies were conducted in high-income countries and five studies in upper middle-income countries. The multi-target interventions targeted women, health providers and health organisations. For this analysis, however, we only consider the components of the intervention that targeted women, which was typically childbirth education. One study came from Chen et al. (2018) [ 18 ] and was graded as having some concerns [ 47 ], two studies from Opiyo et al. (2020) [ 23 ] were graded as having no serious concerns [ 45 , 46 ], and three studies are newly published studies assessed as low [ 44 ] and some concerns about risk of bias [ 48 , 49 ] Table 2 and 3 show characteristics of included studies.

The childbirth education interventions included information about mode of birth, birth process, mental health and coping strategies, pain relief methods, and partners’ roles in birth. Most interventions were delivered in group settings, and only in three studies they were delivered on a one-to-one basis [ 38 , 41 , 42 ]. Only one study explicitly stated that the intervention was individualised to a woman’s unique needs and experiences [ 38 ].

Overall, there was limited theory used to design interventions among the included studies: less than half of interventions (7/17) explicitly used theory in designing the intervention. Among the seven interventions that used theory in intervention development, the theories included the health promotion-disease prevention framework [ 38 ], midwifery counselling framework [ 41 ], cognitive behavioural therapy [ 42 ], Ost’s applied relaxation [ 70 ], conceptual model of parenting [ 71 ], attachment and social cognitive theories [ 37 ], and healthcare improvement scale-up framework [ 46 ]. The remaining 10 studies only relied on previously published studies to design the interventions. We identified very limited process evaluation or implementation outcome evidence related to the included interventions, which is a limitation of the field of CS and clinical interventions more broadly.

• Qualitative comparative analysis

Model 1 – How birth information was provided to women

Model 1 is constructed based on the finding from Kingdon et al. (2018) [ 22 ] that women and communities enjoy learning new birth information, as it opens up new ways of thinking about vaginal birth and CS. Learning new information allows them to understand better the benefits and risks of CS and vaginal births, as well as increase their knowledge about CS [ 22 ].

We used four conditions in constructing model 1 truth table: 1) the provision of information, education, and communication (IEC) materials on what to expect during labour and birth, 2) type of education delivered (antenatal education or psychoeducation), and 3) group-based intervention delivery. We explored this model considering other conditions, such as type of information provided (e.g. information about mode of birth including birth process, mental health and coping strategies, pain relief), delivery technique (e.g. didactic, practical) and frequency and duration of intervention delivery; however these additional conditions did not result in configurations.

Of 16 possible configurations, we identified seven configurations (Table 4 ). The first two row shows perfect consistency of configurations (inclusion = 1) in five studies [ 37 , 68 , 69 , 70 , 71 ] in which all conditions are present, except antenatal education or psychoeducation. The remaining configurations are unsuccessful interventions. Interestingly, when either IEC materials or group-based intervention delivery are present (but not both), implementation is likely to be unsuccessful (rows 3–7).

Boolean minimisation identified two intermediate pathways to successful interventions (Fig.  2 ). The two pathways are similar, except for one condition: type of education. The antenatal education or psychoeducation materials is the content tailored to the type of women they target. Therefore, from the two pathways, we can see that the presence of distribution of IEC materials on birth information and group-based intervention delivery of either antenatal education to the general population of women (e.g. not groups of women with specific risks or conditions) or psychoeducation to women with fear of birth trigger successful interventions. From this solution, we can see that the successful interventions are consistently characterised by the presence of both IEC materials and group-based intervention delivery.

Intermediate pathways from model 1 that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

Model 2 – Emotional support was provided to women

Model 2 was constructed based on the theory that women desire emotional support alongside the communication of information about childbirth [ 22 ]. This includes emotional support from husbands or partners, health professional, or doulas [ 22 ]. Furthermore, Kingdon et al. (2018) describe the importance of two-way conversation and dialogue between women and providers during pregnancy care, particularly to ensure the opportunity for discussion [ 22 ]. Interventions may generate more questions than they answered, creating the need and desire of women to have more dialogue with health professionals [ 22 ]. Women considered intervention content to be most useful when it complements clinical care, is consistent with advice from health professionals and provides a basis for more informed, meaningful dialogue between women and care providers [ 22 ].

Based on this underlying theory, we constructed model 3 truth table by considering three conditions representative of providing emotional support to women, including partner or family member involvement, group-based intervention delivery which provide social or peer support to women, and opportunity for women to interact with health providers. Of 8 possible configurations, we identified six configurations (Table 5 ). The first three rows represent successful interventions with perfect consistency (inclusion = 1). The first row shows successful interventions with all conditions present. The second and third row shows successful interventions with all conditions except partner or family member involvement or interaction with health providers. The remaining rows represent unsuccessful interventions, where at least two conditions are absent.

Boolean minimisation identified two intermediate pathways to successful interventions (Fig.  3 ). In the first pathway, the partner or family members involvement and group-based intervention delivery enable successful interventions. In the second pathway, however, when partner or family members are not involved, successful interventions can happen only when interaction with health providers is included alongside group-based intervention. From these two pathways, we can see that group-based intervention, involvement of partner and family member, and opportunity for women to interact with providers seem to be important in driving intervention success.

Intermediate pathways from model 2 that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

Consolidated model – Essential conditions to prompt successful interventions focusing on women

Using the identified important conditions observed in models 1 and 2, we constructed a consolidated model to examine the final essential conditions which could prompt successful educational interventions targeting women. We merged and tested four conditions: the provision of IEC materials on what to expect during labour and birth, group-based intervention delivery, partner or family member involvement, and opportunity for interaction between women and health providers.

Of the 16 possible configurations, we identified six configurations (Table 6 ). The first three rows show configurations resulting in successful interventions with perfect consistency (inclusion = 1). The first row shows successful interventions with all conditions present; the second and third rows show successful interventions with all conditions present except interaction with health providers or partner or family member involvement. The remaining three rows are configurations of unsuccessful interventions, missing at least two conditions, including the consistent absence of partner or family member involvement.

Boolean minimisation identified two intermediate pathways to successful intervention (Fig.  4 ). The first pathway shows that the opportunity for women to interact with health providers, provision of IEC materials, and group-based intervention delivery prompts successful interventions. The second pathway, however, shows that when there is no opportunity for women to interact with health providers, it is important to have partner or family member involvement alongside group-based intervention delivery and provision of IEC materials. These two pathways suggest that the delivery of educational interventions accompanied by provision of IEC materials and presence of emotional support for women during the intervention is important to trigger successful interventions. These pathways also emphasise that emotional support for women during the intervention can come from either partner, family member, or health provider. For the consolidated model, we did not simplify the solution further, as the intermediate solution is more theoretically sound compared to the most parsimonious solution.

Intermediate pathways from consolidated model that trigger successful interventions targeting pregnant women to optimise CS.  In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid.

Sub-analysis – Interventions targeting both women and health providers or systems

In this sub-analysis, we run the important conditions identified from the consolidated model, added condition of multi-target intervention, and applied it to 17 interventions: 11 interventions targeting women, and six interventions targeting both women and health providers or systems (multi-target interventions).

Of 32 possible configurations, we identified eight configurations (Table 7 ). The first four rows show configurations with successful interventions with perfect consistency (inclusion = 1). The first row is where all the multi-target interventions are clustered, except the unsuccessful intervention Zhang (2020) [ 49 ], and where all the conditions are present. All the conditions in the second to fourth rows are present, except multi-target interventions (all rows), interaction with health providers (third row) and partner and family member involvement (fourth row). The remaining rows are all configurations to unsuccessful interventions, where at least three conditions are missing, except row 8, which is a single case row. This case is the only multi-target intervention that is unsuccessful and in which partner or family members were not involved.

The Boolean minimisation identified two intermediate pathways (Fig.  5 ). The first pathway shows that partner or family involvement, provision of IEC materials, and group-based intervention delivery prompt successful interventions. The first pathway is comprised of all five successful multi-target interventions [ 44 , 45 , 46 , 47 , 48 ] and four of 11 interventions targeting only women [ 37 , 68 , 69 , 71 ]. The second pathway shows that when multi-target interventions are absent, but when interaction with health providers is present, alongside provision of IEC materials and group-based intervention delivery, it prompts successful interventions (3/11 interventions targeting women only [ 37 , 69 , 70 ]). The first pathway shows that there are successful configurations with and without multi-target interventions. Therefore, similar to the interventions targeting women, when implementing multi-target interventions, intervention components targeting women are more likely to be successful when partners or family members are involved, interventions are implemented through group-based intervention delivery, IEC materials were provided, and there is an opportunity for women to interact with health providers.

Intermediate pathways from multi-target interventions sub-analysis that trigger successful interventions targeting pregnant women to optimise CS. In QCA, asterisk (*) denotes an ‘AND’ relationship; Inclusion score (InclS), also known as consistency, indicates the degree to which the evidence is consistent with the hypothesis that there is sufficient relation between the configuration and the outcome; Proportional Reduction in Inconsistency (PRI) refers to the extent in which a configuration is sufficient in triggering successful outcome as well as the negation of the outcome; Coverage score (CovS) refers to percentage of cases in which the configuration is valid

To summarise, there are four essential intervention components which trigger successful educational interventions focusing on pregnant women to reduce CS, this includes 1) group-based intervention delivery, 2) provision of IEC materials on what to expect during labour and birth, 3) partner or family member involvement on the intervention, and 4) opportunity for women to interact with health providers. These conditions do not work in siloed or independently but instead work jointly as parts of configurations to enable successful interventions.

Our extensive QCA identified configurations of essential intervention components which are sufficient to trigger successful interventions to optimised CS. Educational interventions focusing on women were successful by: 1) leveraging social or peer support through group-based intervention delivery, 2) improving women’s knowledge and awareness of what to expect during labour and birth, 3) ensuring women have emotional support through partner or family participation in the intervention, and 4) providing opportunities for women to interact with health providers. We found that the absence of two or more of the above characteristics in an intervention result in unsuccessful interventions. Unlike our logic model, which predicted engagement strategies (i.e. intensity, frequency, technique, recruitment, incentives) to be essential to intervention success, we found that “support” seems to be central in maximising benefits of interventions targeting women.

Group-based intervention delivery is present across all four truth tables and eight pathways leading to successful intervention implementation, suggesting that group-based intervention delivery is an essential component of interventions targeting women. Despite this, we cannot conclude that group-based intervention delivery is a necessary condition, as there may be other pathways not captured in this QCA. The importance of group-based intervention delivery may be due to the group setting providing women with a sense of confidence through peer support and engagement. In group-based interventions, women may feel more confident when learning with others and peer support may motivate women. Furthermore, all group-based interventions in our included studies are conducted at health facilities, which may provide women with more confidence that information is aligned with clinical recommendations. Evidence on benefits of group-based interventions involving women who are pregnant has been demonstrated previously [ 72 , 73 ]. Women reported that group-based interventions reduce their feelings of isolation, provide access to group support, and allow opportunities for them to share their experiences [ 72 , 74 , 75 , 76 ]. This is aligned with social support theory, in which social support through a group or social environment may provide women with feelings of reassurance, compassion, reduce feelings of uncertainty, increase sense of control, access to new contacts to solve problems, and provision of instrumental support, which eventually influence positive health behaviours [ 72 , 77 ]. Women may resolve their uncertainties around mode of birth by sharing their concerns with others and learning at the same time how others cope with it. These findings are consistent with the benefits associated with group-based antenatal care, which is recommended by WHO [ 78 , 79 ].

Kingdon et al. (2018) reported that women and communities liked learning new birth information, as it opens new ways of thinking about vaginal birth and CS, and educates about benefits of different modes of birth, including risks of CS. Our QCA is aligned with this finding where provision of information about birth through education delivery leads to successful interventions but with certain caveats. That is, provision of birth information should be accompanied by IEC materials and through group-based intervention delivery. There is not enough information to distinguish what type of IEC materials lead to successful intervention; however, it is important to note that the format of the IEC materials (such as paper-based or mobile application) may affect success. More work is needed to understand how women and families react to format of IEC materials; for example, will paper-based IEC materials be relegated over more modern methods of reaching women with information through digital applications? The QUALI-DEC (Quality decision-making (QUALI-DEC) by women and healthcare providers for appropriate use of caesarean section) study is currently implementing a decision-analysis tool to help women make an informed decision on preferred mode of birth using both a paper-based and mobile application that may shed some light on this [ 80 ].

Previous research has shown that women who participated in interventions aiming to reduce CS desired emotional support (from partners, doulas or health providers) alongside the communication about childbirth [ 22 ]. Our QCA is aligned with this finding in which emotional support from partners or family members is highly influential in leading to successful interventions. Partner involvement in maternity care has been extensively studied and has been demonstrated to improve maternal health care utilisation and outcomes [ 81 ]. Both women and their partners perceived that partner involvement is crucial as it facilitates men to learn directly from providers, thus promoting shared decision-making among women and partners and enabling partners to reinforce adherence to any beneficial suggestions [ 82 , 83 , 84 , 85 , 86 ]. Partners provide psychosocial support to women, for example through being present during pregnancy and the childbirth process, as well as instrumental support, which includes supporting women financially [ 82 , 83 , 84 ]. Despite the benefits of partner involvement, partner's participation in maternity care is still low [ 82 ], as reflected in this study where only four out of 11 included interventions on this study involved partner or family member involvement. Reasons for this low participation, which include unequal gender norms and limited health system capability [ 82 , 84 , 85 , 86 ], should be explored and addressed to ensure the benefits of the interventions.

Furthermore, our QCA demonstrates the importance of interaction with health providers to trigger successful interventions. The interaction of women with providers in CS decision-making, however, is on a “nexus of power, trust, and risk”, where it may be beneficial but can also reinforce the structural oppression of women [ 13 ]. A recent study on patient-provider interaction in CS decision-making concluded that the interaction between providers who are risk-averse, and women who are cautious about their pregnancies in the health system results in discouragement of vaginal births [ 87 ]. However, this decision could be averted by meaningful communication between women and providers where CS risks and benefits are communicated in an environment where vaginal birth is encouraged [ 87 ]. Furthermore, the reasons women desire interaction with providers can come from opposite directions. Some women see providers as the most trusted and knowledgeable source, in which women can trust the judgement and ensure that the information learned is reliable and evidenced-based [ 22 ]. On the other hand, some women may have scepticism towards providers where women understand that providers’ preference may negatively influence their preferred mode of birth [ 22 ]. Therefore, adequate, two-way interaction is important for women to build a good rapport with providers.

It is also important to note that we have limited evidence (3/17 intervention studies) involving women with previous CS. Vaginal birth after previous CS (VBAC) can be a safe and positive experience for some women, but there are also potential risks depending on their obstetric history [ 88 , 89 , 90 ]. Davis (2020) found that women were motivated to have VBAC due to negative experiences of CS, such as the difficult recovery, and that health providers' roles served as pivotal drivers in motivating women towards VBAC [ 91 ]. Other than this, VBAC also requires giving birth in a suitably staffed and equipped maternity unit, with staff trained on VBAC, equipment for labour monitoring, and resources for emergency CS if needed [ 89 , 90 ]. There is comparatively less research conducted on VBAC and trial of labour after CS [ 88 ]. Therefore, more work is needed to explore if there are potentially different pathways that lead to successful intervention implementation for women with previous CS. It may be more likely that interventions targeting various stakeholders are more crucial in this group of women. For example, both education for women and partners or families, as well as training to upskill health providers might be needed to support VBAC.

Strength and limitations

We found many included studies had poor reporting of the interventions, including the general intervention components (e.g. presence of policies that may support interventions) and process evaluation components, which is reflective of the historical approach to reporting trial data. This poor reporting means we could not engage further in the interventions and thus may have missed important conditions that were not reported. However, we have attempted to compensate for limited process evaluation components by identifying all relevant sibling studies that could contribute to a better understanding of context. Furthermore, there are no studies conducted in low-income countries, despite rapidly increasing CS rates in these settings. Lastly, we were not able to conduct more nuanced analyses about CS, such as exploring how CS interventions impacted changes to emergency versus elective CS, VBAC, or instrumental birth, due to an insufficient number of studies and heterogeneity in outcome measurements. Therefore, it is important to note that we are not necessarily measuring the optimal outcome of interest—reducing unnecessary CS. However, it is unlikely that these non-clinical interventions will interfere with a decision of CS based on clinical indications.

Despite these limitations, this is the first study aiming to understand how certain interventions can be successful in targeting women to optimise CS use. We used the QCA approach and new analytical frameworks to re-analyse existing systematic review evidence to generate new knowledge. We ensure robustness through the use of a logic model and worked backwards in understanding what aspects are different in the intervention across different outcomes. The use of QCA and qualitative evidence synthesis ensured that the results are theory-driven, incorporate participants’ perspectives into the analysis, and explored iteratively to find the appropriate configurations, reducing the risk of data fishing. Lastly, this QCA extends the understanding of effectiveness review conducted by Chen et al. (2018) [ 18 ] by explaining the potential intervention components which may influence heterogeneity.

Implications for practice and research

To aid researchers and health providers to reduce CS in their contexts and designing educational interventions targeting women during pregnancy, we have developed a checklist of key components or questions to consider when designing the interventions that may help lead to successful implementation:

Is the intervention delivered in a group setting?

Are IEC materials on what to expect during labour and birth disseminated to women?

Are women’s partners or families involved in the intervention?

Do women have opportunities to interact with health providers?

We have used this checklist to explore the extent to which the included interventions in our QCA include these components using a matrix model (Fig.  6 ).

Matrix model assessing the extent to which the included intervention studies have essential intervention components identified in the QCA

Additionally, future research on interventions to optimise the use of CS should report the intervention components implemented, including process outcomes such as fidelity, attrition, contextual factors (e.g. policies, details of how the intervention is delivered), and stakeholder factors (e.g. women’s perceptions and satisfaction). These factors are important in not just evaluating whether the intervention is successful or not, but also in exploring why similar interventions can work in one but not in another context. There is also a need for more intervention studies implementing VBAC to reduce CS, to understand how involving women with previous CS may result in successful interventions. Furthermore, more studies understanding impact of the interventions targeting women in LMICs are needed.

This QCA illustrates crucial intervention components and potential pathways that can trigger successful educational interventions to optimise CS, focusing on pregnant women. The following intervention components are found to be sufficient in triggering successful outcomes: 1) group-based delivery, 2) provision of IEC materials, 3) partner or family member involvement, and 4) opportunity for women to interact with health providers. These intervention components do not work in siloed or independently but instead work jointly as parts of configurations to enable successful interventions. Researchers, trialists, hospitals, or other institutions and stakeholders planning interventions focusing on pregnant women can consider including these components to ensure benefits. More studies understanding impact of the interventions targeting women to optimise CS are needed from LMICs. Researchers should clearly describe and report intervention components in trials, and consider how process evaluations can help explain why trials were successful or not. More robust trial reporting and process evaluations can help to better understand mechanisms of action and why interventions may work in one context yet not another.

Availability of data and materials

Additional information files have been provided and more data may be provided upon request to [email protected].

Abbreviations

Coverage score

• Caesarean section

Crisp set qualitative comparative analysis

Fuzzy set qualitative comparative analysis

Information, education, and communication

Inclusion score

Low- and middle-income countries

Proportional reduction in inconsistency

Quality decision-making by women and healthcare providers for appropriate use of caesarean section

Vaginal birth after previous caesarean section

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Acknowledgements

We extend our thanks to Jim Berryman (Brownless Medical Library, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne) for his help in refining the search strategy for sibling studies.

This research was made possible with the support of UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), a co-sponsored programme executed by the World Health Organization (WHO). RIZ is supported by Melbourne Research Scholarship and Human Rights Scholarship from The University of Melbourne. CSEH is supported by a National Health and Medical Research Council (NHMRC) Principal Research Fellowship. MAB’s time is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200100264) and a Dame Kate Campbell Fellowship (University of Melbourne Faculty of Medicine, Dentistry, and Health Sciences). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The contents of this publication are the responsibility of the authors and do not reflect the views of the UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization.

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Logic model in optimizing CS use.

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Risk of bias assessments.

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Coding framework and calibration rules.

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Coding framework as applied to each intervention (data table).

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Zahroh, R.I., Sutcliffe, K., Kneale, D. et al. Educational interventions targeting pregnant women to optimise the use of caesarean section: What are the essential elements? A qualitative comparative analysis. BMC Public Health 23 , 1851 (2023). https://doi.org/10.1186/s12889-023-16718-0

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The case study approach

Sarah crowe.

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

Kathrin Cresswell

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

Ann Robertson

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

Anthony Avery

Aziz sheikh.

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.

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 ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

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 ​ (Table5), 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.

Definitions of a case study

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 ​ (Table1), 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 ​ Tables2, 2 , ​ ,3 3 and ​ and4) 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 ​ (Table2) 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 ​ Tables2 2 and ​ and3, 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 ​ (Table4 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 ​ (Table6). 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 ].

Example of epistemological approaches that may be used in case study research

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 ​ Table7 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 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 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 ​ (Table1) 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 ​ Table3) 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 ​ (Table2 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 ​ (Table1 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 ​ (Table3 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 ​ (Table4 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 ​ Table3, 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 ​ (Table4), 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 ​ Table8 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 ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 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.

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.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

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.

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