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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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Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

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Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

40 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

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Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

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What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

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where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

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As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

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thank you so much, the explanation and examples are really helpful

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• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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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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• 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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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.

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This chapter reviews the strengths and limitations of case study as a research method in social sciences. It provides an account of an evidence base to justify why a case study is best suitable for some research questions and why not for some other research questions. Case study designing around the research context, defining the structure and modality, conducting the study, collecting the data through triangulation mode, analysing the data, and interpreting the data and theory building at the end give a holistic view of it. In addition, the chapter also focuses on the types of case study and when and where to use case study as a research method in social science research.

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A Quick Guide to Case Study with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

A case study is a documented history and detailed analysis of a situation concerning organisations, industries, and markets.

A case study:

• Focuses on discovering new facts of the situation under observation.
• Includes data collection from multiple sources over time.
• Widely used in social sciences to study the underlying information, organisation, community, or event.
• It does not provide any solution to the problem .

When to Use Case Study? 

You can use a case study in your research when:

• The focus of your study is to find answers to how and why questions .
• You don’t have enough time to conduct extensive research; case studies are convenient for completing your project successfully.
• You want to analyse real-world problems in-depth, then you can use the method of the case study.

You can consider a single case to gain in-depth knowledge about the subject, or you can choose multiple cases to know about various aspects of your  research problem .

What are the Aims of the Case Study?

• The case study aims at identifying weak areas that can be improved.
• This method is often used for idiographic research (focuses on individual cases or events).
• Another aim of the case study is nomothetic research (aims to discover new theories through data analysis of multiple cases).

Types of Case Studies

There are different types of case studies that can be categorised based on the purpose of the investigation.

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How to Conduct a Case Study?

• Select the Case to Investigate
• Formulate the Research Question
• Review of Literature
• Choose the Precise Case to Use in your Study
• Select Data Collection and Analysis Techniques
• Collect the Data
• Analyse the Data
• Prepare the Report

Step1: Select the Case to Investigate

The first step is to select a case to conduct your investigation. You should remember the following points.

• Make sure that you perform the study in the available timeframe.
• There should not be too much information available about the organisation.
• You should be able to get access to the organisation.
• There should be enough information available about the subject to conduct further research.

Step2: Formulate the Research Question

It’s necessary to  formulate a research question  to proceed with your case study. Most of the research questions begin with  how, why, what, or what can . 

You can also use a research statement instead of a research question to conduct your research which can be conditional or non-conditional. 

Step 3: Review of Literature

Once you formulate your research statement or question, you need to extensively  review the documentation about the existing discoveries related to your research question or statement.

Step 4: Choose the Precise Case to Use in your Study

You need to select a specific case or multiple cases related to your research. It would help if you treated each case individually while using multiple cases. The outcomes of each case can be used as contributors to the outcomes of the entire study.  You can select the following cases. 

• Representing various geographic regions
• Cases with various size parameters
• Explaining the existing theories or assumptions
• Leading to discoveries
• Providing a base for future research.

Step 5: Select Data Collection and Analysis Techniques

You can choose both  qualitative or quantitative approaches  for  collecting the data . You can use  interviews ,  surveys , artifacts, documentation, newspapers, and photographs, etc. To avoid biased observation, you can triangulate  your research to provide different views of your case. Even if you are focusing on a single case, you need to observe various case angles. It would help if you constructed validity, internal and external validity, as well as reliability.

Example: Identifying the impacts of contaminated water on people’s health and the factors responsible for it. You need to gather the data using qualitative and quantitative approaches to understand the case in such cases.

Construct validity:  You should select the most suitable measurement tool for your research. 

Internal validity:   You should use various methodological tools to  triangulate  the data. Try different methods to study the same hypothesis.

External validity:  You need to effectively apply the data beyond the case’s circumstances to more general issues.

Reliability:   You need to be confident enough to formulate the new direction for future studies based on your findings.

Also Read:  Reliability and Validity

Step 6: Collect the Data

Beware of the following when collecting data:

• Information should be gathered systematically, and the collected evidence from various sources should contribute to your research objectives.
• Don’t collect your data randomly.
• Recheck your research questions to avoid mistakes.
• You should save the collected data in any popular format for clear understanding.
• While making any changes to collecting information, make sure to record the changes in a document.
• You should maintain a case diary and note your opinions and thoughts evolved throughout the study.

Step 7: Analyse the Data

The research data identifies the relationship between the objects of study and the research questions or statements. You need to reconfirm the collected information and tabulate it correctly for better understanding. 

Step 8: Prepare the Report

It’s essential to prepare a report for your case study. You can write your case study in the form of a scientific paper or thesis discussing its detail with supporting evidence. 

A case study can be represented by incorporating  quotations,  stories, anecdotes,  interview transcripts , etc., with empirical data in the result section. 

You can also write it in narrative styles using  textual analysis  or   discourse analysis . Your report should also include evidence from published literature, and you can put it in the discussion section.

Advantages and Disadvantages of Case Study

Frequently Asked Questions

What is the case study.

A case study is a research method where a specific instance, event, or situation is deeply examined to gain insights into real-world complexities. It involves detailed analysis of context, data, and variables to understand patterns, causes, and effects, often used in various disciplines for in-depth exploration.

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A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data.

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Case Studies

Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”

Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.

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Blog Beginner Guides 6 Types of Case Studies to Inspire Your Research and Analysis

6 Types of Case Studies to Inspire Your Research and Analysis

Written by: Ronita Mohan Sep 20, 2021

Case studies have become powerful business tools. But what is a case study? What are the benefits of creating one? Are there limitations to the format?

If you’ve asked yourself these questions, our helpful guide will clear things up. Learn how to use a case study for business. Find out how cases analysis works in psychology and research.

We’ve also got examples of case studies to inspire you.

Haven’t made a case study before? You can easily  create a case study  with Venngage’s customizable case study templates .

Click to jump ahead:

What is a case study?

6 types of case studies, what is a business case study, what is a case study in research, what is a case study in psychology, what is the case study method, benefits of case studies, limitations of case studies, faqs about case studies.

A case study is a research process aimed at learning about a subject, an event or an organization. Case studies are use in business, the social sciences and healthcare.

A case study may focus on one observation or many. It can also examine a series of events or a single case. An effective case study tells a story and provides a conclusion.

Healthcare industries write reports on patients and diagnoses. Marketing case study examples , like the one below, highlight the benefits of a business product.

Now that you know what a case study is, let’s look at the six different types of case studies next.

There are six common types of case reports. Depending on your industry, you might use one of these types.

Descriptive case studies

Explanatory case studies, exploratory case reports, intrinsic case studies, instrumental case studies, collective case reports.

We go into more detail about each type of study in the guide below.

Related:  15+ Professional Case Study Examples [Design Tips + Templates]

When you have an existing hypothesis, you can design a descriptive study. This type of report starts with a description. The aim is to find connections between the subject being studied and a theory.

Once these connections are found, the study can conclude. The results of this type of study will usually suggest how to develop a theory further.

A study like the one below has concrete results. A descriptive report would use the quantitative data as a suggestion for researching the subject deeply.

When an incident occurs in a field, an explanation is required. An explanatory report investigates the cause of the event. It will include explanations for that cause.

The study will also share details about the impact of the event. In most cases, this report will use evidence to predict future occurrences. The results of explanatory reports are definitive.

Note that there is no room for interpretation here. The results are absolute.

The study below is a good example. It explains how one brand used the services of another. It concludes by showing definitive proof that the collaboration was successful.

Another example of this study would be in the automotive industry. If a vehicle fails a test, an explanatory study will examine why. The results could show that the failure was because of a particular part.

Related: How to Write a Case Study [+ Design Tips]

An explanatory report is a self-contained document. An exploratory one is only the beginning of an investigation.

Exploratory cases act as the starting point of studies. This is usually conducted as a precursor to large-scale investigations. The research is used to suggest why further investigations are needed.

An exploratory study can also be used to suggest methods for further examination.

For example, the below analysis could have found inconclusive results. In that situation, it would be the basis for an in-depth study.

Intrinsic studies are more common in the field of psychology. These reports can also be conducted in healthcare or social work.

These types of studies focus on a unique subject, such as a patient. They can sometimes study groups close to the researcher.

The aim of such studies is to understand the subject better. This requires learning their history. The researcher will also examine how they interact with their environment.

For instance, if the case study below was about a unique brand, it could be an intrinsic study.

Once the study is complete, the researcher will have developed a better understanding of a phenomenon. This phenomenon will likely not have been studied or theorized about before.

Examples of intrinsic case analysis can be found across psychology. For example, Jean Piaget’s theories on cognitive development. He established the theory from intrinsic studies into his own children.

Related: What Disney Villains Can Tell Us About Color Psychology [Infographic]

This is another type of study seen in medical and psychology fields. Instrumental reports are created to examine more than just the primary subject.

When research is conducted for an instrumental study, it is to provide the basis for a larger phenomenon. The subject matter is usually the best example of the phenomenon. This is why it is being studied.

Take the example of the fictional brand below.

Assume it’s examining lead generation strategies. It may want to show that visual marketing is the definitive lead generation tool. The brand can conduct an instrumental case study to examine this phenomenon.

Collective studies are based on instrumental case reports. These types of studies examine multiple reports.

There are a number of reasons why collective reports are created:

• To provide evidence for starting a new study
• To find pattens between multiple instrumental reports
• To find differences in similar types of cases
• Gain a deeper understanding of a complex phenomenon
• Understand a phenomenon from diverse contexts

A researcher could use multiple reports, like the one below, to build a collective case report.

Related: 10+ Case Study Infographic Templates That Convert

A business or marketing case study aims at showcasing a successful partnership. This can be between a brand and a client. Or the case study can examine a brand’s project.

There is a perception that case studies are used to advertise a brand. But effective reports, like the one below, can show clients how a brand can support them.

Hubspot created a case study on a customer that successfully scaled its business. The report outlines the various Hubspot tools used to achieve these results.

Hubspot also added a video with testimonials from the client company’s employees.

So, what is the purpose of a case study for businesses? There is a lot of competition in the corporate world. Companies are run by people. They can be on the fence about which brand to work with.

Business reports  stand out aesthetically, as well. They use  brand colors  and brand fonts . Usually, a combination of the client’s and the brand’s.

With the Venngage  My Brand Kit  feature, businesses can automatically apply their brand to designs.

A business case study, like the one below, acts as social proof. This helps customers decide between your brand and your competitors.

Don’t know how to design a report? You can learn  how to write a case study  with Venngage’s guide. We also share design tips and examples that will help you convert.

Related: 55+ Annual Report Design Templates, Inspirational Examples & Tips [Updated]

Research is a necessary part of every case study. But specific research fields are required to create studies. These fields include user research, healthcare, education, or social work.

For example, this UX Design  report examined the public perception of a client. The brand researched and implemented new visuals to improve it. The study breaks down this research through lessons learned.

Clinical reports are a necessity in the medical field. These documents are used to share knowledge with other professionals. They also help examine new or unusual diseases or symptoms.

The pandemic has led to a significant increase in research. For example,  Spectrum Health  studied the value of health systems in the pandemic. They created the study by examining community outreach.

The pandemic has significantly impacted the field of education. This has led to numerous examinations on remote studying. There have also been studies on how students react to decreased peer communication.

Social work case reports often have a community focus. They can also examine public health responses. In certain regions, social workers study disaster responses.

You now know what case studies in various fields are. In the next step of our guide, we explain the case study method.

In the field of psychology, case studies focus on a particular subject. Psychology case histories also examine human behaviors.

Case reports search for commonalities between humans. They are also used to prescribe further research. Or these studies can elaborate on a solution for a behavioral ailment.

The American Psychology Association  has a number of case studies on real-life clients. Note how the reports are more text-heavy than a business case study.

Famous psychologists such as Sigmund Freud and Anna O popularised the use of case studies in the field. They did so by regularly interviewing subjects. Their detailed observations build the field of psychology.

It is important to note that psychological studies must be conducted by professionals. Psychologists, psychiatrists and therapists should be the researchers in these cases.

Related: What Netflix’s Top 50 Shows Can Teach Us About Font Psychology [Infographic]

The case study method, or case method, is a learning technique where you’re presented with a real-world business challenge and asked how you’d solve it.

After working through it independently and with peers, you learn how the actual scenario unfolded. This approach helps develop problem-solving skills and practical knowledge.

This method often uses various data sources like interviews, observations, and documents to provide comprehensive insights. The below example would have been created after numerous interviews.

Case studies are largely qualitative. They analyze and describe phenomena. While some data is included, a case analysis is not quantitative.

There are a few steps in the case method. You have to start by identifying the subject of your study. Then determine what kind of research is required.

In natural sciences, case studies can take years to complete. Business reports, like this one, don’t take that long. A few weeks of interviews should be enough.

The case method will vary depending on the industry. Reports will also look different once produced.

As you will have seen, business reports are more colorful. The design is also more accessible . Healthcare and psychology reports are more text-heavy.

Designing case reports takes time and energy. So, is it worth taking the time to write them? Here are the benefits of creating case studies.

• Collects large amounts of information
• Helps formulate hypotheses
• Builds the case for further research
• Discovers new insights into a subject
• Builds brand trust and loyalty
• Engages customers through stories

For example, the business study below creates a story around a brand partnership. It makes for engaging reading. The study also shows evidence backing up the information.

We’ve shared the benefits of why studies are needed. We will also look at the limitations of creating them.

Related: How to Present a Case Study like a Pro (With Examples)

There are a few disadvantages to conducting a case analysis. The limitations will vary according to the industry.

• Responses from interviews are subjective
• Subjects may tailor responses to the researcher
• Studies can’t always be replicated
• In certain industries, analyses can take time and be expensive
• Risk of generalizing the results among a larger population

These are some of the common weaknesses of creating case reports. If you’re on the fence, look at the competition in your industry.

Other brands or professionals are building reports, like this example. In that case, you may want to do the same.

What makes a case study a case study?

A case study has a very particular research methodology. They are an in-depth study of a person or a group of individuals. They can also study a community or an organization. Case reports examine real-world phenomena within a set context.

How long should a case study be?

The length of studies depends on the industry. It also depends on the story you’re telling. Most case studies should be at least 500-1500 words long. But you can increase the length if you have more details to share.

What should you ask in a case study?

The one thing you shouldn’t ask is ‘yes’ or ‘no’ questions. Case studies are qualitative. These questions won’t give you the information you need.

Ask your client about the problems they faced. Ask them about solutions they found. Or what they think is the ideal solution. Leave room to ask them follow-up questions. This will help build out the study.

How to present a case study?

When you’re ready to present a case study, begin by providing a summary of the problem or challenge you were addressing. Follow this with an outline of the solution you implemented, and support this with the results you achieved, backed by relevant data. Incorporate visual aids like slides, graphs, and images to make your case study presentation more engaging and impactful.

Now you know what a case study means, you can begin creating one. These reports are a great tool for analyzing brands. They are also useful in a variety of other fields.

Use a visual communication platform like Venngage to design case studies. With Venngage’s templates, you can design easily. Create branded, engaging reports, all without design experience.

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Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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

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

Research bias

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

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The 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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Understanding accounting students' learning and its relation to academic achievement: Exploratory case study

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Assessing uncertainty in wind turbine electrical power production using Monte-Carlo simulation: A case study on Sidrap wind turbine, Sulawesi - Indonesia

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Leonard Lisapaly; Assessing uncertainty in wind turbine electrical power production using Monte-Carlo simulation: A case study on Sidrap wind turbine, Sulawesi - Indonesia. AIP Conf. Proc. 19 August 2024; 3039 (1): 020001. https://doi.org/10.1063/5.0209595

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As the number of populations worldwide, including Indonesia, increases annually, the need for electrical energy also increases. With fossil-based energy starting to decrease in some places, and the global need to achieve net zero-emission, renewable energy is gaining popularity as the source of electrical power generation, for example, wind energy. Electrical power generated from wind turbines is one of the popular choices in many countries; among them is the Sidrap wind turbine plant in Sulawesi, Indonesia. Electrical power generated by a wind turbine relies on the wind speed, which varies from time to time and season to season. Therefore, building wind turbines plant should not be based on a deterministic wind speed but should bring the wind speed uncertainty into account. Taking the Sidrap wind turbine plant in Sulawesi, Indonesia, as the case study, this work aims to predict the plant’s average power and uncertainty. This calculation is done by implementing the Monte-Carlo simulation to calculate the production uncertainty. The data used for simulation is public data set between 2004 and 2015, obtained from the EMD International website. The simulation result is then compared to the actual production in 2018 and 2019 for analysis. The result indicates that the product is still in the range estimated using the Monte Carlo simulation. This method can also estimate other electrical power potential and uncertainty in an area before a wind turbine plant is built, given enough data sets containing wind speed variation for years. It is expected that by correctly understanding the uncertainty in electrical power production, the decision-making process to build the plant can be accurately determined.

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The client experience of treatment for generalized anxiety disorder utilizing eye movement desensitization and reprocessing and motivational interviewing: A case study reflection

The purpose of this study is to explore the perceptions of clients in an integration model of EMDR and motivational interviewing (MI) in affecting generalized anxiety disorder.

Dissertation Abstract

“The purpose of this study is to explore the perceptions of clients in an integration model of eye movement desensitization and reprocessing (EMDR) and motivational interviewing (MI) in affecting generalized anxiety disorder (GAD). EMDR is an effective treatment in various anxiety disorders, including post-traumatic stress disorder (PTSD), social anxiety, specific phobia, and reducing symptoms in other anxiety disorders; however, few studies have been conducted on the effectiveness of EMDR for the treatment of GAD and its main symptoms. As MI is a treatment module that directly addresses change, ambivalence, and treatment resistance, it may be effective to integrate with EMDR in the service of improving treatment response for GAD clients. In an integrated model, the primary focus will be less on trauma and more on the processing of motivational factors. Participants will be selected purposefully from the population of those having current or recent experiences with GAD that are negatively affecting the quality of their life. New clients of the therapist will be purposefully informed of the study and selected for the study. The sample size of this study will not exceed eight participants.”

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Moffitt, C. (2024). The client experience of treatment for generalized anxiety disorder utilizing eye movement desensitization and reprocessing and motivational interviewing: A case study reflection. (31484467). [Doctoral dissertation, Texas A&M University]. ProQuest Dissertations Publishing. https://www.proquest.com/openview/608401b66f01d767edcde8f6f8be5a60/1?pq-origsite=gscholar&cbl=18750&diss=y

Date August 15, 2024

Creator(s) Carla Moffitt

Topics Anxiety/Panic/Phobias

Practice & Methods Integrative Therapies

Extent 24 pages

Publisher Texas A&M University

APA Citation Moffitt, C. (2024). The client experience of treatment for generalized anxiety disorder utilizing eye movement desensitization and reprocessing and motivational interviewing: A case study reflection. (31484467). [Doctoral dissertation, Texas A&M University]. ProQuest Dissertations Publishing. https://www.proquest.com/openview/608401b66f01d767edcde8f6f8be5a60/1?pq-origsite=gscholar&cbl=18750&diss=y

Audience EMDR Therapists, Other Mental Health Professionals

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Content Type Thesis/Dissertation

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Call for case studies and best practices on addressing tuberculosis in prisons

The World Health Organization (WHO) Global Tuberculosis Programme is launching a call for case studies and best practices on addressing tuberculosis (TB) in prisons. This includes provision of services for communicable diseases, with a focus on TB prevention and care provided within prisons, as well as on addressing TB in the context of mobility of people between police holding cells, prisons and the community.

An estimated 10.6 million people developed TB in 2022. Despite being preventable and curable, TB remains one of the world’s top infectious killers, accounting for over one million deaths annually. Prisons and other places of detention can be high risk environments for TB transmission due to overcrowding, inadequate infection prevention and control measures, and other determinants such as undernutrition, substance use disorders and inadequate access to health services. The burden of TB disease in prison populations is about 10 times higher than in the general population. In 2019, an estimated 125,105 people in prisons fell ill with TB worldwide, representing about 1% of the global incidence, and only about half of these detected, leaving a large gap of incarcerated people with undiagnosed or unreported TB.

The provision of high-quality health care in prisons, including TB prevention and care, is essential. People in prisons should access health care in the same conditions and of a similar quality as any other person living in the community, throughout their life course. Protecting the human right to health and ensuring universal health coverage are particularly critical in prison settings, where the provision of health services is not always prioritised.

WHO recommendations on TB (prevention, screening, diagnosis, treatment, testing for HIV and comorbidities, treatment support, and infection prevention and control) are applicable to all settings, including prisons. In addition, WHO has specific recommendations on systematic screening for TB disease in prisons and penitentiary institutions, for both prisoners and prison staff as well as systematic testing and treatment for TB infection, which may be considered for people in prisons as well as other at-risk groups including health workers, immigrants from countries with a high TB burden, homeless people and people who use drugs.

WHO has previously issued guidance on the management of TB in prisons, however there have been significant advances in TB prevention and care since this guidance was issued. The WHO Global Tuberculosis Programme is in the process of updating its guidance on TB in prisons. The purpose of the guidance will be to provide operational guidance on the prevention, management and care of TB in prisons, including when people are transferred between police holding cells, prisons and communities. The new WHO guidance on TB in prisons will also feature several case studies illustrating experiences and best practices in addressing TB in prisons.

These case studies may include examples of interventions that are provided within prisons and police holding cells, such as:

·         TB screening and active TB case finding for people in prisons as well as prison staff;

·         Short course TB preventive treatment and effective management and treatment of TB;

·         Screening, diagnosis and care for co-morbidities or other health related risk factors, such as mental health conditions, substance use disorders, HIV, among other conditions;

·         Contact investigation, outbreak management and TB infection prevention and control;

·         Policies and practices that aim to address the social determinants of TB among people in prisons (such as employment, housing and linkages with social protection services);

·         Collaboration between ministries of health and the ministries responsible for prisons and penitentiary institutions;

·         Policies and practices on promotion of human rights and the human right to health;

·         Building the capacity of prison health staff and inmates to effectively prevent and manage TB;

·         Recording and reporting systems on TB in prison settings, and their linkages to the national TB surveillance system, and

·         Policies or practices that ensure continuity of care when people with TB are transferred between prisons or from prisons to the community.

Through this call, WHO invites country officials, UN agencies, technical partners, and other governmental and non-governmental stakeholders within and beyond the health sector involved in the provision of health services within prison settings to submit examples of relevant case studies and best practices to this email address:  [email protected] .

These case studies and best practices should be no longer than 500 words, should feature current examples implemented in the last ten years and should be structured as follows:

·         Background

·         Policy or practice implemented

·         Results achieved as a result of this policy or practice

·         Challenges identified during implementation (and solutions)

·         Way forward/ next steps (as a conclusion)

The deadline for submission of case studies and best practices is  Friday 30 September, 2024 .

All contributors to the selected case studies will be appropriately acknowledged in the WHO guidance on TB in prisons. We thank you in advance for your collaboration, and please do not hesitate to contact us in case of any questions.

Factors influencing formal loan repayment performance of urban women in Tigray, A case study of Dedebit credit and saving institution, Adwa town woreda

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Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study

• Dong Tian 1 , 2   na1 ,
• Yu-Jie Zuo 1 , 3   na1 ,
• Hao-Ji Yan 4   na1 ,
• Heng Huang 1 ,
• Ming-Zhao Liu 2 ,
• Hang Yang 2 ,
• Jin Zhao 2 ,
• Ling-Zhi Shi 2 &
• Jing-Yu Chen 2  

BMC Medical Informatics and Decision Making volume  24 , Article number:  229 ( 2024 ) Cite this article

114 Accesses

Metrics details

Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx.

Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method.

A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P  < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666–0.864) and 0.085 (95% CI, 0.058–0.117) among all ML models, which was superior to the conventional LR model.

Conclusions

The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx.

Peer Review reports

Introduction

Lung transplantation (LTx) has been considered the only effective therapeutic option for end-stage lung diseases. The number of lung transplants has been increasing over the last two decades, with approximately 70,000 adult lung transplants performed worldwide thus far [ 1 ]. Since the first clinical LTx in 1963, airway complications (AC) have resulted in substantial mortality and clinical LTx failure [ 2 ]. In recent years, the occurrence of AC has tended to decrease with improvements in surgical techniques, immunosuppression, and patient allocation [ 3 ]. Nevertheless, large studies have reported that the prevalence of AC remains high.

Airway stenosis (AS) refers to a fixed reduction in the caliber of the airway and is the most common AC after LTx with a reported occurrence rate ranging from 1.6%–32.0% in previous studies [ 4 , 5 , 6 , 7 , 8 , 9 ]. The onset of AS usually occurs between 2 and 9 months after LTx [ 10 , 11 ]. A reduction in the cross-sectional area > 50% is confirmation of severe AS, which reduces the quality of life and increases the morbidity and mortality of patients [ 12 ]. Severe AS requires timely clinical intervention to prevent further progression of AS [ 13 ]. Early detection of AS and treatment by balloon dilation can achieve good efficacy [ 14 ]. However, the early stages of AS are difficult to detect since they often present without specific clinical symptoms. Bronchoscopy is the gold standard for diagnosis, but it is usually used in patients who present with clinical symptoms [ 15 ]. Therefore, early and accurate detection of AS requiring clinical intervention is crucial to guide clinical decision-making about subsequent treatment.

Although the published 2018 International Society for Heart and Lung Transplantation (ISHLT) consensus statement reported risk factors for AC, the risk factors for AS remain unclear [ 4 ]. The risk factors for AS are still controversial due to the inconsistency of risk factors among different institutions [ 16 , 17 ]. In addition, the occurrence of AS is difficult to accurately predict by independent risk factors. Identification of AS status requiring clinical intervention using an accurate prediction model could be valuable to conduct optimal treatment and improve outcomes for LTx patients. However, there has been no satisfactory tool to accurately predict AS requiring clinical intervention. Machine learning (ML) algorithms, a branch of artificial intelligence, can integrate clinical characteristics to achieve accurate predictive outcomes [ 18 ]. Our prior research underscored the efficacy of ML algorithms in predicting survival outcomes in LTx patients. Building on this foundation, we endeavored to extend the application of ML models to address the prediction of AS requiring clinical intervention after LTx [ 19 ]. No published research has reported using ML algorithms to predict AS requiring clinical intervention. In this study, we assessed the clinical characteristics of patients and developed ML models to predict AS requiring clinical intervention. Moreover, the conventional logistic regression (LR) model was fitted by independent risk factors and compared in performance to the optimal ML model.

Patients who underwent LTx in Wuxi People’s Hospital affiliated with Nanjing Medical University between January 2017 and December 2019 were included. The study excluded 3 retransplant patients, 3 pediatric lung transplant patients, 2 patients who were lost to follow-up, and 2 patients with incomplete clinical records. Figure  1 shows a flow chart of the included and excluded patients. All the research procedures were consistent with the ISHLT Ethics statement. The Institutional Review Board of Wuxi People’s Hospital affiliated with Nanjing Medical University approved this study (No. 2020 [374]). Patient consent was waived due to the retrospective nature of the study.

Flow diagram for selection of lung transplant recipients. A total of 391 lung transplant recipients were assessed for eligibility. Of this cohort, patients with re-transplant, pediatric lung transplant, lost follow-up, and incomplete clinical records were excluded from the study leaving 381 patients available for the analysis

Parameter measurements

The following variables were extracted from the database: age, body mass index (BMI), sex, diagnosis, surgical type, extracorporeal membrane oxygenation (ECMO) type, ECMO support, preoperative hormone use, grade 3 primary graft dysfunction at 72 h (72 h PGD 3), operation time, postoperative ventilator time, intensive care unit (ICU) stay, postoperative 6-minute walking test (6MWT), cold-ischemia time, and arterial oxygen tension/inspired oxygen fraction (PaO 2 /FiO 2 ). Diagnoses included interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), pulmonary arterial hypertension (PAH), and others. By definition, 72 h PGD 3 refers to the syndrome of acute lung injury over the first 72 h after LTx and is clinically manifested by diffuse alveolar infiltration on chest radiographs with PaO 2 /FiO 2  < 200 mmHg (10 mmHg = 1.33 kPa) [ 20 ]. Cold-ischemia time in single lung transplantation (SLTx) was defined as the interval between the beginning of cold perfusion of the donor lung and blood reperfusion during LTx surgery. For double lung transplantation (DLTx), the cold-ischemia time was determined at the end of reperfusion of the second lung.

Surgery and perioperative management

Since January 1, 2015, China has stopped using organs from executed prisoners, and voluntary organ donation has become the only legal source. Each bronchial anastomosis was performed in an “end-to-end” technique avoiding telescoping during LTx surgery. All recipients were treated with regular triple immunosuppressive therapy. Patients underwent routine bronchoscopy after LTx, prior to extubation and prior to discharge to assess the condition of the bronchial anastomoses, and the examination frequency was adjusted according to the actual situation. If patients have obvious airflow limitations such as respiratory distress and wheezing, relevant clinical intervention will be activated. An experienced physician (MZL) evaluated the classification of AS based on all definitions and grading systems of AS in the 2018 ISHLT consensus statement [ 4 ].

Development of the LR model and ML model

Univariate LR was used to select factors associated with AS based on our cohort. Multivariate LR included only factors with a P  < 0.05 in univariate LR. A conventional LR model of AS was developed by LR using independent risk factors. For feature selection, three types of methods were used: filtering, wrapping and embedding, which aim to reduce dimension and avoid overfitting of ML models. Within these three categories of feature selection methods, seven methods were utilized , including LR, determination coefficient (DC), Relief, recursive feature elimination (RFE), Boruta, random forest (RF), and least absolute shrinkage and selection operator (LASSO). Finally, 7 groups of features were determined for the subsequent modeling. For the development of ML model , we applied eight ML algorithms, LR, decision tree (DT), k-nearest neighbors (KNN), naïve bayes (NB), support vector machine (SVM), generalized boosted regression modeling (GBRM), random forest (RF), and extreme gradient boosting (XGB). A total of 56 ML models were developed based on the 8 ML algorithms with 7 feature selection methods for predicting AS requiring clinical intervention. The model with the highest the area under the curve (AUC) was identified as the optimal ML model.

Predictive performance of the LR model and ML model

We compared the predictive performance of the conventional LR model with the optimal ML model for AS requiring clinical intervention. The performance of all models was evaluated in terms of discrimination and calibration. The AUC of the receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. The brier score was used to assess the calibration of the model. The brier score ranges from 0 to 1; a score that is close to 0 indicates excellent calibration. Moreover, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were also evaluated. All statistics were internally validated by the bootstrap method with 1000 resamples.

Patients were stratified into high- and low-risk groups in the optimal ML model based on the threshold determined by ROC. Mean decrease accuracy measures the extent to which each feature’s contribution to the model affects the accuracy of the prediction. It was used to identify features that contributed most significantly to the optimal ML model performance. In addition, the relative importance scores of each predictor in the optimal RF model were assessed using two metrics: Percentage Increase in MSE (percentage increase in mean square error) and Increase in Node Purity. Percentage Increase in MSE measures the impact of the variable on the prediction performance, while Increase in Node Purity measures the contribution of the variable to the purity of the decision tree nodes.

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics (version 22.0 Inc., Chicago, IL, USA), R programming language (version 4.2.1, Vienna, Austria) and GraphPad Prism (version 10.1.2, CA, USA). Patient demographics and clinical parameters were summarized as the means ± standard deviations for continuous variables and numbers with percentages for categorical variables. The odds ratio (OR) and 95% confidence interval (CI) were calculated. A value of P  < 0.05 was considered statistically significant in all analyses.

Clinical characteristics

The clinical characteristics of the LTx patients are summarized in Table  1 . A total of 381 patients with 244 males and 137 females were enrolled, and the median age of patients was 57 (range, 19–82) years. In the cohort, most of the indications for LTx were ILD (N = 214) and COPD (N = 67). Regarding surgical type, the numbers of patients with SLTx and DLTx were 201 (52.8%) and 180 (47.2%), respectively. The ECMO type was venoarterial (VA) in 120 cases (31.5%) and venovenous (VV) in 150 cases (39.4%); there were 111 cases (29.1%) that did not involve ECMO. In addition, the operation time, postoperative ventilator time, ICU stay, postoperative 6MWT, cold-Ischemia time and PaO 2 /FiO 2 were 327.76 ± 98.39 min, 5.76 ± 12.42 days, 7.78 ± 10.20 days, 460.84 ± 80.58 m, 7.31 ± 2.05 h and 443.55 ± 66.40, respectively. In this study, forty (10.5%) patients encountered AS requiring clinical intervention during the follow-up period.

Univariate analysis indicated that male (OR = 3.535, 95% CI, 1.445–8.650, P  = 0.006), PAH (OR = 9.651, 95% CI, 2.828–32.930, P  < 0.001), VV-ECMO (OR = 0.267, 95% CI, 0.100–0.711, P  = 0.008), and postoperative 6MWT (OR = 0.995, 95% CI, 0.991–0.998, P  = 0.006) were significantly associated with AS requiring clinical intervention. The multivariate analysis further revealed that male (OR = 7.034, 95% CI, 2.232–22.170, P  < 0.001), PAH (OR = 11.249, 95% CI, 2.554–49.549, P  < 0.001), and postoperative 6MWT (OR = 0.993, 95% CI, 0.988–0.997, P  < 0.001) were independent risk factors for AS requiring clinical intervention (Table  2 ). Conventional LR models were established based on independent risk factors. For the ML model, a total of 5, 5, 7, 8, 7, and 7 features were selected for modeling in the DC, Relief, RF, RFE, Boruta, and LASSO methods, respectively (Table  3 ). The combination of 7 feature selection methods and 8 ML algorithms (56 ML models) is shown in a heatmap (Fig.  2 ). The heatmap shows the AUC for the 56 ML models with a median bootstrapped AUC of 0.679 (range 0.569–0.760). The ML model using an RF algorithm with the DC feature selection method exhibited the highest bootstrapped AUC of 0.760 among the models and was confirmed to be the optimal ML model.

Heatmaps of the ML models for predicting AS requiring clinical intervention after LTx. Heatmaps illustrated the performance of each ML algorithm (columns) with each feature selection method (rows), measured by AUC. LR, logistic regression; DT, decision tree; KNN, k-nearest neighbors; NB, naïve bayes; SVM, support vector machine; GBRM, generalized boosted regression modeling; RF, random forest; XGB, extreme gradient boosting; LASSO, least absolute shrinkage and selection operator; RFE, recursive feature elimination; DC, determination coefficient; ML, machine learning; AS, airway stenosis; LTx, lung transplantation; AUC, the area under the curve

The model performance for the prediction of AS requiring clinical intervention is summarized in Table  4 . The differences emerged in the predicted values of the conventional LR and optimal ML models. The bootstrapped AUC of the optimal ML model was 0.760 (95% CI, 0.666–0.864), which was superior to the conventional LR model of 0.689 (95% CI, 0.545–0.803). The brier score of the optimal ML models was 0.085 (95% CI, 0.058–0.117), outperforming the conventional LR models of 0.091 (95% CI, 0.064–0.125). Furthermore, the sensitivity of the optimal ML model versus the conventional LR model was 0.782 (95% CI, 0.526–1.000) versus 0.680 (95% CI, 0.350–1.000). The specificity of the optimal ML model versus the conventional LR model was 0.689 (95% CI, 0.424–0.917) versus 0.623 (95% CI, 0.305–0.956). The PPV of the optimal ML model versus the conventional LR model was 0.252 (95% CI, 0.133–0.429) versus 0.236 (95% CI, 0.105–0.500). The NPV of the optimal ML model versus the conventional LR model was 0.965 (95% CI, 0.927–1.000) versus 0.952 (95% CI, 0.905–1.000).

A histogram established by the optimal threshold of 0.163 indicates different distributions in the optimal ML model between patients in the high- and low-risk groups (Fig.  3 ). The majority of patients in the high-risk groups stratified by the optimal ML model presented with AS requiring clinical intervention, while the majority of patients in the low-risk group presented without AS requiring clinical intervention.

Histogram of the predicted values in patients with and without AS requiring clinical intervention after LTx. Patients were divided into high- and low-risk patients with a cut-off value of 0.163. Most of the high-risk patients presented with AS requiring clinical intervention, while most of the low-risk patients presented without AS requiring clinical intervention. AS, airway stenosis; LTx, lung transplantation

Figure  4 illustrates the ranking of features by importance in the optimal ML model for predicting AS requiring clinical intervention. Mean decrease accuracy was calculated over the optimal ML model for the features considered in the model. The five features of the DC feature selection method were postoperative 6MWT, diagnosis, sex, ECMO type, and preoperative hormone use, with postoperative 6MWT being the most significant. Figure  5 illustrates the relative importance scores of the predictor variables in the optimal RF model. Postoperative 6MWT showed the highest Percentage Increase in MSE with Increase in Node Purity, implying that it had the greatest impact on the predictive performance of the model and contributed the most to the purity of the decision tree nodes.

Variable importance in the optimal RF model. Mean decrease accuracy calculated over the optimal RF model for the features considered in the model. 6MWT: 6-minute walking test; ECMO, extracorporeal membrane oxygenation; RF, random forest

Relative importance score in the optimal RF model. In the optimal RF model, Percentage Increase in MSE measures the impact of the variable on the prediction performance, while Increase in Node Purity measures the contribution of the variable to the purity of the decision tree nodes. 6MWT, 6-minute walking test; ECMO, extracorporeal membrane oxygenation; Percentage Increase in MSE, percentage increase in mean square error; RF, random forest

Considering the significant value of predicting AS requiring clinical intervention in patients after LTx for treatment guidance, we sought to evaluate the clinical characteristics of the patients and further construct prediction models. The following major findings were revealed in this study: (a) Postoperative 6MWT, diagnosis, sex, ECMO type, and preoperative hormone use are five important features of the optimal ML model. (b) Compared with the conventional LR model, the optimal ML model showed better performance in the prediction of AS requiring clinical intervention. (c) The predictive values of the optimal ML model could obviously distinguish patients with AS requiring clinical intervention. Our study suggests that the optimal ML model may become an effective method for predicting AS requiring clinical intervention.

The 6MWT is used to quantify the functional exercise capacity of patients with moderate to severe lung disease [ 21 ]. The negative correlation between the postoperative 6MWT and AS has been described in previous literature [ 22 ]. In our study, postoperative 6MWT was the feature with the highest importance in the optimal ML model, indicating the importance of the postoperative 6MWT in predicting AS requiring clinical intervention. PAH is a progressive hemodynamic disease characterized by proliferation and remodeling of small pulmonary arteries [ 23 ]. We confirmed that PAH is significantly associated with AS requiring clinical intervention. Patients with PAH are prone to hemodynamic instability in the early postoperative period, which may exacerbate the ischemic condition after LTx by limiting collateral blood flow and lead to development of AS. Sex was usually regarded as a potential contributor to posttransplant complications in LTx patients. The present study found that males were related to an increased probability of AS. Castleberry et al. [ 24 ] also reported similar findings. However, Van De Wauwer et al. [ 25 ] concluded that males have no negative impact on AS since the sex of the donor and recipient generally overlap. In our opinion, males, with higher levels of PGD after LTx, can have an inadequate anastomotic blood flow supply, which may induce abnormal airway remodeling and increase the occurrence of AS [ 26 ]. Additionally, lower estrogen levels in males may lack the protective effect on the airway [ 27 ]. VA-ECMO is the bridging modality for patients with respiratory failure awaiting LTx [ 28 ]. However, patients on VA-ECMO inherently demonstrate a higher risk of AS episodes since VA-ECMO is more likely to result in bleeding and thrombotic complications compared to VV-ECMO [ 29 ]. Our study emphasized the necessity of appropriate use of VV-ECMO rather than VA-ECMO in the LTx perioperative period. The present study also found that preoperative hormone use (prednisone) increased the incidence of AS, which is consistent with the study by Park et al. [ 30 ]. Kim et al. [ 31 ] reported that the AC rate did not vary significantly with preoperative hormone use. Nevertheless, they found that the incidence of AC in the first postoperative year remains high after receiving high doses of preoperative prednisone. Hence, the effects of receiving high doses of prednisone preoperatively cannot be ignored. McAnally et al. [ 32 ] concluded that preoperative hormone use may induce related complications, such as poor bronchial anastomotic healing and severe infections, which may be the reason for the increased risk of AS episodes. Therefore, reducing the preoperative dose of prednisone or discontinuing prednisone may be a feasible way to reduce the risk of AS episodes.

ML algorithm is a scientific tool that focuses on how computers learn from data [ 33 ]. It can be applied to clinical characteristics to develop robust risk prediction models and predict patient outcomes [ 34 ]. In previous studies, Hindocha et al. utilized clinical features to develop, validate, and externally test ML model. They found that the ML model might allow satisfactory predictions of survival after treatment for non-small cell lung cancer [ 18 ]. In this study, we constructed 56 ML models by clinical characteristics, and an optimal ML model was developed based on the most appropriate RF algorithm and DC feature selection method. A conventional LR model was constructed based on three independent risk factors. The discrimination, calibration, sensitivity, and specificity of the models highlighted their performance. Finally, the bootstrap method was used to internally validate the two models. The bootstrapped AUCs of the optimal ML model were higher than 0.750, indicating that the optimal ML model had acceptable discrimination. A brier score of 0.085 proves the calibration of the optimal ML model. Both discrimination and calibration demonstrated that the optimal ML model had better performance in predicting AS requiring clinical intervention compared to the conventional LR model.

The optimal ML model has higher sensitivity and specificity than the conventional LR model, further proving that it is an effective prediction model. Our study is the first to assess the predictive value of the optimal ML model for AS requiring clinical intervention in patients after LTx. The important advantage of the optimal ML model is that it exhibits excellent performance and the application of this method does not require data to conform to statistical assumptions, such as the avoidance of independent variable multicollinearity. Although the optimal ML model exhibits the best performance, not all ML models outperform the conventional LR models. Only the ML model constructed with the most appropriate ML algorithm and feature selection method performed best. Additionally, the results of our study do not completely negate the performance of the conventional LR model since they are applicable to different scenarios respectively [ 35 ].

Historically, the conventional LR model is widely used to predict the effect of variables on disease [ 36 ]. Nevertheless, the conventional LR model assumes that the contribution of all clinical characteristics to the model is linear, which is not applicable to clinical practice. ML models can be better applied to deal with high-dimensional and nonlinear clinical characteristics. Therefore, it is more suitable for clinical practice to achieve good performance. Moreover, the histogram of predicted AS requiring clinical intervention showed that the predicted outcomes and actual outcomes of the optimal ML model were approximately equal, indicating excellent performance. The majority of high-risk patients presented with AS requiring clinical intervention, and the most intensive follow-up could be performed for high-risk populations. In future studies, developing ML model by using large sample size data is warranted. The ML model could be used in clinical trials to help clinicians screen out high-risk patients and improve patient prognosis.

The limitations of this current study are presented as follows. First, being retrospective, the study had some inevitable selection bias and the results are less convincing than prospective studies. However, strict inclusion and exclusion criteria were used to control for bias. Second, we performed this study in a single center with a relatively small sample size, which limited the application of the model. Therefore, investigations with a large sample size are warranted in the future. Third, microbial infection, an important risk factor, was not evaluated in this study. As patients present with an infectious condition, they are administered the appropriate clinical intervention to suppress the infectious response, which would have an impact on our study results. Fourth, the dataset was imbalanced, with only 10% of patients developing AS. This imbalance may affect the results and the generalization ability of the ML model. Fifth, the study was limited by the absence of certain clinical characteristics such as lung function, imaging, or pathological data, which could potentially enhance the accuracy of predictions. Last, the validation process was conducted by bootstrap resampling instead of application of an independent validation set. Considering that the patient cohort consisted of only 381 individuals, we needed to keep as many samples as possible for model training in order to enhance the model’s generalization. However, bootstrapping could not provide comprehensive validation for the model.

In this study, postoperative 6MWT, diagnosis, sex, ECMO type, and preoperative hormone use were identified as five important features of the optimal ML model. We constructed ML models that can effectively predict AS requiring clinical intervention for patients after LTx with good performance. The optimal ML model outperformed the conventional LR model in predicting AS requiring clinical intervention. Multicenter studies with large data samples are warranted to further validate the model. The obtained results may enable early and accurate prediction of AS requiring clinical intervention, guiding clinical decisions for subsequent treatment. Future multi-center studies with large data samples are anticipated to further validate the model. Moreover, the deep learning model could potentially be applied to the personalized treatment of LTx patients in the future.

Availability of data and materials

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Abbreviations

• Airway stenosis

The area under the curve

Body mass index

Chronic obstructive pulmonary disease

Double lung transplantation

Decision tree

Determination coefficient

Extracorporeal membrane oxygenation

Generalized boosted regression modeling

Interstitial lung disease

Intensive care unit

International Society for Heart and Lung Transplantation

K-nearest neighbors

• Lung transplantation

Least absolute shrinkage and selection operator

• Logistic regression
• Machine learning

Naïve bayes

Negative predictive value

Positive predictive value

Percentage increase in mean square error

Pulmonary arterial hypertension

Arterial oxygen tension/inspired oxygen fraction

Random forest

Recursive feature elimination

Receiver operating characteristic

Single lung transplantation

Support vector machine

Venoarterial

Extreme gradient boosting

Grade 3 primary graft dysfunction at 72 h

6- Minute walking test

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Acknowledgements

We would also like to thank American Journal Experts ( https://secure.aje.com/cn/researcher/ ) for editing the English text of a draft of this manuscript.

This study was supported by the National Natural Science Foundation of China (No. 82070059).

Author information

Dong Tian, Yu-Jie Zuo and Hao-Ji Yan contributed equally to this work.

Authors and Affiliations

Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China

Dong Tian, Yu-Jie Zuo & Heng Huang

Wuxi Lung Transplant Center, Wuxi People’s Hospital affiliated to Nanjing Medical University, Wuxi, 214023, China

Dong Tian, Ming-Zhao Liu, Hang Yang, Jin Zhao, Ling-Zhi Shi & Jing-Yu Chen

Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China

Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, 113-8431, Japan

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Contributions

DT: Conceptualization, Methodology, Software, Data collection, Statistical analysis, Features extraction, Original draft. YJZ: Conceptualization, Methodology, Software, Data collection, Statistical analysis, Features extraction, Original draft. HJY: Conceptualization, Methodology, Software, Data collection, Statistical analysis, Features extraction, Original draft. HH: Methodology, Data collection, Statistical analysis, Features extraction, Manuscript editing. MZL: Software, Data collection, Statistical analysis, Manuscript editing. HY: Data collection, Features extraction, Manuscript editing. JZ: Data collection, Manuscript editing. LZS: Conceptualization, Methodology, Statistical analysis, Manuscript editing. JYC: Conceptualization, Methodology, Statistical analysis, Manuscript editing.

Corresponding authors

Correspondence to Dong Tian , Ling-Zhi Shi or Jing-Yu Chen .

Ethics declarations

Ethics approval and consent to participate.

The Institutional Review Board of Wuxi People’s Hospital affiliated with Nanjing Medical University approved this study (No. 2020 [374]). Patient does not need to provide informed consent to participate due to waiver by Institutional Review Board of Wuxi People’s Hospital affiliated with Nanjing Medical University.

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Tian, D., Zuo, YJ., Yan, HJ. et al. Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study. BMC Med Inform Decis Mak 24 , 229 (2024). https://doi.org/10.1186/s12911-024-02635-8

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‘Worst-Case’ Disaster for Antarctic Ice Looks Less Likely, Study Finds

Global warming is putting the continent’s ice at risk of destruction in many forms. But one especially calamitous scenario might be a less pressing concern, a new study found.

By Raymond Zhong

For almost a decade, climate scientists have been trying to get their heads around a particularly disastrous scenario for how West Antarctica’s gigantic ice sheet might break apart, bringing catastrophe to the world’s coasts.

It goes like this: Once enough of the ice sheet’s floating edges melt away, what remains are immense, sheer cliffs of ice facing the sea. These cliffs will be so tall and steep that they are unstable. Great chunks of ice start breaking away from them, exposing even taller, even more-unstable cliffs. Soon, these start crumbling too, and before long you have runaway collapse.

As all this ice tumbles into the ocean, and assuming that nations’ emissions of heat-trapping gases climb to extremely high levels, Antarctica could contribute more than a foot to worldwide sea-level rise before the end of the century.

This calamitous chain of events is still hypothetical, yet scientists have taken it seriously enough to include it as a “low-likelihood, high-impact” possibility in the United Nations’ latest assessment of future sea-level increase.

Now, though, a group of researchers has put forth evidence that the prospect may be more remote than previously thought. As humans burn fossil fuels and heat the planet, West Antarctica’s ice remains vulnerable to destruction in many forms. But this particular form, in which ice cliffs collapse one after the other, looks less likely, according to the scientists’ computer simulations.

“We’re not saying that we’re safe,” said Mathieu Morlighem, a professor of earth science at Dartmouth College who led the research. “The Antarctic ice sheet is going to disappear; this is going to happen. The question is how fast.”

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