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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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Researcher, Academic Writer, Web developer

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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Research Guides

Multiple Case Studies

Nadia Alqahtani and Pengtong Qu

Description

The case study approach is popular across disciplines in education, anthropology, sociology, psychology, medicine, law, and political science (Creswell, 2013). It is both a research method and a strategy (Creswell, 2013; Yin, 2017). In this type of research design, a case can be an individual, an event, or an entity, as determined by the research questions. There are two variants of the case study: the single-case study and the multiple-case study. The former design can be used to study and understand an unusual case, a critical case, a longitudinal case, or a revelatory case. On the other hand, a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena (Lewis-Beck, Bryman & Liao, 2003; Yin, 2017). …a multiple-case study includes two or more cases or replications across the cases to investigate the same phenomena

The difference between the single- and multiple-case study is the research design; however, they are within the same methodological framework (Yin, 2017). Multiple cases are selected so that “individual case studies either (a) predict similar results (a literal replication) or (b) predict contrasting results but for anticipatable reasons (a theoretical replication)” (p. 55). When the purpose of the study is to compare and replicate the findings, the multiple-case study produces more compelling evidence so that the study is considered more robust than the single-case study (Yin, 2017).

To write a multiple-case study, a summary of individual cases should be reported, and researchers need to draw cross-case conclusions and form a cross-case report (Yin, 2017). With evidence from multiple cases, researchers may have generalizable findings and develop theories (Lewis-Beck, Bryman & Liao, 2003).

Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Los Angeles, CA: Sage.

Lewis-Beck, M., Bryman, A. E., & Liao, T. F. (2003). The Sage encyclopedia of social science research methods . Los Angeles, CA: Sage.

Yin, R. K. (2017). Case study research and applications: Design and methods . Los Angeles, CA: Sage.

Key Research Books and Articles on Multiple Case Study Methodology

Yin discusses how to decide if a case study should be used in research. Novice researchers can learn about research design, data collection, and data analysis of different types of case studies, as well as writing a case study report.

Chapter 2 introduces four major types of research design in case studies: holistic single-case design, embedded single-case design, holistic multiple-case design, and embedded multiple-case design. Novice researchers will learn about the definitions and characteristics of different designs. This chapter also teaches researchers how to examine and discuss the reliability and validity of the designs.

Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches . Los Angeles, CA: Sage.

This book compares five different qualitative research designs: narrative research, phenomenology, grounded theory, ethnography, and case study. It compares the characteristics, data collection, data analysis and representation, validity, and writing-up procedures among five inquiry approaches using texts with tables. For each approach, the author introduced the definition, features, types, and procedures and contextualized these components in a study, which was conducted through the same method. Each chapter ends with a list of relevant readings of each inquiry approach.

This book invites readers to compare these five qualitative methods and see the value of each approach. Readers can consider which approach would serve for their research contexts and questions, as well as how to design their research and conduct the data analysis based on their choice of research method.

Günes, E., & Bahçivan, E. (2016). A multiple case study of preservice science teachers’ TPACK: Embedded in a comprehensive belief system. International Journal of Environmental and Science Education, 11 (15), 8040-8054.

In this article, the researchers showed the importance of using technological opportunities in improving the education process and how they enhanced the students’ learning in science education. The study examined the connection between “Technological Pedagogical Content Knowledge” (TPACK) and belief system in a science teaching context. The researchers used the multiple-case study to explore the effect of TPACK on the preservice science teachers’ (PST) beliefs on their TPACK level. The participants were three teachers with the low, medium, and high level of TPACK confidence. Content analysis was utilized to analyze the data, which were collected by individual semi-structured interviews with the participants about their lesson plans. The study first discussed each case, then compared features and relations across cases. The researchers found that there was a positive relationship between PST’s TPACK confidence and TPACK level; when PST had higher TPACK confidence, the participant had a higher competent TPACK level and vice versa.

Recent Dissertations Using Multiple Case Study Methodology

Milholland, E. S. (2015). A multiple case study of instructors utilizing Classroom Response Systems (CRS) to achieve pedagogical goals . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3706380)

The researcher of this study critiques the use of Classroom Responses Systems by five instructors who employed this program five years ago in their classrooms. The researcher conducted the multiple-case study methodology and categorized themes. He interviewed each instructor with questions about their initial pedagogical goals, the changes in pedagogy during teaching, and the teaching techniques individuals used while practicing the CRS. The researcher used the multiple-case study with five instructors. He found that all instructors changed their goals during employing CRS; they decided to reduce the time of lecturing and to spend more time engaging students in interactive activities. This study also demonstrated that CRS was useful for the instructors to achieve multiple learning goals; all the instructors provided examples of the positive aspect of implementing CRS in their classrooms.

Li, C. L. (2010). The emergence of fairy tale literacy: A multiple case study on promoting critical literacy of children through a juxtaposed reading of classic fairy tales and their contemporary disruptive variants . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3572104)

To explore how children’s development of critical literacy can be impacted by their reactions to fairy tales, the author conducted a multiple-case study with 4 cases, in which each child was a unit of analysis. Two Chinese immigrant children (a boy and a girl) and two American children (a boy and a girl) at the second or third grade were recruited in the study. The data were collected through interviews, discussions on fairy tales, and drawing pictures. The analysis was conducted within both individual cases and cross cases. Across four cases, the researcher found that the young children’s’ knowledge of traditional fairy tales was built upon mass-media based adaptations. The children believed that the representations on mass-media were the original stories, even though fairy tales are included in the elementary school curriculum. The author also found that introducing classic versions of fairy tales increased children’s knowledge in the genre’s origin, which would benefit their understanding of the genre. She argued that introducing fairy tales can be the first step to promote children’s development of critical literacy.

Asher, K. C. (2014). Mediating occupational socialization and occupational individuation in teacher education: A multiple case study of five elementary pre-service student teachers . Retrieved from ProQuest Dissertations & Theses Global. (Order Number 3671989)

This study portrayed five pre-service teachers’ teaching experience in their student teaching phase and explored how pre-service teachers mediate their occupational socialization with occupational individuation. The study used the multiple-case study design and recruited five pre-service teachers from a Midwestern university as five cases. Qualitative data were collected through interviews, classroom observations, and field notes. The author implemented the case study analysis and found five strategies that the participants used to mediate occupational socialization with occupational individuation. These strategies were: 1) hindering from practicing their beliefs, 2) mimicking the styles of supervising teachers, 3) teaching in the ways in alignment with school’s existing practice, 4) enacting their own ideas, and 5) integrating and balancing occupational socialization and occupational individuation. The study also provided recommendations and implications to policymakers and educators in teacher education so that pre-service teachers can be better supported.

Multiple Case Studies Copyright © 2019 by Nadia Alqahtani and Pengtong Qu is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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• Knowledge Base
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• 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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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply Psychology

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

Hayden Coombs

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Case Study Research: Single or Multiple?

Https://doi.org/10.5281/zenodo.7106698.

Definition of a Case Study

A case study is a methodological research approach used to generate an in-depth understanding of a contemporary issue or phenomenon in a bounded system.

A case study is one of the most widely used and accepted means of qualitative research methods in the social sciences (Bloomberg & Volpe, 2022). 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 (Crowe et al., 2011). Case studies provide researchers with an opportunity for greater depth of understanding of an issue (Stake, 2010). The case study design is preferred as a research strategy when “how,” “why,” and “what” questions are the interest of the researcher (Yin, 2018).

The two most prominent case study scholars are Robert E. Stake and Robert K. Yin. While case study research has been conducted for some time, Stake established accepted procedures for case study research in 1995 and has produced numerous articles and books about case study methodology and analysis. Two of Stake’s works that continue to impact the academic community are his books, “The Art of Case Study Research,” and, “Multiple Case Study Analysis.” Yin emerged as a leading scholar in case study research and is still producing academic literature today, as he utilizes both quantitative and qualitative approaches to the methodology. Yin’s significant contributions to the development of case study research includes the titles, “Case Study Research and Applications: Designs and Methods,” “Applications of Case Study Research,” and, “The Case Study Anthology.”

Characteristics of a Case Study

• The identification of a case is bounded (a case within a bounded system, which means what is being studied can be defined or described within specific parameters (Creswell & Poth, 2018). A case must be bounded by time and place.
• A case study should provide an in-depth understanding of the case.
• Data is collected through various means, including interviews, focus groups, field notes, documents, autobiographies, historical documents, videos, and more.
• Data analysis differs depending on the case under study. In fact, many case studies are both qualitative and quantitative.
• The successful identification of themes is critical to producing effective descriptions in case study research.
• Case studies offer conclusions provided by the researcher regarding the meaning derived from the case and are important because case studies have continuity in nature.

Types of Case Studies

Case studies are typically defined by the intent of the case analysis. There are three types of case studies: (single) instrumental case study, collective (multiple) case study, and intrinsic case study.

In a single instrumental case study, the researcher focuses on an issue or concern and then selects one bounded case to illustrate the issue (Creswell & Poth, 2018). If the researcher only wants to study one single thing (such as single person from a specific group) or a single group (for example a specific group of people within a bounded system), a single case study is the best choice (Yin, 2017).

In a multiple case study. the researcher selects multiple cases to illustrate the one issue or concern (Creswell & Poth, 2018). Multiple case studies can be used to either augur contrasting results for expected reasons or augur similar results in the studies (Yin, 2017).

In an intrinsic case study, the focus is on the case itself because the case presents a unique situation, thus resembling the focus of narrative research but maintaining the analytic procedures of a case study (Creswell & Poth, 2018).

Bloomberg, L. D., & Volpe, M. (2022).  Completing your qualitative dissertation: A road map from beginning to end . SAGE.

Creswell, J. W., & Poth, C. N. (2018).  Qualitative inquiry and research design: Choosing among five approaches . SAGE.

Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The case study approach.  BMC medical research methodology ,  11 , 100.  https://doi.org/10.1186/1471-2288-11-100 .

Stake, R. E. (2010).  The art of case study research . SAGE.

Yin, R. K. (2018).  Case Study Research and Applications: Designs and Methods  (6th ed.). Sage.

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The Advantages and Limitations of Single Case Study Analysis

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).

Limitations

Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.

References 

Bennett, A. and Checkel, J. T. (2012) ‘Process Tracing: From Philosophical Roots to Best Practice’, Simons Papers in Security and Development, No. 21/2012, School for International Studies, Simon Fraser University: Vancouver.

Bennett, A. and Elman, C. (2006a) ‘Qualitative Research: Recent Developments in Case Study Methods’, Annual Review of Political Science , 9, 455-476.

Bennett, A. and Elman, C. (2006b) ‘Complex Causal Relations and Case Study Methods: The Example of Path Dependence’, Political Analysis , 14, 3, 250-267.

Bennett, A. and Elman, C. (2007) ‘Case Study Methods in the International Relations Subfield’, Comparative Political Studies , 40, 2, 170-195.

Bennett, A. and Elman, C. (2010) Case Study Methods. In C. Reus-Smit and D. Snidal (eds) The Oxford Handbook of International Relations . Oxford University Press: Oxford. Ch. 29.

Berg, B. and Lune, H. (2012) Qualitative Research Methods for the Social Sciences . Pearson: London.

Bryman, A. (2012) Social Research Methods . Oxford University Press: Oxford.

David, M. and Sutton, C. D. (2011) Social Research: An Introduction . SAGE Publications Ltd: London.

Diamond, J. (1992) ‘Economic development and democracy reconsidered’, American Behavioral Scientist , 35, 4/5, 450-499.

Eckstein, H. (1975) Case Study and Theory in Political Science. In R. Gomm, M. Hammersley, and P. Foster (eds) Case Study Method . SAGE Publications Ltd: London.

Flyvbjerg, B. (2006) ‘Five Misunderstandings About Case-Study Research’, Qualitative Inquiry , 12, 2, 219-245.

Geertz, C. (1973) The Interpretation of Cultures: Selected Essays by Clifford Geertz . Basic Books Inc: New York.

Gerring, J. (2004) ‘What is a Case Study and What Is It Good for?’, American Political Science Review , 98, 2, 341-354.

Gerring, J. (2006a) Case Study Research: Principles and Practices . Cambridge University Press: Cambridge.

Gerring, J. (2006b) ‘Single-Outcome Studies: A Methodological Primer’, International Sociology , 21, 5, 707-734.

Gerring, J. (2007) ‘Is There a (Viable) Crucial-Case Method?’, Comparative Political Studies , 40, 3, 231-253.

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

Layne, C. (1994) ‘Kant or Cant: The Myth of the Democratic Peace’, International Security , 19, 2, 5-49.

Levy, J. S. (2008) ‘Case Studies: Types, Designs, and Logics of Inference’, Conflict Management and Peace Science , 25, 1-18.

Lipset, S. M. (1959) ‘Some Social Requisites of Democracy: Economic Development and Political Legitimacy’, The American Political Science Review , 53, 1, 69-105.

Lyotard, J-F. (1984) The Postmodern Condition: A Report on Knowledge . University of Minnesota Press: Minneapolis.

MacMillan, A. (2008) ‘Deviant Democratization in India’, Democratization , 15, 4, 733-749.

Maoz, Z. (2002) Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey and M. Brecher (eds) Evaluating Methodology in International Studies . University of Michigan Press: Ann Arbor.

May, T. (2011) Social Research: Issues, Methods and Process . Open University Press: Maidenhead.

Owen, J. M. (1994) ‘How Liberalism Produces Democratic Peace’, International Security , 19, 2, 87-125.

Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

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

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

Further Reading on E-International Relations

• Identity in International Conflicts: A Case Study of the Cuban Missile Crisis
• Imperialism’s Legacy in the Study of Contemporary Politics: The Case of Hegemonic Stability Theory
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Single case studies vs. multiple case studies: A comparative study

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Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review

Margarithe charlotte schlunegger.

1 Department of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Bern, Switzerland

2 Faculty of Health, School of Nursing Science, Witten/Herdecke University, Witten, Germany

Maya Zumstein-Shaha

Rebecca palm.

3 Department of Health Care Research, Carl von Ossietzky University Oldenburg, Oldenburg, Germany

Associated Data

Supplemental material, sj-docx-1-wjn-10.1177_01939459241263011 for Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review by Margarithe Charlotte Schlunegger, Maya Zumstein-Shaha and Rebecca Palm in Western Journal of Nursing Research

We sought to explore the processes of methodologic and data-analysis triangulation in case studies using the example of research on nurse practitioners in primary health care.

Design and methods:

We conducted a scoping review within Arksey and O’Malley’s methodological framework, considering studies that defined a case study design and used 2 or more data sources, published in English or German before August 2023.

Data sources:

The databases searched were MEDLINE and CINAHL, supplemented with hand searching of relevant nursing journals. We also examined the reference list of all the included studies.

In total, 63 reports were assessed for eligibility. Ultimately, we included 8 articles. Five studies described within-method triangulation, whereas 3 provided information on between/across-method triangulation. No study reported within-method triangulation of 2 or more quantitative data-collection procedures. The data-collection procedures were interviews, observation, documentation/documents, service records, and questionnaires/assessments. The data-analysis triangulation involved various qualitative and quantitative methods of analysis. Details about comparing or contrasting results from different qualitative and mixed-methods data were lacking.

Conclusions:

Various processes for methodologic and data-analysis triangulation are described in this scoping review but lack detail, thus hampering standardization in case study research, potentially affecting research traceability. Triangulation is complicated by terminological confusion. To advance case study research in nursing, authors should reflect critically on the processes of triangulation and employ existing tools, like a protocol or mixed-methods matrix, for transparent reporting. The only existing reporting guideline should be complemented with directions on methodologic and data-analysis triangulation.

Case study research is defined as “an empirical method that investigates a contemporary phenomenon (the ‘case’) in depth and within its real-world context, especially when the boundaries between phenomenon and context may not be clearly evident. A case study relies on multiple sources of evidence, with data needing to converge in a triangulating fashion.” 1 (p15) This design is described as a stand-alone research approach equivalent to grounded theory and can entail single and multiple cases. 1 , 2 However, case study research should not be confused with single clinical case reports. “Case reports are familiar ways of sharing events of intervening with single patients with previously unreported features.” 3 (p107) As a methodology, case study research encompasses substantially more complexity than a typical clinical case report. 1 , 3

A particular characteristic of case study research is the use of various data sources, such as quantitative data originating from questionnaires as well as qualitative data emerging from interviews, observations, or documents. Therefore, a case study always draws on multiple sources of evidence, and the data must converge in a triangulating manner. 1 When using multiple data sources, a case or cases can be examined more convincingly and accurately, compensating for the weaknesses of the respective data sources. 1 Another characteristic is the interaction of various perspectives. This involves comparing or contrasting perspectives of people with different points of view, eg, patients, staff, or leaders. 4 Through triangulation, case studies contribute to the completeness of the research on complex topics, such as role implementation in clinical practice. 1 , 5 Triangulation involves a combination of researchers from various disciplines, of theories, of methods, and/or of data sources. By creating connections between these sources (ie, investigator, theories, methods, data sources, and/or data analysis), a new understanding of the phenomenon under study can be obtained. 6 , 7

This scoping review focuses on methodologic and data-analysis triangulation because concrete procedures are missing, eg, in reporting guidelines. Methodologic triangulation has been called methods, mixed methods, or multimethods. 6 It can encompass within-method triangulation and between/across-method triangulation. 7 “Researchers using within-method triangulation use at least 2 data-collection procedures from the same design approach.” 6 (p254) Within-method triangulation is either qualitative or quantitative but not both. Therefore, within-method triangulation can also be considered data source triangulation. 8 In contrast, “researchers using between/across-method triangulation employ both qualitative and quantitative data-collection methods in the same study.” 6 (p254) Hence, methodologic approaches are combined as well as various data sources. For this scoping review, the term “methodologic triangulation” is maintained to denote between/across-method triangulation. “Data-analysis triangulation is the combination of 2 or more methods of analyzing data.” 6 (p254)

Although much has been published on case studies, there is little consensus on the quality of the various data sources, the most appropriate methods, or the procedures for conducting methodologic and data-analysis triangulation. 5 According to the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) clearinghouse for reporting guidelines, one standard exists for organizational case studies. 9 Organizational case studies provide insights into organizational change in health care services. 9 Rodgers et al 9 pointed out that, although high-quality studies are being funded and published, they are sometimes poorly articulated and methodologically inadequate. In the reporting checklist by Rodgers et al, 9 a description of the data collection is included, but reporting directions on methodologic and data-analysis triangulation are missing. Therefore, the purpose of this study was to examine the process of methodologic and data-analysis triangulation in case studies. Accordingly, we conducted a scoping review to elicit descriptions of and directions for triangulation methods and analysis, drawing on case studies of nurse practitioners (NPs) in primary health care as an example. Case studies are recommended to evaluate the implementation of new roles in (primary) health care, such as that of NPs. 1 , 5 Case studies on new role implementation can generate a unique and in-depth understanding of specific roles (individual), teams (smaller groups), family practices or similar institutions (organization), and social and political processes in health care systems. 1 , 10 The integration of NPs into health care systems is at different stages of progress around the world. 11 Therefore, studies are needed to evaluate this process.

The methodological framework by Arksey and O’Malley 12 guided this scoping review. We examined the current scientific literature on the use of methodologic and data-analysis triangulation in case studies on NPs in primary health care. The review process included the following stages: (1) establishing the research question; (2) identifying relevant studies; (3) selecting the studies for inclusion; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consulting experts in the field. 12 Stage 6 was not performed due to a lack of financial resources. The reporting of the review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review) guideline by Tricco et al 13 (guidelines for reporting systematic reviews and meta-analyses [ Supplementary Table A ]). Scoping reviews are not eligible for registration in PROSPERO.

Stage 1: Establishing the Research Question

The aim of this scoping review was to examine the process of triangulating methods and analysis in case studies on NPs in primary health care to improve the reporting. We sought to answer the following question: How have methodologic and data-analysis triangulation been conducted in case studies on NPs in primary health care? To answer the research question, we examined the following elements of the selected studies: the research question, the study design, the case definition, the selected data sources, and the methodologic and data-analysis triangulation.

Stage 2: Identifying Relevant Studies

A systematic database search was performed in the MEDLINE (via PubMed) and CINAHL (via EBSCO) databases between July and September 2020 to identify relevant articles. The following terms were used as keyword search strategies: (“Advanced Practice Nursing” OR “nurse practitioners”) AND (“primary health care” OR “Primary Care Nursing”) AND (“case study” OR “case studies”). Searches were limited to English- and German-language articles. Hand searches were conducted in the journals Nursing Inquiry , BMJ Open , and BioMed Central ( BMC ). We also screened the reference lists of the studies included. The database search was updated in August 2023. The complete search strategy for all the databases is presented in Supplementary Table B .

Stage 3: Selecting the Studies

Inclusion and exclusion criteria.

We used the inclusion and exclusion criteria reported in Table 1 . We included studies of NPs who had at least a master’s degree in nursing according to the definition of the International Council of Nurses. 14 This scoping review considered studies that were conducted in primary health care practices in rural, urban, and suburban regions. We excluded reviews and study protocols in which no data collection had occurred. Articles were included without limitations on the time period or country of origin.

Inclusion and Exclusion Criteria.

Screening process

After the search, we collated and uploaded all the identified records into EndNote v.X8 (Clarivate Analytics, Philadelphia, Pennsylvania) and removed any duplicates. Two independent reviewers (MCS and SA) screened the titles and abstracts for assessment in line with the inclusion criteria. They retrieved and assessed the full texts of the selected studies while applying the inclusion criteria. Any disagreements about the eligibility of studies were resolved by discussion or, if no consensus could be reached, by involving experienced researchers (MZ-S and RP).

Stages 4 and 5: Charting the Data and Collating, Summarizing, and Reporting the Results

The first reviewer (MCS) extracted data from the selected publications. For this purpose, an extraction tool developed by the authors was used. This tool comprised the following criteria: author(s), year of publication, country, research question, design, case definition, data sources, and methodologic and data-analysis triangulation. First, we extracted and summarized information about the case study design. Second, we narratively summarized the way in which the data and methodological triangulation were described. Finally, we summarized the information on within-case or cross-case analysis. This process was performed using Microsoft Excel. One reviewer (MCS) extracted data, whereas another reviewer (SA) cross-checked the data extraction, making suggestions for additions or edits. Any disagreements between the reviewers were resolved through discussion.

A total of 149 records were identified in 2 databases. We removed 20 duplicates and screened 129 reports by title and abstract. A total of 46 reports were assessed for eligibility. Through hand searches, we identified 117 additional records. Of these, we excluded 98 reports after title and abstract screening. A total of 17 reports were assessed for eligibility. From the 2 databases and the hand search, 63 reports were assessed for eligibility. Ultimately, we included 8 articles for data extraction. No further articles were included after the reference list screening of the included studies. A PRISMA flow diagram of the study selection and inclusion process is presented in Figure 1 . As shown in Tables 2 and ​ and3, 3 , the articles included in this scoping review were published between 2010 and 2022 in Canada (n = 3), the United States (n = 2), Australia (n = 2), and Scotland (n = 1).

PRISMA flow diagram.

Characteristics of Articles Included.

Overview of Within-Method, Between/Across-Method, and Data-Analysis Triangulation.

Research Question, Case Definition, and Case Study Design

The following sections describe the research question, case definition, and case study design. Case studies are most appropriate when asking “how” or “why” questions. 1 According to Yin, 1 how and why questions are explanatory and lead to the use of case studies, histories, and experiments as the preferred research methods. In 1 study from Canada, eg, the following research question was presented: “How and why did stakeholders participate in the system change process that led to the introduction of the first nurse practitioner-led Clinic in Ontario?” (p7) 19 Once the research question has been formulated, the case should be defined and, subsequently, the case study design chosen. 1 In typical case studies with mixed methods, the 2 types of data are gathered concurrently in a convergent design and the results merged to examine a case and/or compare multiple cases. 10

Research question

“How” or “why” questions were found in 4 studies. 16 , 17 , 19 , 22 Two studies additionally asked “what” questions. Three studies described an exploratory approach, and 1 study presented an explanatory approach. Of these 4 studies, 3 studies chose a qualitative approach 17 , 19 , 22 and 1 opted for mixed methods with a convergent design. 16

In the remaining studies, either the research questions were not clearly stated or no “how” or “why” questions were formulated. For example, “what” questions were found in 1 study. 21 No information was provided on exploratory, descriptive, and explanatory approaches. Schadewaldt et al 21 chose mixed methods with a convergent design.

Case definition and case study design

A total of 5 studies defined the case as an organizational unit. 17 , 18 - 20 , 22 Of the 8 articles, 4 reported multiple-case studies. 16 , 17 , 22 , 23 Another 2 publications involved single-case studies. 19 , 20 Moreover, 2 publications did not state the case study design explicitly.

Within-Method Triangulation

This section describes within-method triangulation, which involves employing at least 2 data-collection procedures within the same design approach. 6 , 7 This can also be called data source triangulation. 8 Next, we present the single data-collection procedures in detail. In 5 studies, information on within-method triangulation was found. 15 , 17 - 19 , 22 Studies describing a quantitative approach and the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review.

Qualitative approach

Five studies used qualitative data-collection procedures. Two studies combined face-to-face interviews and documents. 15 , 19 One study mixed in-depth interviews with observations, 18 and 1 study combined face-to-face interviews and documentation. 22 One study contained face-to-face interviews, observations, and documentation. 17 The combination of different qualitative data-collection procedures was used to present the case context in an authentic and complex way, to elicit the perspectives of the participants, and to obtain a holistic description and explanation of the cases under study.

All 5 studies used qualitative interviews as the primary data-collection procedure. 15 , 17 - 19 , 22 Face-to-face, in-depth, and semi-structured interviews were conducted. The topics covered in the interviews included processes in the introduction of new care services and experiences of barriers and facilitators to collaborative work in general practices. Two studies did not specify the type of interviews conducted and did not report sample questions. 15 , 18

Observations

In 2 studies, qualitative observations were carried out. 17 , 18 During the observations, the physical design of the clinical patients’ rooms and office spaces was examined. 17 Hungerford et al 18 did not explain what information was collected during the observations. In both studies, the type of observation was not specified. Observations were generally recorded as field notes.

Public documents

In 3 studies, various qualitative public documents were studied. 15 , 19 , 22 These documents included role description, education curriculum, governance frameworks, websites, and newspapers with information about the implementation of the role and general practice. Only 1 study failed to specify the type of document and the collected data. 15

Electronic health records

In 1 study, qualitative documentation was investigated. 17 This included a review of dashboards (eg, provider productivity reports or provider quality dashboards in the electronic health record) and quality performance reports (eg, practice-wide or co-management team-wide performance reports).

Between/Across-Method Triangulation

This section describes the between/across methods, which involve employing both qualitative and quantitative data-collection procedures in the same study. 6 , 7 This procedure can also be denoted “methodologic triangulation.” 8 Subsequently, we present the individual data-collection procedures. In 3 studies, information on between/across triangulation was found. 16 , 20 , 21

Mixed methods

Three studies used qualitative and quantitative data-collection procedures. One study combined face-to-face interviews, documentation, and self-assessments. 16 One study employed semi-structured interviews, direct observation, documents, and service records, 20 and another study combined face-to-face interviews, non-participant observation, documents, and questionnaires. 23

All 3 studies used qualitative interviews as the primary data-collection procedure. 16 , 20 , 23 Face-to-face and semi-structured interviews were conducted. In the interviews, data were collected on the introduction of new care services and experiences of barriers to and facilitators of collaborative work in general practices.

Observation

In 2 studies, direct and non-participant qualitative observations were conducted. 20 , 23 During the observations, the interaction between health professionals or the organization and the clinical context was observed. Observations were generally recorded as field notes.

In 2 studies, various qualitative public documents were examined. 20 , 23 These documents included role description, newspapers, websites, and practice documents (eg, flyers). In the documents, information on the role implementation and role description of NPs was collected.

Individual journals

In 1 study, qualitative individual journals were studied. 16 These included reflective journals from NPs, who performed the role in primary health care.

Service records

Only 1 study involved quantitative service records. 20 These service records were obtained from the primary care practices and the respective health authorities. They were collected before and after the implementation of an NP role to identify changes in patients’ access to health care, the volume of patients served, and patients’ use of acute care services.

Questionnaires/Assessment

In 2 studies, quantitative questionnaires were used to gather information about the teams’ satisfaction with collaboration. 16 , 21 In 1 study, 3 validated scales were used. The scales measured experience, satisfaction, and belief in the benefits of collaboration. 21 Psychometric performance indicators of these scales were provided. However, the time points of data collection were not specified; similarly, whether the questionnaires were completed online or by hand was not mentioned. A competency self-assessment tool was used in another study. 16 The assessment comprised 70 items and included topics such as health promotion, protection, disease prevention and treatment, the NP-patient relationship, the teaching-coaching function, the professional role, managing and negotiating health care delivery systems, monitoring and ensuring the quality of health care practice, and cultural competence. Psychometric performance indicators were provided. The assessment was completed online with 2 measurement time points (pre self-assessment and post self-assessment).

Data-Analysis Triangulation

This section describes data-analysis triangulation, which involves the combination of 2 or more methods of analyzing data. 6 Subsequently, we present within-case analysis and cross-case analysis.

Mixed-methods analysis

Three studies combined qualitative and quantitative methods of analysis. 16 , 20 , 21 Two studies involved deductive and inductive qualitative analysis, and qualitative data were analyzed thematically. 20 , 21 One used deductive qualitative analysis. 16 The method of analysis was not specified in the studies. Quantitative data were analyzed using descriptive statistics in 3 studies. 16 , 20 , 23 The descriptive statistics comprised the calculation of the mean, median, and frequencies.

Qualitative methods of analysis

Two studies combined deductive and inductive qualitative analysis, 19 , 22 and 2 studies only used deductive qualitative analysis. 15 , 18 Qualitative data were analyzed thematically in 1 study, 22 and data were treated with content analysis in the other. 19 The method of analysis was not specified in the 2 studies.

Within-case analysis

In 7 studies, a within-case analysis was performed. 15 - 20 , 22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized. The individual cases were presented mostly as a narrative description. Quantitative data were integrated into the qualitative description with tables and graphs. Qualitative and quantitative data were also presented as a narrative description.

Cross-case analyses

Of the multiple-case studies, 5 carried out cross-case analyses. 15 - 17 , 20 , 22 Three studies described the cross-case analysis using qualitative data. Two studies reported a combination of qualitative and quantitative data for the cross-case analysis. In each multiple-case study, the individual cases were contrasted to identify the differences and similarities between the cases. One study did not specify whether a within-case or a cross-case analysis was conducted. 23

Confirmation or contradiction of data

This section describes confirmation or contradiction through qualitative and quantitative data. 1 , 4 Qualitative and quantitative data were reported separately, with little connection between them. As a result, the conclusions on neither the comparisons nor the contradictions could be clearly determined.

Confirmation or contradiction among qualitative data

In 3 studies, the consistency of the results of different types of qualitative data was highlighted. 16 , 19 , 21 In particular, documentation and interviews or interviews and observations were contrasted:

• Confirmation between interviews and documentation: The data from these sources corroborated the existence of a common vision for an NP-led clinic. 19
• Confirmation among interviews and observation: NPs experienced pressure to find and maintain their position within the existing system. Nurse practitioners and general practitioners performed complete episodes of care, each without collaborative interaction. 21
• Contradiction among interviews and documentation: For example, interviewees mentioned that differentiating the scope of practice between NPs and physicians is difficult as there are too many areas of overlap. However, a clear description of the scope of practice for the 2 roles was provided. 21

Confirmation through a combination of qualitative and quantitative data

Both types of data showed that NPs and general practitioners wanted to have more time in common to discuss patient cases and engage in personal exchanges. 21 In addition, the qualitative and quantitative data confirmed the individual progression of NPs from less competent to more competent. 16 One study pointed out that qualitative and quantitative data obtained similar results for the cases. 20 For example, integrating NPs improved patient access by increasing appointment availability.

Contradiction through a combination of qualitative and quantitative data

Although questionnaire results indicated that NPs and general practitioners experienced high levels of collaboration and satisfaction with the collaborative relationship, the qualitative results drew a more ambivalent picture of NPs’ and general practitioners’ experiences with collaboration. 21

Research Question and Design

The studies included in this scoping review evidenced various research questions. The recommended formats (ie, how or why questions) were not applied consistently. Therefore, no case study design should be applied because the research question is the major guide for determining the research design. 2 Furthermore, case definitions and designs were applied variably. The lack of standardization is reflected in differences in the reporting of these case studies. Generally, case study research is viewed as allowing much more freedom and flexibility. 5 , 24 However, this flexibility and the lack of uniform specifications lead to confusion.

Methodologic Triangulation

Methodologic triangulation, as described in the literature, can be somewhat confusing as it can refer to either data-collection methods or research designs. 6 , 8 For example, methodologic triangulation can allude to qualitative and quantitative methods, indicating a paradigmatic connection. Methodologic triangulation can also point to qualitative and quantitative data-collection methods, analysis, and interpretation without specific philosophical stances. 6 , 8 Regarding “data-collection methods with no philosophical stances,” we would recommend using the wording “data source triangulation” instead. Thus, the demarcation between the method and the data-collection procedures will be clearer.

Within-Method and Between/Across-Method Triangulation

Yin 1 advocated the use of multiple sources of evidence so that a case or cases can be investigated more comprehensively and accurately. Most studies included multiple data-collection procedures. Five studies employed a variety of qualitative data-collection procedures, and 3 studies used qualitative and quantitative data-collection procedures (mixed methods). In contrast, no study contained 2 or more quantitative data-collection procedures. In particular, quantitative data-collection procedures—such as validated, reliable questionnaires, scales, or assessments—were not used exhaustively. The prerequisites for using multiple data-collection procedures are availability, the knowledge and skill of the researcher, and sufficient financial funds. 1 To meet these prerequisites, research teams consisting of members with different levels of training and experience are necessary. Multidisciplinary research teams need to be aware of the strengths and weaknesses of different data sources and collection procedures. 1

Qualitative methods of analysis and results

When using multiple data sources and analysis methods, it is necessary to present the results in a coherent manner. Although the importance of multiple data sources and analysis has been emphasized, 1 , 5 the description of triangulation has tended to be brief. Thus, traceability of the research process is not always ensured. The sparse description of the data-analysis triangulation procedure may be due to the limited number of words in publications or the complexity involved in merging the different data sources.

Only a few concrete recommendations regarding the operationalization of the data-analysis triangulation with the qualitative data process were found. 25 A total of 3 approaches have been proposed 25 : (1) the intuitive approach, in which researchers intuitively connect information from different data sources; (2) the procedural approach, in which each comparative or contrasting step in triangulation is documented to ensure transparency and replicability; and (3) the intersubjective approach, which necessitates a group of researchers agreeing on the steps in the triangulation process. For each case study, one of these 3 approaches needs to be selected, carefully carried out, and documented. Thus, in-depth examination of the data can take place. Farmer et al 25 concluded that most researchers take the intuitive approach; therefore, triangulation is not clearly articulated. This trend is also evident in our scoping review.

Mixed-methods analysis and results

Few studies in this scoping review used a combination of qualitative and quantitative analysis. However, creating a comprehensive stand-alone picture of a case from both qualitative and quantitative methods is challenging. Findings derived from different data types may not automatically coalesce into a coherent whole. 4 O’Cathain et al 26 described 3 techniques for combining the results of qualitative and quantitative methods: (1) developing a triangulation protocol; (2) following a thread by selecting a theme from 1 component and following it across the other components; and (3) developing a mixed-methods matrix.

The most detailed description of the conducting of triangulation is the triangulation protocol. The triangulation protocol takes place at the interpretation stage of the research process. 26 This protocol was developed for multiple qualitative data but can also be applied to a combination of qualitative and quantitative data. 25 , 26 It is possible to determine agreement, partial agreement, “silence,” or dissonance between the results of qualitative and quantitative data. The protocol is intended to bring together the various themes from the qualitative and quantitative results and identify overarching meta-themes. 25 , 26

The “following a thread” technique is used in the analysis stage of the research process. To begin, each data source is analyzed to identify the most important themes that need further investigation. Subsequently, the research team selects 1 theme from 1 data source and follows it up in the other data source, thereby creating a thread. The individual steps of this technique are not specified. 26 , 27

A mixed-methods matrix is used at the end of the analysis. 26 All the data collected on a defined case are examined together in 1 large matrix, paying attention to cases rather than variables or themes. In a mixed-methods matrix (eg, a table), the rows represent the cases for which both qualitative and quantitative data exist. The columns show the findings for each case. This technique allows the research team to look for congruency, surprises, and paradoxes among the findings as well as patterns across multiple cases. In our review, we identified only one of these 3 approaches in the study by Roots and MacDonald. 20 These authors mentioned that a causal network analysis was performed using a matrix. However, no further details were given, and reference was made to a later publication. We could not find this publication.

Case Studies in Nursing Research and Recommendations

Because it focused on the implementation of NPs in primary health care, the setting of this scoping review was narrow. However, triangulation is essential for research in this area. This type of research was found to provide a good basis for understanding methodologic and data-analysis triangulation. Despite the lack of traceability in the description of the data and methodological triangulation, we believe that case studies are an appropriate design for exploring new nursing roles in existing health care systems. This is evidenced by the fact that case study research is widely used in many social science disciplines as well as in professional practice. 1 To strengthen this research method and increase the traceability in the research process, we recommend using the reporting guideline and reporting checklist by Rodgers et al. 9 This reporting checklist needs to be complemented with methodologic and data-analysis triangulation. A procedural approach needs to be followed in which each comparative step of the triangulation is documented. 25 A triangulation protocol or a mixed-methods matrix can be used for this purpose. 26 If there is a word limit in a publication, the triangulation protocol or mixed-methods matrix needs to be identified. A schematic representation of methodologic and data-analysis triangulation in case studies can be found in Figure 2 .

Schematic representation of methodologic and data-analysis triangulation in case studies (own work).

Limitations

This study suffered from several limitations that must be acknowledged. Given the nature of scoping reviews, we did not analyze the evidence reported in the studies. However, 2 reviewers independently reviewed all the full-text reports with respect to the inclusion criteria. The focus on the primary care setting with NPs (master’s degree) was very narrow, and only a few studies qualified. Thus, possible important methodological aspects that would have contributed to answering the questions were omitted. Studies describing the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review due to the inclusion and exclusion criteria.

Conclusions

Given the various processes described for methodologic and data-analysis triangulation, we can conclude that triangulation in case studies is poorly standardized. Consequently, the traceability of the research process is not always given. Triangulation is complicated by the confusion of terminology. To advance case study research in nursing, we encourage authors to reflect critically on methodologic and data-analysis triangulation and use existing tools, such as the triangulation protocol or mixed-methods matrix and the reporting guideline checklist by Rodgers et al, 9 to ensure more transparent reporting.

Supplemental Material

Acknowledgments.

The authors thank Simona Aeschlimann for her support during the screening process.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material: Supplemental material for this article is available online.

SINGLE CASE STUDIES VS. MULTIPLE CASE STUDIES: A COMPARATIVE STUDY - DIVA PORTAL

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• Published: 30 August 2024

Risk of nosocomial coronavirus disease 2019: comparison between single- and multiple-occupancy rooms

• Hyeon Jae Jo 1 ,
• Pyoeng Gyun Choe 1 , 2 ,
• Ji Seon Kim 2 ,
• Mimi Lee 2 ,
• Minkyeong Lee 1 ,
• Jiyeon Bae 1 ,
• Chan Mi Lee 1 ,
• Chang Kyung Kang 1 ,
• Wan Beom Park 1 &
• Nam Joong Kim 1 , 2  

Antimicrobial Resistance & Infection Control volume  13 , Article number:  95 ( 2024 ) Cite this article

Metrics details

There is an ongoing controversy regarding whether single-occupancy rooms are superior to multiple-occupancy rooms in terms of infection prevention. We investigated whether treatment in a multiple-occupancy room is associated with an increased incidence of nosocomial coronavirus disease 2019 (COVID-19) compared with treatment in a single-occupancy room.

In this retrospective cohort study, every hospitalization period of adult patients aged ≥ 18 years at a tertiary hospital in Korea from January 1, 2022, to December 31, 2022, was analyzed. If COVID-19 was diagnosed more than 5 days after hospitalization, the case was classified as nosocomial. We estimated the association between the number of patients per room and the risk of nosocomial COVID-19 using a Cox proportional hazards regression model.

In total, 25,143 hospitalizations per room type were analyzed. The incidence rate of nosocomial COVID-19 increased according to the number of patients per room; it ranged from 3.05 to 38.64 cases per 10,000 patient-days between single- and 6-bed rooms, respectively. Additionally, the hazard ratios of nosocomial COVID-19 showed an increasing trend according to the number of patients per room, ranging from 0.14 (95% confidence interval 0.001–1.03) to 2.66 (95% confidence interval 1.60–4.85) between single- and 6-bed rooms, respectively.

Conclusions

We demonstrated that the incidence of nosocomial COVID-19 increased according to the number of patients per room. To reduce nosocomial infections by respiratory viruses, the use of multiple-occupancy rooms should be minimized.

Introduction

Nosocomial spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported during the coronavirus disease 2019 (COVID-19) pandemic [ 1 , 2 ]. To prevent nosocomial spread, many hospitals implemented additional strategies beyond the standard precautions. These included testing all patients on admission, improving ventilation, ensuring universal masking, encouraging vaccination of patients and healthcare workers, and isolating patients with confirmed COVID-19 [ 3 , 4 ].

Patients admitted to multiple-occupancy rooms have a higher risk of encountering other patients with transmissible infectious diseases relative to patients in single-occupancy rooms [ 5 , 6 ]. Some studies have demonstrated that the use of single-occupancy rooms significantly reduces the rates of colonization of multidrug-resistant organisms (MDROs) and healthcare-associated infection, such as bloodstream infection or Clostridium difficile infection, compared with treatment in multiple-occupancy rooms [ 7 , 8 , 9 ]. However, there is still controversy regarding the advantages of single-occupancy rooms in reducing multidrug-resistant organism colonization and healthcare-associated infection. This controversy has arisen because most previous studies had low levels of evidence and included many confounding variables, thus hindering interpretation [ 10 , 11 , 12 , 13 ].

MDROs mainly spread via contaminated hands and the environment. In contrast, respiratory viruses, including influenza virus and SARS-CoV-2, mainly spread by droplets or aerosols. Few studies have examined the impact of multiple-occupancy rooms on nosocomial transmission of respiratory viruses. In one previous study, the incidences of nosocomial influenza were 2.0 and 0.7 for 100 patient-days in double- and single-occupancy rooms, respectively [ 5 ]. Several studies have revealed that treatment in multiple-occupancy rooms is a risk factor for nosocomial COVID-19 [ 14 , 15 , 16 , 17 , 18 ]. This study aimed to investigate the impact of multiple-occupancy rooms on the incidence of nosocomial COVID-19.

Study setting

This retrospective observational study was conducted at a tertiary hospital in Seoul, South Korea. This is an 1803-bed university-affiliated hospital with 1367 non-intensive care unit beds for adults, 126 (9.2%) single-bed rooms, 364 (26.6%) 2-bed rooms, 39 (2.9%) 3-bed rooms, 184 (13.5%) 4-bed rooms, 120 (8.8%) 5-bed rooms, and 534 (39.0%) 6-bed rooms. In multiple-occupancy rooms, the beds were placed 7 feet apart and separated by curtains. Among the 126 single-bed rooms, 35 (27.8%) were located in wards with only single-bed rooms, while 91 (72.2%) were located in wards with both single- and multi-bed rooms. This study was performed from January 1, 2022, to December 31, 2022, when the number of confirmed COVID-19 cases was at its peak in Korea. The Delta variant was dominant until January 2022; thereafter, the Omicron BA.1, BA.2, and BA.5 variants were dominant [ 19 ].

During the study period, a SARS-CoV-2 polymerase chain reaction (PCR) assay was performed before hospitalization of all patients, and patients were admitted after a negative result had been confirmed. If the SARS-CoV-2 PCR assay result was positive on admission for patients whose admission was inevitable, those patients were isolated in single-occupancy rooms. Visitors’ access was restricted to individuals with a negative PCR test result obtained within 48 h. Universal masking of patients and healthcare workers was implemented, and vaccination of patients and healthcare workers was encouraged. In addition to screening for all admissions, the SARS-CoV-2 PCR assay was repeated if patients had a fever and/or respiratory symptoms. Patients diagnosed with COVID-19 during admission were isolated in single-occupancy rooms with negative pressure when available, otherwise, single-occupancy rooms without negative pressure were used. Healthcare workers adhered to standard, contact, and droplet precautions for all COVID-19 patients. Airborne precautions were implemented during aerosol-generating procedures. Personal protective equipment included KF94 or equivalent respirators, face shields or goggles, non-sterile gloves, and isolation gowns. During aerosol-generating procedures, N95 or equivalent respirators were used.

When COVID-19 was confirmed in a patient in a multiple-occupancy room, all patients sharing the room were tested with the SARS-CoV-2 PCR assay during the infectious window (defined as 48 h before symptom onset or a positive test in the absence of symptoms). Exposed roommates were placed on droplet precautions if they were inpatients, or on home quarantine if they were being discharged, for 14 days after their last exposure. Considering the median incubation period < 7 days, the quarantine period was reduced to 7 days during the late study period.

Definitions

A case of COVID-19 was defined as a positive SARS-CoV-2 PCR assay result using any respiratory specimens. Patients with a recent history of infection were categorized according to national guidelines, which were based on the Centers for Disease Control and Prevention protocol, as follows [ 20 , 21 ]. Reinfection was defined as a positive test more than 90 days after the last diagnosis (with or without symptoms), a positive test 45–89 days after the last diagnosis (with symptoms), or a history of exposure to a patient with a confirmed positive test result. All other cases were classified as re-positivity. Cases were classified as nosocomial if diagnosed more than 5 days after hospitalization.

Hospital rooms were classified as 1A, 1B, 2, 3, 4, 5, or 6 according to the number of patients per room. 1A refers to a single-bed room in an all single-bed room ward, whereas 1B refers to a single-bed room in a mixed single- and multi-bed room ward.

We retrospectively reviewed the hospitalization periods of adult patients aged ≥ 18 years from January 1, 2022, to December 31, 2022. All hospitalization periods were divided according to the hospital room type. Hospitalization periods were excluded from the analysis based on the following criteria.

If the length of stay in one hospital room was < 5 days, the hospitalization period for that room was excluded.

Hospitalization periods in intensive care units (ICUs) were excluded.

Hospitalization periods after the diagnosis of nosocomial COVID-19 (including periods at the time of re-admission) were excluded.

If nosocomial COVID-19 was diagnosed within 5 days after a room change, hospitalization periods in the pre-and post-movement rooms were excluded.

Hospitalization periods for patients with community-acquired COVID-19 and those with re-positivity results were excluded.

If a patient was hospitalized multiple times during the study period, each hospitalization was included in the analysis.

The following variables were extracted from SUPREME ® , a clinical data warehouse at the study hospital: age, sex, underlying diseases, date of admission, date of discharge, hospitalization room, and SARS-CoV-2 reverse-transcription PCR assay results. Underlying disease data were extracted using International Classification of Diseases 10th revision codes, including diabetes mellitus, chronic kidney disease, cardiovascular disease, heart failure, cerebrovascular accident, liver cirrhosis, chronic obstructive pulmonary disease, interstitial lung disease, rheumatologic disease, asthma, hematologic malignancy, solid malignancy, solid organ transplantation, and hematopoietic stem cell transplantation. Patients were considered vaccinated if they had completed the primary series or received booster vaccinations [ 22 ].

Statistical analysis

Patients’ baseline characteristics were compared across all study groups using the absolute standardized difference (ASD). ASDs of < 0.1 and > 0.25 indicated negligible and large differences, respectively, in the mean or proportion of covariates between two groups [ 23 ]. Statistical significance was defined as a mean ASD of > 0.15 and maximum ASD of > 0.3.

To estimate the incidence rates of nosocomial COVID-19 per room type, the hospitalization period per room was used to calculate the follow-up time when estimating the incidence, with the hospitalization period per room regarded as the analysis unit. The incidence rate was defined as the sum of nosocomial COVID-19 incident cases divided by the total follow-up time. A Poisson regression model was used to test the trend in incidence rate of nosocomial COVID-19 according to the number of patients per room.

The association between the number of patients per room and the risk of nosocomial COVID-19 was estimated using a Cox proportional hazards regression model. Age, sex, and underlying diseases were included in the multivariable model.

Although the vaccination status was an important variable, it could not be extracted from the database of the clinical data warehouse, and it was not feasible to check the vaccination histories of all patients. As an alternative, we reviewed the vaccination histories of all patients with confirmed nosocomial COVID-19. Based on these results, we assumed the vaccination rate of the remaining patients and calculated the number of patients required to estimate the vaccination rate using a precision rate of 5% and the 95% confidence interval (CI). We then reviewed the vaccination histories of the remaining randomly sampled patients. The weighted vaccination rates according to room type were estimated via multiplication of the vaccination rates of patients with and without nosocomial COVID-19 by their sampling weights. Sampling weights were calculated as the inverse of the sampling fraction (number of data points with vaccination information/number of analysis data) per room type and nosocomial COVID-19 status.

Subgroup analysis was performed among patients with known vaccination information to determine the association, adjusted for vaccination status and the above-listed variables. The association was estimated by fitting a Cox proportional hazards model, weighted using the sampling weight.

We also performed sensitivity analysis using a diagnostic cut-off for nosocomial COVID-19 set at 10 days after the date of admission.

Statistical analyses were conducted with support from the Medical Research Collaboration Center and performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA), IBM SPSS Statistics for Windows, version 28.0 (IBM Corp., Armonk, NY, USA), and PASS 2022, v22.0.2 (NCSS, LLC, Kaysville, UT, USA). The threshold for statistical significance was regarded as P  < 0.05.

This study protocol was approved by the Institutional Review Board (IRB No. H-2308-016-1454) and Data Review Board (DRB No. DRB-E(I)-2023-08-07) of Seoul National University Hospital. The requirement for informed consent was waived because of the retrospective nature of the study.

Study cohort

During the study period, 80,702 patients aged ≥ 18 years were hospitalized. Of these patients, 67,890 stayed in only one room type during hospitalization; 12,812 (15.9%) were transferred and stayed in two or more room types during hospitalization. Considering room transfers, 99,797 hospitalizations were analyzed. Among these hospitalizations, we excluded those for which the length of stay was < 5 days (n = 73,214), admissions to intensive care units (n = 1087), hospitalization periods occurring after nosocomial COVID-19 (n = 241), those for which the hospitalization room was a pre- or post-transfer room when nosocomial COVID-19 had been diagnosed within 5 days of transfer (n = 31), and those in which patients were diagnosed with community-acquired COVID-19 or had re-positivity results (n = 81). Finally, 22,757 hospitalizations of 18,577 patients remained. Among these, 1918 (8.4%) patients underwent room transfers, and 25,143 hospitalizations per room type were analyzed (Fig.  1 ).

Study flow diagram and examples of exclusion process. Abbreviations; COVID-19: coronavirus disease-2019, ICU: intensive care unit. a 1A: single-bed room in an all single-bed room ward; 1B: single-bed room in a mixed single- and multi-bed rooms ward. During the study period, there were 99,797 hospitalizations per room type. After excluding hospitalizations according to the eligibility criteria, we analyzed 25,143 hospitalizations per room type. Each exclusion criterion was explained by category

Demographic, baseline characteristics, and vaccination status

The number of hospitalizations per room type and the patients’ baseline characteristics are shown in Table  1 . Seven baseline covariates (age, sex, diabetes mellitus, chronic kidney disease, cardiovascular disease, solid malignancy, and duration of hospitalization) showed large standardized differences regarding means or proportions (mean ASD > 0.15 and maximum ASD > 0.3).

The vaccination rate among patients with nosocomial COVID-19 ranged from 0.0 to 85.1% (Table  2 a). Based on this finding, we assumed a vaccination rate of 80% for the remaining patients and calculated that 246 patients per room type would be required to estimate the vaccination rate with a precision rate of 5% and the 95% CI. Among the randomly sampled 246 patients without nosocomial COVID-19, the vaccination rate ranged from 78.0 to 89.8% (Table  2 b). The estimated vaccination rates per room type were as follows: 1A rooms, 88.8% (95% CI 84.7–92.8); 1B rooms, 84.2% (95% CI 79.7–88.7); 2-bed rooms, 88.0% (95% CI 84.0–91.9); 3-bed rooms, 89.3% (95% CI 85.6–93.1); 4-bed rooms, 77.7% (95% CI 72.6–82.8); 5-bed rooms, 90.3% (95% CI 86.7–93.9); and 6-bed rooms, 88.1% (95% CI 84.2–92.1) (Table  2 c). Overall, vaccination coverage did not significantly differ between patients in single- and multiple-occupancy rooms; however, patients in 4-bed rooms had a lower vaccination rate than patients in the other rooms ( P  < 0.001).

Nosocomial COVID-19

During the 138,997 patient-days of observation, 401 cases of nosocomial COVID-19 were diagnosed. The incidence rate of nosocomial COVID-19 tended to increase according to the number of patients per room, ranging from 3.05 to 38.64 cases per 10,000 patient-days in single- to 6-bed rooms, respectively ( P  < 0.001, Table  3 ).

Risk of nosocomial COVID-19 based on the number of patients per room

The results of multivariable Cox proportional hazards regression are shown in Table  4 . Using 1B rooms as the reference, we observed an increasing trend in the hazard ratios of nosocomial COVID-19 according to the number of patients per room from 0.14 for 1A rooms to 2.66 for 6-bed rooms ( P  < 0.001). Furthermore, the hazard ratios were significantly higher for rooms with ≥ 5 patients than for 1B rooms.

Subgroup analysis, focusing solely on 2627 patients with a known vaccination status, also revealed an increasing trend in the hazard ratio of nosocomial COVID-19 according to the number of patients per room (Supplementary Table 1).

The results of sensitivity analysis, using a diagnostic cut-off for nosocomial COVID-19 set at 10 days after the date of admission, are shown in Supplementary Table 2. The tendency for the risk of nosocomial COVID-19 to increase according to the number of patients per room persisted regardless of the definition of nosocomial COVID-19.

Higher nosocomial COVID-19 rates were detected among patients in multiple-occupancy rooms than among those in single-occupancy rooms. A dose–response relationship was present between the number of patients in a room and the incidence of nosocomial COVID-19. These findings suggest a strong correlation between treatments in multiple-occupancy rooms and the acquisition of SARS-CoV-2 infection.

This study was conducted in Korea in 2022. The prevalence of COVID-19 was relatively low in Korea until late 2021 because of aggressive testing, contact tracing, strict quarantine policies, and high vaccination rates. Despite the high vaccination rates, the prevalence abruptly increased in February 2022 due to the emergence of highly transmissible Omicron variants [ 24 , 25 ]. The incidence of nosocomial COVID-19 increased during the community-wide Omicron outbreak compared with the Delta outbreak [ 26 , 27 ]. We believe that the predominance of highly transmissible Omicron variants in the community highlights the impact of multiple-occupancy rooms on nosocomial COVID-19.

We applied several exclusion criteria, some of which require explanation. ICU stays were excluded due to distinct differences in patient care compared to general wards. The ICU was an open shared space with 10–25 beds, lower patient-to-nurse ratio, and higher patient turnover compared to general wards. In addition, hospitalization periods in pre-and post-movement rooms were excluded when nosocomial COVID-19 was diagnosed within 5 days of a room change. Considering the SARS CoV-2 incubation period of 2–14 days, it was unclear whether transmission occurred before or after the room change. To minimize misclassification, the pre-movement period was excluded.

The criteria for defining nosocomial COVID-19 have not yet been standardized. The incubation period of wild type SARS-CoV-2 ranges from 2 to 14 days (median, 5.1 days) [ 28 ], and that of the Omicron variant is shorter [ 29 , 30 ]. In this study, we selected 5 days after hospitalization as the cut-off for diagnosing nosocomial COVID-19 to cover the median incubation period for COVID-19; this approach also avoided underestimating the incidence of nosocomial COVID-19 [ 28 ]. Other studies also defined nosocomial COVID-19 as a positive SARS-CoV-2 PCR result 5 days after admission in patients who had a negative PCR result on admission [ 14 , 31 ]. When we separately analyzed the data using 10 days as the cut-off (which encompassed 95% of the incubation period), the trends were consistent (Supplementary Table 2).

SARS-CoV-2 mainly spreads through respiratory droplets and/or aerosols; it less frequently spreads through environmental contamination [ 32 , 33 ]. The spread of SARS-CoV-2 after exposure to rooms with multiple occupancies has also been reported [ 1 , 2 , 15 , 34 , 35 , 36 ]. The rate of a second attack rate after exposure to SARS-CoV-2 in a shared room ranges from 19 to 40% [ 15 , 16 , 34 ]. Interventions performed to interrupt the nosocomial spread of respiratory viruses include rapid detection and isolation of patients with transmissible viruses, proper hand hygiene, improved ventilation, implementation of universal masking, and vaccination policies for patients and healthcare personnel [ 3 , 4 ]. Efforts to minimize the use of multiple-occupancy rooms are needed to reduce the nosocomial spread of pathogens transmitted by respiratory secretions. In a prospective observational study, double- or multi-occupancy rooms were independently associated with nosocomial influenza compared with single-occupancy rooms (adjusted odds ratio 3.42; 95% confidence interval 1.29–9.08) [ 37 ]. Another study showed that the relative risk of nosocomial influenza was 2.67 (95% confidence interval 1.05–6.76) in double-occupancy rooms compared with single-occupancy rooms [ 38 ]. We found that the incidence of nosocomial COVID-19 increased according to the number of patients in a room. Patients in shared rooms have minimal close contact with their roommates. Therefore, transmission to roommates might occur via respiratory droplets or aerosols despite universal masking of patients, curtains between patients, and a mean separation distance of 7 feet. A higher number of patients in a room is associated with greater risk of exposure to patients with asymptomatic or symptomatic COVID-19. The incidence of nosocomial COVID-19 was lowest in wards containing only single-bed rooms (1A ward). This suggests that less crowded wards are beneficial for reducing the spread of nosocomial COVID-19. If a patient in a multi-occupancy room had fever or respiratory symptoms in the present study, diagnostic tests were immediately performed to detect COVID-19 and isolate patients with newly detected COVID-19. To minimize nosocomial transmission, droplet precautions were implemented for roommates of COVID-19 patients for 14 days, consistent with the longest incubation period of SARS-CoV-2. However, such efforts are insufficient to prevent the transmission of SARS-CoV-2 in multiple-occupancy rooms because nearly 60% of SARS-CoV-2 transmissions are attributable to asymptomatic or pre-symptomatic individuals [ 39 ]. Several published guidelines recommend single-occupancy rooms for refurbished or new hospital wards [ 40 , 41 ]. The proportion of single-occupancy hospital rooms has increased in many countries [ 42 , 43 ]. We suggest that an increased proportion of single-occupancy rooms is necessary to reduce the spread of nosocomial infections caused by respiratory droplets and/or aerosols.

Although this study demonstrated the impact of multiple-occupancy rooms on the nosocomial spread of COVID-19, it had several limitations. First, we did not analyze genetic relationships of SARS-CoV-2 via molecular methods to confirm spread in shared rooms. Some patients may have been infected by people other than their roommates. Second, we could not investigate the vaccination histories of all patients, although the vaccination rate is an important factor influencing the incidence of nosocomial COVID-19. To minimize this limitation, we examined the vaccination histories of all patients with confirmed COVID-19; we found no significant differences between patients in single- or multiple-occupancy rooms. We also performed a separate analysis of 2627 patients whose vaccination history information was available; the results were consistent with the initial analysis. Third, as mentioned above, the cut-off days to define nosocomial COVID-19 were not standardized. To minimize this limitation, we analyzed data using 10 days as the cut-off; the results were consistent with the initial analysis. Fourth, patients diagnosed with nosocomial COVID-19 after discharge may have been excluded. Fifth, as shown in Fig.  1 , a significant number of hospitalization periods were excluded to minimize misclassification. Although this reduced the sample size, the focus on patients with confidently determined nosocomial spread was prioritized. Considering the year-long study duration, a sufficient number of patients and observation time remained.

We have demonstrated that multiple-occupancy rooms play a role in the spread of nosocomial COVID-19. We suggest minimizing the use of multiple-occupancy rooms to facilitate infection control, especially concerning the spread of respiratory viruses within hospitals.

Availability of data and materials

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

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Acknowledgements

The authors thank Prof. Myoung-jin Jang of the Medical Research Collaborating Center (MRCC) at Seoul National University Hospital for the statistical analysis and consultation.

This work was supported in part by the Bio and Medical Technology Development Program of the National Research Foundation (NRF), the Korean government (MSIT) (grant number 2021M3A9I2080498), and the Creative-Pioneering Researchers Program through Seoul National University.

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Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro Jongno-gu, Seoul, 03080, Republic of Korea

Hyeon Jae Jo, Pyoeng Gyun Choe, Minkyeong Lee, Jiyeon Bae, Chan Mi Lee, Chang Kyung Kang, Wan Beom Park & Nam Joong Kim

Infection Control Office, Seoul National University Hospital, Seoul, Republic of Korea

Pyoeng Gyun Choe, Ji Seon Kim, Mimi Lee & Nam Joong Kim

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Contributions

Concept and design: Kim NJ, Choe PG, Jo HJ Acquisition, analysis, or interpretation of data: Kim NJ, Choe PG, Jo HJ, Lee MM, Kim JS Drafting of the manuscript: Kim NJ, Jo HJ Critical review of the manuscript for important intellectual content: Kim NJ, Park WB, Choe PG, Kang CK, Lee CM, Jo HJ, Bae JY, Lee MK Statistical analysis: Kim NJ, Choe PG, Jo HJ Obtained funding: Kim NJ, Park WB Administrative, technical, or material support: Kim NJ, Park WB, Bae JY, Lee MK, Lee MM, Kim JS Supervision: Kim NJ, Choe PG, Park WB, Kang CK, Lee CM.

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Correspondence to Nam Joong Kim .

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Ethics approval.

This study protocol was approved by the Institutional Review Board (IRB No. H-2308-016-1454) and a Data Review Board (DRB No. DRB-E(I)-2023-08-07) of the Seoul National University Hospital.

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The authors declare no competing interests.

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Single case studies vs. multiple case studies: A comparative study

There are several different definitions and kinds of case studies. Because of different reasons the case studies can be either single or multiple. This study attempts to answer when to write a single case study and when to write a multiple case study. It will further answer the benefits and disadvantages with the different types. The literature review, which is based on secondary sources, is about case studies. Then the literature review is discussed and analysed to reach a conclusion. The conclusion is that there are several different opinions if a single case study or a multiple case study is the best choice. Different causes to consider in the choice to make a single case study or a multiple case study are presented. Some causes are that the amount depends on the context, upon how much is known and how much new information the cases bring. Another conclusion from the case studies I looked among is that it is generally more number of pages in the multiple case studies than in the ...

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Uncertainty assessment of species distribution prediction using multiple global climate models on the tibetan plateau: a case study of gentiana yunnanensis and gentiana siphonantha.

1. Introduction

2. materials and methods, 2.1. study area, 2.2. species data, 2.3. climate and environment data, 2.4. global climate models, 2.5. species distribution modeling, 3.1. model performance, 3.2. current potential distribution, 3.3. future potential distribution simulations, 3.3.1. impacts of gcms on sdm, 3.3.2. range shift under future climate change with mme-4, 4. discussion, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Song, Y.; Xu, X.; Zhang, S.; Chi, X. Uncertainty Assessment of Species Distribution Prediction Using Multiple Global Climate Models on the Tibetan Plateau: A Case Study of Gentiana yunnanensis and Gentiana siphonantha . Land 2024 , 13 , 1376. https://doi.org/10.3390/land13091376

Song Y, Xu X, Zhang S, Chi X. Uncertainty Assessment of Species Distribution Prediction Using Multiple Global Climate Models on the Tibetan Plateau: A Case Study of Gentiana yunnanensis and Gentiana siphonantha . Land . 2024; 13(9):1376. https://doi.org/10.3390/land13091376

Song, Yuxin, Xiaoting Xu, Shuoying Zhang, and Xiulian Chi. 2024. "Uncertainty Assessment of Species Distribution Prediction Using Multiple Global Climate Models on the Tibetan Plateau: A Case Study of Gentiana yunnanensis and Gentiana siphonantha " Land 13, no. 9: 1376. https://doi.org/10.3390/land13091376

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• Open access
• Published: 30 August 2024

Risk factors and clinical outcomes associated with multiple as opposed to single pathogens detected on the gastrointestinal disease polymerase chain reaction assay

• Insa Mannstadt 1 ,
• Alexa M. Choy 4 ,
• Jianhua Li 2 ,
• Daniel A. Green 3 &
• Daniel E. Freedberg 4  

Gut Pathogens volume  16 , Article number:  45 ( 2024 ) Cite this article

Metrics details

The use of gastrointestinal disease multiplex polymerase chain reaction (GI PCR) testing has become common for suspected gastrointestinal infection. Patients often test positive for multiple pathogens simultaneously through GI PCR, although the clinical significance of this is uncertain.

This retrospective cohort study investigated risk factors and clinical outcomes associated with detection of multiple (as opposed to single) pathogens on GI PCR. We included adult patients who underwent GI PCR testing from 2020 to 2023 and had one or more pathogens detected. We compared patients with multiple versus those with single pathogens and hypothesized that immunosuppression would be a risk factor for detection of multiple pathogens. We further hypothesized that, during the 90 days after GI PCR testing, patients with multiple pathogens would have worse clinical outcomes such as increased rates of emergency department (ED) visits, death, hospitalization, or ambulatory care visits.

GI PCR was positive in 1341 (29%) of tested patients; 356 patients had multiple pathogens and 985 had one pathogen. The most common pathogens included Enteropathogenic Escherichia coli (EPEC, 27%), norovirus (17%), and Enteroaggregative E. coli (EAEC, 14%) in both multi- and singly positive patients. Immunosuppression was not associated with multiple pathogens (adjusted odds ratio [aOR] 1.35, 95% CI 0.96, 1.86). The factors most associated with multiple pathogens were Hispanic ethnicity (OR 1.86, 95% CI 1.42, 2.45) and chronic kidney disease (OR 1.69, 95% CI 1.13, 2.49). Patients with multiple pathogens were more likely to have ED visits during the 90 days after GI PCR testing (40% vs. 32%, p < 0.01), but they were not more likely to die, be hospitalized, or to have ambulatory medical visits.

Conclusions

Immunosuppression was not associated with detection of multiple as opposed to single pathogens on GI PCR testing. There were worse clinical outcomes associated with detection of multiple pathogens, although these effects were modest.

Introduction

The gastrointestinal disease multiplex polymerase chain reaction (GI PCR) is common and growing in popularity as a tool to diagnose diarrheal illnesses with greater sensitivity compared to traditional culture. Traditional culture-based testing rarely proves positive for more than one pathogen in a given sample, but GI PCR often detects co-infections with multiple diarrhea-causing pathogens. While GI PCR can identify co-infections, it is not always clear whether all detected pathogens are clinically relevant or if some represent colonization, particularly in patients with altered immune function [ 1 , 2 , 3 , 4 ]. This distinction is crucial as it can significantly impact clinical management decisions [ 5 ].

Despite widespread use of GI PCR, few studies have characterized the prevalence and types of organisms present in samples with multiple positive results. Additionally, there is a lack of understanding of the clinical implications of detecting multiple pathogens as opposed to a single pathogen on patient outcomes. This study aims to fill this knowledge gap and provide valuable insights into the interpretation of GI PCR results, especially in immunocompromised patients.

We hypothesized that immunocompromised patients would be at increased risk for multiple as opposed to single pathogens on GI PCR testing. In individuals with weakened immune systems, such as those with HIV/AIDS or undergoing cancer treatment or organ transplantation, the body’s normal defense mechanisms against colonization by gut pathogens are compromised [ 6 , 7 , 8 , 9 , 10 , 11 ]. Similarly, patients with comorbidities that disrupt the gut microbiome, such as cancer, diabetes, heart failure, chronic kidney disease, or inflammatory bowel disease (IBD), are more prone to enteric infections [ 9 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].

We further hypothesized that the presence of multiple pathogens would be associated with measurably worse clinical outcomes even after adjusting for other factors—i.e., that these patients would have true co-infection which would lead to increased healthcare utilization compared to singly-infected patients. A null hypothesis is that the detection of multiple pathogens usually represents colonization, and that such patients fare similarly to those with just one pathogen present [ 1 , 5 , 19 ].

By examining outcomes in those with multiple as opposed to single pathogens on GI PCR, we aimed to inform the clinical question of infection versus colonization. The overarching goal of the study was to guide future GI PCR testing decisions and to better interpret results when patients test positive for multiple enteric pathogens.

This was a single-center, retrospective cohort study conducted at Columbia University Irving Medical Center (CUIMC). Patients aged 18 years or older who had undergone a GI PCR test between February 2020 and March 2023 were included. Children were excluded because of the differences in gut pathogens affecting adults and children [ 20 ]. The primary analyses were focused on the subset of patients who tested positive for one or more pathogens (i.e., a positive GI PCR test result). In instances where multiple positive stool tests were recorded for a single patient, the first test result was selected to ensure that each included test represented a unique individual. To minimize loss to follow-up, only individuals who had received primary care or specialist outpatient care within the two-month period preceding the assay GI PCR test were included. The study protocol was approved by the institutional review board of CUIMC.

GI PCR testing

Patients were classified as testing positive for multiple pathogens if they had two or more organisms detected on GI PCR; they were classified positive for a single pathogen if only one organism was detected. The stool samples collected from the patients were processed using the FilmArray GI Panel (BioFire Diagnostics, Salt Lake City, UT) according to the manufacturer’s instructions. Freshly excreted stool samples were collected by nurses and an aliquot of stool was placed directly into Cary Blair transport media at the bedside. These samples were mixed with the manufacturer’s reagents, loaded onto a cartridge, and placed in the FilmArray instrument for automated analysis. The FilmArray GI Panel utilizes a closed-system disposable pouch to qualitatively detect DNA or RNA from 22 different gastrointestinal pathogens including bacteria, parasites, and viruses [ 21 ]. The treating physicians had access to the GI PCR results when formulating treatment plans for their patients.

Classification of immunosuppression

The main focus of interest was immunosuppression, which was classified categorically. Patients were classified as immunosuppressed if they had auto-immune diseases, history of solid organ transplant, or if they took an immunosuppressive medication in the 90 days before GI PCR testing (Supplemental Table  1 ) [ 22 , 23 , 24 , 25 , 26 ].

Co-variables

Using automated queries of the electronic medical record, we gathered demographic, clinical characteristics, and comorbidities and classified them based on codes documented using the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) coding system at the time of the GI PCR test (Supplemental Table  1 ). The ICD-10 system is used for billing in all U.S. healthcare settings and contains hierarchically structured medical diagnoses and reasons for healthcare visits [ 22 ]. (Supplemental Table  1 ). As of May 2024, ICD-10 is a medical classification list that standardizes disease and health condition coding across the globe and is maintained by the World Health Organization [ 27 ]. Age and BMI were split into quartiles. Laboratory values were defined as normal or abnormal based on the institutional laboratory reference ranges as of May 1, 2024 [ 28 ]. Age and serum markers were categorized to aid in risk stratification and clinical decision-making, providing clearer ranges for interpretation.

Clinical outcomes

We compared clinical outcomes during the 90 days after GI PCR testing including mortality, hospitalization, ED visits, receipt of antibiotics, and ambulatory medicine visits, between groups testing positive for multiple versus single pathogens by gathering data from electronic medical records with automated queries and classifying outcomes as present or absent.

Statistical approach

Continuous data were expressed as means with standard deviations (SD) or as medians with interquartile ranges if the data were not normally distributed. Data were compared using t-tests for continuous data or chi-squared tests or Fisher’s exact tests for categorical data. Two multivariable models were constructed. First, a model was constructed for the outcome of testing positive for multiple as opposed to single pathogens on GI PCR. This model included immunosuppression a priori, with additional variables added stepwise, retaining those in the final model that independently predicted multiple pathogens. Second, a model was constructed for each clinical outcome. The primary focus of interest in this model was testing positive for multiple as opposed to single pathogens and we additionally pre-specified that age, immunosuppression, and insurance status would be included because these factors are likely to associate with poor outcomes. Logistic regression modeling was used to investigate risk factors for detection of multiple as opposed to single pathogens on GI PCR. Crude (unadjusted) odds ratios were used for descriptive purposes and adjusted odds ratios were used to control for potential confounding variables and to estimate the independent effects of predictor variables. Two-tailed test with a p-value of ≤ 0.05 was considered statistically significant. All statistical analysis was performed using RStudio [ 44 ], using packages forestplot [ 45 ], lubridate [ 46 ], olsrr [ 47 ], vtable [ 48 ], checkmate [ 49 ], report [ 50 ], tibble [ 51 ], abind [ 52 ], R language and environment [ 53 ], Table  1 [ 54 ], reshape [ 55 ], ggplot2 [ 56 ], stringr [ 57 ], forcats [ 58 ], tidyverse [ 59 ], dplyr [ 60 ], purrr [ 61 ], readr [ 62 ], tidyr [ 63 ], and kableExtra [ 64 ].

Patient characteristics

There were 4704 patients who underwent GI PCR testing during the study period. Of these, 29% tested positive (either singly or multiply) and were included in the main analyses. The majority of patients were female (60%), with a median age of 53 years (IQR 35–68). Over half were White (52%), and nearly a quarter were Hispanic (24%), (Table  1 ).

Multiple as opposed to single pathogens detected

Out of the total patients tested, 1341 (29%) had a positive result on the GI PCR test. Of those with a positive GI PCR test, 985 (73%) were positive for a single pathogen, while 356 (27%) had multiple pathogens detected. Among the identified pathogens, the most prevalent were Enteropathogenic E. coli (EPEC), norovirus, and Enteroaggregative E. coli (EAEC). These accounted for approximately 70% of GI PCR results in patients with a single pathogen detected and 60% of PCR results in patients with multiple pathogens detected (Fig.  1 ). Patients with multiple positive GI PCR were slightly more likely to be immunosuppressed without reaching statistical significance (19% vs 16%, p = 0.07). They were more likely to be Hispanic (38% vs. 24%, p < 0.01) and to have end-stage renal disease (12% vs. 8%, p = 0.01) (Table  1 ). Among those with multiple pathogens detected on GI PCR, heat maps showed that the pathogen combinations most often co-present were EPEC and EAEC, EAEC and norovirus, and EPEC and norovirus. Higher rates of observed compared to expected combinations of co-positivity were seen for Enterotoxigenic E. coli (ETEC) and EAEC, Shiga toxin-producing E. coli (STEC) and ETEC, STEC and EAEC, and Giardia and Campylobacter (all p < 0.01) (Fig.  2 ).

Pie chart analyses of the study population’s fecal samples. The left pie chart represents the distribution of pathogens in samples with only one detected pathogen, while the right pie chart shows the breakdown for samples containing multiple pathogens

Heat map illustrating the prevalence of co-infecting pathogens in patients with multi positive PCR results. Highlighted squares represent combinations of pathogens that occurred more frequently than expected, as determined by McNamar’s test with a statistical significance threshold of p < 0.05

Logistic regression model for multiple vs. single pathogens

In the final model, immunosuppression was not significantly associated with multiple pathogens (aOR 1.35, 95% CI 0.96–1.86) (Table  2 ). Hispanic ethnicity was associated with increased risk for multiple pathogens (aOR 1.86, 95% CI 1.42–2.45).

Detection of multiple pathogens and clinical outcomes

Within 90 days of GI PCR testing, 24 (0.5%) patients died, 568 (12%) recorded ED or urgent care visits, 2673 (57%) recorded ambulatory medicine visits, and 161 (3.4%) were hospitalized. Patients with multiple positive pathogens were more likely to have ED/urgent care visits compared to those with single positive PCR results (40% vs. 32%, p < 0.01) but were not more likely to experience any of the other outcomes (Fig.  3 ). Next, we used logistic regression modeling to investigate the independent association between multiple pathogens and 90 day ED visits. After adjusting for other factors, detection of multiple (as opposed to single) pathogens was associated with increased risk for ED visits (aOR 1.44, 95% CI 1.11–1.87) (Table  3 ). Other factors that were independently associated with ED visits were immunosuppression (aOR 1.95, 95% CI 1.43–2.66), and Medicaid insurance (aOR 2.51, 95% CI 1.74, 3.62). The rates of receiving an antibiotic prescription were similar between patients with multiple vs. single positive GI PCR results (33% vs 32%). When looking specifically at the rates of receiving two or more antibiotic prescriptions, patients with multiple positive results had slightly higher rates compared to those with a single positive result (23% vs 19%, p = 0.03).

Bar graph of the 90 day clinical outcomes or disease courses in patients with single (blue) or multiple (yellow) positive pathogen results on GI PCR assay. The graph compares the outcomes between patients with infections with a single organism versus patients with multiple concurrent infections

This study assessed the clinical significance of detecting multiple as opposed to single pathogens on the GI PCR test, a common occurrence that was observed in 24% of all positive tests. We assessed risk factors for multiple pathogens, including immunosuppression. We also characterized the prevalence and types of enteric infections and the differences in clinical course and outcomes, comparing patients who tested positive for multiple gut pathogens versus those who tested positive for one pathogen alone. Overall, the baseline characteristics and outcomes of the two groups were more similar than we expected. A priori, we hypothesized that patients positive for multiple pathogens would be more likely to be immunosuppressed and would have increased medical comorbidities. We found that immunosuppression was not statistically associated with multiple pathogens. Downstream from this, we found only very modest differences in clinical outcomes when comparing those with multiple pathogens versus a single pathogen. Patients positive for multiple pathogens, including the more commonly detected but less clinically relevant EAEC, had a slightly higher rate of emergency room visits than those positive for a single pathogen, suggesting a potential additional health burden. However, the overall similarity between these two groups in terms of risk factors and the lack of thorough measures of clinical outcomes, such as severity of disease, duration of symptoms, or antibiotic requirement, makes it difficult to conclusively determine whether co-infection with multiple enteric pathogens represents a substantial health burden or is more likely an incidental finding. The higher prevalence of EAEC, an organism with less certain clinical relevance, among patients with multiple pathogens further supports the notion that these co-infections may not necessarily lead to worse clinical outcomes. While the increased emergency room visits among patients with multiple pathogens points to some additional health burden, the clinical course after GI PCR testing otherwise appeared largely similar between the two groups.

In contradiction to our results, prior studies have suggested that immunocompromise is associated with multiple gut pathogens, although many prior studies focus on enteric viruses and on children [ 3 , 6 , 29 ]. Specific immunocompromised subpopulations including children who are solid organ transplant recipients [ 29 ], those with HIV/AIDS [ 9 , 12 ], and liver and stem cell transplant recipients [ 30 , 31 ] are associated with higher rate of multiple pathogens on stool testing. The use of corticosteroids has also been associated with an increased likelihood of multiple pathogens on GI PCR among patients with IBD [ 32 ]. In a study of GI PCR testing in patients with HIV, Axelrad et al. found that 25% of men who have sex with men patients had multiple gut pathogens regardless of their degree of immunosuppression [ 11 , 33 ]. Our study was not powered to look at specific categories of immunosuppression and it is likely that there was heterogeneity in the degree of immunosuppression within the diverse group of immunosuppressed patients included in the study.

Interestingly, Hispanic ethnicity was the most important predictor of multiple pathogens on GI PCR. Prior research has documented differences in microbiome structure between racial and ethnic groups [ 34 , 35 , 36 , 37 ]. Hispanic ethnicity may be associated with the detection of multiple pathogens on GI PCR due to a combination of host genetics, geographic location, and socioeconomic factors such as diet, living environment, pathogen exposures, access to medical care, travel, and other social constructs that shape the gut microbiome and influence susceptibility to enteric pathogens [ 35 , 36 , 37 , 38 ]. This study could not determine the specific factors underlying the association between Hispanic ethnicity and higher incidence of multiple pathogens on GI PCR testing.

Prior studies of viral diarrhea in children have suggested that there is greater severity of diarrhea when multiple viruses are detected, but less is known in adults and with bacterial enteropathogens [ 39 ]. After adjusting for other factors including age, insurance status, and immunosuppression, detection of multiple pathogens was associated with a 44% increased risk for subsequent ED visits compared to detection of a single pathogen. Those with multiple pathogens were also more likely to receive more than one antibiotic, although overall rates of antibiotic use were similar comparing those with multiple vs. single pathogens. There was no association between multiple pathogens and other clinical outcomes (death, hospitalization, or increased likelihood of an ambulatory care visit). Future diagnostics—particularly those using sequencing technologies—may provide more granular clinical information by reporting on the relative abundance of a given organism which could influence the decision of whether and how to treat.

When we looked at patterns of co-positivity, we found several pathogen pairs which appeared at a rate greater than expected by pure chance: ETEC and EAEC, STEC and ETEC, STEC and EAEC, and Giardia and Campylobacter . Whether these represent synergistic relationships or rather shared environmental risk factors is unknown. In prior studies, ETEC has been found to co-occur more often with EPEC, and with Campylobacter [ 40 ]. Prior studies have also suggested that bacteria-bacteria pairs appear together more frequently than virus-bacteria pairs [ 40 , 41 ]. It is plausible that viral-bacterial coinfection could augment the severity of diarrhea [ 42 ]. One study employed a cluster analysis and hierarchical clustering approach to PCR-based data and demonstrated that such co-infections were likely to be clinically relevant [ 43 ].

This study has strengths, and some limitations. This study builds on the limited body of existing research that investigates patient variables and clinical outcomes associated with multiple pathogens on GI PCR. It was relatively large and looked at the presence of multiple pathogens from several angles. Limitations include a retrospective design, lack of granular data related to hygiene and lifestyle factors which may influence GI PCR positivity, and lack of detailed patient symptom and severity data. Future studies should investigate the impact of GI pathogens on patient outcomes and explore strategies to prevent and manage these infections.

In conclusion, we found that patients testing positive for multiple pathogens on GI PCR did not exhibit substantially different baseline characteristics or clinical outcomes compared to those testing positive for a single pathogen. The unexpected finding of Hispanic ethnicity as a predictor of multiple pathogens highlights the complex interplay between environmental, socioeconomic factors, and enteric infections. Patients who tested positive for multiple pathogens were more likely to have ER visits afterwards compared to those who tested positive for single pathogens, but no other harm was observed to be associated with multiple pathogens (no increased rate of death or hospitalization). On balance, these results argue that in many multi-positive GI PCR patients, one or more of the organisms is likely to be a colonizer.

Data availability

No datasets were generated or analysed during the current study.

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Supplementary Material 1. Table 1: Immunosuppression classification criteria including ICD-10 codes related to immune-mediated disease and immunosuppressants in 90 days prior to PCR

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Mannstadt, I., Choy, A., Li, J. et al. Risk factors and clinical outcomes associated with multiple as opposed to single pathogens detected on the gastrointestinal disease polymerase chain reaction assay. Gut Pathog 16 , 45 (2024). https://doi.org/10.1186/s13099-024-00638-4

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Case study showcases effectiveness of multimodality approaches for diagnosing hydrocephalus

by Beijing Institute of Technology Press Co., Ltd

A recent case report published in Cyborg Bionic Systems details the diagnosis of idiopathic normal pressure hydrocephalus (iNPH) using multimodality diagnostic approaches, highlighting significant advancements in medical diagnostics and patient care. The study conducted by a team of researchers from Tianjin Medical University General Hospital, Tianjin, China, presents a comprehensive case study of a 68-year-old male patient diagnosed with iNPH, showcasing the effectiveness of these advanced diagnostic techniques.

iNPH is a condition characterized by the accumulation of cerebrospinal fluid (CSF) causing ventricular dilation, often mistaken for brain atrophy due to similar symptoms such as cognitive impairment and gait disturbances. The prevalence of this condition increases with age, affecting approximately 1.30% of individuals over 65 and rising to 5.9% among those over 80.

In the documented case, the patient suffered from deteriorated gait, cognitive decline, and urinary incontinence , symptoms that gradually worsened over several years. Initially misdiagnosed, his condition prompted the use of multimodality diagnostic approaches after traditional methods provided inconclusive results. The diagnostic process included brain imaging, cerebrospinal fluid tap tests (CSFTT), continuous intracranial pressure monitoring, and a novel infusion study, which collectively led to an accurate diagnosis and subsequent treatment.

The infusion study, a critical component of the diagnosis, involves the measurement of cerebrospinal fluid resistance (Rcsf), which has been identified as a crucial physical marker for diagnosing hydrocephalus. In this case, an Rcsf level exceeding the normal range significantly indicated the presence of hydrocephalus, confirming the necessity for surgical intervention.

Following the diagnosis, the patient underwent a ventriculoperitoneal shunt surgery, which involves the insertion of a tube to drain excess CSF from the brain to the abdominal cavity. The surgery was successful, with the patient showing remarkable improvement in symptoms and overall quality of life.

This case underscores the vital role of multimodality diagnostic approaches in the medical field. Not only do these techniques enhance diagnostic accuracy, but they also reduce clinical costs and time spent on diagnosis, providing a quicker path to recovery for patients. The effectiveness of these approaches in complex cases like iNPH demonstrates their potential for broader application, promising significant improvements in the diagnosis and treatment of similar conditions.

Moreover, the study advocates for the adoption of these techniques in standard medical practice, suggesting that they could significantly reduce the rates of misdiagnosis and improve clinical outcomes. As medical technology continues to advance, the integration of such multimodal diagnostic tools holds the promise of transforming patient care , offering more precise, efficient, and cost-effective solutions for challenging medical diagnoses.

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Higher education in the era of artificial intelligence: academic freedom as a case study

• Open access
• Published: 29 August 2024
• Volume 5 , article number  220 , ( 2024 )

Cite this article

You have full access to this open access article

• Noura Joudieh 1 ,
• Hassan Harb 2   na1 ,
• Chamseddine Zaki 2   na1 ,
• Alaaeddine Ramadan 3   na1 ,
• Louai Saker 2   na1 ,
• Nour Mostafa 2   na1 &
• Layla Tannoury 4   na1  

Higher education is crucial for the development of states and societies and improving the overall quality of life. However, entry into higher education is often influenced by factors beyond qualifications, and individuals in the field face suppression from the controlling parties. These challenges undermine the value of education and the integrity of democratic processes like elections. In this paper, we study academic freedom in Lebanon and propose a technique that dynamically extracts the factors that might affect academic freedom. This technique comprises multiple stages: data collection, data preprocessing, static extraction of factors, dynamic extraction of factors, and evaluation. In the data collection stage, data was obtained from 254 participants through a questionnaire that discusses various facets of academic freedom. The preprocessing stage enhances data quality through cleaning, normalizing, and transforming. For static extraction, factors impacting academic freedom are identified using naive K-means clustering. In dynamic extraction, the Apriori algorithm identifies key metrics. Finally, a customized K-means algorithm clusters data based on a specific metric. This algorithm was applied on both, the statically and dynamically extracted metrics, and comparison was done based on the accuracy of the resultant clustering. This comparison demonstrates the effectiveness of the proposed technique in identifying and analyzing factors impacting academic freedom.

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• Artificial Intelligence

Avoid common mistakes on your manuscript.

1 Introduction

The world is changing. Development, science, and technology are evolving at a fast pace, producing complex issues in different sectors and industries. This diversity challenges today’s generations to cope with and contribute to those issues, creating a scene of innovation in the heart of every industry. Higher education intervenes to prepare students to be up to those challenges with the spirit of determination and grit. It is one of the key drivers of growth performance, prosperity, and competitiveness in national and global economies [ 1 ]. The state of artificial intelligence in higher education has seen a rapid rise in publications, with new trends emerging in terms of research locations, researcher affiliations, and subject domains [ 2 ]. In this article [ 2 ], the authors conducted a systematic review of Artificial Intelligence in higher education from 2016 to 2022, using a priori, and grounded coding, the data from the 138 articles were extracted, analyzed, and coded. The vast importance of higher education lies in its impact on society, economy, education, students, and world development. It aids students to acquire skills related to critical thinking, innovation, stepping out of comfort zones, teamwork, oral communication, and problem-solving. Designed to broaden an individual’s knowledge and experience, higher education provides its holders with higher employability, creating a cloud of knowledge and civilization for society as a whole. By doing this, it provides countries with higher revenues, inducing governments to allocate a portion of their funds to this specific sector. Unfortunately, this vital sector faces a host of challenges that threaten its value. Applicants find themselves judged based on their political and religious background rather than the qualifications and capabilities they have gained in their lives. The better the connections the greater the possibility of acceptance is, regardless of value and educational level acquired. Empirical evidence shows that in Lebanon, academic freedom and admissions are influenced by sectarianism and political affiliations. Various studies, such as those by Altbach [ 3 ], discuss how political and religious factors impact higher education in Lebanon and other countries. Similarly, Agbo and Lenshie [ 4 ] explore how academic freedom in African countries is often compromised by political pressures. The stereotyping and discrimination observed in Lebanon can be generalized to other countries with similar socio-political dynamics. For instance, in Saudi Arabia, academic freedom is influenced by gender and age, as shown by Al-Saeed [ 5 ]. In Turkey, academic freedom is debated more philosophically than factually, indicating a need for more empirical studies, as highlighted by Ertem [ 6 ].

The socio-political system plays a crucial role in embedding academia. For example, in the United States, academic freedom is seen as a key indicator of liberal democracy but faces threats due to attacks on individual liberties, as discussed by Cole [ 7 ]. These examples underscore the importance of considering the socio-political context when examining academic freedom and the factors that influence it. Stereotyping can lead to discriminatory hiring practices where individuals are judged based on characteristics unrelated to their qualifications. This creates an environment where meritocracy is undermined, affecting both hiring and retention rates. In Pakistan, for example, gender inequality affects academic freedom and employment opportunities, with females facing more barriers than males [ 8 ]. In Africa, the lack of funding and political interference are significant barriers to academic freedom and employment stability [ 4 ]. These examples highlight the pervasive impact of socio-political factors on academic freedom and employment practices globally.

Adopting this mentality, academic institutions are impeded to achieve their lofty goal of building a knowledge-based society. Entities concerned such as professors, students, librarians, educational institutions, and society as a whole are subject to the rule of repression and restrictions that threaten the act of pursuing knowledge and research [ 9 ]. Reporting the right to educate and propose suggestions that appear awkward for authority or political parties, as absent also entails difficulties. In addition, in their decisions, proposals and opinions, the entities concerned may be deemed to be regulated, where it is forbidden to address any contentious material that could compromise the reputation of the political or religious parties supporting their presence in this sector. Deprived freedoms in this sector include the freedom in research and publications, the freedom to write and speak about any topic, and the classroom freedom of discussion. To sum up all these issues, it can be claimed that this sector is under the denial of academic freedom attack.

As Louis Menand once wrote, “Academic freedom is not just a nice job perk. It is the philosophical key to the whole enterprise of higher education” [ 10 ]. Regarded as a cornerstone of higher education, multiple aspects can influence academic freedom. In this paper, we propose a technique that dynamically extracts these metrics that play a role in the existence of academic freedom. Multiple stages can be outlined in the proposed method, where the preprocessing step was initiated after gathering real data from participants using a questionnaire. All missing values and irrelevant attributes were cleaned from the data and then converted into two versions, one consisting of pure nominal data and another of numeric data. After data collection and processing, the data are ready to enter the analysis stages. Further phases are aimed at extracting from a given dataset the metrics that shape academic freedom. Firstly, the extraction process is carried out statically by either adopting what is established in studies and research or implementing the clustering algorithm of K-Means and attempting to recognize common factors in a certain cluster of low or high academic freedom. Secondly, this operation, using the Apriori algorithm, is performed dynamically. The condition for considering an attribute as a metric of the given dataset is to have this attribute repeated in most of the strongly generated rules by the algorithm. Finally, a comparison is made to measure the performance of dynamic extraction. It is carried out using a developed new version of K-Means algorithm that clusters a given data according to a given attribute. This clustering is conducted for each of the selected metrics and a comparison of the sum of square errors (SSE) of the resulting clusters is performed. The lower the SSE, the better the metric selected would be. Final findings revealed that dynamic extraction was able to obtain better metrics evidenced by the lower SSE values garnered after clustering.

The remainder of this paper is organized as follows. Section 2 presents an overview of techniques proposed for studying the metrics that might affect academic freedom. In section 3 , we present a detailed illustration of the technique while exploring each of its stages. Section 4 exposes the simulation and the discussion of the obtained results. Finally, section 5 concludes the paper and gives directions for future work.

2 Related work

Academic freedom is not a modern concept; it has long been discussed and researched in a variety of countries. Many researchers debated its existence, others studied its impact and relation with higher education. [ 5 ] studied how academic freedom is being applied in a Saudi Arabia university. From data collected from different faculty members, the study showed that female members claim more academic freedom than their male peers, and younger members are more aware of their academic rights than older professors and academic staff. The study emphasized that the smooth operation of the university administration is often disrupted by this state of uncertainty of academic rights and freedom. In [ 11 ], a more narrow view of academic freedom as a professional privilege rather than a human right is previewed. Science, teaching, and speech independence were the categories included in this concept. It was also contended that intellectual freedom requires equality from authority, whether governmental, religious, or social. Academic freedom in Turkish literature was the core of what [ 6 ] studied. After reviewing sixty-one studies on research and scholarships in the Turkish literature, they found out that academic freedom was debated as a national, local, and structural term, but that most of these debates were philosophical rather than factually focused. More empirical studies of academic freedom are required, according to the paper, especially in the Turkish context. Moving to the United States, [ 7 ], argued that the presence of academic freedom is a key indicator of liberal democracy. Additionally, they stated that the creativity, discovery, research, and academic freedom of industrialized nations, including the United States are being exposed to jeopardy because of the attacks on the principles of individual liberty and freedom of expression. A study was conducted by [ 12 ], on the universities of Bologna mirroring the academic values in the European context, and the National University of Singapore is seen as a spot involved and engaged in the region’s university developments. To fulfill the study’s goal of gaining knowledge about academic freedom from various angles, interviews with a sample of participants who are diverse in their disciplines, career stages, and genders were done. After analyzing those interviews, the definition of academic freedom, its importance, its existence, associated obligations, and limitations were extracted to result out by stating that academic freedom varies from one country to another, and from one age to another as younger ages need to enjoy an equal amount of freedom as their supervisors to be fully integrated in the academic world. Also, funding was one of the main factors spotted out by the results, as it plays a role in the choice of research conducted whether theoretical or practical, because as stated practical research is favored as it consumes less time. While [ 13 ] tried to shape academic freedom and frame it in an academic scope, integrating the limits that might be imposed by the Federal Constitution of 1988. Along with this frame, [ 13 ], concluded that academic freedom is an umbrella for various freedoms including the freedom of teachers to teach, students to learn, researchers to research, and knowledge to be shared. As for the Federal Constitution of 1988, it enshrines the teacher’s right to expose only reasonable ideas and positions, i.e. considering the right to education as a mechanism for preserving ideological pluralism, rather than a standalone freedom. [ 14 ] went further to elaborate on academic freedom being a stiff wall protecting universities and their members from any governmental or other universities’ intrusion. In that scope, the author expressed the danger of exceptional cases where academic freedom is used by professors in a classroom to harass, bully, or verbally assault students the idea of putting limits to academic freedom. But, at last, he emphasized by the assistance of the First Amendment that protects the speech of all thoughts, including those that are controversial, uncivil, repulsive, or worrisome, that intrusions on academic freedom must be battled from the beginning, and if those challenging cases were encouraged, they will gain momentum in suppressing free expression, and it will become more difficult to defend the respected speech. The research by [ 15 ] identified sixteen significant challenges facing universities in the United Kingdom, each posing a threat to their core mission. Among these, the issue of instrumentalism stands out. This mentality shifts the university’s focus from the pursuit of knowledge for its own sake to objectives such as social mobility, career development, sustainable futures, or economic rejuvenation. This shift frames academic freedom in materialistic terms, undermining its noble intent. Additionally, marketisation poses a threat to academic freedom by transforming students into customers and education into a commercial product. Financial crises also emerged as a challenge, highlighted ironically by the observation that while universities claim to be financially strained, they still invest heavily in non-academic roles.

In a related vein, [ 16 ] introduced the influence of social media on academic freedom. The features of social media, combined with academic institutions’ concern for their reputation, have created an environment where expressions made outside university walls are more vulnerable and perilous than before. Universities now monitor and respond to faculty members’ social media posts, sometimes criticizing, repudiating, or even punishing them for their comments. To foster a more supportive environment for such expressions, [ 16 ] recommended updating the guidelines of the American Association of University Professors (AAUP). This update would aim to protect social media posts and positions, addressing the current exploitation of the AAUP’s silence on this issue by institutions to impose limits on individuals, which conflicts with the protections stated in First Amendment case law. Where [ 17 ] defines academic freedom in a simpler and broader approach saying that it is the freedom to do academic work. Using this conception, six freedoms were inserted under this definition, where academic freedom was defined as the freedom to teach, learn, and question, and considered as a type of intellectual freedom unique to academic positions and perspective, critical at all levels of education and in any other educational settings, collaborative, and institutional, and intrinsic to the academic credibility of any academic journey or organization. All this is to state that, the entirety of academic freedom can not be accomplished without understanding its relevance to all academics and its position in all academic contexts. Cormac McGrath et al. [ 18 ] examine the attitudes of university teachers toward the adoption of artificial intelligence (AI) in higher education, employing an experimental philosophy approach. Through an online survey involving three distinct scenarios, focusing on first-generation students, a typical student, and students with learning disabilities, and 18 consistent questions, the study gathered responses from 194 out of 1773 teachers. The findings highlight varying perceptions of responsibility and equity in AI implementation, with a notable willingness to use AI tools to support equitable outcomes, particularly for first-generation and disabled students. Additionally, the results show significant differences in responses based on demographic factors such as gender, age, and academic position in certain cases. The study also uncovers prevalent concerns among teachers regarding fairness, responsibility, and their understanding and resources for integrating AI into teaching practices.

Specialized to countries, [ 4 ] explores the relationship between the state and academics to determine the dialectics of African academic freedom struggles. In Africa, the challenges of intellectual freedom through academic institutions are tied to the lack of funding and oppressive state authority, where the political class when gained power after independence, was hesitant to permit academic freedom to intellectuals on whom they depended heavily for fear of losing power. So academic freedom was only allowed to the degree that it does not coup the citizens against the state. [ 4 ] also stated that the reluctance of African academics to criticize Africa’s political elites is to blame for the system’s deterioration, where they have also been charged with being quiet while encouraging African leaders to plunder wealth for personal benefit by using religion, territory, race, and other primal identities to mislead the population. Moving to Pakistan, [ 8 ], found that higher education is not equal for both genders, where females’ academic freedom is less than that of males, where societal values clash with corporate politics, placing female workers at a disadvantage. In that scope, the author suggested that the Higher Education Community (HEC) should introduce educational programs to teach male workers about the importance of women’s jobs and responsibilities in society which necessitates a shift in mindsets to be more accepting of female employees and the ’cleansing of the male mentality’. Finally, [ 19 ] conducted a comparative study using systematic data analysis to define academic freedom in India and the United States and explore where academic freedom is more safeguarded. As a result, the author found that both have similar definitions of academic freedom, yet different protection mechanisms. India was found to have breaches in the Indian Penal Code, used to outlaw freedom of expression when it is incompatible with the country’s dignity and reputation, the government has the power to curb it and the insanity activities designed to insult religious sentiments and expression that encourages religious hatred within Higher Educational Institutions (HEI). Thus the study proposes that some particular provisions of the Indian Penal Code be revised to secure academic freedom in India.

3 Proposed technique

As a definition, academic freedom might seem plain, but it is deep. It lies at the core of every educational institution’s mission. The existence of academic freedom can be closely linked to the development of the higher education system. It can be stated as the freedom of the professor to teach and the freedom of the student to learn [ 3 ]. This simple definition holds the profound meaning of having no external control over the professor and no limitations on the curiosity of students to ask. However, academic freedom is struggling to exist in an environment that comprises several factors influencing it. In this section, we introduce a technique that provides the ability to extract dynamically the metrics that affect academic freedom. This operation of extracting metrics can be done statically, but using the proposed technique, better factors are extracted. This is evident by the accuracy of resulting clusters of clustering data based on these factors. The proposed technique relies mainly on the Apriori algorithm and a customized version of the K-Means clustering algorithm. Figure 1 illustrates the five stages of the technique, with the algorithm and procedure done in each of them. In what follows, is an exploration of each stage.

Architecture of the approach

3.1 Sample selection

Participants were selected based on their expertise in the higher education sector. They included employees and academic staff from both government and private sector institutions in Lebanon. The participants were aware of the content of the questionnaire and voluntarily accepted participation. To ensure the reliability of the questionnaire, it was pre-tested with a smaller group of similar participants to check for consistency in responses. Content validity was established by having the questionnaire reviewed by experts in higher education and academic freedom. Construct validity was ensured through factor analysis to confirm that the questions effectively measured the intended constructs.

3.2 Data collection

The primary stage of the proposed technique is gathering data about academic freedom and its level of dependence in Lebanon. To collect data, a questionnaire was formulated comprising 69 questions tackling different aspects of interest. Two versions of the questionnaire were prepared, one in English and the other in Arabic. A web-based application was created to conduct this questionnaire. The link to this application was distributed to employees and academic staff in official and private departments in Lebanon, and responses were accepted in the period between 11 May, 2023, and 24 June. In total from both the Arabic and English version, 254 participants registered a record. The questions of the survey were perfectly studied to cover a variety of titles. Each title can be regarded as a section in the questionnaire, containing a set of questions. Those sections can be listed as such:

Demographic questions : As the title states, questions about age, gender, country of residence, workplace, university, and position are asked in this section. Seven questions are involved under this title.

Academic Freedom in General : This type of question aims to collect information about the current state of academic freedom in educational institutions and how participants look to academic freedom. For this purpose, seven questions are asked under this title.

Essentials of Academic Freedom : Four questions aiming to understand if the essentials of academic freedom do exist were specialized to this part of the questionnaire. Questions included asking about freedom in teaching and discussion in classes, freedom in suggesting ideas, especially controversial ones, and whether there exists any political or religious prejudice when considering job decisions.

Flow of Knowledge : Questions in this part enquire about freedom of thought, decisions, and whether there are protecting laws for academic freedom. Four questions were dedicated to fulfilling the aim.

Environmental Issues : As the title implies, the four questions in this part are all related to the link between academic freedom and society in general.

External Forces : In this survey, our mere goal is to examine the factors that might influence academic freedom. Eight questions in this section were dedicated to collect the needed information about factors like politics, religion, positions, and others.

Educational Objectives and Policies : Funding preferences for academic freedom, research, access to libraries, and the extent of acquiring academic freedom are all interrogated in a set of seven questions under this title.

Institutional Accountability : Three questions to shape the responsibility of the academic institutions were put in this section.

Rights and Freedoms of Higher-Education Teaching Personnel : The two questions in this section are bounded to ask if the entrance to the higher education sector is judged solely by qualifications or other factors that contribute.

Terms and Conditions of Employment : This is one profound part of the survey that enquires about the bond between employment in particular in academic institutions and academic freedom. 12 questions related to the job positions, salaries, perks, employing criteria, promotions, and other job-related aspects, were inserted under this title in the questionnaire.

Free Teaching Profession : The majority of the ten questions in this section are to be answered by a number from 1 to 10. The scope of this section is testing the extent of the influence of factors that affect academic freedom. For instance, political, economic, and religious challenges are questioned.

Out of the 69 questions, only 65 of them were promoted to the second stage of the technique. The four eliminated attributes were the ones that hold multiple answers resulting from multiple-choice questions. This measure was considered to mitigate the complexity in the following levels of analysis.

3.3 Data preprocessing

The trivial tunnel that data shall pass through after being collected is processing. Applied to the used dataset are cleaning, integrating, and transformation.

Cleaning: Upon collection of data, participants may leave intentionally or unintentionally an empty field. Those missing values are in particular treated in the cleaning stage, in addition to the removal of irrelevant data. The type of all data fields is either a number or a word, whenever a number is missing it is replaced by 0, while if a word is missing it is replaced by “none”. This criterion was considered because the percentage of missing values was small. As for the removal of irrelevant data, the date of filling the survey that was added to the records was removed because it lies out of the scope of our interest.

Integration: As mentioned earlier, two copies of the survey were prepared and the records collected from each can be seen in Table 1 . To integrate the records from both surveys, the Arabic records were translated to English to end up with a single final English version of 254 records. This version is to be used in further stages.

Transformation: In this phase of processing, the data were rendered to create two versions: one consisting purely of nominal values and another consisting purely of numeric values. This step was of profound importance because the used machine learning algorithms require a specific type of data. In particular, the Apriori algorithm only accepts nominal attributes. The K-Means clustering algorithm can accept both, yet it depends on the criteria for calculating the difference between any two records. In our case, the difference is computed via the Euclidean distance which calls for numeric values to be given as parameters. To perform this operation, first, we studied the nature of the used attributes as presented in Table 2 .

To create a pure nominal version, the nine numeric values had to be treated. Those values are answers to questions that ask “To what extent...”, and so they range from 1 to 10. The treatment of those attributes is illustrated in Table 3 .

To create a pure numeric version, all 60 nominal attributes had to be treated. The yes-no questions were simply replaced by 1(yes) and 2(no). Similarly, other types of questions were replaced, for example, if a question has five distinct answers, then they are replaced by numbers from 1 to 5 respectively.

3.4 Static metrics

Organizational innovative strategies in the hyperdynamic environment are locked in the historical path of decision-making. The reason why organizations lose their flexibility and fluidness and become sticky and rigid relies on the drawn paths they form intentionally or unintentionally over time. Awkward practices, built-in rational maps, and group culture and thinking constitute the major conditions that lead organizations and universities to become path dependent according to the literature. Their strategies become irreversible and past events map future actions; contrary of strategic management. Decisions become historically inured. The educational institutions if path dependent, will be rigid with conditioned innovation and pre-drawn nonergodic outcomes [ 20 ]. According to [ 21 ], path dependence refers to complex nonergodic processes that are ‘unable to shake free of their history’ [ 22 , 23 ].

[ 20 ] determined “three developmental phases of path dependence (Fig. 2 ), starting with (1) singular historical events, (2) which may, under certain conditions, transform themselves into self-reinforcing dynamics, and (3) possibly end up in an organizational lock-in”. each phase develops under different administrations, but the path continues to be shaped. Different studies identify self-reinforcing practices as dynamics that tend to build up a specific path of decision with a state of total inflexibility. The first phase is the preformation phase where the adoption of a choice is unpredictable. The picked decision, or critical juncture, becomes the push towards a self-reinforcing process. The second phase comes when a new regime reinforces the afore dynamics and makes the system more irreversible. Hence, the extent of choices diminishes and the decision processes remain contingent but nonergodic. Constrictions increase to reach the third phase, the lock-in phase with a patterned decision. The organizations end up with a repeated predominant approach with an inefficient system. However, the social character of organizations gives them a narrow range of unpredictable decisions that effectively will not alter the routine action pattern.

As a result, to test whether academic freedom is path dependent, a longitudinal study would be appropriate or more conveniently, a cross-sectional study of academics comparing three age groups that will represent the trend of thought of an ensemble of academics over a long time as per ergodic studies [ 23 ].

Thus to extract the factors that might impact academic freedom, in this phase we counted on what research and studies have accomplished. Another approach could have been considered, which is applying the simple K-Means clustering, and trying to observe common factors among clusters. Yet in our case, this was not efficient because the data were not strongly correlated. Being so, we counted on research and studies to get static metrics. As a result, we considered age and description as two metrics that affect academic freedom. The age in our dataset can have three values “Less than 35 years”, “Between 35 and 50”, and “More than 50”. Whereas the description attribute specifies the position of the filling participant. This attribute can hold the following values: “A researcher”, “A full-time professor”, “A part-time professor”, “An hourly-paid lecturer”, “An academic-related staff”.

Developmental phases of path dependence [ 20 ]

3.5 Dynamic metrics

This phase of the chain is the core of this technique. In an attempt to dynamically extract the factors that influence academic freedom, the Apriori algorithm was used. The Apriori algorithm is a very simple algorithm for identifying frequent itemsets from large transactions in the database. The name of the Apriori algorithm is derived from the fact that this algorithm uses previous knowledge of frequent itemsets for the next iteration process [ 24 ]. The resulting itemsets undergo rules analysis to generate rules having support, confidence, and lift values meeting the conditions. The equations to calculate those criteria are provided below respectively:

Support value: The support value of an association rule is a measure of how frequently the itemset appears in the dataset. Specifically, it is the proportion of transactions in the dataset where both X and Y occur together, where T is the total number of transactions.

Confidence value: Is a measure of the reliability of an association rule. The confidence value measures the number of transactions containing both X and Y and the number of transactions containing X.

Lift value: Is a measure of the strength of an association rule compared to the expected frequency of Y if X and Y were independent. It shows the validity of the transaction process, where the confidence of a transaction \(conf(X \rightarrow Y)\) is normalized by the support \(sup(X \rightarrow Y)\) .

The application of this algorithm in the scope of this technique is examined in Algorithm 1. First, the dataset is transformed into a set of “records” to be ready to enter the algorithm. Each data record in the dataset is transformed into an Apriori record. Those records take the form of transactions having items, as it is well known when using Apriori. So for instance, as seen in Fig. 3 , “A=Yes” and “B=No” are two itemsets.

Apriori Algorithm

Record Formulation

Considering A, B, C, D, and E as attributes, the data record illustrated becomes as seen in the figure. After applying the same criteria to all data records, the Apriori records are sent to the algorithm, in addition to specified confidence and support. As for the lift value, only rules with lift > 1 are accepted.

After the Apriori algorithm is applied, and rules complying with the conditions of specified support(sup), confidence(conf), and lift are generated, those rules are treated to extract the attributes found in each rule. For instance,

Example of rules:R1: {A=Yes} \(\rightarrow\) {B=Yes, C=Yes, D=Yes} (conf: 0.958, supp: 0.906, lift: 1.006) R2: {A=No} \(\rightarrow\) {E=Yes} (conf: 0.968, supp: 0.926, lift: 1.306)

Attributes extracted from the rules are:R1:{A, B, C, D}R2:{A, E}

To extract the metrics, the algorithm is repeated on a set of values for confidence and support. For each support sup and confidence conf , we count the repetition of each extracted attribute from the generated rules, the most repeated item is regarded as a metric. For different values of confidence and support, multiple metrics can be extracted. In this example, attribute A is mostly repeated, then “A” is considered a metric.

3.6 Evaluation

This is the final stage of the technique, where the comparison between the accuracy of the factors extracted statically and dynamically is examined. To do this comparison, the usage of the K-Means clustering algorithm was required. Yet, the used algorithm is not the naive simple one, but a customized version developed for the purpose. In this study, K-Means Clustering was employed to categorize the survey data into distinct clusters based on 69 attributes related to academic freedom. The attributes were selected through a comprehensive review of existing literature, expert consultations, and preliminary data analysis to ensure their relevance to academic freedom. The clustering process involved initializing centroids and iteratively assigning data points to the nearest centroids based on Euclidean distance, refining the clusters until convergence was achieved. This method allowed us to identify patterns and group respondents with similar perceptions and experiences of academic freedom, providing a nuanced understanding of the factors influencing academic freedom in different contexts. The K-Means is generally an iterative algorithm in which the process begins by choosing an initial centroid randomly for each cluster and the number of clusters to be created. Then, using the Euclidean distance, each data point is allocated to the nearest centroid, and the first cluster creation round is done. The cluster centroids are then modified and the procedure is replicated before convergence is achieved (Algorithm 2). As for the customized K-Means algorithm used in this technique, the process begins by specifying additionally a metric to cluster upon (Algorithm 2). The difference in application is that, when calculating the distance between two data points, this metric, which is originally an attribute found in the dataset, is removed from the calculation process.

Customized K-Means Clustering Algorithm

After applying the K-Means clustering algorithm, the accuracy of the resultant clusters must be tested. For this purpose, the sum of square errors (SSE) is used. The SSE value is computed using the equation below. To interpret this value, it is stated that the lower the value is, the higher the accuracy and the better the results are.

where \(C_j\) is the \(j^{th}\) cluster; \(m_j\) presents the centroid of \(C_j\) ; \(distance(x,m_j)\) is the distance between a data point x and the centroid \(m_j\) .

4 Performance evaluation

In this section, we will emphasize the importance and accuracy of the proposed technique to extract dynamically better metrics that mostly influence academic freedom. The implementation was accomplished using the Python language.

4.1 Attribute definitions

Before presenting the detailed results, we define the key attributes used in our analysis:

Gender: The gender identity of the respondent.

Age: Categorized into three groups:–Less than 35 years–Between 35 and 50 years–More than 50 years

Professional Description: The professional role of the respondent (e.g., researcher, professor).

HighestDeg: The highest degree obtained by the respondent.

UnivYears: The number of years the respondent has worked in a university setting.

SECHigherEdu?: Whether the respondent believes higher education plays a role in social equity and change.

ReshapeEdutoSEChanges?: The respondent’s view on whether education should be reshaped to promote social equity.

TPParticipate?: Whether the respondent participates in political or social activities.

AcedCommVulnerableToPoliticalPressures?: Perception of the academic community’s vulnerability to political pressures.

RightToEduEnjoyedInAnAtmosphereOfAcademicFreedom?: Whether the respondent enjoys their right to education in an atmosphere of academic freedom.

TypesOfDiscriminationInEdu?: Types of discrimination faced in the educational setting.

RightToPutNewIdeasWithoutLosingYourJob?: The right to express new ideas without the fear of job loss.

FreedomInTeachingAndDiscussionInClass?: Freedom to teach and discuss topics in class.

FiredIf_OfDifferentPoliticalParty_RefuseToRevealUrPoliticalBeliefs?: The risk of being fired for political beliefs or affiliations.

HinderedUFromUrRightToPursueTruthInUrOwnWay?: If respondents feel hindered in their pursuit of academic truth.

LoseJobForPublishingIdeasUnfavorableTo?: Risk of losing a job for publishing controversial ideas.

AcFreImportantForSociety?: This attribute measures the perceived importance of academic freedom for societal development.

VulnerableToPoliticalPressure: Indicates the perceived susceptibility of academic freedom to political influence.

The attributes used in this analysis encompass a broad range of factors affecting academic freedom. These include:

Demographic Factors: Such as age, gender, and country of origin, which can influence personal experiences and perceptions of academic freedom.

Institutional Characteristics: Such as the level of institutional independence from state control and the existence of policies supporting academic freedom.

Personal Experiences: Such as the ability to publish freely, engage in political debates, and participate in international academic collaborations.

Each attribute was selected for its potential impact on academic freedom, allowing us to create a comprehensive profile of the academic environment as experienced by respondents. This detailed analysis enables us to pinpoint specific areas where academic freedom is either upheld or compromised.

4.2 Apriori implementation and results

As stated before, the Apriori algorithm was used in the mission of extracting metrics dynamically. The algorithm was tested for 16 different combinations of confidence and support values, attaining a lift value greater than one to ensure a positive correlation between the different sides of the rule. The number of rules generated in each of those 16 permutations of confidence and support values are presented in the graph of Fig. 4 .

Number of generated rules for different support and confidence values

After generating the rules, and the rules are treated by extracting the comprising attributes from each, the frequency of the detected attributes is calculated. The result of this step is a graph generated from code for each value of confidence and support, showing the detected attributes and the frequency of each in the generated rules.

Rules and attributes detected for (conf ≥ 0.6, sup ≥ 0.7)

Rules and attributes detected for (conf ≥ 0.7, sup ≥ 0.9)

Rules and attributes detected for (conf ≥ 0.8, sup ≥ 0.8)

Rules and attributes detected for (conf ≥ 0.9, sup ≥ 0.6)

Figures 5 , 6 , 7 , and 8 show the most frequent attribute in the rules generated for different values of confidence and support. The results for the 16 graphs of the 16 different combinations of confidence and support values, with the most frequent attribute are presented in Table 4 .

Extracting the dynamic metrics and concerning Table 4 , we would end up with three attributes: “AcFreImportantForSociety?”, “ReshapeEduToSE”, and “VulnerableToPoliticalPressure”.

4.3 K-means and comparison

After performing the above steps, it is time to validate and prove the added value of this technique to the world of academic freedom and data analysis. To do this, we applied the customized K-Means clustering algorithm (Algorithm 2) according to the aforementioned static (Age and Description) and dynamic (AcFreImportantForSociety?, ReshapeEduToSE, and VulnerableToPoliticalPressure) chosen metrics. Then we calculated the sum of square errors of the resulting clusters in each of the cases. The final results of the metrics with the SSE, using four as the number of clusters created in each case, are presented in Table 5 .

In an examination of the SSE values illustrated in Table 5 , we can see that the SSE value of the clusters resulting from any dynamic metric is lower than all SSE values of clusters resulting from the static metrics. The lower the SSE, the more accurate the cluster results. Starting from this interpretation we can conclude that the proposed technique was able to provide dynamically, better metrics affecting academic freedom than statically.

5 Conclusion, study implications and future works

In conclusion, this research delved into the elements impacting freedom, in Lebanon using K-Means clustering to pinpoint characteristics and their connections. The analysis uncovered that factors like religious ties, age and institutional policies play a role in determining academic freedom. The study shed light on the difficulties academics in Lebanon face due to pressures and discrimination linked to political and religious views.

5.1 Implications of the study

The results emphasize the need to address cultural biases within settings. Policymakers and educational leaders must acknowledge these biases in order to foster an open academic atmosphere. The study methodology provides a framework for examining freedom in similar circumstances offering valuable insights for regions with comparable sociopolitical landscapes. Promoting freedom requires implementing policies that reduce influences and promote inclusivity ensuring that educational institutions serve as impartial hubs of knowledge and research.

5.2 Future directions

Subsequent research should build upon this study by exploring factors influencing freedom, such as economic pressures and gender dynamics. Longitudinal studies could offer an understanding of how these elements change over time. Moreover applying the framework to regions, with varying sociopolitical backgrounds can help validate the findings and broaden the applicability of the results. Working with scholars from, around the world can offer viewpoints. Enrich the overall comprehension of academic freedom, on a global scale.

Data availability

The data that support the findings of this study are available from the authors upon request due to restrictions eg privacy or ethics.

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Hassan Harb, Chamseddine Zaki, Alaaeddine Ramadan, Louai Saker, Nour Mostafa and Layla Tannoury have contributed equally to this work.

Authors and Affiliations

Faculty of Sciences, Lebanese University, Beirut, Lebanon

Noura Joudieh

College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Hassan Harb, Chamseddine Zaki, Louai Saker & Nour Mostafa

College of Engineering and Computing, American University of Bahrain, Riffa, Bahrain

Alaaeddine Ramadan

Faculty of Business Administration and Economics, Lebanese University, Beirut, Lebanon

Layla Tannoury

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All authors contributed to the study conception, literature review, and design. N.J., H.H., and C.Z. performed material preparation, data collection, and analysis. Validation was done by A.R., N.M., L.S., and L.T. The first draft of the manuscript was written by N.J. H.H., A.R., and C.Z. reviewed and edited the manuscript. All authors commented on previous versions of the manuscript. All authors participated in follow-up meetings related to the research. All authors read and approved the final manuscript.

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Joudieh, N., Harb, H., Zaki, C. et al. Higher education in the era of artificial intelligence: academic freedom as a case study. Discov Sustain 5 , 220 (2024). https://doi.org/10.1007/s43621-024-00425-w

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Received : 05 May 2024

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DOI : https://doi.org/10.1007/s43621-024-00425-w

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• Artificial intelligence
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• Apriori algorithm

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