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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Case Study Methods and Examples

By Janet Salmons, PhD Manager, Sage Research Methods Community

What is Case Study Methodology ?

Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it. Or, case study can refer to a type of academic writing that typically delves into a problem, process, or situation.

Case study methodology can entail the study of one or more "cases," that could be described as instances, examples, or settings where the problem or phenomenon can be examined. The researcher is tasked with defining the parameters of the case, that is, what is included and excluded. This process is called bounding the case , or setting boundaries.

Case study can be combined with other methodologies, such as ethnography, grounded theory, or phenomenology. In such studies the research on the case uses another framework to further define the study and refine the approach.

Case study is also described as a method, given particular approaches used to collect and analyze data. Case study research is conducted by almost every social science discipline: business, education, sociology, psychology. Case study research, with its reliance on multiple sources, is also a natural choice for researchers interested in trans-, inter-, or cross-disciplinary studies.

The Encyclopedia of case study research provides an overview:

The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case.

It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed methods because this they use either more than one form of data within a research paradigm, or more than one form of data from different paradigms.

A case study inquiry could include multiple types of data:

multiple forms of quantitative data sources, such as Big Data + a survey

multiple forms of qualitative data sources, such as interviews + observations + documents

multiple forms of quantitative and qualitative data sources, such as surveys + interviews

Case study methodology can be used to achieve different research purposes.

Robert Yin , methodologist most associated with case study research, differentiates between descriptive , exploratory and explanatory case studies:

Descriptive : A case study whose purpose is to describe a phenomenon. Explanatory : A case study whose purpose is to explain how or why some condition came to be, or why some sequence of events occurred or did not occur. Exploratory: A case study whose purpose is to identify the research questions or procedures to be used in a subsequent study.

Robert Yin’s book is a comprehensive guide for case study researchers!

You can read the preface and Chapter 1 of Yin's book here . See the open-access articles below for some published examples of qualitative, quantitative, and mixed methods case study research.

Mills, A. J., Durepos, G., & Wiebe, E. (2010).  Encyclopedia of case study research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412957397

Yin, R. K. (2018). Case study research and applications (6th ed.). Thousand Oaks: SAGE Publications.

Open-Access Articles Using Case Study Methodology

As you can see from this collection, case study methods are used in qualitative, quantitative and mixed methods research.

Ang, C.-S., Lee, K.-F., & Dipolog-Ubanan, G. F. (2019). Determinants of First-Year Student Identity and Satisfaction in Higher Education: A Quantitative Case Study. SAGE Open. https://doi.org/10.1177/2158244019846689

Abstract. First-year undergraduates’ expectations and experience of university and student engagement variables were investigated to determine how these perceptions influence their student identity and overall course satisfaction. Data collected from 554 first-year undergraduates at a large private university were analyzed. Participants were given the adapted version of the Melbourne Centre for the Study of Higher Education Survey to self-report their learning experience and engagement in the university community. The results showed that, in general, the students’ reasons of pursuing tertiary education were to open the door to career opportunities and skill development. Moreover, students’ views on their learning and university engagement were at the moderate level. In relation to student identity and overall student satisfaction, it is encouraging to state that their perceptions of studentship and course satisfaction were rather positive. After controlling for demographics, student engagement appeared to explain more variance in student identity, whereas students’ expectations and experience explained greater variance in students’ overall course satisfaction. Implications for practice, limitations, and recommendation of this study are addressed.

Baker, A. J. (2017). Algorithms to Assess Music Cities: Case Study—Melbourne as a Music Capital. SAGE Open. https://doi.org/10.1177/2158244017691801

Abstract. The global  Mastering of a Music City  report in 2015 notes that the concept of music cities has penetrated the global political vernacular because it delivers “significant economic, employment, cultural and social benefits.” This article highlights that no empirical study has combined all these values and offers a relevant and comprehensive definition of a music city. Drawing on industry research,1 the article assesses how mathematical flowcharts, such as Algorithm A (Economics), Algorithm B (Four T’s creative index), and Algorithm C (Heritage), have contributed to the definition of a music city. Taking Melbourne as a case study, it illustrates how Algorithms A and B are used as disputed evidence about whether the city is touted as Australia’s music capital. The article connects the three algorithms to an academic framework from musicology, urban studies, cultural economics, and sociology, and proposes a benchmark Algorithm D (Music Cities definition), which offers a more holistic assessment of music activity in any urban context. The article concludes by arguing that Algorithm D offers a much-needed definition of what comprises a music city because it builds on the popular political economy focus and includes the social importance of space and cultural practices.

Brown, K., & Mondon, A. (2020). Populism, the media, and the mainstreaming of the far right: The Guardian’s coverage of populism as a case study. Politics. https://doi.org/10.1177/0263395720955036

Abstract. Populism seems to define our current political age. The term is splashed across the headlines, brandished in political speeches and commentaries, and applied extensively in numerous academic publications and conferences. This pervasive usage, or populist hype, has serious implications for our understanding of the meaning of populism itself and for our interpretation of the phenomena to which it is applied. In particular, we argue that its common conflation with far-right politics, as well as its breadth of application to other phenomena, has contributed to the mainstreaming of the far right in three main ways: (1) agenda-setting power and deflection, (2) euphemisation and trivialisation, and (3) amplification. Through a mixed-methods approach to discourse analysis, this article uses  The Guardian  newspaper as a case study to explore the development of the populist hype and the detrimental effects of the logics that it has pushed in public discourse.

Droy, L. T., Goodwin, J., & O’Connor, H. (2020). Methodological Uncertainty and Multi-Strategy Analysis: Case Study of the Long-Term Effects of Government Sponsored Youth Training on Occupational Mobility. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 147–148(1–2), 200–230. https://doi.org/10.1177/0759106320939893

Abstract. Sociological practitioners often face considerable methodological uncertainty when undertaking a quantitative analysis. This methodological uncertainty encompasses both data construction (e.g. defining variables) and analysis (e.g. selecting and specifying a modelling procedure). Methodological uncertainty can lead to results that are fragile and arbitrary. Yet, many practitioners may be unaware of the potential scale of methodological uncertainty in quantitative analysis, and the recent emergence of techniques for addressing it. Recent proposals for ‘multi-strategy’ approaches seek to identify and manage methodological uncertainty in quantitative analysis. We present a case-study of a multi-strategy analysis, applied to the problem of estimating the long-term impact of 1980s UK government-sponsored youth training. We use this case study to further highlight the problem of cumulative methodological fragilities in applied quantitative sociology and to discuss and help develop multi-strategy analysis as a tool to address them.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Lai, D., & Roccu, R. (2019). Case study research and critical IR: the case for the extended case methodology. International Relations , 33 (1), 67-87. https://doi.org/10.1177/0047117818818243

Abstract. Discussions on case study methodology in International Relations (IR) have historically been dominated by positivist and neopositivist approaches. However, these are problematic for critical IR research, pointing to the need for a non-positivist case study methodology. To address this issue, this article introduces and adapts the extended case methodology as a critical, reflexivist approach to case study research, whereby the case is constructed through a dynamic interaction with theory, rather than selected, and knowledge is produced through extensions rather than generalisation. Insofar as it seeks to study the world in complex and non-linear terms, take context and positionality seriously, and generate explicitly political and emancipatory knowledge, the extended case methodology is consistent with the ontological and epistemological commitments of several critical IR approaches. Its potential is illustrated in the final part of the article with reference to researching the socioeconomic dimension of transitional justice in Bosnia and Herzegovina.

Lynch, R., Young, J. C., Boakye-Achampong, S., Jowaisas, C., Sam, J., & Norlander, B. (2020). Benefits of crowdsourcing for libraries: A case study from Africa . IFLA Journal. https://doi.org/10.1177/0340035220944940

Abstract. Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14 (2), 164-183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers .  International Journal of Qualitative Methods .  https://doi.org/10.1177/1609406919862424

Abstract. Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation. These issues often lead to confusion, wastage of valuable time, and wrong decisions that affect the overall outcome of the research. This article presents a checklist comprised of four phases, that is, foundation phase, prefield phase, field phase, and reporting phase. The objective of this article is to provide novice researchers with practical application of this checklist by linking all its four phases with the authors’ experiences and learning from recently conducted in-depth multiple case studies in the organizations of New Zealand. Rather than discussing case study in general, a targeted step-by-step plan with real-time research examples to conduct a case study is given.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods, 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

More Sage Research Methods Community Posts about Case Study Research

Use research cases as the basis for individual or team activities that build skills.

Find an 10-step process for using research cases to teach methods with learning activities for individual students, teams, or small groups. (Or use the approach yourself!)

How do you decide which methodology fits your study? In this dialogue Linda Bloomberg and Janet Boberg explain the importance of a strategic approach to qualitative research design that stresses alignment with the purpose of the study.

Case study methods are used by researchers in many disciplines. Here are some open-access articles about multimodal qualitative or mixed methods designs that include both qualitative and quantitative elements.

Case study methodology is both unique, and uniquely confusing. It is unique given one characteristic: case studies draw from more than one data source.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Find discussion of case studies and published examples.

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What is a Case Study in Research? Definition, Methods, and Examples

Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies. Its diverse applications, such as advancing theory, program evaluation, and intervention development, make it an invaluable tool for driving meaningful research and fostering positive change.[ 1]  

Table of Contents

What is a Case Study?  

A case study method involves a detailed examination of a single subject, such as an individual, group, organization, event, or community, to explore and understand complex issues in real-life contexts. By focusing on one specific case, researchers can gain a deep understanding of the factors and dynamics at play, understanding their complex relationships, which might be missed in broader, more quantitative studies.  

When to do a Case Study?  

A case study design is useful when you want to explore a phenomenon in-depth and in its natural context. Here are some examples of when to use a case study :[ 2]  

• Exploratory Research: When you want to explore a new topic or phenomenon, a case study can help you understand the subject deeply. For example , a researcher studying a newly discovered plant species might use a case study to document its characteristics and behavior.  
• Descriptive Research: If you want to describe a complex phenomenon or process, a case study can provide a detailed and comprehensive description. For instance, a case study design   could describe the experiences of a group of individuals living with a rare disease.  
• Explanatory Research: When you want to understand why a particular phenomenon occurs, a case study can help you identify causal relationships. A case study design could investigate the reasons behind the success or failure of a particular business strategy.  
• Theory Building: Case studies can also be used to develop or refine theories. By systematically analyzing a series of cases, researchers can identify patterns and relationships that can contribute to developing new theories or refining existing ones.  
• Critical Instance: Sometimes, a single case can be used to study a rare or unusual phenomenon, but it is important for theoretical or practical reasons. For example , the case of Phineas Gage, a man who survived a severe brain injury, has been widely studied to understand the relationship between the brain and behavior.  
• Comparative Analysis: Case studies can also compare different cases or contexts. A case study example involves comparing the implementation of a particular policy in different countries to understand its effectiveness and identifying best practices.  

How to Create a Case Study – Step by Step  

Step 1: select a case  .

Careful case selection ensures relevance, insight, and meaningful contribution to existing knowledge in your field. Here’s how you can choose a case study design :[ 3]  

• Define Your Objectives: Clarify the purpose of your case study and what you hope to achieve. Do you want to provide new insights, challenge existing theories, propose solutions to a problem, or explore new research directions?  
• Consider Unusual or Outlying Cases: Focus on unusual, neglected, or outlying cases that can provide unique insights.  
• Choose a Representative Case: Alternatively, select a common or representative case to exemplify a particular category, experience, or phenomenon.   
• Avoid Bias: Ensure your selection process is unbiased using random or criteria-based selection.  
• Be Clear and Specific: Clearly define the boundaries of your study design , including the scope, timeframe, and key stakeholders.   
• Ethical Considerations: Consider ethical issues, such as confidentiality and informed consent.  

Step 2: Build a Theoretical Framework  

To ensure your case study has a solid academic foundation, it’s important to build a theoretical framework:   

• Conduct a Literature Review: Identify key concepts and theories relevant to your case study .  
• Establish Connections with Theory: Connect your case study with existing theories in the field.  
• Guide Your Analysis and Interpretation: Use your theoretical framework to guide your analysis, ensuring your findings are grounded in established theories and concepts.   

Step 3: Collect Your Data  

To conduct a comprehensive case study , you can use various research methods. These include interviews, observations, primary and secondary sources analysis, surveys, and a mixed methods approach. The aim is to gather rich and diverse data to enable a detailed analysis of your case study .  

Step 4: Describe and Analyze the Case  

How you report your findings will depend on the type of research you’re conducting. Here are two approaches:   

• Structured Approach: Follows a scientific paper format, making it easier for readers to follow your argument.  
• Narrative Approach: A more exploratory style aiming to analyze meanings and implications.  

Regardless of the approach you choose, it’s important to include the following elements in your case study :   

• Contextual Details: Provide background information about the case, including relevant historical, cultural, and social factors that may have influenced the outcome.  
• Literature and Theory: Connect your case study to existing literature and theory in the field. Discuss how your findings contribute to or challenge existing knowledge.  
• Wider Patterns or Debates: Consider how your case study fits into wider patterns or debates within the field. Discuss any implications your findings may have for future research or practice.  

What Are the Benefits of a Case Study   

Case studies offer a range of benefits , making them a powerful tool in research.  

1. In-Depth Analysis  

• Comprehensive Understanding: Case studies allow researchers to thoroughly explore a subject, understanding the complexities and nuances involved.  
• Rich Data: They offer rich qualitative and sometimes quantitative data, capturing the intricacies of real-life contexts.  

2. Contextual Insight  

• Real-World Application: Case studies provide insights into real-world applications, making the findings highly relevant and practical.  
• Context-Specific: They highlight how various factors interact within a specific context, offering a detailed picture of the situation.  

3. Flexibility  

• Methodological Diversity: Case studies can use various data collection methods, including interviews, observations, document analysis, and surveys.  
• Adaptability: Researchers can adapt the case study approach to fit the specific needs and circumstances of the research.  

4. Practical Solutions  

• Actionable Insights: The detailed findings from case studies can inform practical solutions and recommendations for practitioners and policymakers.  
• Problem-Solving: They help understand the root causes of problems and devise effective strategies to address them.  

5. Unique Cases  

• Rare Phenomena: Case studies are particularly valuable for studying rare or unique cases that other research methods may not capture.  
• Detailed Documentation: They document and preserve detailed information about specific instances that might otherwise be overlooked.  

What Are the Limitations of a Case Study   

While case studies offer valuable insights and a detailed understanding of complex issues, they have several limitations .  

1. Limited Generalizability  

• Specific Context: Case studies often focus on a single case or a small number of cases, which may limit the generalization of findings to broader populations or different contexts.  
• Unique Situations: The unique characteristics of the case may not be representative of other situations, reducing the applicability of the results.  

2. Subjectivity  

• Researcher Bias: The researcher’s perspectives and interpretations can influence the analysis and conclusions, potentially introducing bias.  
• Participant Bias: Participants’ responses and behaviors may be influenced by their awareness of being studied, known as the Hawthorne effect.  

3. Time-Consuming  

• Data Collection and Analysis: Gathering detailed, in-depth data requires significant time and effort, making case studies more time-consuming than other research methods.  
• Longitudinal Studies: If the case study observes changes over time, it can become even more prolonged.  

4. Resource Intensive  

• Financial and Human Resources: Conducting comprehensive case studies may require significant financial investment and human resources, including trained researchers and participant access.  
• Access to Data: Accessing relevant and reliable data sources can be challenging, particularly in sensitive or proprietary contexts.  

5. Replication Difficulties  

• Unique Contexts: A case study’s specific and detailed context makes it difficult to replicate the study exactly, limiting the ability to validate findings through repetition.  
• Variability: Differences in contexts, researchers, and methodologies can lead to variations in findings, complicating efforts to achieve consistent results.  

By acknowledging and addressing these limitations , researchers can enhance the rigor and reliability of their case study findings.  

Key Takeaways  

Case studies are valuable in research because they provide an in-depth, contextual analysis of a single subject, event, or organization. They allow researchers to explore complex issues in real-world settings, capturing detailed qualitative and quantitative data. This method is useful for generating insights, developing theories, and offering practical solutions to problems. They are versatile, applicable in diverse fields such as business, education, and health, and can complement other research methods by providing rich, contextual evidence. However, their findings may have limited generalizability due to the focus on a specific case.  

Frequently Asked Questions  

Q: What is a case study in research?  

A case study in research is an impactful tool for gaining a deep understanding of complex issues within their real-life context. It combines various data collection methods and provides rich, detailed insights that can inform theory development and practical applications.  

Q: What are the advantages of using case studies in research?  

Case studies are a powerful research method, offering advantages such as in-depth analysis, contextual insights, flexibility, rich data, and the ability to handle complex issues. They are particularly valuable for exploring new areas, generating hypotheses, and providing detailed, illustrative examples that can inform theory and practice.  

Q: Can case studies be used in quantitative research?  

While case studies are predominantly associated with qualitative research, they can effectively incorporate quantitative methods to provide a more comprehensive analysis. A mixed-methods approach leverages qualitative and quantitative research strengths, offering a powerful tool for exploring complex issues in a real-world context. For example , a new medical treatment case study can incorporate quantitative clinical outcomes (e.g., patient recovery rates and dosage levels) along with qualitative patient interviews.  

Q: What are the key components of a case study?  

A case study typically includes several key components:   

• Introductio n, which provides an overview and sets the context by presenting the problem statement and research objectives;  
• Literature review , which connects the study to existing theories and prior research;  
• Methodology , which details the case study design , data collection methods, and analysis techniques;   
• Findings , which present the data and results, including descriptions, patterns, and themes;   
• Discussion and conclusion , which interpret the findings, discuss their implications, and offer conclusions, practical applications, limitations, and suggestions for future research.  

Together, these components ensure a comprehensive, systematic, and insightful exploration of the case.  

References  

• de Vries, K. (2020). Case study methodology. In  Critical qualitative health research  (pp. 41-52). Routledge.  
• Fidel, R. (1984). The case study method: A case study.  Library and Information Science Research ,  6 (3), 273-288.  
• Thomas, G. (2021). How to do your case study.  How to do your case study , 1-320.  

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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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Distinguishing case study as a research method from case reports as a publication type

The purpose of this editorial is to distinguish between case reports and case studies. In health, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. As a qualitative methodology, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. The depth and richness of case study description helps readers understand the case and whether findings might be applicable beyond that setting.

Single-institution descriptive reports of library activities are often labeled by their authors as “case studies.” By contrast, in health care, single patient retrospective descriptions are published as “case reports.” Both case reports and case studies are valuable to readers and provide a publication opportunity for authors. A previous editorial by Akers and Amos about improving case studies addresses issues that are more common to case reports; for example, not having a review of the literature or being anecdotal, not generalizable, and prone to various types of bias such as positive outcome bias [ 1 ]. However, case study research as a qualitative methodology is pursued for different purposes than generalizability. The authors’ purpose in this editorial is to clearly distinguish between case reports and case studies. We believe that this will assist authors in describing and designating the methodological approach of their publications and help readers appreciate the rigor of well-executed case study research.

Case reports often provide a first exploration of a phenomenon or an opportunity for a first publication by a trainee in the health professions. In health care, case reports are familiar ways of sharing events or efforts of intervening with single patients with previously unreported features. Another type of study categorized as a case report is an “N of 1” study or single-subject clinical trial, which considers an individual patient as the sole unit of observation in a study investigating the efficacy or side effect profiles of different interventions. Entire journals have evolved to publish case reports, which often rely on template structures with limited contextualization or discussion of previous cases. Examples that are indexed in MEDLINE include the American Journal of Case Reports , BMJ Case Reports, Journal of Medical Case Reports, and Journal of Radiology Case Reports . Similar publications appear in veterinary medicine and are indexed in CAB Abstracts, such as Case Reports in Veterinary Medicine and Veterinary Record Case Reports .

As a qualitative methodology, however, case study research encompasses a great deal more complexity than a typical case report and often incorporates multiple streams of data combined in creative ways. Distinctions include the investigator’s definitions and delimitations of the case being studied, the clarity of the role of the investigator, the rigor of gathering and combining evidence about the case, and the contextualization of the findings. Delimitation is a term from qualitative research about setting boundaries to scope the research in a useful way rather than describing the narrow scope as a limitation, as often appears in a discussion section. The depth and richness of description helps readers understand the situation and whether findings from the case are applicable to their settings.

CASE STUDY AS A RESEARCH METHODOLOGY

Case study as a qualitative methodology is an exploration of a time- and space-bound phenomenon. As qualitative research, case studies require much more from their authors who are acting as instruments within the inquiry process. In the case study methodology, a variety of methodological approaches may be employed to explain the complexity of the problem being studied [ 2 , 3 ].

Leading authors diverge in their definitions of case study, but a qualitative research text introduces case study as follows:

Case study research is defined as a qualitative approach in which the investigator explores a real-life, contemporary bounded system (a case) or multiple bound systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information, and reports a case description and case themes. The unit of analysis in the case study might be multiple cases (a multisite study) or a single case (a within-site case study). [ 4 ]

Methodologists writing core texts on case study research include Yin [ 5 ], Stake [ 6 ], and Merriam [ 7 ]. The approaches of these three methodologists have been compared by Yazan, who focused on six areas of methodology: epistemology (beliefs about ways of knowing), definition of cases, design of case studies, and gathering, analysis, and validation of data [ 8 ]. For Yin, case study is a method of empirical inquiry appropriate to determining the “how and why” of phenomena and contributes to understanding phenomena in a holistic and real-life context [ 5 ]. Stake defines a case study as a “well-bounded, specific, complex, and functioning thing” [ 6 ], while Merriam views “the case as a thing, a single entity, a unit around which there are boundaries” [ 7 ].

Case studies are ways to explain, describe, or explore phenomena. Comments from a quantitative perspective about case studies lacking rigor and generalizability fail to consider the purpose of the case study and how what is learned from a case study is put into practice. Rigor in case studies comes from the research design and its components, which Yin outlines as (a) the study’s questions, (b) the study’s propositions, (c) the unit of analysis, (d) the logic linking the data to propositions, and (e) the criteria for interpreting the findings [ 5 ]. Case studies should also provide multiple sources of data, a case study database, and a clear chain of evidence among the questions asked, the data collected, and the conclusions drawn [ 5 ].

Sources of evidence for case studies include interviews, documentation, archival records, direct observations, participant-observation, and physical artifacts. One of the most important sources for data in qualitative case study research is the interview [ 2 , 3 ]. In addition to interviews, documents and archival records can be gathered to corroborate and enhance the findings of the study. To understand the phenomenon or the conditions that created it, direct observations can serve as another source of evidence and can be conducted throughout the study. These can include the use of formal and informal protocols as a participant inside the case or an external or passive observer outside of the case [ 5 ]. Lastly, physical artifacts can be observed and collected as a form of evidence. With these multiple potential sources of evidence, the study methodology includes gathering data, sense-making, and triangulating multiple streams of data. Figure 1 shows an example in which data used for the case started with a pilot study to provide additional context to guide more in-depth data collection and analysis with participants.

Key sources of data for a sample case study

VARIATIONS ON CASE STUDY METHODOLOGY

Case study methodology is evolving and regularly reinterpreted. Comparative or multiple case studies are used as a tool for synthesizing information across time and space to research the impact of policy and practice in various fields of social research [ 9 ]. Because case study research is in-depth and intensive, there have been efforts to simplify the method or select useful components of cases for focused analysis. Micro-case study is a term that is occasionally used to describe research on micro-level cases [ 10 ]. These are cases that occur in a brief time frame, occur in a confined setting, and are simple and straightforward in nature. A micro-level case describes a clear problem of interest. Reporting is very brief and about specific points. The lack of complexity in the case description makes obvious the “lesson” that is inherent in the case; although no definitive “solution” is necessarily forthcoming, making the case useful for discussion. A micro-case write-up can be distinguished from a case report by its focus on briefly reporting specific features of a case or cases to analyze or learn from those features.

DATABASE INDEXING OF CASE REPORTS AND CASE STUDIES

Disciplines such as education, psychology, sociology, political science, and social work regularly publish rich case studies that are relevant to particular areas of health librarianship. Case reports and case studies have been defined as publication types or subject terms by several databases that are relevant to librarian authors: MEDLINE, PsycINFO, CINAHL, and ERIC. Library, Information Science & Technology Abstracts (LISTA) does not have a subject term or publication type related to cases, despite many being included in the database. Whereas “Case Reports” are the main term used by MEDLINE’s Medical Subject Headings (MeSH) and PsycINFO’s thesaurus, CINAHL and ERIC use “Case Studies.”

Case reports in MEDLINE and PsycINFO focus on clinical case documentation. In MeSH, “Case Reports” as a publication type is specific to “clinical presentations that may be followed by evaluative studies that eventually lead to a diagnosis” [ 11 ]. “Case Histories,” “Case Studies,” and “Case Study” are all entry terms mapping to “Case Reports”; however, guidance to indexers suggests that “Case Reports” should not be applied to institutional case reports and refers to the heading “Organizational Case Studies,” which is defined as “descriptions and evaluations of specific health care organizations” [ 12 ].

PsycINFO’s subject term “Case Report” is “used in records discussing issues involved in the process of conducting exploratory studies of single or multiple clinical cases.” The Methodology index offers clinical and non-clinical entries. “Clinical Case Study” is defined as “case reports that include disorder, diagnosis, and clinical treatment for individuals with mental or medical illnesses,” whereas “Non-clinical Case Study” is a “document consisting of non-clinical or organizational case examples of the concepts being researched or studied. The setting is always non-clinical and does not include treatment-related environments” [ 13 ].

Both CINAHL and ERIC acknowledge the depth of analysis in case study methodology. The CINAHL scope note for the thesaurus term “Case Studies” distinguishes between the document and the methodology, though both use the same term: “a review of a particular condition, disease, or administrative problem. Also, a research method that involves an in-depth analysis of an individual, group, institution, or other social unit. For material that contains a case study, search for document type: case study.” The ERIC scope note for the thesaurus term “Case Studies” is simple: “detailed analyses, usually focusing on a particular problem of an individual, group, or organization” [ 14 ].

PUBLICATION OF CASE STUDY RESEARCH IN LIBRARIANSHIP

We call your attention to a few examples published as case studies in health sciences librarianship to consider how their characteristics fit with the preceding definitions of case reports or case study research. All present some characteristics of case study research, but their treatment of the research questions, richness of description, and analytic strategies vary in depth and, therefore, diverge at some level from the qualitative case study research approach. This divergence, particularly in richness of description and analysis, may have been constrained by the publication requirements.

As one example, a case study by Janke and Rush documented a time- and context-bound collaboration involving a librarian and a nursing faculty member [ 15 ]. Three objectives were stated: (1) describing their experience of working together on an interprofessional research team, (2) evaluating the value of the librarian role from librarian and faculty member perspectives, and (3) relating findings to existing literature. Elements that signal the qualitative nature of this case study are that the authors were the research participants and their use of the term “evaluation” is reflection on their experience. This reads like a case study that could have been enriched by including other types of data gathered from others engaging with this team to broaden the understanding of the collaboration.

As another example, the description of the academic context is one of the most salient components of the case study written by Clairoux et al., which had the objectives of (1) describing the library instruction offered and learning assessments used at a single health sciences library and (2) discussing the positive outcomes of instruction in that setting [ 16 ]. The authors focus on sharing what the institution has done more than explaining why this institution is an exemplar to explore a focused question or understand the phenomenon of library instruction. However, like a case study, the analysis brings together several streams of data including course attendance, online material page views, and some discussion of results from surveys. This paper reads somewhat in between an institutional case report and a case study.

The final example is a single author reporting on a personal experience of creating and executing the role of research informationist for a National Institutes of Health (NIH)–funded research team [ 17 ]. There is a thoughtful review of the informationist literature and detailed descriptions of the institutional context and the process of gaining access to and participating in the new role. However, the motivating question in the abstract does not seem to be fully addressed through analysis from either the reflective perspective of the author as the research participant or consideration of other streams of data from those involved in the informationist experience. The publication reads more like a case report about this informationist’s experience than a case study that explores the research informationist experience through the selection of this case.

All of these publications are well written and useful for their intended audiences, but in general, they are much shorter and much less rich in depth than case studies published in social sciences research. It may be that the authors have been constrained by word counts or page limits. For example, the submission category for Case Studies in the Journal of the Medical Library Association (JMLA) limited them to 3,000 words and defined them as “articles describing the process of developing, implementing, and evaluating a new service, program, or initiative, typically in a single institution or through a single collaborative effort” [ 18 ]. This definition’s focus on novelty and description sounds much more like the definition of case report than the in-depth, detailed investigation of a time- and space-bound problem that is often examined through case study research.

Problem-focused or question-driven case study research would benefit from the space provided for Original Investigations that employ any type of quantitative or qualitative method of analysis. One of the best examples in the JMLA of an in-depth multiple case study that was authored by a librarian who published the findings from her doctoral dissertation represented all the elements of a case study. In eight pages, she provided a theoretical basis for the research question, a pilot study, and a multiple case design, including integrated data from interviews and focus groups [ 19 ].

We have distinguished between case reports and case studies primarily to assist librarians who are new to research and critical appraisal of case study methodology to recognize the features that authors use to describe and designate the methodological approaches of their publications. For researchers who are new to case research methodology and are interested in learning more, Hancock and Algozzine provide a guide [ 20 ].

We hope that JMLA readers appreciate the rigor of well-executed case study research. We believe that distinguishing between descriptive case reports and analytic case studies in the journal’s submission categories will allow the depth of case study methodology to increase. We also hope that authors feel encouraged to pursue submitting relevant case studies or case reports for future publication.

Editor’s note: In response to this invited editorial, the Journal of the Medical Library Association will consider manuscripts employing rigorous qualitative case study methodology to be Original Investigations (fewer than 5,000 words), whereas manuscripts describing the process of developing, implementing, and assessing a new service, program, or initiative—typically in a single institution or through a single collaborative effort—will be considered to be Case Reports (formerly known as Case Studies; fewer than 3,000 words).

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• Introduction

The case study creation process

Types of case studies, benefits and limitations.

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• Table Of Contents

case study , detailed description and assessment of a specific situation in the real world created for the purpose of deriving generalizations and other insights from it. A case study can be about an individual, a group of people, an organization, or an event, among other subjects.

By focusing on a specific subject in its natural setting, a case study can help improve understanding of the broader features and processes at work. Case studies are a research method used in multiple fields, including business, criminology , education , medicine and other forms of health care, anthropology , political science , psychology , and social work . Data in case studies can be both qualitative and quantitative. Unlike experiments, where researchers control and manipulate situations, case studies are considered to be “naturalistic” because subjects are studied in their natural context . ( See also natural experiment .)

The creation of a case study typically involves the following steps:

• The research question to be studied is defined, informed by existing literature and previous research. Researchers should clearly define the scope of the case, and they should compile a list of evidence to be collected as well as identify the nature of insights that they expect to gain from the case study.
• Once the case is identified, the research team is given access to the individual, organization, or situation being studied. Individuals are informed of risks associated with participation and must provide their consent , which may involve signing confidentiality or anonymity agreements.
• Researchers then collect evidence using multiple methods, which may include qualitative techniques, such as interviews, focus groups , and direct observations, as well as quantitative methods, such as surveys, questionnaires, and data audits. The collection procedures need to be well defined to ensure the relevance and accuracy of the evidence.
• The collected evidence is analyzed to come up with insights. Each data source must be reviewed carefully by itself and in the larger context of the case study so as to ensure continued relevance. At the same time, care must be taken not to force the analysis to fit (potentially preconceived) conclusions. While the eventual case study may serve as the basis for generalizations, these generalizations must be made cautiously to ensure that specific nuances are not lost in the averages.
• Finally, the case study is packaged for larger groups and publication. At this stage some information may be withheld, as in business case studies, to allow readers to draw their own conclusions. In scientific fields, the completed case study needs to be a coherent whole, with all findings and statistical relationships clearly documented.

Case studies have been used as a research method across multiple fields. They are particularly popular in the fields of law, business, and employee training; they typically focus on a problem that an individual or organization is facing. The situation is presented in considerable detail, often with supporting data, to discussion participants, who are asked to make recommendations that will solve the stated problem. The business case study as a method of instruction was made popular in the 1920s by instructors at Harvard Business School who adapted an approach used at Harvard Law School in which real-world cases were used in classroom discussions. Other business and law schools started compiling case studies as teaching aids for students. In a business school case study, students are not provided with the complete list of facts pertaining to the topic and are thus forced to discuss and compare their perspectives with those of their peers to recommend solutions.

In criminology , case studies typically focus on the lives of an individual or a group of individuals. These studies can provide particularly valuable insight into the personalities and motives of individual criminals, but they may suffer from a lack of objectivity on the part of the researchers (typically because of the researchers’ biases when working with people with a criminal history), and their findings may be difficult to generalize.

In sociology , the case-study method was developed by Frédéric Le Play in France during the 19th century. This approach involves a field worker staying with a family for a period of time, gathering data on the family members’ attitudes and interactions and on their income, expenditures, and physical possessions. Similar approaches have been used in anthropology . Such studies can sometimes continue for many years.

Case studies provide insight into situations that involve a specific entity or set of circumstances. They can be beneficial in helping to explain the causal relationships between quantitative indicators in a field of study, such as what drives a company’s market share. By introducing real-world examples, they also plunge the reader into an actual, concrete situation and make the concepts real rather than theoretical. They also help people study rare situations that they might not otherwise experience.

Because case studies are in a “naturalistic” environment , they are limited in terms of research design: researchers lack control over what they are studying, which means that the results often cannot be reproduced. Also, care must be taken to stay within the bounds of the research question on which the case study is focusing. Other limitations to case studies revolve around the data collected. It may be difficult, for instance, for researchers to organize the large volume of data that can emerge from the study, and their analysis of the data must be carefully thought through to produce scientifically valid insights. The research methodology used to generate these insights is as important as the insights themselves, for the latter need to be seen in the proper context. Taken out of context, they may lead to erroneous conclusions. Like all scientific studies, case studies need to be approached objectively; personal bias or opinion may skew the research methods as well as the results. ( See also confirmation bias .)

Business case studies in particular have been criticized for approaching a problem or situation from a narrow perspective. Students are expected to come up with solutions for a problem based on the data provided. However, in real life, the situation is typically reversed: business managers face a problem and must then look for data to help them solve it.

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What is a Case Study? Definition & Examples

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Case Study Definition

A case study is an in-depth investigation of a single person, group, event, or community. This research method involves intensively analyzing a subject to understand its complexity and context. The richness of a case study comes from its ability to capture detailed, qualitative data that can offer insights into a process or subject matter that other research methods might miss.

A case study strives for a holistic understanding of events or situations by examining all relevant variables. They are ideal for exploring ‘how’ or ‘why’ questions in contexts where the researcher has limited control over events in real-life settings. Unlike narrowly focused experiments, these projects seek a comprehensive understanding of events or situations.

In a case study, researchers gather data through various methods such as participant observation, interviews, tests, record examinations, and writing samples. Unlike statistically-based studies that seek only quantifiable data, a case study attempts to uncover new variables and pose questions for subsequent research.

A case study is particularly beneficial when your research:

• Requires a deep, contextual understanding of a specific case.
• Needs to explore or generate hypotheses rather than test them.
• Focuses on a contemporary phenomenon within a real-life context.

Learn more about Other Types of Experimental Design .

Case Study Examples

Various fields utilize case studies, including the following:

• Social sciences : For understanding complex social phenomena.
• Business : For analyzing corporate strategies and business decisions.
• Healthcare : For detailed patient studies and medical research.
• Education : For understanding educational methods and policies.
• Law : For in-depth analysis of legal cases.

For example, consider a case study in a business setting where a startup struggles to scale. Researchers might examine the startup’s strategies, market conditions, management decisions, and competition. Interviews with the CEO, employees, and customers, alongside an analysis of financial data, could offer insights into the challenges and potential solutions for the startup. This research could serve as a valuable lesson for other emerging businesses.

See below for other examples.

Types of Case Studies

Several standard types of case studies exist that vary based on the objectives and specific research needs.

Illustrative Case Study : Descriptive in nature, these studies use one or two instances to depict a situation, helping to familiarize the unfamiliar and establish a common understanding of the topic.

Exploratory Case Study : Conducted as precursors to large-scale investigations, they assist in raising relevant questions, choosing measurement types, and identifying hypotheses to test.

Cumulative Case Study : These studies compile information from various sources over time to enhance generalization without the need for costly, repetitive new studies.

Critical Instance Case Study : Focused on specific sites, they either explore unique situations with limited generalizability or challenge broad assertions, to identify potential cause-and-effect issues.

Pros and Cons

As with any research study, case studies have a set of benefits and drawbacks.

• Provides comprehensive and detailed data.
• Offers a real-life perspective.
• Flexible and can adapt to discoveries during the study.
• Enables investigation of scenarios that are hard to assess in laboratory settings.
• Facilitates studying rare or unique cases.
• Generates hypotheses for future experimental research.
• Time-consuming and may require a lot of resources.
• Hard to generalize findings to a broader context.
• Potential for researcher bias.
• Cannot establish causality .
• Lacks scientific rigor compared to more controlled research methods .

Crafting a Good Case Study: Methodology

While case studies emphasize specific details over broad theories, they should connect to theoretical frameworks in the field. This approach ensures that these projects contribute to the existing body of knowledge on the subject, rather than standing as an isolated entity.

The following are critical steps in developing a case study:

• Define the Research Questions : Clearly outline what you want to explore. Define specific, achievable objectives.
• Select the Case : Choose a case that best suits the research questions. Consider using a typical case for general understanding or an atypical subject for unique insights.
• Data Collection : Use a variety of data sources, such as interviews, observations, documents, and archival records, to provide multiple perspectives on the issue.
• Data Analysis : Identify patterns and themes in the data.
• Report Findings : Present the findings in a structured and clear manner.

Analysts typically use thematic analysis to identify patterns and themes within the data and compare different cases.

• Qualitative Analysis : Such as coding and thematic analysis for narrative data.
• Quantitative Analysis : In cases where numerical data is involved.
• Triangulation : Combining multiple methods or data sources to enhance accuracy.

A good case study requires a balanced approach, often using both qualitative and quantitative methods.

The researcher should constantly reflect on their biases and how they might influence the research. Documenting personal reflections can provide transparency.

Avoid over-generalization. One common mistake is to overstate the implications of a case study. Remember that these studies provide an in-depth insights into a specific case and might not be widely applicable.

Don’t ignore contradictory data. All data, even that which contradicts your hypothesis, is valuable. Ignoring it can lead to skewed results.

Finally, in the report, researchers provide comprehensive insight for a case study through “thick description,” which entails a detailed portrayal of the subject, its usage context, the attributes of involved individuals, and the community environment. Thick description extends to interpreting various data, including demographic details, cultural norms, societal values, prevailing attitudes, and underlying motivations. This approach ensures a nuanced and in-depth comprehension of the case in question.

Learn more about Qualitative Research and Qualitative vs. Quantitative Data .

Morland, J. & Feagin, Joe & Orum, Anthony & Sjoberg, Gideon. (1992). A Case for the Case Study . Social Forces. 71(1):240.

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Definition of case study

Examples of case study in a sentence.

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'case study.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1914, in the meaning defined at sense 1

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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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McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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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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What Is a Case Study?

Weighing the pros and cons of this method of research

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Making the case for artisanal and small-scale mining

• Morgan Sherburne

EXPERT Q&A

Artisanal and small-scale mining plays a critical role in supplying the world with minerals vital for decarbonization, but this kind of mining typically lacks regulation and can be socially and environmentally harmful.

Despite ASM’s significant challenges, University of Michigan researchers argue that artisanal and small-scale mining, or ASM, should be embraced.

Minerals such as cobalt, copper, lithium and nickel are necessary components of electric vehicle batteries, wind turbines, photovoltaic systems and battery storage units. Artisanal and small-scale mining, or ASM, provides as much as 20% of the global supply of these minerals.

“There is no decarbonization without mining these and other minerals. To move away from coal, oil and gas, the global energy system must mine critical minerals,” said Brandon Marc Finn , a researcher at the U-M School for Environment and Sustainability. “At the same time, we argue that ASM should be recognized as an essential social and environmental justice issue of our time.”

ASM plays a central role in local economies as well, Finn says. About 40.5 million people around the world participate in ASM in 80 different countries, and up to 270 million people depend on it. However, ASM is linked to environmental degradation, can be detrimental to health outcomes, and is frequently associated with human rights abuses, including child labor. ASM workers generally operate with limited capital, for minimal pay, and without social safety nets.

“ASM workers choose artisanal mining because they often do not have comparable alternative livelihood options and this work supports them and their families. They adopt informal, often unregulated working practices because these typically have lower barriers to entry into the market,” said Finn, also a core faculty member at the U-M Center for Sustainable Systems.

To better understand ASM, Finn conducted three fieldwork trips with mining communities in the Democratic Republic of the Congo to trace the start of the cobalt and copper supply chains. He discusses the challenges and future of artisanal and small-scale mining in a study published in the journal Energy Research and Social Science. His co-authors are Adam Simon, professor of earth and environmental sciences, and Joshua Newell, professor of environment and sustainability.

What is artisanal and small scale mining?

Artisanal and small-scale mining is typically mining that is very labor intensive. Miners use mostly rudimentary tools such as picks, axes and shovels. It generally does not use overly mechanized processes such as large scale diggers or dump trucks. These mines are usually composed of hand-dug tunnels extending tens of meters deep, open pit mines, or the mining minerals from old waste (tailings) from industrial mines. It is typically above ground and therefore more easily accessible.

ASM miners may lack transparency around pricing, the weighing of their ore and the grade of ore they have mined. The argument we’re making regarding ASM is to act in good faith and not pretend these problems don’t exist. We document many of the challenges in the paper. While many regard the problems as symptomatic of ASM mining, we argue that ASM itself is symptomatic of long-term marginalization.

Many large tech companies seek to exclude ASM from their supply chains. This threatens to worsen the living conditions and livelihoods of millions of people. Hundreds of millions of dollars have been spent trying to “formalize” ASM mining practices and have largely failed. This is, in part, because they misunderstand the problem. ASM workers choose artisanal mining because they do not have comparable alternative livelihood options and this work supports them and their families. They adopt informal, often unregulated working practices because these typically have lower barriers to entry into the market.

Rather than look away from the problem and try to exclude ASM miners from global mineral supply chains, we should turn towards them, and to the social, economic and geopolitical reasons that make ASM necessary in the first place. This is all the more essential given soaring demand for critical minerals, such as cobalt and lithium. With hundreds of billions of dollars invested into low-carbon technologies, some of this money should support the most vulnerable parts of the workforce that mine the metals that enable our lives and will sustain the global energy transition.

What are some of the challenges associated with artisanal and small-scale mining?

The dangers or risks emerge because there’s often very little regulation and enforcement of occupational standards. If you overregulate, then you potentially cut people out from participating in ASM. But if you don’t regulate at all, you also face issues such as tunnels that are structurally unsound, or the complete lack of assessment of breathing conditions when you’re 30 or 40 meters underground.

There are significant issues in terms of health for miners such as dust inhalation, silicosis and exposure to mercury. Mercury also goes into water bodies, with impacts extending beyond the individual mining site. There are issues of dust pollution and severe environmental pollution, deforestation and others, all of which are not exclusive to ASM. They occur at and beyond industrial mines too.

To me, these issues are relatively straightforward to regulate and improve upon if you have governance structures in place. The honest, transparent and pragmatic position is that these conditions exist and we should mitigate as much harm as possible, and distribute the wealth generated from industrial mining more fairly.

Another very challenging issue with ASM is that of child labor. Recent pathbreaking scholarship from Andrew Gulley estimates around 2% of cobalt comes from child labor specifically, which sounds pretty low, but when you realize that industrial mining employs a small fraction of the workforce that ASM mining employs, you realize it takes many more hands to produce one percent of ASM-mined ore than it does for industrially produced ore.

I do have an abolitionist view on child labor: I don’t want children working in mines or with dangerous equipment but I think the reason they do is because the alternatives that families have are nearly nonexistent. A way to deal with that is to take a more realistic perspective in terms of regulating ASM in designated ASM areas with enforceable standards and fair pay to workers.

What are some solutions to these issues?

I think the first kind of solution is for us to be able to understand how artisanal and small-scale mining happens and why it happens. Because of the socioeconomic realities that people face, people make rational decisions to get into ASM mining because they need to provide for themselves and for their families. I think when we understand why it happens, we have to take a more holistic approach on how we deal with ASM. And it’s not just on the mining side: it’s the politics and the economic structures that inform the conditions that create ASM.

It’s also the royalties and taxation received by national governments from industrial mines, which do not sufficiently benefit ordinary people on the ground. Industrial mining companies owe more to impacted communities than they pay. This is especially because they are sometimes implicated in housing displacement and widespread environmental pollution, but also because they extract so much value from the ground. Industrial mining companies have a fundamental duty and a moral obligation to the people whose land they’re on and resources they are extracting from. This includes supporting ASM workers. Bringing industrial mining into the artisanal mining debate is important because I want to try to hold them more accountable and show how they are related.

On the other hand, I think development organizations like The World Bank need to adopt more heterodox thinking to ASM mining and development. The World Bank has spent hundreds of millions to try to formalize artisanal and small-scale mining, but I think that’s often not a conducive approach. You can’t start by trying to instill the outcome you want if that very outcome threatens peoples’ ability to earn a livelihood. You need to find some middle ground between doing nothing and overextending your reach to formalize an industry, and thereby risk threatening livelihoods.

There do need to be minimum standards for ASM. For example, ASM mining zones need to be designated to miners, and these zones must have decent and abundant ore grades. Once you have a reasonable designated zone, you’re able to enforce the occupational, environmental and social standards. However, this is only part of the problem. Minimal standards need to apply to the point of sale, too. ASM miners should be compensated fairly and transparently for the material they produce. This point of sale should be regulated as much as the mining itself.

These areas should have the requirement of not using child labor. But regulating bodies should offer alternatives which put families in better positions without child labor than they would have been in with their child being on the mining site in the first place. This also entails paying miners market rates for the ore they produce.

We also need to ask miners themselves what they want, rather than assuming solutions without their input and expertise. Artisanal miners know why they mine, and have excellent ideas on what could improve their working and living conditions. We should ask them questions and listen to their answers.

What’s the future you’d like to see of artisanal and small scale mining?

The future I’d like to see is that we understand mining, both artisanal and industrial mining, in its full complexity. I think we need to know the role ASM plays in providing the backbone of survival for miners and their families.

I think we, as consumers, as tech companies, whoever has to rely on these minerals, need to be able to understand that a different kind of mining may be possible for ASM and for industrial mining. We need to acknowledge and appreciate the work that ASM miners do to enable our daily lives, and to enable decarbonization in the future. We need to bring ASM into discussions on social and environmental justice—not turn away in outrage or feign plausible deniability.

We need to try to understand the notion of justice beyond just the U.S. It’s not just the U.S. decarbonizing as an inherent moral good. We have to know that these supply chains are not only necessary for modern technology and the modern world, but they link us morally to one another. It’s easy to forget this, because it’s happening somewhere far away in a place we haven’t heard of and to populations of people that have been absolutely marginalized for hundreds of years. I don’t think the solution lies in us shaming each other as individuals: I think part of the solution probably also lies in holding political elites and major corporate actors’ feet to the fire.

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• Open access
• Published: 07 September 2024

A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews

• Cathalijn H. C. Leenaars   ORCID: orcid.org/0000-0002-8212-7632 1 ,
• Frans R. Stafleu 2 ,
• Christine Häger 1 &
• André Bleich 1  

Systematic Reviews volume  13 , Article number:  230 ( 2024 ) Cite this article

Metrics details

While undisputedly important, and part of any systematic review (SR) by definition, evaluation of the risk of bias within the included studies is one of the most time-consuming parts of performing an SR. In this paper, we describe a case study comprising an extensive analysis of risk of bias (RoB) and reporting quality (RQ) assessment from a previously published review (CRD42021236047). It included both animal and human studies, and the included studies compared baseline diseased subjects with controls, assessed the effects of investigational treatments, or both. We compared RoB and RQ between the different types of included primary studies. We also assessed the “informative value” of each of the separate elements for meta-researchers, based on the notion that variation in reporting may be more interesting for the meta-researcher than consistently high/low or reported/non-reported scores. In general, reporting of experimental details was low. This resulted in frequent unclear risk-of-bias scores. We observed this both for animal and for human studies and both for disease-control comparisons and investigations of experimental treatments. Plots and explorative chi-square tests showed that reporting was slightly better for human studies of investigational treatments than for the other study types. With the evidence reported as is, risk-of-bias assessments for systematic reviews have low informative value other than repeatedly showing that reporting of experimental details needs to improve in all kinds of in vivo research. Particularly for reviews that do not directly inform treatment decisions, it could be efficient to perform a thorough but partial assessment of the quality of the included studies, either of a random subset of the included publications or of a subset of relatively informative elements, comprising, e.g. ethics evaluation, conflicts of interest statements, study limitations, baseline characteristics, and the unit of analysis. This publication suggests several potential procedures.

Peer Review reports

Introduction

Researchers performing systematic reviews (SRs) face bias at two potential levels: first, at the level of the SR methods themselves, and second, at the level of the included primary studies [ 1 ]. To safeguard correct interpretation of the review’s results, transparency is required at both levels. For bias at the level of the SR methods, this is ensured by transparent reporting of the full SR methods, at least to the level of detail as required by the PRISMA statement [ 2 ]. For bias at the level of the included studies, study reporting quality (RQ) and/or risk of bias (RoB) are evaluated at the level of the individual included study. Specific tools are available to evaluate RoB in different study types [ 3 ]. Also, for reporting of primary studies, multiple guidelines and checklists are available to prevent missing important experimental details and more become available for different types of studies over time [ 4 , 5 ]. Journal endorsement of these types of guidelines has been shown to improve study reporting quality [ 6 ].

While undisputedly important, evaluation of the RoB and/or RQ of the included studies is one of the most time-consuming parts of an SR. Experienced reviewers need 10 min to an hour to complete an individual RoB assessment [ 7 ], and every included study needs to be evaluated by two reviewers. Besides spending substantial amounts of time on RoB or RQ assessments, reviewers tend to become frustrated because of the scores frequently being unclear or not reported (personal experience from the authors, colleagues and students). While automation of RoB seems to be possible without loss of accuracy [ 8 , 9 ], so far, this automation has not had significant impact on the speed; in a noninferiority randomised controlled trial of the effect of automation on person-time spent on RoB assessment, the confidence interval for the time saved ranged from − 5.20 to + 2.41 min [ 8 ].

In any scientific endeavour, there is a balance between reliability and speed; to guarantee reliability of a study, time investments are necessary. RoB or RQ assessment is generally considered to be an essential part of the systematic review process to warrant correct interpretation of the findings, but with so many studies scoring “unclear” or “not reported”, we wondered if all this time spent on RoB assessments is resulting in increased reliability of reviews.

Overall unclear risk of bias in the included primary studies is a conclusion of multiple reviews, and these assessments are useful in pinpointing problems in reporting, thereby potentially improving the quality of future publications of primary studies. However, the direct goal of most SRs is to answer a specific review question, and in that respect, unclear RoB/not reported RQ scores contribute little to the validity of the review’s results. If all included studies score “unclear” or “high” RoB on at least one of the analysed elements, the overall effect should be interpreted as inconclusive.

While it is challenging to properly evaluate the added validity value of a methodological step, we had data available allowing for an explorative case study to assess the informative value of various RoB and RQ elements in different types of studies. We previously performed an SR of the nasal potential difference (nPD) for cystic fibrosis (CF) in animals and humans, aiming to quantify the predictive value of animal models for people with CF [ 10 , 11 ]. That review comprised between-subject comparisons of both baseline versus disease-control and treatment versus treatment control. For that review, we performed full RoB and RQ analyses. This resulted in data allowing for comparisons of RoB and RQ between animal and human studies, but also between baseline and treatment studies, which are both presented in this manuscript. RoB evaluations were based on the Cochrane collaboration’s tool [ 12 ] for human studies and SYRCLE’s tool [ 13 ] for animal studies. RQ was tested based on the ARRIVE guidelines [ 14 ] for animal studies and the 2010 CONSORT guidelines [ 15 ] for human studies. Brief descriptions of these tools are provided in Table  1 .

All these tools are focussed on interventional studies. Lacking more specific tools for baseline disease-control comparisons, we applied them as far as relevant for the baseline comparisons. We performed additional analyses on our RQ and RoB assessments to assess the amount of distinctive information gained from them.

The analyses described in this manuscript are based on a case study SR of the nPD related to cystic fibrosis (CF). That review was preregistered on PROSPERO (CRD42021236047) on 5 March 2021 [ 16 ]. Part of the results were published previously [ 10 ]. The main review questions are answered in a manuscript that has more recently been published [ 11 ]. Both publications show a simple RoB plot corresponding to the publication-specific results.

For the ease of the reader, we provide a brief summary of the overall review methods. The full methods have been described in our posted protocol [ 16 ] and the earlier publications [ 10 , 11 ]. Comprehensive searches were performed in PubMed and Embase, unrestricted for publication date or language, on 23 March 2021. Title-abstract screening and full-text screening were performed by two independent reviewers blinded to the other’s decision (FS and CL) using Rayyan [ 17 ]. We included animal and/or human studies describing nPD in CF patients and/or CF animal models. We restricted to between-subject comparisons, either CF versus healthy controls or experimental CF treatments versus CF controls. Reference lists of relevant reviews and included studies were screened (single level) for snowballing. Discrepancies were all resolved by discussions between the reviewers.

Data were extracted by two independent reviewers per reference in several distinct phases. Relevant to this manuscript, FS and CL extracted RoB and RQ data in Covidence [ 18 ], in two separate projects using the same list of 48 questions for studies assessing treatment effects and studies assessing CF-control differences. The k  = 11 studies that were included in both parts of the overarching SR were included twice in the current data set, as RoB was separately scored for each comparison. Discrepancies were all resolved by discussions between the reviewers. In violation of the protocol, no third reviewer was involved.

RoB and SQ data extraction followed our review protocol, which states the following: “For human studies, risk of bias will be assessed with the Cochrane Collaboration’s tool for assessing risk of bias. For animal studies, risk of bias will be assessed with SYRCLE’s RoB tool. Besides, we will check compliance with the ARRIVE and CONSORT guidelines for reporting quality”. The four tools contain overlapping questions. To prevent unnecessary repetition of our own work, we created a single list of 48 items, which were ordered by topic for ease of extraction. For RoB, this list contains the same elements as the original tools, with the same response options (high/unclear/low RoB). For RQ, we created checklists with all elements as listed in the original tools, with the response options reported yes/no. For (RQ and RoB) elements specific to some of the included studies, the response option “irrelevant” was added. We combined these lists, only changing the order and merging duplicate elements. We do not intend this list to replace the individual tools; it was created for this specific study only.

In our list, each question was preceded by a short code indicating the tool it was derived from (A for ARRIVE, C for CONSORT, and S for SYRCLE’s) to aid later analyses. When setting up, we started with the animal-specific tools, with which the authors are more familiar. After preparing data extraction for those, we observed that all elements from the Cochrane tool had already been addressed. Therefore, this list was not explicit in our extractions. The extraction form always allowed free text to support the response. Our extraction list is provided with our supplementary data.

For RoB, the tools provide relatively clear suggestions for which level to score and when, with signalling questions and examples [ 12 , 13 ]. However, this still leaves some room for interpretation, and while the signalling questions are very educative, there are situations where the response would in our opinion not correspond to the actual bias. The RQ tools have been developed as guidelines on what to report when writing a manuscript, and not as a tool to assess RQ [ 14 , 15 ]. This means we had to operationalise upfront which level we would find sufficient to score “reported”. Our operationalisations and corrections of the tools are detailed in Table  2 .

Data were exported from Covidence into Microsoft’s Excel, where the two projects were merged and spelling and capitalisation were harmonised. Subsequent analyses were performed in R [ 21 ] version 4.3.1 (“Beagle Scouts”) via RStudio [ 22 ], using the following packages: readxl [ 23 ], dplyr [ 24 ], tidyr [ 25 ], ggplot2 [ 26 ], and crosstable [ 27 ].

Separate analyses were performed for RQ (with two levels per element) and RoB (with three levels per element). For both RoB and RQ, we first counted the numbers of irrelevant scores overall and per item. Next, irrelevant scores were deleted from further analyses. We then ranked the items by percentages for reported/not reported, or for high/unclear/low scores, and reported the top and bottom 3 (RoB) or 5 (RQ) elements.

While 100% reported is most informative to understand what actually happened in the included studies, if all authors continuously report a specific element, scoring of this element for an SR is not the most informative for meta-researchers. If an element is not reported at all, this is bad news for the overall level of confidence in an SR, but evaluating it per included study is also not too efficient except for highlighting problems in reporting, which may help to improve the quality of future (publications of) primary studies. For meta-researchers, elements with variation in reporting may be considered most interesting because these elements highlight differences between the included studies. Subgroup analyses based on specific RQ/RoB scores can help to estimate the effects of specific types of bias on the overall effect size observed in meta-analyses, as has been done for example randomisation and blinding [ 28 ]. However, these types of subgroup analyses are only possible if there is some variation in the reporting. Based on this idea, we defined a “distinctive informative value” (DIV) for RQ elements, based on the optimal variation being 50% reported and either 0% or 100% reporting being minimally informative. Thus, this “DIV” was calculated as follows:

Thus, the DIV could range from 0 (no informative value) to 50 (maximally informative), visualised in Fig.  1 .

Visual explanation of the DIV value

The DIV value was only used for ranking. The results were visualised in a heatmap, in which the intermediate shades correspond to high DIV values.

For RoB, no comparable measure was calculated. With only 10 elements but at 3 distinct levels, we thought a comparable measure would sooner hinder interpretation of informative value than help it. Instead, we show the results in an RoB plot split by population and study design type.

Because we are interested in quantifying the predictive value of animal models for human patients, we commonly perform SRs including both animal and human data (e.g. [ 29 , 30 ]). The dataset described in the current manuscript contained baseline and intervention studies in animals and humans. Because animal studies are often held responsible for the reproducibility crisis, but also to increase the external validity of this work, explorative chi-square tests (the standard statistical test for comparing percentages for binary variables) were performed to compare RQ and RoB between animal and human studies and between studies comparing baselines and treatment effects. They were performed with the base R “chisq.test” function. No power calculations were performed, as these analyses were not planned.

Literature sample

We extracted RoB and RQ data from 164 studies that were described in 151 manuscripts. These manuscripts were published from 1981 through 2020. Overall, 164 studies comprised 78 animal studies and 86 human studies, 130 comparisons of CF versus non-CF control, and 34 studies assessing experimental treatments. These numbers are detailed in a crosstable (Table  3 ).

The 48 elements in our template were completed for these 164 studies, which results in 7872 assessed elements. In total, 954 elements (12.1%) were irrelevant for various reasons (mainly for noninterventional studies and for human studies). The 7872 individual scores per study are available from the data file on OSF.

Of the 48 questions in our extraction template, 38 addressed RQ, and 10 addressed RoB.

Overall reporting quality

Of the 6232 elements related to RQ, 611 (9.8%) were deemed irrelevant. Of the remainder, 1493 (26.6% of 5621) were reported. The most reported elements were background of the research question (100% reported), objectives (98.8% reported), interpretation of the results (98.2% reported), generalisability (86.0% reported), and the experimental groups (83.5% reported). The least-reported elements were protocol violations, interim analyses + stopping rules and when the experiments were performed (all 0% reported), where the experiments were performed (0.6% reported), and all assessed outcome measures (1.2% reported).

The elements with most distinctive variation in reporting (highest DIV, refer to the “ Methods ” section for further information) were as follows: ethics evaluation (64.6% reported), conflicts of interest (34.8% reported), study limitations (29.3% reported), baseline characteristics (26.2% reported), and the unit of analysis (26.2% reported). RQ elements with DIV values over 10 are shown in Table  4 .

Overall risk of bias

Of the 1640 elements related to RoB, 343 (20.9%) were deemed irrelevant. Of the remainder, 219 (16.9%) scored high RoB, and 68 (5.2%) scored low RoB. The overall RoB scores were highest for selective outcome reporting (97.6% high), baseline group differences (19.5% high), and other biases (9.8% high); lowest for blinding of participants, caregivers, and investigators (13.4% low); blinding of outcome assessors (11.6% low) and baseline group differences (8.5% low); and most unclear for bias due to animal housing (100% unclear), detection bias due to the order of outcome measurements (99.4% unclear), and selection bias in sequence generation (97.1% unclear). The baseline group differences being both in the highest and the lowest RoB score are explained by the baseline values being reported better than the other measures, resulting in fewer unclear scores.

Variation in reporting is relatively high for most of the elements scoring high or low. Overall distinctive value of the RoB elements is low, with most scores being unclear (or, for selective outcome reporting, most scores being high).

Animal versus human studies

For RQ, the explorative chi-square tests indicated differences in reporting between animal and human studies for baseline values ( Χ 1  = 50.3, p  < 0.001), ethical review ( Χ 1  = 5.1, p  = 0.02), type of study ( Χ 1  = 11.2, p  < 0.001), experimental groups ( Χ 1  = 3.9, p  = 0.050), inclusion criteria ( Χ 1  = 24.6, p  < 0.001), the exact n value per group and in total ( Χ 1  = 26.0, p  < 0.001), (absence of) excluded datapoints ( Χ 1  = 4.5, p  = 0.03), adverse events ( Χ 1  = 5.5, p  = 0.02), and study limitations ( Χ 1  = 8.2, p  = 0.004). These explorative findings are visualised in a heatmap (Fig.  2 ).

Heatmap of reporting by type of study. Refer to Table  3 for absolute numbers of studies per category

For RoB, the explorative chi-square tests indicated differences in risk of bias between animal and human studies for baseline differences between the groups ( Χ 2  = 34.6, p  < 0.001) and incomplete outcome data ( Χ 2  = 7.6, p  = 0.02). These explorative findings are visualised in Fig.  3 .

Risk of bias by type of study. Refer to Table  3 for absolute numbers of studies per category. Note that the data shown in these plots overlap with those in the two preceding publications [ 10 , 11 ]

Studies assessing treatment effects versus studies assessing baseline differences

For RQ, the explorative chi-square tests indicated differences in reporting between comparisons of disease with control versus comparisons of treatment effects for the title listing the type of study ( X 1  = 5.0, p  = 0.03), the full paper explicitly mentioning the type of study ( X 1  = 14.0, p  < 0.001), explicit reporting of the primary outcome ( X 1  = 11.7, p  < 0.001), and reporting of adverse events X 1  = 25.4, p  < 0.001). These explorative findings are visualised in Fig.  2 .

For RoB, the explorative chi-square tests indicated differences in risk of bias between comparisons of disease with control versus comparisons of treatment effects for baseline differences between the groups ( Χ 2  = 11.4, p  = 0.003), blinding of investigators and caretakers ( Χ 2  = 29.1, p  < 0.001), blinding of outcome assessors ( Χ 2  = 6.2, p  = 0.046), and selective outcome reporting ( Χ 2  = 8.9, p  = 0.01). These explorative findings are visualised in Fig.  3 .

Overall, our results suggest lower RoB and higher RQ for human treatment studies compared to the other study types.

This literature study shows that reporting of experimental details is low, frequently resulting in unclear risk-of-bias assessments. We observed this both for animal and for human studies, with two main study designs: disease-control comparisons and, in a smaller sample, investigations of experimental treatments. Overall reporting is slightly better for elements that contribute to the “story” of a publication, such as the background of the research question, interpretation of the results and generalisability, and worst for experimental details that relate to differences between what was planned and what was actually done, such as protocol violations, interim analyses, and assessed outcome measures. The latter also results in overall high RoB scores for selective outcome reporting.

Of note, we scored this more stringently than SYRCLE’s RoB tool [ 13 ] suggests and always scored a high RoB if no protocol was posted, because only comparing the “Methods” and “Results” sections within a publication would, in our opinion, result in an overly optimistic view. Within this sample, only human treatment studies reported posting protocols upfront [ 31 , 32 ]. In contrast to selective outcome reporting, we would have scored selection, performance, and detection bias due to sequence generation more liberally for counterbalanced designs (Table  2 ), because randomisation is not the only appropriate method for preventing these types of bias. Particularly when blinding is not possible, counterbalancing [ 33 , 34 ] and Latin-square like designs [ 35 ] can decrease these biases, while randomisation would risk imbalance between groups due to “randomisation failure” [ 36 , 37 ]. We would have scored high risk of bias for blinding for these types of designs, because of increased sequence predictability. However, in practice, we did not include any studies reporting Latin-square-like or other counterbalancing designs.

One of the “non-story” elements that is reported relatively well, particularly for human treatment studies, is the blinding of participants, investigators, and caretakers. This might relate to scientists being more aware of potential bias of participants; they may consider themselves to be more objective than the general population, while the risk of influencing patients could be considered more relevant.

The main strength of this work is that it is a full formal analysis of RoB and RQ in different study types: animal and human, baseline comparisons, and treatment studies. The main limitation is that it is a single case study from a specific topic: the nPD test in CF. The results shown in this paper are not necessarily valid for other fields, particularly as we hypothesise that differences in scientific practice between medical fields relate to differences in translational success [ 38 ]. Thus, it is worth to investigate field-specific informative values before selecting which elements to score and analyse in detail.

Our comparisons of different study and population types show lower RoB and higher RQ for human treatment studies compared to the other study types for certain elements. Concerning RQ, the effects were most pronounced for the type of experimental design being explicitly mentioned and the reporting of adverse events. Concerning RoB, the effects were most pronounced for baseline differences between the groups, blinding of investigators and caretakers, and selective outcome reporting. Note, however, that the number of included treatment studies is a lot lower than the number of included baseline studies, and that the comparisons were based on only k  = 12 human treatment studies. Refer to Table  3 for absolute numbers of studies per category. Besides, our comparisons may be confounded to some extent by the publication date. The nPD was originally developed for human diagnostics [ 39 , 40 ], and animal studies only started to be reported at a later date [ 41 ]. Also, the use of the nPD as an outcome in (pre)clinical trials of investigational treatments originated at a later date [ 42 , 43 ].

Because we did not collect our data to assess time effects, we did not formally analyse them. However, we had an informal look at the publication dates by RoB score for blinding of the investigators and caretakers, and by RQ score for ethics evaluation (in box plots with dot overlay), showing more reported and fewer unclear scores in the more recent publications (data not shown). While we thus cannot rule out confounding of our results by publication date, the results are suggestive of mildly improved reporting of experimental details over time.

This study is a formal comparison of RoB and RQ scoring for two main study types (baseline comparisons and investigational treatment studies), for both animals and humans. Performing these comparisons within the context of a single SR [ 16 ] resulted in a small, but relatively homogeneous sample of primary studies about the nPD in relation to CF. On conferences and from colleagues in the animal SR field, we heard that reporting would be worse for animal than for human studies. Our comparisons allowed us to show that particularly for baseline comparisons of the nPD in CF versus control, this is not the case.

The analysed tools [ 12 , 13 , 15 ] were developed for experimental interventional studies. While some of the elements are less appropriate for other types of studies, such as animal model comparisons, our results show that many of the elements can be used and could still be useful, particularly if the reporting quality of the included studies would be better.

Implications

To correctly interpret the findings of a meta-analysis, awareness of the RoB in the included studies is more relevant than the RQ on its own. However, it is impossible to evaluate the RoB if the experimental details have not been reported, resulting in many unclear scores. With at least one unclear or high RoB score per included study, the overall conclusions of the review become inconclusive. For SRs of overall treatment effects that are performed to inform evidence-based treatment guidelines, RoB analyses remain crucial, even though the scores will often be unclear. Ideally, especially for SRs that will be used to plan future experiments/develop treatment guidelines, analyses should only include those studies consistently showing low risk of bias (i.e. low risk on all elements). However, in practice, consistently low RoB studies in our included literature samples (> 20 SRs to date) are too scarce for meaningful analyses. For other types of reviews, we think it is time to consider if complete RoB assessment is the most efficient use of limited resources. While these assessments regularly show problems in reporting, which may help to improve the quality of future primary studies, the unclear scores do not contribute much to understanding the effects observed in meta-analyses.

With PubMed already indexing nearly 300,000 mentioning the term “systematic review” in the title, abstract, or keywords, we can assume that many scientists are spending substantial amounts of time and resources on RoB and RQ assessments. Particularly for larger reviews, it could be worthwhile to restrict RoB assessment to either a random subset of the included publications or a subset of relatively informative elements. Even a combination of these two strategies may be sufficiently informative if the results of the review are not directly used to guide treatment decisions. The subset could give a reasonable indication of the overall level of evidence of the SR while saving resources. Different suggested procedures are provided in Table  5 . The authors of this work would probably have changed to such a strategy during their early data extraction phase, if the funder would not have stipulated full RoB assessment in their funding conditions.

We previously created a brief and simple taxonomy of systematised review types [ 44 ], in which we advocate RoB assessments to be a mandatory part of any SR. We would still urge anyone calling their review “systematic” to stick to this definition and perform some kind of RoB and/or RQ assessment, but two independent scientists following a lengthy and complex tool for all included publications, resulting in 74.6% of the assessed elements not being reported, or 77.9% unclear RoB, can, in our opinion, in most cases be considered inefficient and unnecessary.

Our results show that there is plenty of room for improvement in the reporting of experimental details in medical scientific literature, both for animal and for human studies. With the current status of the primary literature as it is, full RoB assessment may not be the most efficient use of limited resources, particularly for SRs that are not directly used as the basis for treatment guidelines or future experiments.

Availability of data and materials

The data described in this study are available from the Open Science Platform ( https://osf.io/fmhcq/ ) in the form of a spreadsheet file. In the data file, the first tab shows the list of questions that were used for data extraction with their respective short codes. The second tab shows the full individual study-level scores, with lines per study and columns per short code.

Abbreviations

• Cystic fibrosis

High risk of bias

Low risk of bias

No, not reported

• Nasal potential difference
• Risk of bias
• Reporting quality

Systematic review

Unclear risk of bias

Yes, reported

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Acknowledgements

The authors kindly acknowledge Dr. Hendrik Nieraad for his help in study classification.

Open Access funding enabled and organized by Projekt DEAL. This research was funded by the BMBF, grant number 01KC1904. During grant review, the BMBF asked for changes in the review design which we incorporated. Publication of the review results was a condition of the call. Otherwise, the BMBF had no role in the collection, analysis and interpretation of data, or in writing the manuscript.

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CL and AB acquired the grant to perform this work and designed the study. CL performed the searches. FS and CL extracted the data. CL performed the analyses. CH performed quality controls for the data and analyses. CL drafted the manuscript. All authors revised the manuscript and approved of the final version.

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Leenaars, C.H.C., Stafleu, F.R., Häger, C. et al. A case study of the informative value of risk of bias and reporting quality assessments for systematic reviews. Syst Rev 13 , 230 (2024). https://doi.org/10.1186/s13643-024-02650-w

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Business process reengineering (BPR) is the radical redesign of core business processes to achieve dramatic improvements in performance, efficiency and effectiveness. BPR examples are not one-time projects, but rather examples of a continuous journey of innovation and change focused on optimizing end-to-end processes and eliminating redundancies. The purpose of BPR is to streamline  workflows , eliminate unnecessary steps and improve resource utilization.

BPR involves business process redesign that challenges norms and methods within an organization. It typically focuses on achieving dramatic, transformative changes to existing processes. It should not be confused with  business process management (BPM) , a more incremental approach to optimizing processes, or business process improvement (BPI), a broader term that encompasses any systematic effort to improve current processes. This blog outlines some BPR examples that benefit from a BPM methodology.

BPR emerged in the early 1990s as a management approach aimed at radically redesigning business operations to achieve business transformation. The methodology gained prominence with the publication of a 1990 article in the Harvard Business Review, “Reengineering Work: Don’t Automate, Obliterate,” by Michael Hammer, and the 1993 book by Hammer and James Champy, Reengineering the Corporation . An early case study of BPR was Ford Motor Company, which successfully implemented reengineering efforts in the 1990s to streamline its manufacturing processes and improve competitiveness.

Organizations of all sizes and industries implement business process reengineering. Step 1 is to define the goals of BPR, and subsequent steps include assessing the current state, identifying gaps and opportunities, and process mapping.

Successful implementation of BPR requires strong leadership, effective change management and a commitment to continuous improvement. Leaders, senior management, team members and stakeholders must champion the BPR initiative and provide the necessary resources, support and direction to enable new processes and meaningful change.

Streamlining supply chain management

Using BPR for supply chain optimization involves a meticulous reassessment and redesign of every step, including logistics, inventory management and procurement . A comprehensive supply chain overhaul might involve rethinking procurement strategies, implementing just-in-time inventory systems, optimizing production schedules or redesigning transportation and distribution networks. Technologies such as supply chain management software (SCM), enterprise resource planning (ERP) systems, and advanced analytics tools can be used to automate and optimize processes. For example, predictive analytics can be used to forecast demand and optimize inventory levels, while blockchain technology can enhance transparency and traceability in the supply chain.

• Improved efficiency
• Reduced cost
• Enhanced transparency

Customer relationship management (CRM)

BPR is a pivotal strategy for organizations that want to overhaul their customer relationship management (CRM) processes. Steps of business process reengineering for CRM include integrating customer data from disparate sources, using advanced analytics for insights, and optimizing service workflows to provide personalized experiences and shorter wait times.

BPR use cases for CRM might include:

• Implementing integrated CRM software to centralize customer data and enable real-time insights
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Using BPR, companies can establish a comprehensive view of each customer, enabling anticipation of their needs, personalization of interactions and prompt issue resolution.

• 360-degree customer view
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Digitizing administrative processes

Organizations are increasingly turning to BPR to digitize and automate administrative processes to reduce human errors. This transformation entails replacing manual, paper-based workflows with digital systems that use technologies like Robotic Process Automation (RPA) for routine tasks.

This might include streamlining payroll processes, digitizing HR operations or automating invoicing procedures. This can lead to can significant improvements in efficiency, accuracy and scalability and enable the organization to operate more effectively.

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• Increased adaptability

Improving product development processes

BPR plays a crucial role in optimizing product development processes, from ideation to market launch. This comprehensive overhaul involves evaluating and redesigning workflows, fostering cross-functional collaboration and innovating by using advanced technologies. This can involve implementing cross-functional teams to encourage communication and knowledge sharing, adopting agile methodologies to promote iterative development and rapid prototyping, and by using technology such as product lifecycle management (PLM) software to streamline documentation and version control.

BPR initiatives such as these enable organizations to reduce product development cycle times, respond more quickly to market demands, and deliver innovative products that meet customer needs.

• Faster time-to-market
• Enhanced innovation
• Higher product quality

Updating technology infrastructure

In an era of rapid technological advancement, BPR serves as a vital strategy for organizations that need to update and modernize their technology infrastructure. This transformation involves migrating to cloud-based solutions, adopting emerging technologies like artificial intelligence (AI) and machine learning (ML) , and integrating disparate systems for improved data management and analysis, which enables more informed decision making. Embracing new technologies helps organizations improve performance, cybersecurity and scalability and positioning themselves for long-term success.

• Enhanced performance
• Improved security
• Increased innovation

Reducing staff redundancy

In response to changing market dynamics and organizational needs, many companies turn to BPR to restructure their workforce and reduce redundancy. These strategic initiatives can involve streamlining organizational hierarchies, consolidating departments and outsourcing non-core functions. Optimizing workforce allocation and eliminating redundant roles allows organizations to reduce costs, enhance operational efficiency and focus resources on key priorities.

• Cost savings
• Increased efficiency
• Focus on core competencies

Cutting costs across operations

BPR is a powerful tool to systematically identify inefficiencies, redundancies and waste within business operations. This enables organizations to streamline processes and cut costs.

BPR focuses on redesigning processes to eliminate non-value-added activities, optimize resource allocation, and enhance operational efficiency. This might entail automating repetitive tasks, reorganizing workflows for minimizing bottlenecks, renegotiating contracts with suppliers to secure better terms, or by using technology to improve collaboration and communication. This can enable significant cost savings and improve profitability.

• Lower costs
• Enhanced competitiveness

Improving output quality

BPR can enhance the quality of output across various business processes, from manufacturing to service delivery. BPR initiatives generally boost key performance indicators (KPIs).

Steps for improving output quality involve implementing quality control measures, fostering a culture of continuous improvement, and using customer feedback and other metrics to drive innovation.

Technology can also be used to automate processes. When employees are freed from distracting processes, they can increase their focus on consistently delivering high-quality products and services. This builds customer trust and loyalty and supports the organization’s long-term success.

• Higher customer satisfaction
• Enhanced brand image

Human resource (HR) process optimization

BPR is crucial for optimizing human resources (HR) processes. Initiatives might include automating the onboarding process with easy-to-use portals, streamlining workflows, creating self-service portals and apps, using AI for talent acquisition , and implementing a data-driven approach to performance management.

Fostering employee engagement can also help attract, develop and retain top talent. Aligning HR processes with organizational goals and values can enhance workforce productivity, satisfaction and business performance.

• Faster recruitment cycles
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• Strategic talent allocation

The following case study examples demonstrate a mix of BPR methodologies and use cases working together to yield client benefits.

Bouygues becomes the AI standard bearer in French telecom

Bouygues Telecom , a leading French communications service provider, was plagued by legacy systems that struggled to keep up with an enormous volume of support calls. The result? Frustrated customers were left stranded in call lines and Bouygues at risk of being replaced by its competitors. Thankfully, Bouygues had partnered with IBM previously in one of our first pre- IBM watsonx™ AI deployments. This phase 1 engagement laid the groundwork perfectly for AI’s injection into the telecom’s call center during phase 2.

Today, Bouygues greets over 800,000 calls a month with IBM watsonx Assistant™, and IBM watsonx Orchestrate™ helps alleviate the repetitive tasks that agents previously had to handle manually, freeing them for higher-value work. In all, agents’ pre-and-post-call workloads were reduced by 30%. 1 In addition, 8 million customer-agent conversations—which were, in the past, only partially analyzed—have now been summarized with consistent accuracy for the creation of actionable insights.

Taken together, these technologies have made Bouygues a disruptor in the world of customer care, yielding a USD 5 million projected reduction in yearly operational costs and placing them at the forefront of AI technology. 1

Finance of America promotes lifetime loyalty via customer-centric transformation

By co-creating with IBM, mortgage lender Finance of America was able to recenter their operations around their customers, driving value for both them and the prospective home buyers they serve.

To accomplish this goal, FOA iterated quickly on both new strategies and features that would prioritize customer service and retention. From IBM-facilitated design thinking workshops came roadmaps for a consistent brand experience across channels, simplifying the work of their agents and streamlining the application process for their customers.

As a result of this transformation, FOA is projected to double their customer base in just three years. In the same time frame, they aim to increase revenue by over 50% and income by over 80%. Now, Finance of America is primed to deliver enhanced services—such as debt advisory—that will help promote lifetime customer loyalty. 2

Business process reengineering (BPR) with IBM takes a critical look at core processes to spot and redesign areas that need improvement. By stepping back, strategists can analyze areas like supply chain, customer experience and finance operations. BPR services experts can embed emerging technologies and overhaul existing processes to improve the business holistically. They can help you build new processes with intelligent workflows that drive profitability, weed out redundancies, and prioritize cost saving.

1. IBM Wow Story: Bouygues Becomes the AI Standard-Bearer in French Telecom. Last updated 10 November 2023.

2. IBM Wow Story: Finance of America Promotes Lifetime Loyalty via Customer-Centric Transformation. Last updated 23 February 2024.

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The state-of-the-art of mycobacterium chimaera infections and the causal link with health settings: a systematic review.

1. Introduction

2. materials and methods, 4. discussion, 4.1. mycobacterium chimaera’s characteristics and ecosystem, 4.2. heater-cooler units, medical devices, water, and air-conditioned implants, 4.3. incubation period and symptoms presentation, 4.4. presence in the lung system, 4.5. modality of transmission, 4.6. detection, 4.7. disinfection, 4.8. causal link assessment, 5. limitations, 6. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Bolcato, V.; Bassetti, M.; Basile, G.; Bianco Prevot, L.; Speziale, G.; Tremoli, E.; Maffessanti, F.; Tronconi, L.P. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare 2024 , 12 , 1788. https://doi.org/10.3390/healthcare12171788

Bolcato V, Bassetti M, Basile G, Bianco Prevot L, Speziale G, Tremoli E, Maffessanti F, Tronconi LP. The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review. Healthcare . 2024; 12(17):1788. https://doi.org/10.3390/healthcare12171788

Bolcato, Vittorio, Matteo Bassetti, Giuseppe Basile, Luca Bianco Prevot, Giuseppe Speziale, Elena Tremoli, Francesco Maffessanti, and Livio Pietro Tronconi. 2024. "The State-of-the-Art of Mycobacterium chimaera Infections and the Causal Link with Health Settings: A Systematic Review" Healthcare 12, no. 17: 1788. https://doi.org/10.3390/healthcare12171788

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