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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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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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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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A case study is an in-depth examination of a specific instance or phenomenon within a real-world context, often used in research to explore complex issues. This method allows researchers to gather detailed qualitative and quantitative data, providing valuable insights into specific subjects, such as individuals, groups, or organizations. Case studies are particularly useful for understanding unique circumstances and can help highlight challenges and ethical considerations related to the subject matter.

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• Learning - 14.4 Challenges and Ethical Considerations in Learning Research

5 Must Know Facts For Your Next Test

• Case studies can provide deep insights into the complexities of real-life situations and help inform practice and policy.
• They often involve multiple data sources, including interviews, observations, documents, and sometimes quantitative data.
• Researchers must be mindful of ethical considerations when conducting case studies, especially regarding informed consent and confidentiality of participants.
• Case studies can sometimes lead to generalizations or theories based on the findings, but they are primarily focused on a specific instance rather than broad population conclusions.
• This method is often used in various fields such as psychology, education, business, and health to investigate specific challenges or phenomena.

Review Questions

• Case studies differ from other research methods by focusing on a specific instance or phenomenon within its real-world context. They often utilize a variety of data collection methods, such as interviews, observations, and document analysis, to gather rich qualitative insights. This approach allows researchers to explore complex issues in depth, while other methods may focus on broader patterns or correlations across larger populations.
• Researchers must address several ethical considerations when conducting case studies, including obtaining informed consent from participants, ensuring their confidentiality, and being transparent about the study's purpose. It's crucial to respect the autonomy of participants and protect them from any potential harm or exploitation. These ethical guidelines help maintain the integrity of the research process and promote trust between researchers and participants.
• Case studies can be highly effective for understanding complex learning challenges in educational settings because they provide a detailed examination of specific instances that illuminate broader issues. By analyzing individual or group experiences within their context, researchers can uncover nuanced factors affecting learning outcomes. However, while case studies offer valuable insights, their findings may not always be generalizable due to their focus on particular situations. Therefore, combining case studies with other research methods could enhance the overall understanding of learning challenges.

Related terms

Qualitative Research : A research approach that focuses on understanding the meaning individuals or groups ascribe to social or human problems, often using interviews and observations.

Ethical Considerations : The principles and guidelines that govern the conduct of research, ensuring respect for participants, confidentiality, and integrity in data collection and reporting.

Mixed Methods : A research approach that combines both qualitative and quantitative techniques to provide a more comprehensive understanding of a research question.

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What is a Case Study?: Definition, Examples, & Methods

Published on July 9th, 2024

I. What is a Case Study?: Introduction

Case study definition.

A case study is a research method involving an in-depth examination of a single subject, group, event, or phenomenon within its real-world context. Widely used across various disciplines such as social sciences, business, law, medicine, and education, case studies provide comprehensive insights into complex issues that broader surveys or experimental research cannot capture. The essence of a case study is to explore and analyze real-life situations to uncover patterns, identify causes, and propose practical solutions. Case study examples illustrate how theoretical knowledge can be applied to practical scenarios, making them invaluable for both academic research and problem-solving.

Importance in Research and Business

Case studies are crucial in both research and business due to their ability to provide detailed and nuanced insights. In academic research, case studies enable in-depth analysis of complex issues, helping researchers understand the how and why of phenomena, and leading to the development of new theories or the refinement of existing ones. In business, case studies help understand market dynamics, consumer behavior, and the effectiveness of strategies. They showcase successes and failures, offering valuable lessons for future projects. In education, especially in business schools, case studies help students develop critical thinking and problem-solving skills by analyzing real-world scenarios.

Brief History of Case Study Methodology

The case study methodology has a rich history, originating in the early 20th century in sociology. The Chicago School of Sociology used case studies to explore urban life and social issues. This approach was later adopted in psychology by figures like Sigmund Freud, who used detailed case studies to develop his theories on psychoanalysis. By the mid-20th century, Harvard Business School had popularized case studies as a teaching tool, encouraging students to analyze real-world business scenarios. Over the years, standardized templates have been developed to ensure consistency in data collection and analysis.

In modern times, case studies have adapted to the digital age with advanced data analysis software and AI tools, which ensures the originality and accuracy of case study content. This evolution highlights the adaptability and enduring relevance of case studies as a powerful tool for gaining in-depth understanding and generating valuable insights across various fields.

II. Types of Case Studies

Let’s learn about the different types of case studies that help researchers choose the appropriate method to gain deep insights into their subject.

A. Explanatory Case Studies

Explanatory case studies are designed to explore cause-and-effect relationships. They aim to explain how and why certain events occur and what factors influence these outcomes. This type of case study is often used in social sciences, business, and policy research to test theories and develop new insights. For example, an explanatory case study might investigate how a specific business strategy led to increased market share or how a new policy affected public health outcomes. By focusing on detailed and comprehensive analysis, explanatory case studies help researchers define case study contexts and understand complex phenomena.

B. Exploratory Case Studies

Exploratory case studies are used to explore a topic or issue when there are no clear outcomes or established theories. They serve as a preliminary step that can help to identify research questions and hypotheses for further study. This type of case study is particularly useful when the subject matter is new or not well understood. For instance, researchers might conduct an exploratory case study to investigate the impact of emerging technologies on consumer behavior. Exploratory case studies are flexible and open-ended, allowing researchers to gather rich, qualitative data that can guide future research directions.

C. Descriptive Case Studies

Descriptive case studies provide a detailed account of a specific subject, event, or phenomenon. They aim to describe the context, characteristics, and outcomes without necessarily investigating causal relationships. This type of case study is useful for documenting and understanding the particulars of a situation. For example, a descriptive case study might provide an in-depth look at a company's organizational structure and culture. By offering a comprehensive overview, descriptive case studies help to illustrate and contextualize complex issues, making them easier to understand and analyze.

D. Multiple-Case Studies

Multiple-case studies, also known as comparative case studies, involve the analysis of several cases to understand similarities and differences. This type of case study allows researchers to compare and contrast different instances of a phenomenon, which can lead to more robust and generalizable findings. For example, a multiple-case study might examine several companies that have implemented similar business strategies to identify common factors that contribute to success. By analyzing multiple cases, researchers can draw broader conclusions and develop more nuanced insights.

E. Intrinsic Case Studies

Intrinsic case studies focus on a specific case because it is unique or interesting in its own right. The primary aim is to gain a deeper understanding of the case itself, rather than to generalize findings to other contexts. This type of case study is often used when the case has particular significance or offers unique insights. For example, an intrinsic case study might investigate a rare medical condition to understand its characteristics and implications. By delving deeply into the specifics of the case, intrinsic case studies provide valuable, detailed knowledge that can inform practice and theory.

F. Instrumental Case Studies

Instrumental case studies use a specific case to gain insights into a broader issue or to refine a theoretical concept. The case itself is of secondary interest; it serves as a means to understand something else. For example, a researcher might use an instrumental case study of a particular organization to explore general principles of organizational behavior. This type of case study is useful for developing and testing theories, as it allows researchers to apply and examine theoretical frameworks in real-world contexts.

G. Collective Case Studies

Collective case studies, also known as multiple-case studies, involve studying a group of cases simultaneously or sequentially. This approach aims to investigate a phenomenon, population, or general condition by analyzing multiple instances. Collective case studies are valuable for identifying patterns and trends across different cases. For instance, a collective case study might examine several educational programs across different schools to understand common factors that contribute to student success. By studying multiple cases, researchers can enhance the reliability and validity of their findings and develop a more comprehensive understanding of the topic.

Each type of case study offers unique advantages and serves different research purposes. Whether researchers aim to explain causal relationships, explore new topics, provide detailed descriptions, compare multiple instances, or gain insights into broader issues, case studies are versatile tools that can be tailored to fit various research needs. Using tools like case study templates and following a structured case study format can help ensure that the research is thorough and well-organized. By understanding the different types of case studies, researchers can choose the most appropriate method to achieve their objectives and generate meaningful insights.

Also read: Uncover the power of our recruitment automation through customer stories, read our customer stories .

III. The Structure of a Case Study

A well-structured case study is essential for effectively communicating the research findings and insights, ensuring clarity and comprehensiveness.

Title and Abstract : The title should be clear, concise, and reflective of the main focus of the case study. The abstract provides a summary, usually between 150-250 words, outlining the purpose, methodology, key findings, and conclusions of the study. This section helps readers quickly understand the essence of the case study.

B. Background Information

Background Information : This section sets the context for the case study by providing relevant information about the subject being studied. It includes details about the history, environment, and circumstances surrounding the case. For example, if the case study is about a business, the background information might cover the company’s history, industry context, and market conditions.

C. Introduction and Problem Statement

Introduction and Problem Statement : The introduction offers an overview of the case study’s purpose and scope. The problem statement clearly defines the specific issue or research question that the case study aims to address. This section explains why the problem is significant and warrants investigation. For example, a problem statement might highlight a decline in customer satisfaction at a company and the need to understand the underlying causes.

D. Methodology

Methodology : The methodology section details the research design and approach used to conduct the study. It includes the methods and procedures for data collection and analysis. This section should provide enough detail to allow replication of the study. Common methodologies include qualitative methods like interviews and observations, quantitative methods like surveys and statistical analysis, or a combination of both.

E. Data Collection and Analysis

Data Collection and Analysis : This section describes the specific techniques used to gather data and the process of analyzing it. It includes information on data sources, sampling methods, and data collection instruments. The analysis part explains how the data was processed and interpreted to arrive at the findings. For example, in a business case study, data collection might involve employee interviews and customer surveys, while analysis might involve thematic coding and statistical correlation.

F. Findings and Analysis

Findings and Analysis : The findings section presents the results of the study, detailing what the data revealed about the problem. The analysis interprets these findings, explaining their significance and implications. This section should be organized logically, often using headings and subheadings to guide the reader through different aspects of the findings. For instance, findings might show a correlation between employee training and customer satisfaction, with the analysis explaining how training improves service quality.

G. Proposed Solutions and Recommendations

Proposed Solutions and Recommendations : Based on the findings, this section suggests practical actions or strategies to address the identified problems. It outlines specific steps that stakeholders can take to implement these solutions. Recommendations should be feasible, backed by the data, and aligned with the study’s goals. For example, recommendations might include implementing a new training program for employees or adopting new customer service policies.

H. Conclusion

Conclusion : The conclusion summarizes the main findings and their implications. It reinforces the significance of the study and may suggest areas for further research. This section ties together the entire case study, providing a final perspective on the problem and the proposed solutions. The conclusion should leave the reader with a clear understanding of what was learned and why it matters.

I. References and Appendices

References and Appendices : The references section lists all the sources cited in the case study, following a standard citation format (e.g., APA, MLA). This ensures proper attribution and allows readers to locate the original sources. The appendices include supplementary materials that support the case study, such as raw data, detailed tables, questionnaires, or interview transcripts. These materials provide additional context and evidence for the study’s findings and conclusions.

By adhering to this comprehensive structure, researchers can ensure their case studies are thorough, and well-organized, and effectively communicate their findings and insights to the audience.

IV. The Case Study Process

The process of conducting a case study involves several systematic steps that ensure thorough and credible research.

A. Identifying the Research Question

The first step in the case study process is to define a clear and focused research question. This question should address a specific issue or problem that the case study aims to explore. The research question guides the entire study, helping to determine the scope and objectives. For instance, a business case study might pose the question, "How does employee training impact customer satisfaction in retail settings?"

B. Selecting the Case and Determining Data-Gathering Techniques

Once the research question is established, the next step is to select a case that provides the best opportunity to explore this question. The case can be an individual, group, organization, event, or phenomenon. The selection should be purposeful and based on specific criteria relevant to the research question. Additionally, researchers must determine the most appropriate data-gathering techniques, such as interviews, surveys, observations, or document analysis, to collect the necessary information.

C. Preparing to Collect Data

Before data collection begins, researchers must develop a detailed plan outlining the procedures and tools to be used. This preparation includes creating data collection instruments (e.g., interview guides, and survey questionnaires), obtaining necessary permissions and ethical approvals, and ensuring logistical arrangements are in place. Proper preparation ensures that data collection is systematic and consistent, minimizing potential biases and errors.

D. Collecting Data in the Field

Data collection involves gathering information directly from the selected case using predetermined techniques. This phase requires careful attention to detail and adherence to the planned methods. For example, conducting interviews requires skilled questioning and active listening, while observations necessitate systematic note-taking. Ensuring data quality and integrity is crucial during this phase to maintain the credibility of the study.

E. Evaluating and Analyzing the Data

After data collection, researchers must evaluate and analyze the gathered information to draw meaningful conclusions. This process involves organizing the data, coding for themes and patterns, and using analytical techniques to interpret the findings. Qualitative data might be analyzed through thematic analysis, while quantitative data could be subjected to statistical analysis. The goal is to identify key insights that address the research question and provide a deeper understanding of the case.

F. Reporting the Findings

The final step in the case study process is to compile the findings into a comprehensive report. This report should follow a structured format, including sections such as the introduction, methodology, findings, analysis, proposed solutions, and conclusion. The report should clearly communicate the research question, the process followed, the data collected, and the insights gained. Visual aids like charts, graphs, and tables can enhance the presentation of data. Additionally, the report should provide actionable recommendations based on the findings, and it should be tailored to the intended audience, whether academic, professional, or general readers.

By following these steps, researchers can ensure a rigorous and systematic approach to conducting case studies, resulting in credible and valuable insights that contribute to knowledge and practice in their respective fields.

V. Benefits of Case Studies

Case studies offer numerous benefits that make them a valuable research method in various fields.

A. In-depth Analysis of Complex Issues

In-depth Analysis of Complex Issues : Case studies allow researchers to conduct a thorough and detailed examination of complex issues. This method provides a deep understanding of the subject matter by exploring multiple facets and perspectives. For instance, a case study on a company’s turnaround strategy can delve into the financial, operational, and cultural changes that contributed to its success. This in-depth analysis is often impossible to achieve through other research methods that provide more generalized data.

B. Real-world Application of Theories

Real-world Application of Theories : Case studies bridge the gap between theory and practice by applying theoretical concepts to real-world scenarios. They demonstrate how abstract theories can be implemented and tested in practical situations. For example, a case study on leadership styles in crisis management can show how different theoretical approaches to leadership are applied in real-life crises, providing valuable insights for both academics and practitioners.

C. Generation of New Hypotheses

Generation of New Hypotheses : Through detailed investigation and observation, case studies often reveal new insights and patterns that can lead to the generation of new hypotheses. These hypotheses can then be tested in future research, contributing to the advancement of knowledge in the field. For example, a case study on consumer behavior might uncover new trends or factors influencing purchasing decisions, prompting further research into these areas.

D. Versatility Across Various Fields

Versatility Across Various Fields : Case studies are a versatile research method that can be applied in various fields, including business, education, medicine, law, and social sciences. They can be used to study a wide range of topics, from individual behaviors to organizational practices and societal phenomena. This versatility makes case studies a popular choice for researchers seeking to understand diverse and complex issues.

VI. Challenges in Conducting Case Studies

Despite their benefits, conducting case studies also presents several challenges that researchers need to be aware of and address.

A. Potential for Researcher Bias

Potential for Researcher Bias : One of the primary challenges of case studies is the potential for researcher bias. Since case studies often involve close interaction between the researcher and the subject, there is a risk that the researcher’s perspectives and preconceptions may influence the findings. To mitigate this, researchers must strive for objectivity, use multiple sources of evidence, and employ techniques like triangulation to validate their findings.

B. Limited Generalizability

Limited Generalizability : Case studies typically focus on a single case or a small number of cases, which can limit the generalizability of the findings. The insights gained from a specific case may not necessarily apply to other contexts or populations. To address this limitation, researchers should clearly define the scope of their study and acknowledge the extent to which their findings can be generalized.

C. Time-consuming Nature

Time-consuming Nature : Conducting a thorough case study can be time-consuming, requiring extensive data collection, analysis, and reporting. This can be a significant drawback, especially for researchers with limited time and resources. To manage this challenge, researchers should plan their study carefully, set realistic timelines, and ensure they have the necessary resources to complete the study effectively.

D. Ethical Considerations

Ethical Considerations : Case studies often involve collecting detailed information about individuals or organizations, which raises important ethical considerations. Researchers must ensure that they obtain informed consent from participants, protect their privacy and confidentiality, and avoid any potential harm. Adhering to ethical guidelines and obtaining necessary approvals from ethics committees are crucial steps in conducting ethical case study research.

By understanding and addressing these challenges, researchers can enhance the reliability and credibility of their case studies, ensuring that their findings provide valuable contributions to their respective fields.

VII. Case Studies in Different Fields

Case studies are a versatile research method that can be applied across a wide range of fields, each benefiting from the in-depth analysis and practical insights they provide.

Business and Management : In the field of business and management, case studies are widely used to analyze organizational strategies, market dynamics, leadership practices, and operational processes. They offer detailed insights into how companies address challenges, implement changes, and achieve success. 

For example, a business case study might explore how a company successfully navigated a financial crisis, providing lessons on crisis management, financial planning, and leadership. These case studies are valuable for both academic purposes and practical applications, helping managers and executives learn from real-world examples.

Psychology and Social Sciences : Case studies in psychology and social sciences provide an in-depth examination of individual or group behavior, social phenomena, and cultural practices. They are particularly useful for exploring complex psychological conditions, social interactions, and cultural contexts. 

For instance, a psychological case study might investigate the development and treatment of a specific mental health disorder in a patient, offering insights into therapeutic approaches and patient experiences. In social sciences, case studies can explore social issues such as poverty, education, and community development, contributing to a deeper understanding of societal challenges and potential solutions.

Medicine and Healthcare : In medicine and healthcare, case studies are essential for understanding unique medical conditions, treatment outcomes, and healthcare practices. They provide detailed accounts of patient histories, diagnoses, treatments, and responses, contributing to medical knowledge and practice. 

For example, a medical case study might document a rare disease, detailing the symptoms, diagnostic process, treatment plan, and patient recovery. These studies are valuable for medical education, helping practitioners learn from specific cases and improve patient care. They also play a crucial role in advancing medical research by highlighting unusual cases that can lead to new discoveries.

Law and Criminal Justice : Case studies in law and criminal justice offer comprehensive analyses of legal cases, criminal behavior, law enforcement practices, and judicial decisions. They help understand the intricacies of legal principles, the application of laws, and the effectiveness of criminal justice policies. 

For instance, a legal case study might analyze a landmark Supreme Court decision, examining the legal arguments, judicial reasoning, and broader implications for society. In criminal justice, case studies can explore crime patterns, investigative techniques, and rehabilitation programs, providing valuable insights for law enforcement and policy-making.

Education : In the field of education, case studies are used to explore teaching methods, learning outcomes, educational policies, and institutional practices. They provide detailed examinations of specific educational settings, programs, and student experiences. 

For example, an educational case study might investigate the implementation of a new teaching strategy in a classroom, analyzing its impact on student engagement and academic performance. These studies are valuable for educators, administrators, and policymakers, offering practical insights into effective educational practices and innovations. Case studies in education help identify best practices, address challenges, and improve the overall quality of education.

VIII. Tools and Techniques for Case Study Research

The effectiveness of case study research often hinges on the tools and techniques used for data collection and analysis. Here are some key methods and tools that enhance the quality and depth of case study research.

Interviews and Surveys : 

Interviews and surveys are fundamental techniques for gathering qualitative and quantitative data in case studies. Interviews allow for in-depth exploration of subjects' experiences, perspectives, and insights. They can be structured, semi-structured, or unstructured, depending on the research goals. Surveys, on the other hand, provide a means to collect data from a larger sample, offering quantifiable insights that can complement qualitative findings. For example, in a business case study, interviews with key stakeholders can reveal detailed insights into organizational culture, while surveys can gauge employee satisfaction across the company.

Observation Methods : 

Observation involves systematically recording behaviors, events, and interactions as they occur naturally. This method is particularly useful for understanding the context and dynamics of the case under study. Participant observation, where the researcher becomes part of the group being studied, and non-participant observation, where the researcher observes from a distance, are common techniques. For instance, in an educational case study, observing classroom interactions can provide valuable data on teaching methods and student engagement.

Document Analysis : 

Document analysis involves reviewing and interpreting existing documents related to the case. These documents can include reports, memos, letters, emails, meeting minutes, policy documents, and other records. Analyzing these documents can provide insights into the historical context, organizational processes, and key events relevant to the case. For example, in a legal case study, analyzing court documents, legal briefs, and case law can help understand the legal arguments and judicial decisions.

Data Analysis Software : 

Data analysis software helps researchers organize, code, and analyze qualitative and quantitative data efficiently. Tools like NVivo, ATLAS.ti, and MAXQDA are commonly used for qualitative data analysis, enabling researchers to code text, identify themes, and visualize relationships. For quantitative data, software like SPSS, Stata, and R can perform statistical analysis, providing detailed insights into data patterns and correlations. These tools enhance the rigor and reliability of the analysis, making it easier to manage large volumes of data and derive meaningful conclusions.

AI Tools like HireQuotient's AI Detector :

HireQuotient's AI Detector is an advanced tool designed to ensure the originality and integrity of written content. It uses artificial intelligence to detect plagiarism, analyze text for unique patterns, and verify the authenticity of research material.

How It Can Be Used in Case Study Research : In case study research, HireQuotient's AI Detector can be used to check the originality of the case study report, ensuring that the content is free from plagiarism. This tool can also help in verifying the authenticity of sources and data used in the case study, providing an additional layer of validation. By analyzing text for unique patterns, the AI Detector can assist researchers in maintaining the quality and credibility of their work.

Benefits of Using AI in Case Study Analysis : Using AI tools like HireQuotient's AI Detector in case study analysis offers several benefits. First, it enhances the credibility and reliability of the research by ensuring that all content is original and properly cited. Second, it saves time and effort in manually checking for plagiarism and verifying sources, allowing researchers to focus on more critical aspects of the study. Third, AI tools can process large volumes of data quickly and accurately, identifying patterns and insights that might be missed through manual analysis. Overall, integrating AI into case study research improves the efficiency, accuracy, and integrity of the research process.

By leveraging these tools and techniques, researchers can conduct comprehensive and rigorous case studies that provide valuable insights and contribute to the advancement of knowledge in their respective fields.

IX. Writing and Presenting Case Studies

Effectively writing and presenting case studies is crucial for conveying research findings in a clear and impactful manner. Here are key considerations for each aspect of this process.

A. Choosing a Compelling Narrative Style

The narrative style chosen for a case study can significantly influence its readability and engagement. A compelling narrative weaves facts and analysis into a cohesive story that captures the reader’s attention. Depending on the audience and purpose, the narrative style can be:

• Descriptive : Providing a detailed account of events and contexts, often used for educational purposes.
• Analytical : Focusing on the interpretation and implications of the findings, suitable for academic and research audiences.
• Persuasive : Aiming to convince the reader of a particular viewpoint or course of action, commonly used in business and policy-making contexts.
• Reflective : Incorporating personal insights and reflections, which can be effective in educational and professional development settings.

Selecting a narrative style that aligns with the objectives of the case study and the preferences of the target audience helps ensure that the message is conveyed effectively.

B. Structuring the Case Study Report

A well-structured case study report enhances clarity and coherence, making it easier for readers to follow the research process and understand the findings. A typical structure includes:

• Title and Abstract : Concise summary of the study’s focus and key findings.
• Introduction : Overview of the research question, objectives, and significance of the study.
• Background Information : Contextual information about the subject or case being studied.
• Problem Statement : Clear definition of the problem or issue addressed by the study.
• Methodology : Detailed description of the research methods and procedures used for data collection and analysis.
• Findings and Analysis : Presentation and interpretation of the research results.
• Proposed Solutions and Recommendations : Practical suggestions based on the findings.
• Conclusion : Summary of the main insights and their implications.
• References and Appendices : List of sources cited and supplementary materials.

Using headings and subheadings to organize these sections helps guide the reader through the report and ensures all key components are covered.

C. Using Visuals and Data Representation

Visual aids such as charts, graphs, tables, and diagrams can significantly enhance the presentation of data and findings in a case study. Effective use of visuals can:

• Clarify Complex Information : Simplifying complex data and relationships.
• Highlight Key Points : Drawing attention to important findings and trends.
• Enhance Engagement : Making the report more visually appealing and easier to digest.

When using visuals, it’s important to ensure they are clearly labeled, accurately represent the data, and are integrated seamlessly into the narrative. Visuals should complement and reinforce the textual content rather than distract from it.

D. Tailoring the Presentation to the Audience

The presentation of a case study should be tailored to the specific needs and preferences of the intended audience. Consider the following:

• Academic Audience : Focus on methodological rigor, theoretical contributions, and detailed analysis. Use formal language and provide extensive references.
• Business Audience : Emphasize practical implications, actionable recommendations, and real-world applications. Use clear, concise language and highlight key insights and solutions.
• General Audience : Make the content accessible and engaging by using simple language, storytelling techniques, and relatable examples. Avoid jargon and technical terms that may be unfamiliar.

By paying careful attention to narrative style, report structure, use of visuals, and audience tailoring, researchers can create compelling and impactful case studies that effectively convey their findings and insights.

X. Case Studies vs. Other Research Methods

Experimental research involves manipulating one or more variables to observe the effect on another variable, typically in a controlled environment. This method is highly effective for establishing cause-and-effect relationships and testing hypotheses. In contrast, case studies focus on the in-depth exploration of a single subject or small group within its real-life context. While experiments prioritize control and generalizability, case studies emphasize detailed understanding and contextual relevance. Case studies are particularly valuable when the research question requires exploring complex phenomena that cannot be easily isolated in an experimental setting.

Surveys and questionnaires are quantitative research methods designed to gather data from a large population, often through structured questions with predefined response options. These methods are useful for identifying trends, measuring attitudes, and making statistical generalizations. In contrast, case studies employ qualitative methods such as interviews and observations to provide rich, detailed insights into a specific case. While surveys and questionnaires offer breadth, case studies provide depth, allowing researchers to uncover nuanced information and develop a comprehensive understanding of the subject.

Ethnographic studies involve immersive, long-term fieldwork where researchers observe and interact with participants in their natural environment to understand cultural practices and social behaviors. Both ethnographic studies and case studies prioritize in-depth, qualitative analysis and contextual understanding. However, ethnography typically focuses on entire communities or cultures, while case studies concentrate on specific individuals, groups, or events. Case studies may use ethnographic techniques but are usually narrower in scope and duration.

Mixed methods research combines qualitative and quantitative approaches to provide a more comprehensive understanding of the research problem. Case studies can be an integral part of mixed methods research by incorporating both qualitative data (e.g., interviews, observations) and quantitative data (e.g., surveys, statistical analysis). This integration allows researchers to explore the case in detail while also quantifying certain aspects, enhancing the robustness and validity of the findings. Mixed methods research benefits from the detailed insights of case studies and the generalizability of quantitative data.

XI. The Future of Case Study Research

Technological Advancements in Data Collection and Analysis : Advances in technology are revolutionizing the way data is collected and analyzed in case study research. Tools such as mobile apps, online surveys, and digital recording devices facilitate efficient and accurate data collection. Data analysis software like NVivo and ATLAS.ti enables researchers to organize, code, and interpret large volumes of qualitative data. Additionally, big data analytics and machine learning algorithms offer new possibilities for identifying patterns and insights from complex datasets, enhancing the depth and precision of case study analysis.

Increasing Focus on Cross-Cultural Case Studies : Globalization and interconnectedness have heightened the importance of understanding cultural differences and similarities. Cross-cultural case studies are gaining prominence as researchers seek to compare and contrast cases from different cultural contexts. These studies provide valuable insights into how cultural factors influence behaviors, practices, and outcomes. By examining cases from diverse settings, researchers can develop more comprehensive and culturally sensitive theories and solutions.

The Role of AI and Machine Learning in Case Study Research : AI and machine learning are transforming case study research by automating data analysis and enhancing accuracy. Tools like HireQuotient's AI Detector help ensure the originality and integrity of case study content by detecting plagiarism and verifying sources. AI algorithms can analyze large datasets quickly, identifying patterns and correlations that may be overlooked by human researchers. These technologies enable more efficient data processing, allowing researchers to focus on interpreting and applying the findings.

Emerging Trends in Case Study Methodology : New trends in case study methodology are shaping the future of research. One trend is the increasing use of digital ethnography, where researchers study online communities and virtual environments. Another trend is the emphasis on participatory case studies, involving stakeholders in the research process to ensure their perspectives are represented. Additionally, there is a growing interest in longitudinal case studies that track changes over time, providing deeper insights into dynamic processes and long-term outcomes.

XII. Conclusion

Case studies are a versatile and valuable research method that offers in-depth analysis, real-world applications, and the ability to generate new hypotheses. They differ from other research methods in their focus on detailed, contextual understanding.

Thus, undertake your own case studies, leveraging the tools and techniques discussed to explore complex issues and contribute to their fields. With advancements in technology and methodology, conducting case studies is more accessible and impactful than ever. Whether for academic research, business analysis, or personal interest, case studies offer a powerful means to gain deep, actionable insights.

Soujanya Varada

As a technical content writer and social media strategist, Soujanya develops and manages strategies at HireQuotient. With strong technical background and years of experience in content management, she looks for opportunities to flourish in the digital space. Soujanya is also a dance fanatic and believes in spreading light!

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

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What is a Case Study?

A case study is defined as an in-depth analysis of a particular subject, often a real-world situation, individual, group, or organization. 

It is a research method that involves the comprehensive examination of a specific instance to gain a better understanding of its complexities, dynamics, and context. 

Case studies are commonly used in various fields such as business, psychology, medicine, and education to explore and illustrate phenomena, theories, or practical applications.

In a typical case study, researchers collect and analyze a rich array of qualitative and/or quantitative data, including interviews, observations, documents, and other relevant sources. The goal is to provide a nuanced and holistic perspective on the subject under investigation.

The information gathered here is used to generate insights, draw conclusions, and often to inform broader theories or practices within the respective field.

Case studies offer a valuable method for researchers to explore real-world phenomena in their natural settings, providing an opportunity to delve deeply into the intricacies of a particular case. They are particularly useful when studying complex, multifaceted situations where various factors interact. 

Additionally, case studies can be instrumental in generating hypotheses, testing theories, and offering practical insights that can be applied to similar situations. Overall, the comprehensive nature of case studies makes them a powerful tool for gaining a thorough understanding of specific instances within the broader context of academic and professional inquiry.

Key Characteristics of Case Study

Case studies are characterized by several key features that distinguish them from other research methods. Here are some essential characteristics of case studies:

• In-depth Exploration: Case studies involve a thorough and detailed examination of a specific case or instance. Researchers aim to explore the complexities and nuances of the subject under investigation, often using multiple data sources and methods to gather comprehensive information.
• Contextual Analysis: Case studies emphasize the importance of understanding the context in which the case unfolds. Researchers seek to examine the unique circumstances, background, and environmental factors that contribute to the dynamics of the case. Contextual analysis is crucial for drawing meaningful conclusions and generalizing findings to similar situations.
• Holistic Perspective: Rather than focusing on isolated variables, case studies take a holistic approach to studying a phenomenon. Researchers consider a wide range of factors and their interrelationships, aiming to capture the richness and complexity of the case. This holistic perspective helps in providing a more complete understanding of the subject.
• Qualitative and/or Quantitative Data: Case studies can incorporate both qualitative and quantitative data, depending on the research question and objectives. Qualitative data often include interviews, observations, and document analysis, while quantitative data may involve statistical measures or numerical information. The combination of these data types enhances the depth and validity of the study.
• Longitudinal or Retrospective Design: Case studies can be designed as longitudinal studies, where the researcher follows the case over an extended period, or retrospective studies, where the focus is on examining past events. This temporal dimension allows researchers to capture changes and developments within the case.
• Unique and Unpredictable Nature: Each case study is unique, and the findings may not be easily generalized to other situations. The unpredictable nature of real-world cases adds a layer of authenticity to the study, making it an effective method for exploring complex and dynamic phenomena.
• Theory Building or Testing: Case studies can serve different purposes, including theory building or theory testing. In some cases, researchers use case studies to develop new theories or refine existing ones. In others, they may test existing theories by applying them to real-world situations and assessing their explanatory power.

Understanding these key characteristics is essential for researchers and practitioners using case studies as a methodological approach, as it helps guide the design, implementation, and analysis of the study.

Key Components of a Case Study

A well-constructed case study typically consists of several key components that collectively provide a comprehensive understanding of the subject under investigation. Here are the key components of a case study:

• Provide an overview of the context and background information relevant to the case. This may include the history, industry, or setting in which the case is situated.
• Clearly state the purpose and objectives of the case study. Define what the study aims to achieve and the questions it seeks to answer.
• Clearly identify the subject of the case study. This could be an individual, a group, an organization, or a specific event.
• Define the boundaries and scope of the case study. Specify what aspects will be included and excluded from the investigation.
• Provide a brief review of relevant theories or concepts that will guide the analysis. This helps place the case study within the broader theoretical context.
• Summarize existing literature related to the subject, highlighting key findings and gaps in knowledge. This establishes the context for the current case study.
• Describe the research design chosen for the case study (e.g., exploratory, explanatory, descriptive). Justify why this design is appropriate for the research objectives.
• Specify the methods used to gather data, whether through interviews, observations, document analysis, surveys, or a combination of these. Detail the procedures followed to ensure data validity and reliability.
• Explain the criteria for selecting the case and any sampling considerations. Discuss why the chosen case is representative or relevant to the research questions.
• Describe how the collected data will be coded and categorized. Discuss the analytical framework or approach used to identify patterns, themes, or trends.
• If multiple data sources or methods are used, explain how they complement each other to enhance the credibility and validity of the findings.
• Present the key findings in a clear and organized manner. Use tables, charts, or quotes from participants to illustrate the results.
• Interpret the results in the context of the research objectives and theoretical framework. Discuss any unexpected findings and their implications.
• Provide a thorough interpretation of the results, connecting them to the research questions and relevant literature.
• Acknowledge the limitations of the study, such as constraints in data collection, sample size, or generalizability.
• Highlight the contributions of the case study to the existing body of knowledge and identify potential avenues for future research.
• Summarize the key findings and their significance in relation to the research objectives.
• Conclude with a concise summary of the case study, its implications, and potential practical applications.
• Provide a complete list of all the sources cited in the case study, following a consistent citation style.
• Include any additional materials or supplementary information, such as interview transcripts, survey instruments, or supporting documents.

By including these key components, a case study becomes a comprehensive and well-rounded exploration of a specific subject, offering valuable insights and contributing to the body of knowledge in the respective field.

Sampling in a Case Study Research

Sampling in case study research involves selecting a subset of cases or individuals from a larger population to study in depth. Unlike quantitative research where random sampling is often employed, case study sampling is typically purposeful and driven by the specific objectives of the study. Here are some key considerations for sampling in case study research:

• Criterion Sampling: Cases are selected based on specific criteria relevant to the research questions. For example, if studying successful business strategies, cases may be selected based on their demonstrated success.
• Maximum Variation Sampling: Cases are chosen to represent a broad range of variations related to key characteristics. This approach helps capture diversity within the sample.
• Selecting Cases with Rich Information: Researchers aim to choose cases that are information-rich and provide insights into the phenomenon under investigation. These cases should offer a depth of detail and variation relevant to the research objectives.
• Single Case vs. Multiple Cases: Decide whether the study will focus on a single case (single-case study) or multiple cases (multiple-case study). The choice depends on the research objectives, the complexity of the phenomenon, and the depth of understanding required.
• Emergent Nature of Sampling: In some case studies, the sampling strategy may evolve as the study progresses. This is known as theoretical sampling, where new cases are selected based on emerging findings and theoretical insights from earlier analysis.
• Data Saturation: Sampling may continue until data saturation is achieved, meaning that collecting additional cases or data does not yield new insights or information. Saturation indicates that the researcher has adequately explored the phenomenon.
• Defining Case Boundaries: Clearly define the boundaries of the case to ensure consistency and avoid ambiguity. Consider what is included and excluded from the case study, and justify these decisions.
• Practical Considerations: Assess the feasibility of accessing the selected cases. Consider factors such as availability, willingness to participate, and the practicality of data collection methods.
• Informed Consent: Obtain informed consent from participants, ensuring that they understand the purpose of the study and the ways in which their information will be used. Protect the confidentiality and anonymity of participants as needed.
• Pilot Testing the Sampling Strategy: Before conducting the full study, consider pilot testing the sampling strategy to identify potential challenges and refine the approach. This can help ensure the effectiveness of the sampling method.
• Transparent Reporting: Clearly document the sampling process in the research methodology section. Provide a rationale for the chosen sampling strategy and discuss any adjustments made during the study.

Sampling in case study research is a critical step that influences the depth and richness of the study’s findings. By carefully selecting cases based on specific criteria and considering the unique characteristics of the phenomenon under investigation, researchers can enhance the relevance and validity of their case study.

Case Study Research Methods With Examples

• Interviews:
• Interviews involve engaging with participants to gather detailed information, opinions, and insights. In a case study, interviews are often semi-structured, allowing flexibility in questioning.
• Example: A case study on workplace culture might involve conducting interviews with employees at different levels to understand their perceptions, experiences, and attitudes.
• Observations:
• Observations entail direct examination and recording of behavior, activities, or events in their natural setting. This method is valuable for understanding behaviors in context.
• Example: A case study investigating customer interactions at a retail store may involve observing and documenting customer behavior, staff interactions, and overall dynamics.
• Document Analysis:
• Document analysis involves reviewing and interpreting written or recorded materials, such as reports, memos, emails, and other relevant documents.
• Example: In a case study on organizational change, researchers may analyze internal documents, such as communication memos or strategic plans, to trace the evolution of the change process.
• Surveys and Questionnaires:
• Surveys and questionnaires collect structured data from a sample of participants. While less common in case studies, they can be used to supplement other methods.
• Example: A case study on the impact of a health intervention might include a survey to gather quantitative data on participants’ health outcomes.
• Focus Groups:
• Focus groups involve a facilitated discussion among a group of participants to explore their perceptions, attitudes, and experiences.
• Example: In a case study on community development, a focus group might be conducted with residents to discuss their views on recent initiatives and their impact.
• Archival Research:
• Archival research involves examining existing records, historical documents, or artifacts to gain insights into a particular phenomenon.
• Example: A case study on the history of a landmark building may involve archival research, exploring construction records, historical photos, and maintenance logs.
• Longitudinal Studies:
• Longitudinal studies involve the collection of data over an extended period to observe changes and developments.
• Example: A case study tracking the career progression of employees in a company may involve longitudinal interviews and document analysis over several years.
• Cross-Case Analysis:
• Cross-case analysis compares and contrasts multiple cases to identify patterns, similarities, and differences.
• Example: A comparative case study of different educational institutions may involve analyzing common challenges and successful strategies across various cases.
• Ethnography:
• Ethnography involves immersive, in-depth exploration within a cultural or social setting to understand the behaviors and perspectives of participants.
• Example: A case study using ethnographic methods might involve spending an extended period within a community to understand its social dynamics and cultural practices.
• Experimental Designs (Rare):
• While less common, experimental designs involve manipulating variables to observe their effects. In case studies, this might be applied in specific contexts.
• Example: A case study exploring the impact of a new teaching method might involve implementing the method in one classroom while comparing it to a traditional method in another.

These case study research methods offer a versatile toolkit for researchers to investigate and gain insights into complex phenomena across various disciplines. The choice of methods depends on the research questions, the nature of the case, and the desired depth of understanding.

Best Practices for a Case Study in 2024

Creating a high-quality case study involves adhering to best practices that ensure rigor, relevance, and credibility. Here are some key best practices for conducting and presenting a case study:

• Clearly articulate the purpose and objectives of the case study. Define the research questions or problems you aim to address, ensuring a focused and purposeful approach.
• Choose a case that aligns with the research objectives and provides the depth and richness needed for the study. Consider the uniqueness of the case and its relevance to the research questions.
• Develop a robust research design that aligns with the nature of the case study (single-case or multiple-case) and integrates appropriate research methods. Ensure the chosen design is suitable for exploring the complexities of the phenomenon.
• Use a variety of data sources to enhance the validity and reliability of the study. Combine methods such as interviews, observations, document analysis, and surveys to provide a comprehensive understanding of the case.
• Clearly document and describe the procedures for data collection to enhance transparency. Include details on participant selection, sampling strategy, and data collection methods to facilitate replication and evaluation.
• Implement measures to ensure the validity and reliability of the data. Triangulate information from different sources to cross-verify findings and strengthen the credibility of the study.
• Clearly define the boundaries of the case to avoid scope creep and maintain focus. Specify what is included and excluded from the study, providing a clear framework for analysis.
• Include perspectives from various stakeholders within the case to capture a holistic view. This might involve interviewing individuals at different organizational levels, customers, or community members, depending on the context.
• Adhere to ethical principles in research, including obtaining informed consent from participants, ensuring confidentiality, and addressing any potential conflicts of interest.
• Conduct a rigorous analysis of the data, using appropriate analytical techniques. Interpret the findings in the context of the research questions, theoretical framework, and relevant literature.
• Offer detailed and rich descriptions of the case, including the context, key events, and participant perspectives. This helps readers understand the intricacies of the case and supports the generalization of findings.
• Communicate findings in a clear and accessible manner. Avoid jargon and technical language that may hinder understanding. Use visuals, such as charts or graphs, to enhance clarity.
• Seek feedback from colleagues or experts in the field through peer review. This helps ensure the rigor and credibility of the case study and provides valuable insights for improvement.
• Connect the case study findings to existing theories or concepts, contributing to the theoretical understanding of the phenomenon. Discuss practical implications and potential applications in relevant contexts.
• Recognize that case study research is often an iterative process. Be open to revisiting and refining research questions, methods, or analysis as the study progresses. Practice reflexivity by acknowledging and addressing potential biases or preconceptions.

By incorporating these best practices, researchers can enhance the quality and impact of their case studies, making valuable contributions to the academic and practical understanding of complex phenomena.

Interested in learning more about the fields of product, research, and design? Search our articles here for helpful information spanning a wide range of topics!

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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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The case study creation process

Types of case studies, benefits and limitations.

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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?

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."

The case study as a type of qualitative research

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What Is a Case Study?

March 11, 2024 |, contributors.

Case studies give marketers a deep dive into a specific problem, process, or achievement within a company. They offer detailed insights into real-life business challenges and triumphs. Case studies provide a narrative that's informative and engaging, allowing readers to glean practical knowledge from real-world scenarios.

Case studies stand out for their ability to present complex data in a digestible format. They turn abstract numbers and statistics into relatable stories, making them an invaluable resource in the arsenal of any marketer. By showcasing specific examples of strategies that worked (or didn’t), case studies provide a unique learning opportunity that can be applied to similar situations in different organizations.

Benefits and Limitations of Case Studies

The power of detailed insight.

One of the most significant benefits of case studies is their capacity to provide detailed insights. Unlike broader market research, case studies delve into the specifics of a single situation, offering an in-depth understanding of the dynamics at play. This level of detail can be incredibly useful for businesses looking to understand the nuances of a particular strategy or problem. It allows for a more nuanced approach to problem-solving, as the specificity of a case study often highlights unique variables and outcomes that broader analyses may overlook.

Real-World Application

Case studies are grounded in real-world scenarios, making their lessons and findings directly applicable to similar situations in other organizations. By examining how a particular strategy or decision played out in a real context, businesses can better anticipate potential outcomes in their own situations.

Limitations: Generalizability and Bias

However, the very strength of case studies – their detailed focus on a single instance – is also their limitation. The findings of a case study might not apply to all situations. Since case studies often focus on unique cases, there’s a risk of drawing conclusions that don’t apply broadly.

Another limitation is the potential for bias. Since case studies are often retrospective and rely on the interpretation of events, there’s a risk of subjective bias in both the collection and interpretation of data. This can skew the insights and lessons derived from the study.

Despite these limitations, case studies are a powerful tool in the marketer’s toolkit, offering a unique blend of storytelling and empirical investigation.

Types of Case Studies

Explanatory case studies.

Explanatory case studies are often used in fields like social sciences to explain how or why certain events occurred. In a business context, explanatory case studies can be instrumental in unraveling the intricacies of business processes or market dynamics. They typically involve a detailed analysis of a situation or series of events to understand the underlying causes and effects. An explanatory case study's strength is its ability to provide clear, logical insights into complex scenarios.

Exploratory Case Studies

Exploratory case studies are typically conducted before a more in-depth investigation. Their primary purpose is to identify the key variables and potential relationships in a situation, setting the stage for more detailed analysis later. In marketing, exploratory case studies can help identify the factors contributing to a product's success or failure in the market. They are particularly useful in the early stages of research, where the goal is to gather as much information as possible to formulate more precise questions for further study.

Collective Case Studies

Collective case studies involve studying a group of cases simultaneously or sequentially. This approach is beneficial when the objective is to understand a phenomenon, population, or general condition more comprehensively. By examining multiple cases, marketers and business leaders can compare and contrast different scenarios, leading to a more robust and well-rounded understanding of the subject. Collective case studies are particularly useful for observing variations across different contexts, offering insights that might be overlooked when focusing on a single case.

Each type of case study serves a unique purpose and offers different insights, making them versatile tools for understanding complex business scenarios.

Writing and Analysis Tips

Structuring and formatting a case study.

When crafting a case study, be mindful of structure and format. A well-structured case study ensures clarity and enhances the reader's engagement and comprehension. Typically, a case study should begin with an introduction that sets the context and outlines the problem or situation being addressed. This is followed by a detailed presentation of the facts, including background information and a description of the events or processes involved.

The next section should involve an analysis of the case. This is where the data is interpreted and insights are drawn. It's crucial to link the theoretical framework to the practical aspects of the case, demonstrating how specific concepts apply to real-world scenarios. Finally, conclude with a section that synthesizes the findings, offering conclusions and if applicable, recommendations. This closing part should summarize the key insights and highlight the implications for practice and further research.

Developing Theories and Analyzing Results

Developing theories in case studies involves identifying patterns and relationships within the data. The goal is to move beyond the description to interpret the significance of what has been observed. This process requires critical thinking and the ability to link empirical evidence to broader concepts and theories.

Analyzing results in a case study involves scrutinizing the data to understand the 'why' and 'how' of the case. It's about digging deeper into the findings to unearth underlying principles or truths. This analysis should be rigorous and creative, combining empirical evidence with insightful interpretation.

Ensuring Validity and Reliability

To ensure the validity and reliability of a case study, use robust and transparent methods in data collection and analysis. Validity refers to the accuracy and truthfulness of the findings, while reliability pertains to the consistency of the results over time. Triangulation of data sources, where information is corroborated from multiple sources or methods, can enhance validity and reliability.

Furthermore, being transparent about the limitations of the case study, such as potential biases or constraints, helps maintain the integrity of the research. Providing a clear and detailed description of the methodology also allows others to assess the credibility of the findings.

These tips, when applied, can enhance the quality and impact of a case study, making it a powerful tool in business and marketing research.

Case Study Examples

Here are a few links to case studies done by 97th Floor:

SOLD.com : An agent-centric approach fuels SOLD.com's 322% ROI increase amidst housing market Challenges

Gigamon : The holistic SEO approach that won Gigamon their #1 keyword

Tuft & Needle : Connecting with Sleep-Deprived Parents to Increase Tuft and Needle’s Revenue by 57% YoY

These case studies provide tangible examples of how different strategies and innovations can be effectively applied in real-world business scenarios.

Case studies provide a unique blend of narrative storytelling and empirical investigation, making abstract concepts and strategies relatable and understandable. Whether explanatory, exploratory, or collective, each type of case study offers a different lens through which to view and solve business problems.

The insights from well-crafted case studies illuminate past successes and failures and pave the way for future innovations and strategies. By providing detailed analysis and real-world applications, case studies serve as a crucial tool for marketers and business leaders aiming to navigate the ever-evolving landscape of business challenges.

Ultimately, the value of a case study lies in its ability to inspire, inform, and guide. It's a resource that combines theoretical knowledge with practical experience, offering a comprehensive understanding. For any business looking to grow, adapt, and excel, leveraging the power of case studies is not just beneficial—it's essential.

A good case study is well-structured, detailed, and provides clear insights. It should present a real-world problem, outline the steps taken to address it and detail the results. Clarity, relevance, and the ability to engage the reader are key elements.

The length of a case study can vary depending on the complexity of the subject. Generally, it should be long enough to cover all relevant aspects of the case but concise enough to maintain the reader's interest. Typically, anywhere from 500 to 1500 words is standard.

While case studies provide valuable insights, they should not replace traditional market research. They are best used in conjunction with other forms of research to provide a comprehensive understanding of a market or problem.

Ensuring objectivity involves using a systematic approach in gathering and analyzing data, avoiding bias in selecting cases, and being transparent about the limitations of the study.

Yes, case studies are versatile and can be beneficial for businesses of all types and sizes. They are particularly useful for understanding specific situations in depth and can provide valuable insights regardless of the industry.

Small businesses can use case studies to learn from the experiences of others, understand market dynamics, and formulate strategies based on proven methods. They can also create their own case studies to showcase their successes and attract customers or investors.

In digital marketing, case studies can be used as powerful content pieces to demonstrate expertise, build trust, and provide value to the audience. They can be shared across various digital platforms, including social media, blogs, and email newsletters.

The frequency depends on the business's goals and resources. Regularly publishing case studies can keep the content fresh and relevant, but it's more important to focus on quality and relevance than frequency.

Absolutely. Case studies are excellent tools for employee training, as they provide real-life examples and scenarios for employees to learn from and discuss.

The effectiveness can be measured by its impact, such as increased website traffic, engagement rates, lead generation, and feedback from readers. Analyzing these metrics can provide insights into how well the case study resonates with the target audience.

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

What is the definition of a case study.

A case study is typically a research paper to generate an in-depth and multi-faced understanding of any complicated issue in a life scenario. It is a well-written research design that is very commonly used in a wide range of disciplines.

Looking for fast and professional case study assignment help online ? Choose Casestudyhelp.com and enjoy high-quality case study assistance and the lowest rate!

Also Read:  A Complete Guide to Writing an Effective Case Study

Case Study Examples

• Marketing case study examples: Case studies in marketing are written to show your success, and you must always prominently showcase your buoyant suits. You can use bright, bold colours with many contesting fonts, shapes, and simple icons to highlight your case study.
• You need to highlight your big win on the 2nd page with a bright orange colour with highlighted circles.
• Make the essential data stand out exceptionally to track your prospective customers.
• Marketing all the critical data is very important in your marketing case study.

Use a straightforward and crystal clear layout of the case study.

• Using a straightforward layout in any case study is very effective. For example, keeping a spotless white background and drawing slim lines helps to separate these sections in a specific way for formatting the case study.
• Making the information clear helps draw attention to the results and helps to improve the accessibility of the design.
• The case study examples must sit nicely with more extended reports and a consistent layout.

Need Help with Writing a Case Study?

Casestudyhelp.com is the right place that can help you.

What Are the Types of Case Studies?

Case studies can be categorized into several types based on their focus and purpose. Here are some common types of case studies:

• Collective Case Studies : These types of case studies involve investigating any group of individuals. Here, the researchers need to study a group of people in a specific setting or any community. Ex: Psychologists must explore how access to the resources in any society can affect people’s mental wellness.
• Descriptive Case Studies: These involve starting with any descriptive theory. The subjects are then observed, and the gathered information is compared to the preexisting approaches.
• Explanatory Case Studies: These types of case studies are primarily used to conduct any casual investigation. Here, the researchers are more interested in looking for the factors that caused specific results.
• Exploratory Case Studies : These case studies are conducted when researchers want to explore a new or relatively unexplored topic. They are more open-ended and aim to generate hypotheses and ideas for further research.
• Instrumental Case Studies : These case studies are selected because they provide insights into a broader issue or theory. The case is used as a means to investigate a more general phenomenon.
• Intrinsic Case Studies : In these case studies, the case itself is of particular interest due to its uniqueness or rarity. The goal is not to generalize findings to a larger population but to understand the specific case deeply.
• Pilot Case Studies : Pilot case studies are conducted as a preliminary investigation before launching a larger study. They help researchers refine their research questions, methods, and procedures.
• Problem-Oriented Case Studies : These case studies focus on solving a specific problem or addressing a particular issue. Researchers aim to provide practical solutions based on their analysis of the case.
• Ethnographic Case Studies : Ethnographic case studies involve immersing the researcher in the subject’s environment to gain an in-depth cultural understanding. This is often used in anthropology and sociology.
• Longitudinal Case Studies : Longitudinal studies involve observing and analyzing a case over an extended period of time. This allows researchers to track changes, developments, and trends that occur over time.
• Comparative Case Studies : Comparative case studies involve comparing two or more cases to draw similarities, differences, and patterns between them. This type of study is often used to test hypotheses or theories.
• Critical Instance Case Studies : Critical instance cases are chosen because they represent a crucial or pivotal event that can provide insights into a larger issue or theory.

Each type of case study serves a different purpose and is designed to answer specific research questions. Researchers choose the type of case study that best aligns with their objectives and the nature of the phenomenon they are investigating.

Also, Check Out –  Why Is Everyone Talking About Case Study Help?

What Are the Methods of a Case Study?

A   case study research   is a qualitative research design. It is often used in the social sciences since it involves observing the cases or subjects in their settings with the most minor interference from the researcher.

In the case study method, the researchers pose a definite question raging any individual or group for testing their hypotheses or theories. This is done by gathering data from the interviews with the essential data.

Case study research is a perfect way to understand the nuances of any matter often neglected in quantitative research methods. A case study is distinct from any other qualitative study in the following ways:

• Focused on the effect of any set of circumstances in any group or any individual
• It mostly begins with any specific question regarding one or more cases
• It usually focuses on the individual accounts and its experiences

The primary features of case study research methods are as follows:

• The case study methods   must involve the researcher asking a few questions of one person or a small group of people who are known as the respondents for testing the survey.
• The case study in the research mythology might apply triangulation to collect data. It is then analyzed and interpreted to form a hypothesis to be tested through further research or validated by other researchers.
• Concepts are defined using objective language with references to the Preconceived Notions. These individuals may have about them. A researcher sets out to discover by asking any specific question on how people think about their findings.
• The case study method needs a clear concept and theory to guide the processes. A well-organized research question is fundamental while conducting any case study since its results depend on it. The best approach for answering the research questions is challenging the preexisting theories, assumptions or hypotheses.

Benefits and Limitations of Case Studies

The benefits of case studies are as follows:

• Case studies give many details to be collected and will be easily obtained by the other research designs. The collected data is mostly richer than that can be funded via different experimental methods.
• Case studies are primarily conducted on the rare cases where more extensive samples of similar participants are unavailable.
• Within certain case studies, scientific experiments can also be conducted.
• The case studies can also help the experimenters adapt the ideas and produce novel hypotheses for later testing.

Disadvantages of Case Studies

• One of the main criticisms in case studies is that the collected data cannot necessarily be generated for any broader population. This can lead to data being collected over any case study that is only sometimes relevant or useful.
• Some of the case studies still need to be scientific. Many scientists used case studies for conducting several experiments, the results of which were only sometimes very successful.
• Case studies are primarily based on one person, so it can be only one experimenter who is collecting the data. This can lead to a bias in data collection that can influence the results in frequent designs.
• Drawing any definite cause or effect from many case studies is sometimes challenging.

Importance of Case Study

• A case study is a particular research h method involving an up-close and in-depth investigation of any subject, and it is related to a contextual position. These are produced by following a research form. The case study helps in bringing the understanding of any complex issue. This can extend experience or add strength to the already existing knowledge via the previous research. The contextual analysis revolves around a small number of events or situations.
• Researchers have used case studies for an extended period, and they have been successfully applied in various disciplines like social sciences.

Writing the best case study paper on any subject is a challenging task. Thus, you will always need the best service provider in this regard. The  CaseStudyHelp.com   is the top choice for you.

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

6 Types of Case Studies to Inspire Your Research and Analysis

Written by: Ronita Mohan Sep 20, 2021

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

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

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

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

Click to jump ahead:

What is a case study?

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

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

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

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

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

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

Descriptive case studies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Take the example of the fictional brand below.

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

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

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

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

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

Related: 10+ Case Study Infographic Templates That Convert

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What makes a case study a case study?

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

How long should a case study be?

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

What should you ask in a case study?

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

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

How to present a case study?

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

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

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

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What is the Case Study Method?

Simply put, the case method is a discussion of real-life situations that business executives have faced.

On average, you'll attend three to four different classes a day, for a total of about six hours of class time (schedules vary). To prepare, you'll work through problems with your peers.

How the Case Method Creates Value

Often, executives are surprised to discover that the objective of the case study is not to reach consensus, but to understand how different people use the same information to arrive at diverse conclusions. When you begin to understand the context, you can appreciate the reasons why those decisions were made. You can prepare for case discussions in several ways.

Case Discussion Preparation Details

In self-reflection.

The time you spend here is deeply introspective. You're not only working with case materials and assignments, but also taking on the role of the case protagonist—the person who's supposed to make those tough decisions. How would you react in those situations? We put people in a variety of contexts, and they start by addressing that specific problem.

In a small group setting

The discussion group is a critical component of the HBS experience. You're working in close quarters with a group of seven or eight very accomplished peers in diverse functions, industries, and geographies. Because they bring unique experience to play you begin to see that there are many different ways to wrestle with a problem—and that’s very enriching.

In the classroom

The faculty guides you in examining and resolving the issues—but the beauty here is that they don't provide you with the answers. You're interacting in the classroom with other executives—debating the issue, presenting new viewpoints, countering positions, and building on one another's ideas. And that leads to the next stage of learning.

Beyond the classroom

Once you leave the classroom, the learning continues and amplifies as you get to know people in different settings—over meals, at social gatherings, in the fitness center, or as you are walking to class. You begin to distill the takeaways that you want to bring back and apply in your organization to ensure that the decisions you make will create more value for your firm.

How Cases Unfold In the Classroom

Pioneered by HBS faculty, the case method puts you in the role of the chief decision maker as you explore the challenges facing leading companies across the globe. Learning to think fast on your feet with limited information sharpens your analytical skills and empowers you to make critical decisions in real time.

To get the most out of each case, it's important to read and reflect, and then meet with your discussion group to share your insights. You and your peers will explore the underlying issues, compare alternatives, and suggest various ways of resolving the problem.

How to Prepare for Case Discussions

There's more than one way to prepare for a case discussion, but these general guidelines can help you develop a method that works for you.

Preparation Guidelines

Read the professor's assignment or discussion questions.

The assignment and discussion questions help you focus on the key aspects of the case. Ask yourself: What are the most important issues being raised?

Read the first few paragraphs and then skim the case

Each case begins with a text description followed by exhibits. Ask yourself: What is the case generally about, and what information do I need to analyze?

Reread the case, underline text, and make margin notes

Put yourself in the shoes of the case protagonist, and own that person's problems. Ask yourself: What basic problem is this executive trying to resolve?

Note the key problems on a pad of paper and go through the case again

Sort out relevant considerations and do the quantitative or qualitative analysis. Ask yourself: What recommendations should I make based on my case data analysis?

Case Study Best Practices

The key to being an active listener and participant in case discussions—and to getting the most out of the learning experience—is thorough individual preparation.

We've set aside formal time for you to discuss the case with your group. These sessions will help you to become more confident about sharing your views in the classroom discussion.

Participate

Actively express your views and challenge others. Don't be afraid to share related "war stories" that will heighten the relevance and enrich the discussion.

If the content doesn't seem to relate to your business, don't tune out. You can learn a lot about marketing insurance from a case on marketing razor blades!

Actively apply what you're learning to your own specific management situations, both past and future. This will magnify the relevance to your business.

People with diverse backgrounds, experiences, skills, and styles will take away different things. Be sure to note what resonates with you, not your peers.

Being exposed to so many different approaches to a given situation will put you in a better position to enhance your management style.

Frequently Asked Questions

What can i expect on the first day, what happens in class if nobody talks, does everyone take part in "role-playing".

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Making Co-Design More Responsible: Case Study on the Development of an AI-Based Decision Support System in Dementia Care

Dirk r m lukkien.

1 Vilans Centre of Expertise of Long Term Care, Utrecht, Netherlands

2 Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, Netherlands

Sima Ipakchian Askari

3 Human Technology Interaction, Eindhoven University of Technology, Eindhoven, Netherlands

Nathalie E Stolwijk

Bob m hofstede, henk herman nap, wouter p c boon, alexander peine.

4 Faculty of Humanities, Open University of The Netherlands, Heerlen, Netherlands

Ellen H M Moors

Mirella m n minkman.

5 Tilburg Institute for Advanced Studies School for Business and Society, Tilburg University, Tilburg, Netherlands

Associated Data

The responsible innovation survey.

Emerging technologies such as artificial intelligence (AI) require an early-stage assessment of potential societal and ethical implications to increase their acceptability, desirability, and sustainability. This paper explores and compares 2 of these assessment approaches: the responsible innovation (RI) framework originating from technology studies and the co-design approach originating from design studies. While the RI framework has been introduced to guide early-stage technology assessment through anticipation, inclusion, reflexivity, and responsiveness, co-design is a commonly accepted approach in the development of technologies to support the care for older adults with frailty. However, there is limited understanding about how co-design contributes to the anticipation of implications.

This paper empirically explores how the co-design process of an AI-based decision support system (DSS) for dementia caregivers is complemented by explicit anticipation of implications.

This case study investigated an international collaborative project that focused on the co-design, development, testing, and commercialization of a DSS that is intended to provide actionable information to formal caregivers of people with dementia. In parallel to the co-design process, an RI exploration took place, which involved examining project members’ viewpoints on both positive and negative implications of using the DSS, along with strategies to address these implications. Results from the co-design process and RI exploration were analyzed and compared. In addition, retrospective interviews were held with project members to reflect on the co-design process and RI exploration.

Our results indicate that, when involved in exploring requirements for the DSS, co-design participants naturally raised various implications and conditions for responsible design and deployment: protecting privacy, preventing cognitive overload, providing transparency, empowering caregivers to be in control, safeguarding accuracy, and training users. However, when comparing the co-design results with insights from the RI exploration, we found limitations to the co-design results, for instance, regarding the specification, interrelatedness, and context dependency of implications and strategies to address implications.

Conclusions

This case study shows that a co-design process that focuses on opportunities for innovation rather than balancing attention for both positive and negative implications may result in knowledge gaps related to social and ethical implications and how they can be addressed. In the pursuit of responsible outcomes, co-design facilitators could broaden their scope and reconsider the specific implementation of the process-oriented RI principles of anticipation and inclusion.

Introduction

In the long-term care for older adults with frailty, caregivers and clients are increasingly being assisted by artificial intelligence (AI)–based technologies [ 1 - 5 ]. AI-based technologies can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or web-based environments, thereby using machine or human-based data and input [ 6 ]. For instance, AI is being used in decision support systems (DSSs) that acquire relevant data about care needs or processes; present the relevant data to users (eg, caregivers); and translate raw data into actionable information, such as alerts, risk assessments, or recommendations about care strategies [ 7 - 10 ]. Notwithstanding the opportunities and advantages, it is broadly acknowledged that the use of AI-based technologies entails societal and ethical implications. The long-term data collection in the context of monitoring older people’s health and well-being and the mediating or even leading role of algorithms in interpreting these data to arrive at care-related decisions pose implications related to, among others, undermining people’s privacy, autonomy, and self-determination; the discrimination and stigmatization of old age; and surveillance capitalism [ 1 , 11 - 15 ].

Due to the impact technologies such as DSSs have on people’s lives and the potential resistance that might emerge during implementation, an early-stage assessment of their implications is called for. This paper explores and compares 2 of these assessment approaches: the responsible innovation (RI) framework originating from technology studies and the co-design approach originating from design studies. The term RI refers to the aim to ensure the ethical acceptability, societal desirability, and sustainability of innovation processes and outcomes [ 16 , 17 ]. To guide RI into practice, Owen et al [ 17 ] suggest that four process-oriented principles should guide technology research and development: (1) anticipation of the potential positive and negative implications; (2) inclusion of users and other stakeholders; (3) reflexivity of actors upon their own practices, assumptions, values, and interests; and (4) responsiveness to insights that emerge during the innovation process.

Co-design can be used as an umbrella term for approaches that actively involve users and other stakeholders of innovations in any stage of the design process to ensure that the outcomes meet their needs [ 18 , 19 ]. It is a commonly accepted approach in the development of technologies to support the long-term care for older adults [ 20 - 22 ]. On a conceptual level, co-design resonates with RI. Both approaches share a focus on developing technologies to match human needs and abilities, similar to research fields such as human factors, human-computer interaction, and cognitive engineering. In fact, co-design has increasingly received attention as a way to support RI [ 23 ]. Similar to RI, the co-design approach describes a research and development process in which innovators inclusively deliberate and reflect on the needs and values of different stakeholders and iteratively design and adapt innovations based on these insights [ 23 ]. However, in contrast to RI, co-design does not explicitly impose on innovators the need to anticipate potential societal and ethical implications (henceforth, abbreviated as “implications”). Co-design can yield insights into potential unintended side effects and value creation that stakeholders do not want from innovation, but this is generally not an explicit aim in co-design. Against this background, this paper empirically explores how the explicit anticipation of implications can complement co-design.

More specifically, this paper presents a case study on an international collaborative project that focuses on the development of a DSS to support formal caregivers involved in long-term dementia care. A co-design process involving intended users and other stakeholders (henceforth, abbreviated as “users”) is central to the development of the DSS. In addition, a separate line of research of the project under investigation explicitly anticipated implications of using DSSs in dementia care, along with strategies to address these implications, thereby fostering RI in AI-assisted decision-making. This so-called RI exploration largely took place in parallel to (ie, not as part of) the co-design activities and focused on soliciting the perspectives of project members (PMs) rather than those of users. This paper describes the empirical exploration of how the co-design process of an AI-based DSS for dementia caregivers is complemented by the explicit anticipation of implications.

The Healthy Ageing Eco-System for People With Dementia Project

The case presented in this paper is the Healthy Ageing Eco-system for People With Dementia (HAAL) project, which is part of the European Active and Assisted Living (AAL) program (AAL Europe, 2021; project AAL-2020-7-229-CP). In HAAL, an international consortium comprising care organizations, research institutes, and commercial firms from the Netherlands, Italy, Taiwan, and Denmark collaborates on the co-design, development, testing, and commercialization of a DSS that is intended to provide actionable information to formal caregivers of people with dementia, with the aim of reducing their workload and increasing the quality of care [ 24 ]. The DSS developed in HAAL concerns a dashboard that integrates various types of data about the physical activity, eating and sleeping patterns, cognitive functioning, mood, social contact, and medication intake of people with dementia. These data can be collected via several digital technologies (henceforth, “HAAL technologies”) throughout various stages of dementia. Besides integrating the data from HAAL technologies into 1 dashboard, possibilities to provide caregivers only the most relevant data in the form of summary overviews, alerts, predictions about emergency situations, and recommendations about care strategies were explored. To this end, both preprogrammed, rule-based algorithms and data-driven algorithms rooted in machine learning are used to process data.

With these predefined directions as a starting point, a series of iterative co-design activities involving dementia caregivers, or more correctly “proxy users” who represent these eventual users (see the study by Stewart and Hyysalo [ 25 ]), and other stakeholders were organized to feed the actual design and development of the dashboard. The co-design activities focused on exploring the relevance and possibilities of translating the data from HAAL technologies into useful information and prioritizing data that are relevant to be presented in the dashboard [ 24 , 26 ]. In addition, the co-design activities focused on determining functionalities of the dashboard and designing and evaluating different pages of the dashboard’s user interface.

The RI exploration in HAAL, which took place largely in parallel to the co-design activities, initially focused on raising PMs’ general awareness about RI and exploring their perspectives on both positive and negative implications of using the HAAL dashboard, along with strategies to address these implications.

For this case study, results from the co-design process and RI exploration within the HAAL project were incorporated and analyzed. In addition, retrospective interviews were held with individual PMs to reflect on the co-design process and RI exploration. Because the co-design process and RI exploration were largely organized in parallel, the HAAL project provided sufficient data within a specific time and context to perform a retrospective analysis on how the explicit anticipation of implications can complement co-design. Figure 1 shows a timeline of activities.

Timeline of the co-design process, responsible innovation (RI) exploration, and retrospective interviews. "Month" refers to the month (count in project) in which the activity took place. HAAL: Healthy Ageing Eco-system for People With Dementia.

Co-Design Process

Table 1 describes the 4 specific steps taken in the co-design process. The co-design activities in HAAL were conducted in 4 countries: the Netherlands, Italy, Taiwan and Denmark. The organizations from Denmark are unsubsidized partners in the HAAL project and did not participate in co-design steps 3 to 4. Despite differences in dementia care systems across these countries, such as types of caregivers involved in home-based and institutionalized care settings, formal caregivers of people with dementia were perceived as the primary target group for (using) the dashboard in all countries. Hence, a variety of formal caregivers of people with dementia, such as (homecare) nurses, case managers, psychologists, psychotherapists, social workers, and specialists in the care of older adults, were involved in the co-design activities. In addition, other stakeholders, such as innovation staff, data analysts at care organizations, and people working in (care) alarm centrals, were involved in some steps of the co-design process to broadly explore requirements for the dashboard. As indicated in Table 1 , two intermediate steps were taken without the direct involvement of users. Further, at the end of step 4, participants were implicitly asked about RI-related themes (autonomy and transparency). Throughout the co-design activities, data were collected in the form of notes, audio and video recordings, photos, drawings, and (web-based) canvasses and by conducting surveys.

Steps taken in the co-design process.

a Intermediate step: after analyzing results from step 1, user personas and desired dashboard functionalities were defined and translated into a preliminary mock-up for the dashboard (iteration 1). The motivational goal model of Taveter et al [ 32 ] was used for this translation.

b HAAL: Healthy Ageing Eco-system for People With Dementia.

c MoSCoW: must have, should have, could have, and won’t have this time.

d Intermediate step: after analyzing results from step 3, insights about user requirements were again plotted on the motivational goal model to define design requirements. These design requirements were used to translate the preliminary mock-up into a clickable mock-up (iteration 2).

e HUBBI: eHealth usability benchmarking instrument.

f RI: responsible innovation.

RI Exploration

The RI exploration was primarily based on a qualitative survey among PMs, which was preceded by 2 workshops and followed by a third workshop with PMs. The first 2 workshops with PMs were held in a hybrid setting (web based and physical) during collective consortium meetings. The goal of the first workshop was to explain the notion of RI to PMs and discuss their thoughts about the relevance of and ways to address RI in HAAL. In the second workshop, based on the guidance ethics approach of Verbeek and Tijink [ 33 ], potential positive and negative implications of using the envisioned HAAL dashboard were explored, along with ways to address these implications.

Next, a dedicated qualitative RI survey was developed and conducted among PMs ( Multimedia Appendix 1 ). The goal of the RI survey was to reveal PMs’ viewpoints on how to responsibly develop AI-based analytical functionalities and the dashboard user interface in the HAAL project. The survey first explained that AI, as in the HAAL dashboard, provides opportunities for descriptive, diagnostic, predictive, and prescriptive analyses with differing levels of complexity and automation [ 34 , 35 ]. Next, questions were asked in relation to 2 distinct imaginary scenarios that outline different roles for AI within the HAAL dashboard. The first scenario (A) described a descriptive and largely rule-based dashboard through which users can assess the data from HAAL technologies and how the situations of clients have changed over time. This scenario was inspired by the dashboard that was aimed to be developed in the HAAL project. The second scenario (B) took a more speculative turn and described a proactive and partially self-learning dashboard that automatically translates the data into diagnostic, predictive, and prescriptive information to prompt caregivers to take certain actions. The scenarios were used as input to inspire respondents about directions the project could take in terms of developing AI and to enable them to articulate their expectations and considerations regarding the opportunities and implications of an advanced AI-based DSS (see also the study by Noortman et al [ 36 ]). After presenting each scenario, questions were asked about the positive and negative implications of using the respective dashboard. Thereafter, respondents were asked which scenario they preferred in terms of ethical acceptability, societal desirability, and technical feasibility and why they preferred it. Next, the survey introduced six principles for responsible AI innovation, adopted from guidelines from the World Health Organization: (1) protecting human autonomy; (2) promoting human well-being and safety and the public interest; (3) ensuring transparency, explainability, and intelligibility; (4) fostering responsibility and accountability; (5) ensuring inclusiveness and equity; and (6) promoting AI that is responsive and sustainable [ 37 ]. Respondents were asked how these principles might be relevant to and could be applied in the HAAL project. The survey was completed by 12 respondents representing 7 different organizations from all 4 countries. In addition, 5 respondents partially filled in the survey anonymously.

Finally, the RI survey was followed by a third hybrid workshop in which PMs were invited to jointly discuss what they learned from answering the RI survey.

Retrospective Interviews With PMs

In addition to the co-design activities and RI exploration, semistructured interviews were held with 6 PMs: 4 co-design facilitators (n=1, 25% working in the Netherlands; n=2, 50% working in Taiwan; and n=1, 25% working in Italy) and 2 software developers (working in Italy). The goal of the interviews was to uncover possible rationales behind the co-design process, choices made throughout the co-design process, and input given by co-design participants. All interviews lasted between 30 and 40 minutes and were fully transcribed by a professional transcription service.

The analysis of data was performed by DRML, SIA, NES, and BMH. The data collected during the co-design activities and RI exploration were first analyzed independently by these 4 researchers. While the co-design data were previously analyzed by HAAL PMs to learn about the dashboard requirements, they were analyzed again for the purposes of this paper. Taking the 6 responsible AI principles from the World Health Organization guidelines [ 37 ] as a starting point, the researchers performed an inductive thematic analysis [ 38 ] to uncover conditions for the responsible design and deployment of the HAAL dashboard, including potential negative implications and strategies to address them. In doing so, they examined how certain insights regarding these conditions emerged in the co-design activities, the RI exploration, or both. In other words, the analysis focused, first, on identifying themes common within and between the co-design and RI exploration results and, second, on examining how the results from the RI exploration complement those from the co-design activities, or vice versa, in terms of RI. Subsequently, the transcripts of the retrospective interviews were analyzed independently by DRML, SIA, and BMH to uncover new conditions for RI and explore the complementarity between the co-design process and RI exploration. An additional focus was on why certain insights about conditions for RI may have emerged less explicitly in either the co-design process or the RI exploration. While analyzing the data, the researchers applied open coding and kept track of their reflections by writing them down as memos. After the data were independently analyzed by the researchers, the findings and memos were regularly discussed and reviewed by the researchers to reconcile major discrepancies in the coding and to reach agreement on the final coding scheme. Both physical and digital meetings were held to ensure the consistency of the analysis and reach convergence.

Ethical Considerations

The authors of this study followed the guidelines in the Declaration of Helsinki and the Dutch code of conduct for scientific integrity. Ethical approval for the interviews, not subject to the medical scientific research act involving human subjects, was granted by an independent board of the lead author's department (Vilans), including a privacy officer and legal expert [ 39 ].

For each co-design step, general information about the goal and procedure was provided, and the participants were asked to read and sign an informed consent form. The original consent covers secondary analysis of the data for the purposes of this study. The data gathered through the co-design steps and RI exploration were pseudonymized before analysis. Study participants did not receive any financial compensation.

Seven overarching and interlinked themes representing conditions for the responsible development and deployment of the HAAL dashboard were extracted: (1) develop a proactive dashboard, (2) prevent cognitive overload, (3) protect privacy, (4) provide transparency, (5) empower caregivers to be in control, (6) safeguard accuracy, and (7) train users. We explicate how insights related to each theme emerged in the co-design activities, the RI exploration, or both. In addition, insights from the interviews with PMs are provided. In doing so, for each theme, we discuss how the explicit anticipation of implications (ie, the RI exploration) complements the co-design process in the HAAL project. Textbox 1 excerpts the results.

Analysis of complementarities between the co-design process and responsible innovation (RI) exploration per theme.

• The co-design results clearly indicate a perceived need for a proactive dashboard and provide concrete arguments to this end. The RI exploration also indicated the need for a proactive dashboard, albeit with less concrete arguments. Besides, limitations were raised regarding the short-term feasibility of a proactive dashboard.
• The co-design process and RI exploration yielded similar insights, that is, that too much data in one place would overload caregivers’ cognitive workload and that focus of the dashboard should be on providing actionable and only the most relevant information. However, this insight only emerged late in the co-design process (step 4 of 4).
• The need for privacy protection emerged strongly in the co-design process, and participants clearly pointed to the need for a proactive dashboard in privacy terms. The theme was discussed only briefly in the RI exploration, although some practical suggestions were provided, such as the use of encryption and passwords.
• While the importance of the transparency of the dashboard’s information emerged in the co-design process, practical suggestions on how to provide transparency (eg, training users in correctly interpreting information and explanations) were given only in the RI exploration.
• The main contribution from co-design was the proposition to gradually expand the application of artificial intelligence (AI) functions in practice so that users can get used to an increasing role of AI. In comparison, the RI exploration yielded more in-depth insights and suggestions. The RI exploration stressed that it is important for caregivers not to become too reliant on the results of AI and to have a critical mindset and keep the context in mind.
• During co-design, the importance of accurate dashboard information was mentioned but not discussed in depth. In the RI exploration, concrete suggestions were made to ensure accuracy, such as including feedback buttons for users.
• The importance of training, also in relation to other themes such as empowering caregivers to be in control and safeguarding accuracy, frequently appeared in the RI exploration but was raised by only one of the participants in the co-design process. In the RI exploration, suggestions were also provided regarding the focus of training, for instance, on creating awareness about the mediating role of AI in decision-making.

Theme 1: Develop a Proactive Dashboard

The co-design participants generally agreed that the HAAL dashboard should support decision-making proactively, by actively generating and pointing users to relevant insights, rather than passively, by merely showing data from the HAAL technologies. In contrast, the results from the RI survey showed varying viewpoints among PMs regarding the dashboard’s required level of proactiveness with regard to supporting decision-making.

Co-design steps 1 and 2 showed that the data from HAAL technologies could be potentially useful for both daily caregivers and caregivers who are less frequently involved (eg, general practitioners). In these co-design steps, there was limited reflection on the possibilities of a dashboard beyond data integration. However, in co-design steps 3 and 4, most participants expressed an interest in a dashboard that also interprets data to provide new information and inspire users. That is, participants suggested that the dashboard should provide insights into or predictions about outliers from usual patterns and distinguish between urgent (eg, a fall) and nonurgent (eg, a deviation in sleeping pattern) outliers to prompt caregivers to take appropriate action. As one of the caregivers at a Taiwanese care center argued, “What I would like is an alert service, more centered on urgency than on daily, routine patient follow-up .” In addition, the dashboard was seen as a way to encourage caregivers to consider signs that might otherwise have been neglected or perceived too late. Besides, some participants of co-design steps 3 and 4 proposed that the dashboard could provide recommendations on how to prevent or address certain deviations from usual patterns.

In the RI survey, most PMs shared pros and cons related to both a descriptive dashboard (scenario A) and a proactive dashboard (scenario B). Most PMs argued that a proactive dashboard could potentially add the most value, especially in terms of enhancing prevention and reducing caregivers’ cognitive load (see also theme 2). At the same time, all PMs expressed doubts about the feasibility of developing a proactive dashboard due to the complexity and relatively limited time span of the HAAL project. Some PMs stressed that the initial acceptance and adoption of a proactive dashboard by caregivers might be low, arguing that the more proactive the dashboard is, the more it may infringe on job satisfaction. As one of the PMs explained, “Caregivers might enjoy the part in their work where they investigate the status of the client, and this is then (partially) taken over by machines.” However, although market introduction was questioned, some PMs advocated exploring possibilities for and experimenting with the more progressive concept of a proactive dashboard to iteratively learn and generate ideas and lessons for future research and development. In an interview, one of the PMs explained, “We know that we could do bigger, smarter things with AI, but you cannot start with high-level AI...But I think that these kinds of projects are useful also to build knowledge and literacy, by making people consider what technology and artificial intelligence could do . ”

Theme 2: Prevent Cognitive Overload

The need to prevent cognitive overload was another recurring argument for developing a proactive dashboard in both the co-design process and RI survey. In co-design step 4, it was stressed by multiple participants that too much data or information in one place could exceed caregivers’ cognitive load and cause problems regarding the prioritization of which client, or what aspect of a client’s life, needs attention first. Similarly, in the RI survey, PMs suggested multiple times that a descriptive dashboard may require additional time for caregivers in terms of checking the data, rather than save time, and increase mental strain. As one of the PMs stated, “Adding more data in one place without elaborating on it would not really reduce the caregiver burden.”

Theme 3: Protect Privacy

While privacy was a prominent theme throughout all co-design steps, it was only briefly discussed in the RI exploration. During co-design step 3, multiple participants suggested that from a privacy perspective, a (proactive) dashboard that provides only the most relevant data patterns, notifications, and alerts may be preferred over a (descriptive) dashboard that directly discloses all data about the evolving status of clients in relation to various indicators. This link between the need for a proactive dashboard (scenario B) and privacy concerns was not discussed in the RI survey.

Further, privacy concerns raised in the co-design activities were related to the storage of large amounts of data collected about people with dementia and how these data would be handled. As one of the participants stated, “A lot of personal information is gathered, so you can get to know a lot about people . ” In line with this, most participants stated that compliance with the European General Data Protection Regulation should be ensured, and some practical suggestions were made, for instance, to show the client’s home address or room number in the dashboard rather than their names in case of alarms.

The importance of privacy protection was mentioned by various PMs in the first 2 RI workshops, but in the RI survey, only 3 (18%) of the 17 PMs provided input on privacy issues. One of the PMs stated that ways must be found to balance the benefits of large-scale and long-term data collection (eg, in terms of prevention) with downsides such as a feeling of intrusion. Complementary to the co-design process, PMs also provided practical suggestions on privacy protection in the RI survey, such as using a private log-in to the dashboard for caregivers, encryption, or even facial recognition to protect data.

Another privacy concern, raised during co-design step 3, was data accessibility. Several participants reported about who should have access to the dashboard. Some participants proposed that access should be limited to specific caregivers with the specific assignment to learn from the dashboard. In contrast, others reported that all caregivers, including informal carers (eg, family), should have access to the dashboard, if desired. There was no consensus among participants about whether a distinction should be made between different users who are able to see different client data.

Theme 4: Provide Transparency

In co-design step 4, participants proposed that a condition for the use of a proactive dashboard is that users need to understand the reasons (eg, data patterns) behind information provided by the dashboard. In this respect, one of the PMs discussed in an interview that caregivers should not be overloaded with too many details about how specific dashboard information comes about (see also theme 2). In contrast, some co-design participants stressed that users should always be able to examine all data from the different HAAL technologies. Hence, this could be in conflict with the previously discussed insight from co-design that making all data available may be less preferable from a privacy perspective (see also theme 3).

The co-design participants also made various remarks regarding the context specificity of transparency needs. Multiple participants expressed that a need for transparency may not always, or for every user, mean the same. For instance, in case of alarms about certain urgent situations, it may be irrelevant or even distracting to immediately show all data that triggered the alarm. However, users may want to view all the data at a later stage to gain insights into the context and possible causes for the urgent situation, for instance, for training and prevention purposes. A similar insight was raised in the RI exploration, where it was, for example, suggested that in-depth explanations could be provided but only after users ask for it, for instance, by clicking through.

Further, during co-design step 4, it was suggested that once caregivers have built a certain level of trust in the dashboard, less detailed explanations clarifying how the dashboard reaches its conclusions might be sufficient. However, as one of the PMs added in an interview, in the long run, excessive trust might lead to caregivers making certain decisions too easily based on the dashboard’s information without critical reflection: “The long-term risk is that users end up trusting the system too much ” (see also theme 5).

Although co-design participants highlighted the importance of transparency in HAAL, they did not provide practical suggestions about ways to provide transparency. In the retrospective interviews, various possible explanations were given. For instance, 2 PMs argued that issues such as transparency may have been discussed with limited depth throughout co-design because they pertain more to the backend of the system (ie, algorithms and web services) than the front end (ie, interface) with which users directly interact and because participants may place a certain degree of trust in developers to deal with such issues. Besides, 2 PMs discussed that it may have been hard for co-design participants to formulate requirements regarding transparency during early phases of design because the dashboard concept was still relatively abstract. As suggested, gaining in-depth insights into issues such as these may be easier when practically demonstrating and testing the dashboard in field tests, as users can then actually experience the system and its limitations.

While practical suggestions on providing transparency in HAAL were absent in the co-design results, they were discussed in the RI survey. For instance, PMs suggested (1) showing which specific data were included by algorithms to provide certain information; (2) creating abstractions easy to understand for users to explain the logic behind data analyses, for instance, by giving explanatory examples of common use cases; and (3) training users in interpreting the information and their explanations (see also theme 7).

Theme 5: Empower Caregivers to Be in Control

It was raised in co-design step 4 that people should be in charge of decision-making, regardless of whether human decisions are in line with the dashboard’s information. In the same line, multiple PMs argued in the RI survey that people (ie, caregivers) should always be making the final decisions, and they should make these decisions only after carefully valuing the dashboard’s information in light of the specific context. It was also suggested during co-design that caregivers may at first instance not be ready yet to get extensive advice from a dashboard. A gradual expansion of AI-functions in real practice was suggested. For instance, in the beginning, the dashboard could provide only generic insights (eg, patterns), alarms, and predictions. In a later stage, when reliability has improved and trust in and experience with the system have been gained, recommendations or conclusions about follow-up steps could be provided. Apart from the above, the importance of people making the final decisions was not further reported by co-design participants.

In contrast, the importance of caregivers being and remaining to be in control of decision-making was more prominent in the RI exploration. In the RI survey, 3 PMs suggested that the long-term use of a proactive dashboard might slowly deprive the intuition of caregivers and maintain an automated and predefined focus whereby one might overlook the person (ie, person with dementia) behind the data. One of the PMs even stated, “There may be a tendency to rely more on AI than own observations and assessments because ‘the computer is always right.’” To encourage caregivers to make autonomous decisions while using the dashboard, training was put forward as an important factor by several PMs (see also theme 7).

Theme 6: Safeguard Accuracy

The importance of accurate dashboard information was reflected to a limited extent in the co-design process. During all co-design steps, participants reported a couple of times that the accuracy of the data and data analyses should be regularly evaluated. However, in an interview, a PM suggested that co-design participants mainly shared this requirement as a general condition that must be met before the dashboard could be put into practice, rather than giving concrete ideas on how to achieve this.

The importance of accurate dashboard information and ways to achieve this were more prominently discussed in the RI survey. Multiple PMs argued that information provided by the dashboard should not lead to any faulty judgments by caregivers and that both the data and the algorithms processing data should, therefore, be accurate, without significant biases. For instance, one of the PMs stated, “The dashboard should not give unnecessary warnings to caregivers because the false warning could stimulate the caregivers to impose unnecessary boundaries to people with dementia . ” One of the PMs explicitly linked accuracy to being sensitive toward the diversity among clients and suggested that the dashboard be fed with data from heterogeneous clients to reduce bias. In contrast to the co-design process, PMs also provided practical suggestions about particular ways of involving users to safeguard accuracy, such as enabling users to (1) provide feedback on data or insights through a button, (2) personalize certain thresholds for alarms to the individual client, (3) keep track of their responses and follow-up actions on the dashboard’s information, (4) report nonplausible suggestions and malfunctions, and (5) periodically evaluate the dashboard’s functioning. Again, training was put forward as an important factor in this case for users to be able to be involved (see also theme 7).

Theme 7: Train Users

During the co-design activities, one of the participants commented that the proper use of the dashboard would require training and practical learning. In the RI survey, multiple PMs pointed out that training users is an important measure to tackle challenges related to the autonomy of users and the accuracy of the dashboard’s information (see also themes 5 and 6). It was suggested that the training should focus on making the users become acquainted with the HAAL technologies; data types; and information provided by the dashboard, including underlying data analyses, and on understanding the impact that the use of the dashboard might have on decision-making. One of the PMs said, “Caregivers should be taught that they will always in some degree be influenced by the information on the dashboard, and be recommended to make their own judgements first.” Another PM argued that training should prevent caregivers to become overreliant on the dashboard. In addition, training was suggested to prepare some users for active involvement in maintaining the accuracy of the dashboard information (see also theme 6).

Principal Findings

This paper empirically explores how the co-design process of an AI-based DSS for dementia caregivers is complemented by the explicit anticipation of implications. A total of 7 overarching and interlinked themes representing conditions for the responsible development and deployment of the DSS were extracted: develop a proactive dashboard, prevent cognitive overload, protect privacy, provide transparency, empower caregivers to be in control, safeguard accuracy (eg, by reducing false positives), and train users. Because these conditions are interlinked, it is essential for various actors, including developers and users of the DSS, to work together to cohesively address them in practice. Moreover, some conditions, such as to develop a proactive dashboard and empower caregivers to be in charge or to provide transparency through detailed information and prevent cognitive overload, can be at odds with each other and need to be carefully balanced. To gain a deeper understanding about appropriate and responsible levels of proactivity by the DSS, where the contributions of AI and human input in decision-making are balanced, future studies could expand upon prior research in fields such as human factors by exploring and contextualizing notions such as automation bias [ 40 , 41 ] and human automation coordination [ 42 , 43 ] in the context of AI-assisted decision-making in long-term dementia care. Scenarios that may lead to excessive reliance on the automated execution of functions, such as AI-driven data interpretation, could be anticipated, and strategies could be devised to mitigate such scenarios [ 40 ].

As our analysis points out, the general expectation of both co-design participants and PMs was that a dashboard that proactively supports decision-making would be most valuable to dementia caregivers. To this regard, the perspectives of co-design participants were fairly aligned; there was a consensus that the dashboard should not show all available data from care technologies. Rather, it should focus on information about significant changes in the data that, for instance, indicate a deterioration of well-being. AI itself was positioned as a technical fix (see also the study by Wehrens et al [ 44 ]) to mitigate specific risks related to the remote technology-based monitoring of people with dementia, that is, the infringement of clients’ privacy and cognitive overload of caregivers. This is in line with previous studies that show that too much information [ 45 - 47 ] and insufficient time can lead to information overload [ 48 ]. The same suggestion of using AI to actually support the responsible embedding of technology in care practice was also found in a scoping review on practical approaches to responsible AI innovation in the context of long-term care [ 49 ]. In comparison to the co-design results, the perspectives of PMs in the RI exploration were less unanimous; some PMs shared doubts about the short-term feasibility and acceptance of a proactive dashboard. This discrepancy between results may have been owing to the co-design process being focused on exploring opportunities for innovation, while the RI exploration explicitly invited PMs to reflect on opportunities as well as risks of AI-based analytical functionalities.

Throughout both the co-design process and the RI exploration, various conditions were defined for the responsible development and deployment of a proactive DSS. Similar conditions emerged in the co-design process and RI exploration. However, despite considering and addressing usability requirements, such as minimizing memory load [ 31 , 50 ], in the co-design process, co-design participants generally went into less detail. Compared to PMs in the RI exploration, co-design participants provided fewer practical suggestions on how to meet the RI conditions, except for conditions related to privacy protection. In addition, multiple conditions (ie, preventing cognitive overload, empowering caregivers to be in control, and safeguarding accuracy) emerged in a relatively late stage of the co-design process, once prototyping and reflection on prototypes stood central. Relevant input on implications and conditions for RI emerged more naturally in these phases of co-design, regardless of 2 RI questions related to autonomy and transparency being asked at the end of the last co-design step. Again, these differences in results could potentially be explained by the focus of co-design activities being mainly on opportunities, while the RI exploration was focused on both opportunities and risks.

Hence, the explicit anticipation of implications (ie, the RI exploration) was found to complement the insights from the co-design process in the project under investigation. At the same time, a number of deficiencies can be mentioned regarding the insights that have been gained about social and ethical implications of the DSS. For instance, potential tensions were found between conditions set by different co-design participants. More specifically, to protect privacy, some co-design participants proposed to limit access to information provided by the DSS to specific caregivers. Other participants advocated more transparency and data availability. It is premature to draw conclusions from such contrasting insights. However, it can be stated that insufficient insights were gained into people’s individual views on such matters, the interrelatedness of conditions, and potential trade-offs between them. Further, it stood out that both the co-design process and RI exploration yielded limited insights into the dependency of different conditions on context (eg, time, place, and culture). Although it was indicated that trust in the dashboard and transparency needs may change over time, limited insights were gained into how conditions for RI may depend on other contextual factors, such as place and culture. Despite the co-design activities being carried out in multiple countries, no cross-country differences in conditions for the responsible design and deployment of the dashboard were found.

Practical Implications

As argued by Fischer et al [ 22 ], differences regarding who is involved in the co-design of care technologies, and how, when, and why they are involved, result in different types of outcomes. To this respect, we discuss 4 considerations that designers and co-design facilitators could take into account to increase the potential for co-design processes to contribute to ethically acceptable, societally desirable, and sustainable deployments of AI-based care technologies.

First, one could strive for balanced attention on both positive and negative implications throughout co-design processes. The co-design process in this case study was focused mostly on functional (ie, what the technology must do) and nonfunctional (eg, usability and reliability) requirements. However, rather than merely eliciting information on the needs, preferences, and requirements of users, co-design processes should go back and forth between needs and opportunities for innovation on the one hand and associated implications on the other hand. In addition, RI necessitates striking a balance in co-design practices between focusing on design aspects, such as usability and esthetics, and considering ethical and social implications. Adhering to specific design standards holds importance to meaningful field tests and the implementation of innovations in practice. However, excessive emphasis on these aspects during early phases of innovation may detract from fostering the innovation’s desirability and acceptability. Although research and development projects that integrate anticipatory elements into co-design may yield more in-depth insights and be able to more flexibly adapt to insights than projects that anticipate implications separate from the co-design process, a few remarks can be made here. For instance, implications of innovation may need to be anticipated and addressed not only as part of co-design but also in parallel to and beyond the co-design process through methods such as impact assessments, ethical reviews, and foresight exercises. Besides, caution should be exercised to prevent co-design processes from becoming dominated by the anticipation of long-term and wider societal implications, as this may go at the expense of fast iterative design cycles exploring and addressing requirements and direct benefits for users. Further, Sumner et al [ 21 ] argued that co-design may require the commitment of a significant amount of time and resources and that some projects may have to rationalize limited resources. Naturally, the same applies to anticipating implications as part of or in parallel to co-design.

Second, one could engage with the perspectives of people who are willing and able to imagine how their interests and their role as users of technology evolve over time (ie, future users), rather than merely involve people from contemporary care practices in co-design. Innovators should not just examine the needs of current users because they may then be insufficiently able to respond to future needs [ 51 ]. For instance, in the context of the HAAL project, which was investigated in this study, this could concern the involvement of progressive and technology-savvy dementia caregivers who reflect on how the adoption of increasingly advanced DSSs and other AI technologies will change their work.

Third, one could deliberate on which stakeholders, apart from users, should actually participate in co-design and regularly evaluate how their views guide the underlying direction of innovation. Due to the focus of co-design often being on the needs, expectations, and contexts of individual users, innovators may fail to address potential negative implications, especially implications for other stakeholders or in the long run [ 52 ]. Accordingly, it might be relevant to involve certain stakeholders such as intermediary user organizations or social advocacy groups in co-design to articulate societal demands and consider societal implications from a systemic perspective [ 25 , 53 , 54 ]. For instance, in the context of the HAAL project, this could concern involving nongovernmental organizations that are committed to the privacy interests of older people.

Fourth, one could not only invite but also actively enable users to contribute to the anticipation of implications in co-design. As users are often no experts in (responsible) innovation, they may have difficulties in explicating implications and how they could be addressed, even if explicitly asked for. In this case study, it became more natural for co-design participants to come up with implications in the later phases of co-design (ie, steps 3 and 4) when the dashboard concept had become more tangible. To enable the anticipation of implications early in the co-design process, it may be useful to develop inspirational tools that use, for instance, examples of negative impacts of AI technologies [ 55 ], envisioning cards [ 56 ], or design fiction [ 36 , 57 ] to evoke consideration of the possible intended and unintended short- and long-term effects of future technologies. In addition, in the context of AI-based innovation, one could ensure through training that co-design participants have a basic understanding of what AI can do and how its behavior may be unpredictable and change over time while accumulating data [ 58 , 59 ].

In sum, for co-design processes to result in more RI outcomes, designers and co-design facilitators may need to broaden their scope and reconsider the specific implementation of the process-oriented RI principles of anticipation and inclusion [ 17 , 60 ]. Even though there are still many uncertainties about the potential uses and consequences of technology during early phases of co-design and before users can “experience” the technology in practice, the anticipation of implications with users ideally starts early, before the technology design has been locked in and change becomes difficult, time-consuming, and expensive [ 61 ]. Besides, anticipation should be a recurring element of the innovation process, as people’s values and perspectives on what is responsible may evolve over time and under the influence of technological innovation [ 62 ].

Limitations and Suggestions for Future Research

Given that this paper studies merely a single case, our aim is not to generalize, but rather to illustrate a typical co-design process of an AI-based technology to support the care for older adults and contribute to building a nuanced view on the relation between co-design and RI [ 63 ]. Although we use a broad definition for co-design, we acknowledge that there are multiple ways, methods, and instruments to integrate users into the innovation process [ 21 ]. Therefore, our findings about the role of anticipating implications in co-design are not generally applicable to co-design. For instance, it is plausible that projects that adopt the value-sensitive design approach yield different results, as this approach aims to explicitly consider the values of users and other stakeholders and how these values are affected by the envisioned technology [ 64 - 66 ]. In other words, some approaches to co-design may in themselves impose on facilitators to explore the values at stake and thereby the implications of innovation. Future research could examine to what extent such approaches support RI.

Further, we recognize that there are limitations to the RI exploration that was part of our study and thus to the insights gained into conditions for the responsible development and deployment of DSSs in dementia care. Our RI exploration initially focused on the perspectives of PMs to stimulate and facilitate whole-team participation in exploring how RI could be addressed throughout the HAAL project. The underlying assumption was that RI cannot be prescribed to innovators but needs to be conceptualized and addressed “in context” by those who actually perform the research, design, development, and testing with users [ 67 , 68 ]. However, soliciting PMs’ perspectives provided neither a complete nor necessarily an accurate picture about implications and ways they can be addressed. To this end, future studies could consider embedding trained ethicists in the research team who can provide top-down guidance and inspiration (eg, contextualized ethics principles) during bottom-up engagement with users and other stakeholders [ 49 , 69 ]. Besides, future research could explore the perspectives of users on RI in the context of AI-based care technologies, such as DSSs, for instance, what values come to matter most to them, what positive and negative implications they foresee, how they perceive the urgency of (other) known implications in their context, and how they look at certain strategies to address implications (eg, see the study of Lukkien et al [ 70 ]). In doing so, the perspectives of stakeholders from different care contexts (eg, care organizations or countries) can be captured with sufficient detail and be compared to learn how to account for the context specificity of values in technology design and deployment [ 71 , 72 ]. In addition, the perspectives of people with dementia should be clarified, even when they are only a passive user of the technology (as is often the case with DSSs), and despite these people often having difficulties in expressing their needs [ 73 , 74 ].

Finally, even though all co-design activities and the RI exploration had already been completed by the time the objectives for this case study were established, the RI exploration had a minor effect on the co-design process. For instance, some co-design researchers were also participants in the RI exploration, which could have affected the co-design activities. Besides, at the request of DRML (who led the RI exploration), the usability study (co-design step 4) included 2 RI-related questions. In our results, we explicated that co-design participants already discussed more implications before these 2 questions were asked. Without this minor effect, there may have been a greater knowledge gap between the results from the co-design process and RI exploration in HAAL. However, to gain more robust results into the role of the anticipation of implications in co-design, future research could study co-design processes completely separately from an exploration of associated implications.

In this paper, we explored how the co-design process of an AI-based DSS for dementia caregivers is complemented by the explicit anticipation of social and ethical implications. Co-design is an essential means to feed the development and deployment of AI-based care technologies with insights about needs of targeted users and collectively translate these needs into requirements for technology design. Besides, as found in this empirical study, certain implications and strategies to address these implications may be naturally anticipated in co-design, even though users may not necessarily think in terms of implications or risks, but rather in terms of conditions before the technology can be used. At the same time, this case study indicates that a co-design process that focuses on opportunities rather than balancing attention for both positive and negative implications may result in knowledge gaps related to implications and how they can be addressed. In the pursuit of responsible outcomes, co-design facilitators could consider broadening the scope of co-design processes, for instance, by moving back and forth between opportunities and associated implications of innovation, involving future users and social advocacy groups in such an inquiry, and ensuring that co-design participants are provided with inspiration and have basic knowledge and skills to contribute to anticipating implications. Explicit anticipation of implications in co-design and broader inclusion of stakeholders in doing so increase opportunities for innovators to start addressing implications of innovation before the technology design has been locked in.

Acknowledgments

The authors gratefully acknowledge support from the Active and Assisted Living (AAL) program, cofinanced by the European Commission through the Horizon 2020 Societal Challenge Health, Demographic Change, and Wellbeing. In particular, the work reported here has been supported by the AAL Healthy Ageing Eco-system for People With Dementia (HAAL) project (AAL-2020-7-229-CP). In addition, the authors thank their HAAL project partners in the Netherlands, Italy, Taiwan, and Denmark for organizing and participating in the research activities that provided the basis for this study.

Abbreviations

Multimedia Appendix 1

Authors' Contributions: DRML contributed to conceptualization, methodology, validation, investigation, formal analysis, writing the original draft, reviewing and editing the manuscript, and funding acquisition. SIA contributed to methodology, investigation, formal analysis, and reviewing and editing the manuscript. NES contributed to methodology, investigation, and reviewing and editing the manuscript. BMH contributed to formal analysis and reviewing and editing the manuscript. HHN contributed to conceptualization, methodology, validation, reviewing and editing the manuscript, project administration, and funding acquisition. WPCB contributed to conceptualization, methodology, and reviewing and editing the manuscript. AP contributed to conceptualization, methodology, and reviewing and editing the manuscript. EHMM contributed to conceptualization, methodology, and reviewing and editing the manuscript. MMNM contributed to conceptualization and methodology. All authors contributed to writing (original draft).

Conflicts of Interest: None declared.

• Heron’s Formula Class 9 Case Study Questions Maths Chapter 10

Last Updated on September 8, 2024 by XAM CONTENT

Hello students, we are providing case study questions for class 9 maths. Case study questions are the new question format that is introduced in CBSE board. The resources for case study questions are very less. So, to help students we have created chapterwise case study questions for class 9 maths. In this article, you will find case study questions for CBSE Class 9 Maths Chapter 10 Heron’s Formula. It is a part of Case Study Questions for CBSE Class 9 Maths Series.

Table of Contents

Case Study Questions on Heron’s Formula

Mayank bought a triangle shape field and wants to grow potato and wheat on his field. He divided his field by joining opposite sides. On the largest park he grew wheat and on the rest part he grew potato. The dimensions of a park are shown in the park.

On the basis of the above information, solve the following questions:

Q 1. Find the length of AC in ΔABC.

Q 2. Find the area of ΔABC.

Q 3. If the cost of ploughing park is ₹5 per cm 2 , then find the total cost of ploughing the park.

1. In right angled $\triangle A B C$, use Pythagoras theorem,

$$ \begin{aligned} A C & =\sqrt{(A B)^2+(B C)^2}=\sqrt{(12)^2+(5)^2} \\ \vdots & =\sqrt{144+25}=\sqrt{169}=13 \mathrm{~m} \end{aligned} $$

Hence, length of $A C$ is 13 m .

2. Area of $\triangle \mathrm{ABC}$

$$ \begin{aligned} & =\frac{1}{2} \times A B \times B C \\ & =\frac{1}{2} \times 12 \times 5=30 \mathrm{~m}^2 \end{aligned} $$

3. Since, the total area of the park $=30 \mathrm{~m}^2$ $\because$ The cost of ploughing the park in $1 \mathrm{~m}^2=5$ $\therefore$ The cost of ploughing the park in $30 \mathrm{~m}^2$

$$ \begin{aligned} & = 5\times 30\\ & =150 \end{aligned} $$

Understanding Heron’s Formula

Area of Triangle: The total space occupied inside the boundary of the triangle is said to be an area of triangle.

Perimeter of Triangle: Sum of lengths of all three sides of a triangle.

$\begin{aligned} 2 s & =a+b+c \\ s & =\frac{a+b+c}{2}\end{aligned}$

Right-angled Triangle: It is a triangle with one right angle.

1. Area $=\frac{1}{2} \times a \times b$ 2. Altitude $=a$ 3. Perimeter$=a+b+\sqrt{a^2+b^2}$

where ‘a’ and ‘b’ are the sides that includes to the right angle.

Isosceles Triangle: Triangle that has two equal sides and corresponding two equal angles.

1. Area $=\frac{b}{4} \sqrt{4 a^2-b^2}$ 2. Perimeter $=2 a+b$ 3. Altitude $=\frac{1}{2} \sqrt{4 a^2-b^2}$

where ‘a’ is length of two equal sides and ‘b’ is base.

Equilateral Triangle: Triangle with all sides and all angles equal (each being 60°)

1. Area $=\frac{\sqrt{3}}{4} a^2$ 2. Perimeter $=3 a$ 3. Altitude $=\frac{\sqrt{3}}{2} a$

where ‘a’ is side.

Heron‘s Formula: The formula given by Heron about the area of a triangle.

Area of triangle $=\sqrt{s(s-a)(s-b)(s-c)}$

where a, b and c are the sides of triangle and s is its semi-perimeter.

Boost your knowledge

(i) The length of longest altitude is the perpendicular distance from the opposite vertex to the smallest side of a triangle. (ii) The length of smallest altitude is the perpendicular distance from the opposite vertex to the largest side of a triangle. (iii) Heron‘s formula is helpful when it is not possible to find the height of the triangle easily. (iv) Heron‘s formula is applicable to all types of triangles whether it is a right triangle or an isosceles or an equilateral triangle

• Circles Class 9 Case Study Questions Maths Chapter 9
• Quadrilaterals Class 9 Case Study Questions Maths Chapter 8
• Triangles Class 9 Case Study Questions Maths Chapter 7
• Lines and Angles Class 9 Case Study Questions Maths Chapter 6
• Introduction to Euclid’s Geometry Class 9 Case Study Questions Maths Chapter 5
• Linear Equations in Two Variables Class 9 Case Study Questions Maths Chapter 4
• Coordinate Geometry Class 9 Case Study Questions Maths Chapter 3

Polynomials Class 9 Case Study Questions Maths Chapter 2

Number systems class 9 case study questions maths chapter 1, topics from which case study questions may be asked.

• Quadrilaterals
• Parallelograms
• Properties of a parallelogram
• Mid-point theorem
• Converse of Mid-point theorem

A trapezium is not a parallelogram (as only one pair of opposite sides is parallel in a trapezium and we require both pairs to be parallel in a parallelogram)

Case study questions from the above given topic may be asked.

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Frequently Asked Questions (FAQs) on Heron’s Formula Case Study

Q1: what is heron’s formula.

A1: Heron’s Formula is used to calculate the area of a triangle when the lengths of all three sides are known. The formula is: Area of triangle $=\sqrt{s(s-a)(s-b)(s-c)}$

Q2: How is Heron’s Formula derived?

A2: Heron’s formula is derived from the general area formula of a triangle. It simplifies the calculation of the area without needing the height. This is particularly useful for triangles where the height is difficult to determine directly. The formula is based on the triangle’s semi-perimeter and each of its sides.

Q3: When should Heron’s Formula be used?

A3: Heron’s Formula should be used when the lengths of all three sides of a triangle are given, and the height is unknown. It allows you to find the area of any triangle (scalene, isosceles, or equilateral) as long as you know the sides.

Q4: Can Heron’s Formula be used for right-angled triangles?

A4: Yes, Heron’s Formula can be used for right-angled triangles, although for right-angled triangles, a simpler method involving the base and height (half the product of the base and height) can also be used to find the area.

Q5: What is the significance of the semi-perimeter in Heron’s Formula?

A5: The semi-perimeter (s) is half the perimeter of the triangle. It acts as a key variable in the formula, simplifying the calculation of the area by incorporating all three sides of the triangle in a single expression.

Q6: Can Heron’s Formula be applied to a quadrilateral?

A6: No, Heron’s Formula specifically applies to triangles. However, if a quadrilateral can be divided into two triangles, the area of each triangle can be found using Heron’s Formula, and the sum of the areas will give the total area of the quadrilateral.

Q7: How is Heron’s Formula applied in real-life problems?

A7: Heron’s Formula is used in fields like civil engineering, architecture, and land surveying where determining the area of irregular land plots or structures is necessary, especially when only the lengths of the boundaries are known.

Q8: Are there any online resources or tools available for practicing Circles case study questions?

A9: We provide case study questions for CBSE Class 9 Maths on our website. Students can visit the website and practice sufficient case study questions and prepare for their exams. If you need more case study questions, then you can visit Physics Gurukul website. they are having a large collection of case study questions for all classes.

Related Posts

• 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

Drucker AM, Fleming P, Chan AW. Research techniques made simple: assessing risk of bias in systematic reviews. J Invest Dermatol. 2016;136(11):e109–14.

Article   PubMed   CAS   Google Scholar  

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Article   PubMed   PubMed Central   Google Scholar  

Page MJ, McKenzie JE, Higgins JPT. Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review. BMJ Open. 2018;8(3):e019703.

Wang X, Chen Y, Yang N, Deng W, Wang Q, Li N, et al. Methodology and reporting quality of reporting guidelines: systematic review. BMC Med Res Methodol. 2015;15:74.

Zeng X, Zhang Y, Kwong JS, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015;8(1):2–10.

Article   PubMed   Google Scholar  

Turner L, Shamseer L, Altman DG, Schulz KF, Moher D. Does use of the CONSORT statement impact the completeness of reporting of randomised controlled trials published in medical journals? A Cochrane review Syst Rev. 2012;1:60.

Savovic J, Weeks L, Sterne JA, Turner L, Altman DG, Moher D, et al. Evaluation of the Cochrane collaboration’s tool for assessing the risk of bias in randomized trials: focus groups, online survey, proposed recommendations and their implementation. Syst Rev. 2014;3:37.

Arno A, Thomas J, Wallace B, Marshall IJ, McKenzie JE, Elliott JH. Accuracy and efficiency of machine learning-assisted risk-of-bias assessments in “real-world” systematic reviews : a noninferiority randomized controlled trial. Ann Intern Med. 2022;175(7):1001–9.

Jardim PSJ, Rose CJ, Ames HM, Echavez JFM, Van de Velde S, Muller AE. Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system. BMC Med Res Methodol. 2022;22(1):167.

Leenaars C, Hager C, Stafleu F, Nieraad H, Bleich A. A systematic review of the effect of cystic fibrosis treatments on the nasal potential difference test in animals and humans. Diagnostics (Basel). 2023;13(19):3098.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Leenaars CHC, Stafleu FR, Hager C, Nieraad H, Bleich A. A systematic review of animal and human data comparing the nasal potential difference test between cystic fibrosis and control. Sci Rep. 2024;14(1):9664.

Higgins JPT, Savović J, Page MJ, Elbers RG, Sterne JAC. Chapter 8: Assessing risk of bias in a randomized trial. Cochrane Handbook for Systematic Reviews of Interventions. 2022.

Google Scholar  

Hooijmans CR, Rovers MM, de Vries RB, Leenaars M, Ritskes-Hoitinga M, Langendam MW. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014;14:43.

Kilkenny C, Browne WJ, Cuthill IC, Emerson M, Altman DG. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 2010;8(6):e1000412.

Begg C, Cho M, Eastwood S, Horton R, Moher D, Olkin I, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement JAMA. 1996;276(8):637–9.

CAS   Google Scholar  

Leenaars C, Stafleu F, Bleich A. The nasal potential difference test for diagnosing cystic fibrosis and assessing disease severity: a systematic review. 2021.

Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.

Covidence systematic review software Melbourne, Australia: Veritas Health Innovation. Available from: www.covidence.org .

Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 20: updated guidelines for reporting animal research. J Cereb Blood Flow Metab. 2020;40(9):1769–77.

Knowles MR, Gatzy JT, Boucher RC. Aldosterone metabolism and transepithelial potential difference in normal and cystic fibrosis subjects. Pediatr Res. 1985;19(7):676–9.

Team RC. a language and environment for statistical computing. R Foundation for Statistical Computing. 2021.

RStudio_Team. RStudio: integrated development for R. Boston, MA.: RStudio, Inc.; 2019. Available from: http://www.rstudio.com/ .

Wickham H, Bryan J. readxl: read Excel files. R package version 1.3.1. 2019.

Wickham H, François R, Henry L, Müller K. dplyr: a grammar of data manipulation. R package version 1.0.3. 2021.

Wickham H, Girlich M. tidyr: tidy messy data. R package version 1.2.0. 2022.

Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.

Book   Google Scholar  

Chaltiel D. Crosstable: crosstables for descriptive analyses. R package version 0.5.0. 2022.

Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U, Donnan GA. Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. 2008;39(10):2824–9.

Leenaars C, Stafleu F, de Jong D, van Berlo M, Geurts T, Coenen-de Roo T, et al. A systematic review comparing experimental design of animal and human methotrexate efficacy studies for rheumatoid arthritis: lessons for the translational value of animal studies. Animals (Basel). 2020;10(6):1047.

Leenaars CHC, Kouwenaar C, Stafleu FR, Bleich A, Ritskes-Hoitinga M, De Vries RBM, et al. Animal to human translation: a systematic scoping review of reported concordance rates. J Transl Med. 2019;17(1):223.

Kerem E, Konstan MW, De Boeck K, Accurso FJ, Sermet-Gaudelus I, Wilschanski M, et al. Ataluren for the treatment of nonsense-mutation cystic fibrosis: a randomised, double-blind, placebo-controlled phase 3 trial. Lancet Respir Med. 2014;2(7):539–47.

Rowe SM, Liu B, Hill A, Hathorne H, Cohen M, Beamer JR, et al. Optimizing nasal potential difference analysis for CFTR modulator development: assessment of ivacaftor in CF subjects with the G551D-CFTR mutation. PLoS ONE. 2013;8(7): e66955.

Reese HW. Counterbalancing and other uses of repeated-measures Latin-square designs: analyses and interpretations. J Exp Child Psychol. 1997;64(1):137–58.

Article   Google Scholar  

Zeelenberg R, Pecher D. A method for simultaneously counterbalancing condition order and assignment of stimulus materials to conditions. Behav Res Methods. 2015;47(1):127–33.

Richardson JTE. The use of Latin-square designs in educational and psychological research. Educ Res Rev. 2018;24:84–97.

King G, Nielsen R, Coberley C, Pope JE, Wells A. Avoiding randomization failure in program evaluation, with application to the Medicare Health Support program. Popul Health Manag. 2011;14(Suppl 1):S11-22.

Meier B, Nietlispach F. Fallacies of evidence-based medicine in cardiovascular medicine. Am J Cardiol. 2019;123(4):690–4.

Van de Wall G, Van Hattem A, Timmermans J, Ritskes-Hoitinga M, Bleich A, Leenaars C. Comparing translational success rates across medical research fields - a combined analysis of literature and clinical trial data. Altex. 2023;40(4):584–94.

PubMed   Google Scholar  

Knowles MR, Gatzy JT, Boucher RC. Increased bioelectric potential differences across respiratory epithelia in cystic fibrosis. N Engl Med. 1981;305:1489–95.

Article   CAS   Google Scholar  

Unal-Maelger OH, Urbanek R. Status of determining the transepithelial potential difference (PD) of the respiratory epithelium in the diagnosis of mucoviscidosis. Monatsschr Kinderheilkd. 1988;136(2):76–80.

PubMed   CAS   Google Scholar  

Dorin JR, Dickinson P, Alton EW, Smith SN, Geddes DM, Stevenson BJ, et al. Cystic fibrosis in the mouse by targeted insertional mutagenesis. Nature. 1992;359(6392):211–5.

Alton EW, Middleton PG, Caplen NJ, Smith SN, Steel DM, Munkonge FM, et al. Non-invasive liposome-mediated gene delivery can correct the ion transport defect in cystic fibrosis mutant mice. Nat Genet. 1993;5(2):135–42.

Caplen NJ, Alton EW, Middleton PG, Dorin JR, Stevenson BJ, Gao X, et al. Liposome-mediated CFTR gene transfer to the nasal epithelium of patients with cystic fibrosis. Nat Med. 1995;1(1):39–46.

Leenaars C, Tsaioun K, Stafleu F, Rooney K, Meijboom F, Ritskes-Hoitinga M, et al. Reviewing the animal literature: how to describe and choose between different types of literature reviews. Lab Anim. 2021;55(2):129–41.

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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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Creation of Comprehensive Utilization Alternatives for Coal Mine Water Based on Multi-Criteria Decision Analysis: A Case Study of the Mengcun Coal Mine

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Jiangtao He

The efficient use of mine water is a critical part of alleviating water scarcity in the Yellow River Basin. The treatment cost and water quality of mine water significantly influence the selection of comprehensive reuse pathways. This paper summarizes the existing treatment process parameters and reuse pathways for high-TDS coal mine water, detailing the water quality limits and local regulatory requirements for each reuse pathway. By integrating the SWOT (strengths, weaknesses, opportunities, and threats) and PESTEL methods, this study determines the prioritization of reuse pathways. The aim of this study is to propose an alternative evaluation scheme for the comprehensive utilization of high-TDS coal mine water based on the combination of a "treatment process + reuse pathway" to improve utilization efficiency and mitigate water resource shortages in the Yellow River Basin. The methodology was validated through a case study of the Mengcun Coal Mine in Shaanxi Province, which is in the Yellow River Basin. By identifying potential reuse pathways and water consumption in the study area, three conventional treatment process chains and three zero discharge treatment process chains were selected. The prioritization of each comprehensive reuse pathway was determined through evaluation, resulting in the creation of six different alternative scenarios. Each scenario was analyzed for performance in terms of the water reuse rate, residual TDS, operation and maintenance costs, and CO2 emissions. The proposed methodology for creating alternative scenarios can be used to evaluate comprehensive industrial wastewater reuse systems under multiple criteria, providing scientific support to relevant decisionmakers.

Keywords: Comprehensive utilization of coal mine water, Creating alternatives, Treatment process chains, Reuse pathway prioritization, Multicriteria decision analysis.

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