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Quantitative study designs: Case Studies/ Case Report/ Case Series

Quantitative study designs.

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Case Study / Case Report / Case Series

Some famous examples of case studies are John Martin Marlow’s case study on Phineas Gage (the man who had a railway spike through his head) and Sigmund Freud’s case studies, Little Hans and The Rat Man. Case studies are widely used in psychology to provide insight into unusual conditions.

A case study, also known as a case report, is an in depth or intensive study of a single individual or specific group, while a case series is a grouping of similar case studies / case reports together.

A case study / case report can be used in the following instances:

• where there is atypical or abnormal behaviour or development
• an unexplained outcome to treatment
• an emerging disease or condition

The stages of a Case Study / Case Report / Case Series

Which clinical questions does Case Study / Case Report / Case Series best answer?

Emerging conditions, adverse reactions to treatments, atypical / abnormal behaviour, new programs or methods of treatment – all of these can be answered with case studies /case reports / case series. They are generally descriptive studies based on qualitative data e.g. observations, interviews, questionnaires, diaries, personal notes or clinical notes.

What are the advantages and disadvantages to consider when using Case Studies/ Case Reports and Case Series ?

What are the pitfalls to look for?

One pitfall that has occurred in some case studies is where two common conditions/treatments have been linked together with no comprehensive data backing up the conclusion. A hypothetical example could be where high rates of the common cold were associated with suicide when the cohort also suffered from depression.

Critical appraisal tools 

To assist with critically appraising Case studies / Case reports / Case series there are some tools / checklists you can use.

JBI Critical Appraisal Checklist for Case Series

JBI Critical Appraisal Checklist for Case Reports

Real World Examples

Some Psychology case study / case report / case series examples

Capp, G. (2015). Our community, our schools : A case study of program design for school-based mental health services. Children & Schools, 37(4), 241–248. A pilot program to improve school based mental health services was instigated in one elementary school and one middle / high school. The case study followed the program from development through to implementation, documenting each step of the process.

Cowdrey, F. A. & Walz, L. (2015). Exposure therapy for fear of spiders in an adult with learning disabilities: A case report. British Journal of Learning Disabilities, 43(1), 75–82. One person was studied who had completed a pre- intervention and post- intervention questionnaire. From the results of this data the exposure therapy intervention was found to be effective in reducing the phobia. This case report highlighted a therapy that could be used to assist people with learning disabilities who also suffered from phobias.

Li, H. X., He, L., Zhang, C. C., Eisinger, R., Pan, Y. X., Wang, T., . . . Li, D. Y. (2019). Deep brain stimulation in post‐traumatic dystonia: A case series study. CNS Neuroscience & Therapeutics. 1-8. Five patients were included in the case series, all with the same condition. They all received deep brain stimulation but not in the same area of the brain. Baseline and last follow up visit were assessed with the same rating scale.

References and Further Reading  

Greenhalgh, T. (2014). How to read a paper: the basics of evidence-based medicine. (5th ed.). New York: Wiley.

Heale, R. & Twycross, A. (2018). What is a case study? Evidence Based Nursing, 21(1), 7-8.

Himmelfarb Health Sciences Library. (2019). Study design 101: case report. Retrieved from https://himmelfarb.gwu.edu/tutorials/studydesign101/casereports.cfm

Hoffmann T., Bennett S., Mar C. D. (2017). Evidence-based practice across the health professions. Chatswood, NSW: Elsevier.

Robinson, O. C., & McAdams, D. P. (2015). Four functional roles for case studies in emerging adulthood research. Emerging Adulthood, 3(6), 413-420.

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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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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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.

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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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What is case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

Analyze case study research

Dovetail streamlines case study research to help you uncover and share actionable insights

• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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

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

Research bias

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

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

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

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative vs Quantitative Research Methods & Data Analysis

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The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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The case study approach

Sarah crowe.

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

Kathrin Cresswell

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

Ann Robertson

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

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

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

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

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

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

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

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

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

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

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

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

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

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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A geographical analysis of social enterprises: the case of Ireland

Social Enterprise Journal

ISSN : 1750-8614

Article publication date: 29 April 2024

Issue publication date: 4 July 2024

This study aims to conduct a geographical analysis of the distribution and type of activities developed by social enterprises in rural and urban areas of Ireland.

Design/methodology/approach

The study analyses data of more than 4,000 social enterprises against a six-tier rural/urban typology, using descriptive statistics and non-parametric tests to test six hypotheses.

The study shows a geographical rural–urban pattern in the distribution of social enterprises in Ireland, with a positive association between the remoteness of an area and the ratio of social enterprises, and a lack of capital-city effect related to the density of social enterprises. The analysis also shows a statistically significant geographical rural–urban pattern for the types of activities developed by social enterprises. The authors observe a positive association between the remoteness of the areas and the presence of social enterprises operating in the community and local development sector whereas the association is not significant for social enterprises developing welfare services.

Research limitations/implications

The paper shows the potential of using recently developed rural–urban typologies and tools such as geographical information systems for conducting geographical research on social enterprises. The findings also have implications for informing spatially sensitive policymaking on social enterprises.

Originality/value

The merging of a large national data set of social enterprises with geographical tools and data at subregional level contributes to the methodological advancement of the field of social enterprises, providing tools and frameworks for a nuanced and spatially sensitive analysis of these organisations.

• Rural social enterprises
• Urban social enterprises
• Quantitative research
• Social economy organisations

Olmedo, L. , O. Shaughnessy, M. and Holloway, P. (2024), "A geographical analysis of social enterprises: the case of Ireland", Social Enterprise Journal , Vol. 20 No. 4, pp. 499-521. https://doi.org/10.1108/SEJ-09-2023-0105

Emerald Publishing Limited

Copyright © 2024, Lucas Olmedo, Mary O. Shaughnessy and Paul Holloway.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Social and solidarity economy organisations, and especially social enterprises, have recently been brought to the fore by international institutions including the European Commission, the organisation for economic co-operation and development (OECD) and the United Nations ( European Commission, 2021 ; OECD, 2022 ; United Nations, 2023 ). These institutions acknowledge the contribution and potential of social enterprises to address complex challenges such as climate change, ageing population and lack of access to employment for vulnerable groups; namely, due to the combination of social and/or environmental aims with an economic activity and democratic decision-making which characterise social enterprises ( Galera and Borzaga, 2009 ; Defourny and Nyssens, 2017 ).

Social enterprises in Ireland have been traditionally considered relevant actors providing goods and services to disadvantaged communities and enabling work integration of vulnerable groups ( O’Hara and O’Shaughnessy, 2021 ). In 2019, the Irish Government launched the first National Social Enterprise Policy for Ireland, representing a milestone for the recognition and institutionalisation of social enterprises in the country ( Olmedo et al. , 2021 ). This policy establishes an official definition of social enterprises as follows:

An enterprise whose objective is to achieve a social, societal or environmental impact, rather than maximising profit for its owners or shareholders. It pursues its objectives by trading on an ongoing basis through the provision of goods and/or services, and by reinvesting surpluses into achieving social objectives. It is governed in a fully accountable and transparent manner and is independent of the public sector. If dissolved, it should transfer its assets to another organisation with a similar mission ( Government of Ireland, 2019 , p. 8).

This policy recognises, in line with previous research reports on Irish social enterprises ( Hynes, 2016 ; European Commission, 2020 ), the contribution of Irish social enterprises to deliver a wide range of goods and services, as well as supporting the attainment of government policy goals in areas such as labour market activation but also in health care, climate action, social cohesion and rural development.

Despite common features shared across social enterprises, previous research has highlighted differences between social enterprises operating in rural and urban areas in terms of their community focus, leadership style and funding sources ( Smith and McColl, 2016 ; Barraket et al. , 2019 ). The geographical context where social enterprises operate has been acknowledged as a relevant factor for explaining the work of these organisations ( Steiner and Teasdale, 2019 ; Olmedo et al. , 2023 ) and their contribution to urban and rural development ( Angelidou and Mora, 2019 ; Olmedo and O’Shaughnessy, 2022 ). Geographically sensitive research on social enterprises has been developed mainly at the local level ( Mazzei, 2017 ; Jammulamadaka and Chakraborty, 2018 ; Pinch and Sunley, 2016 ), with some research also conducted at the regional level ( Buckingham et al. , 2011 ; Woo and Jung, 2023 ); however, less is known about the differences in the distribution and the type of activities that social enterprises develop in different rural–urban areas of a country. Therefore, the aim of this paper is to explore the distribution and type of activities developed by social enterprises in different rural and urban areas in Ireland.

To achieve this, we use a six-tier rural–urban typology developed by the Irish Central Statistics Office ( CSO, 2019 ) combined with data on 4,335 social enterprises collected in Ireland. Using geographical information systems (GIS) we georeferenced social enterprises and tested six hypotheses. The spatially sensitive and quantitative empirical data analysis provided by this study adds knowledge to previous calls for geographical research on social enterprises ( Munoz, 2010 ) and provides relevant evidence for the development of spatially sensitive policies for social enterprises ( Mazzei and Roy, 2017 ).

The rest of the paper is structured as follows, Section 2 presents a literature review on previous geographical research on social enterprises. Section 3 outlines the research framework and the hypotheses of this study. Section 4 explains the methodology used in the research. Section 5 presents the findings of this study, with a subsection presenting descriptive statistics and another presenting the analysis of the hypotheses’ tested. Section 6 discusses the findings and Section 7 outlines the conclusions and limitations of this study, ending with some proposals for further research.

2. Literature review – geographical research on social enterprises

The publication in 2010 of a seminal call “Towards a geographical research agenda for social enterprise” ( Munoz, 2010 ) meant a significant milestone for the development of a geographically sensitive perspective towards the study of social enterprises. Within this body of research [ 1 ], some authors have adopted a micro-geographical perspective to study social enterprises as spaces of well-being ( Munoz et al. , 2015 ). For example, Farmer et al. (2020) used GIS to link specific sites within a social enterprise to the well-being experienced by the employees of three Australian Work Integration Social Enterprises. Their findings show how the social enterprises studied acted as “socially-supportive workplaces which focus on deploying, developing and supporting talents and not simply allocating people to one job in one location for all time” ( Farmer et al. , 2020 , p. 9).

Another stream of studies has focused on the local geography of social enterprises ( Jammulamadaka and Chakraborty, 2018 ). Some of these studies have a specific urban focus. For example, Pinch and Sunley (2016) investigated whether social enterprises in four major UK cities benefited from urban agglomeration effects, concluding that agglomeration enables greater demand for social enterprises goods and services and better access to institutional support, funding, knowledge and networks. Similarly, Mazzei (2017) stressed the influence of “place” on the incentives and opportunities for two social enterprises operating within English cities.

Previous research has also taken a geographical perspective to study social enterprises in rural areas. Drawing from social network theory, Richter (2019) showed how social enterprises operating in rural Austria and Poland act as embedded intermediaries between their localities and supra-regional networks. In studies conducted in rural Scotland, Steiner and Steinerowska-Streb (2012) and Steiner and Teasdale (2019) stated that rural areas are a fertile ground for social enterprises due to characteristics associated to rurality, such as reduced market competitors and high levels of social capital. Moreover, these studies further explain how rural social enterprises use advantages of the rural context, such as the skills and knowledge of retired people who moved to rural localities, to develop social entrepreneurial activities. In a study conducted in rural Scotland exploring social enterprises in addressing social isolation and loneliness, Kelly et al. (2019) concluded that despite these organisations offer more flexible solutions than statutory services, relying on social enterprises as solutions to these challenges is not realistic. This was posited to features associated with the rural context of the study, such as remoteness, small labour markets and depopulation.

This echoes research on social enterprises in rural Ireland conducted by O’Shaughnessy and O’Hara (2016) , who stated that geographic isolation and limited job creation associated to the rural context challenges the development of social enterprises. More recently, Olmedo et al. (2023) showed how social enterprises in three Irish rural localities, through a process of “placial substantive hybridity”, harness and (re)valorise untapped local resources and complement these with extra-local resources to foster social innovation and contribute to an integrated development of their localities.

Geographical research has also been conducted comparing social enterprises operating in rural and urban localities. Smith and McColl (2016) explored the influence of the context in four social enterprises based in Scottish urban and rural communities. The authors found that rural social enterprises show a great linkage between the geographical characteristics of where they are based, their community identity and ownership and type of business developed. Contrarily in the urban social enterprises they studied, it was a social need rather than a geographical aspect which drove the organisations’ aim. In a study conducted in Australia, Barraket et al. (2019) compared 11 locally oriented urban and rural social enterprises resourcefulness strategies. The authors showed the great relevance of community networks within rural based social enterprises to access financial and physical assets; however, those social enterprises based in urban areas were more inclined to leverage public funding related to welfare objectives and resources from corporates.

Despite the plethora of research investigating social enterprises at the urban and rural levels, few studies have researched social enterprises through a regional perspective. In this regard, Buckingham et al. (2011) attempted to unmask the “enigmatic regional geography of social enterprises in the UK” using statistical data from different surveys related to social enterprises conducted between 2005 and 2009. The authors concluded that interregional variations (north–south and east–west) were relatively small and without statistical significance; except for high levels of social enterprise activity in London due to its dynamic and innovative business environment and the effect that headquarters location of national social enterprises (mainly in London) might have in the data. More recently, Woo and Jung (2023) have explored the regional determinants of the emergence of social enterprises in South Korea. Combining longitudinal data sets (2012–2019) from the Korea Social Enterprise Promotion Agency and Korea Statistics and using an entrepreneurial ecosystems perspective, the authors concluded that the emergence of social enterprises is especially significant in regions experiencing government or market failure and in regions with greater incidences of start-ups, human capital and financial resources.

At the national (country) and international level, research on social enterprises has been mainly conducted from an institutional perspective, influenced by the seminal work of Kerlin (2013) and the international comparative social enterprise models project ( Defourny and Nyssens, 2017 ; Defourny et al. , 2020 ), with scarce studies adopting a geographical perspective. A notable exception can be found in a study conducted by Douglas et al. (2018) exploring social enterprises in Fiji, in which the geography of the country, a small remote island in the Pacific Ocean, is considered (together with its history, social, economic, political and cultural institutions) a determinant factor shaping social enterprises in the country.

In summary (see Table 1 ), the review of the literature shows how geographical research on social enterprises has been conducted at various levels, from micro-organisational to national level; however, to-date this research has predominantly focused on the influence of local geographical elements in shaping the work of social enterprises. Within the local level, urban and rural localities have been subject to research and some differences have been identified in the ways rural–urban social enterprises operate. Regarding the methodologies used by studies, most geographical research on social enterprises have used qualitative methods, with some exceptions in studies that take a regional perspective. In these instances, studies have predominantly used existing survey data and registers of social enterprises ( Woo and Jung, 2023 ). In terms of theoretical perspectives, some studies are based on economic geography theories such as agglomeration and cluster theory (e.g. Pinch and Sunley, 2016 ; Jammulamadaka and Chakraborty, 2018 ) and concepts such as “place” borrowed from human geography (e.g. Mazzei, 2017 ; Olmedo et al. , 2023 ). However, generally the studies reviewed rather use theories from disciplines such as sociology, e.g. social network theory, and business/entrepreneurship, e.g. entrepreneurial ecosystems, complementing these with spatially sensitive elements such as the use of methodological tools such as GIS in their analysis ( Farmer et al. , 2020 ), the multi-scalar analysis of networks ( Richter, 2019 ) or a spatial rural–urban comparison of the cases studied ( Barraket et al. , 2019 ).

Despite the significant progress of geographical research on social enterprises in recent years, studies have focused on how geographical elements of the context influence the features and work of social enterprises, rather than exploring the basic and critical question (for research and policy) of how social enterprises are geographically distributed, and why. According to Buckingham et al. (2011 , p. 90), it “seems likely that the most significant geographical differences in the distribution of social enterprises are to be found at the sub-regional level […] and there is clearly a need for further, more fine-grained investigation”, see also Steiner et al. (2019) . This study aims to fill this gap for the case of Ireland by exploring the distribution and type of activities developed by social enterprises in rural and urban areas. To do so, we draw from a combination of increasingly complex thinking about rural–urban spatial heterogeneity, the advancement of methodological tools for rural–urban spatial classification at sub-regional level and from statistical information gathered on Irish social enterprises.

3. Research framework and hypothesis

3.1 territorial, rural–urban and classifications.

This paper is based on a geographical perspective towards the study of social enterprises in Ireland, and more specifically on the analysis of social enterprises in rural and urban areas. The definition of what constitutes a rural and urban area has been subject to extensive debate (see, for example, Mantino et al. , 2023 ; Eurostat, 2021 ). Within Europe there is no definitive agreement between Member States of what is considered as a rural/urban area; for example, in Ireland, rural areas are defined in terms of settlements with a population of less than 1,500 persons ( CSO, 2019 ), whereas in Spain rural areas are considered as those municipalities with less than 5,000 inhabitants but also those with less than 30,000 inhabitants and a density lower than 100 inhabitants/km 2 ( Government of Spain, 2007 ). These definitions classify rural–urban areas mainly in terms of population densities.

population density;

the percentage of the population of a region living in rural communities; and

the presence of large urban centres in such regions.

According to these criteria, NUTS 3 [ 2 ] regions are classified into Predominantly Rural; Intermediate and Predominantly Urban ( OECD, 2006 ) [ 3 ]. This methodology has been revised by Eurostat (2021) incorporating finer-grain data at Local Administrative Units Level 2 (LAU2) and grid cells of 1 km 2 to categorised territories into cities, towns, semi-dense areas and rural areas. Eurostat (2021) has also included a further subclassification based on population density and size. Towns and semi-dense areas were sub-divided into dense towns, semi-dense towns and suburban or peri-urban areas. Rural areas were also sub-divided into villages, dispersed rural areas and mostly inhabited rural areas. This finer analysis allows for a more precise analysis of the rural–urban continuum overcoming an abrupt differentiation between urban and rural areas but approaching it rather as a continuum that acknowledges the heterogeneity of rural and urban areas.

Besides the classification of rural–urban areas based on population density and size, classifications based on the functions and relations between areas have also been developed ( Mantino et al. , 2023 ). These classifications tend to incorporate indicators related to economic factors, for example, the economic growth/decline, the degree of productive activities (agriculture, forestry, manufacturing and construction) and consumption activities (tourism, recreation, housing and services) ( Copus et al. , 2011 ). Environmental indicators, for example, related to ecosystems functions (climate regulation, water supply and regulation, soil retention and formation, biodiversity) are also incorporated to classify rural–urban areas based on their (multi)functionality ( Mantino et al. , 2023 ). A key aspect of the relationship between rural–urban areas includes the mobility of workers and the access to services. In this regard indicators of proximity related, for example, to the time needed to access to services and infrastructures have also been considered in the classification of rural–urban areas ( Eurostat, 2021 ).

These functional classifications are usually interlinked with the abovementioned rural–urban classifications based on population density creating increasingly nuanced typologies through the multiple criteria that reflects the complexity of relationships between urban and rural areas ( Perpiñá Castillo et al. , 2022 ). In this line, the Central Statistics Office of Ireland (CSO) developed in 2019 a six-tier rural–urban typology ( CSO, 2019 ). This typology was developed using the place of work as a measure of distance to services and amenities, combined with population density from Census 2016. The typology is applied to small area population (SAP), and includes the following six categories: cities, satellite urban towns, independent urban towns, rural areas with high urban influence, rural areas with moderate rural influence and highly rural/remote rural areas (see Table 2 ).

3.2 Hypothesis development

Our study uses the typology developed by the CSO to conduct a geographical analysis of social enterprises in Ireland. Based on this framework, and some of the characteristics of social enterprises presented in the literature review of this paper, six hypotheses have been developed.

Previous studies have suggested that social enterprises are influenced by their geographical context with differences in the spread of social enterprises in rural and urban areas ( Buckingham et al. , 2011 ; CEIS and Social Value Lab, 2023 ). Some studies stress that rural areas represent a fertile ground for social enterprises ( Steiner and Steinerowska-Streb, 2012 ) and that social enterprises tend to emerge and develop in regions experiencing government or market failure ( Woo and Jung, 2023 ). However, the studies of Buckingham et al. (2011) and Pinch and Sunley (2016) suggest a capital-city effect attraction for social enterprises due to its dynamic and innovative business environment, the presence of headquarters location of national social enterprises, greater demand for social enterprises goods and services and better access to institutional support, funding, knowledge and networks, therefore, more supportive social entrepreneurial ecosystems (see also Diaz Gonzalez and Dentchev, 2020 ).

States that the presence of social enterprises is significantly associated with the type of rural–urban areas.

States that the presence of social enterprises is positively associated with areas with lower population density and greater distance to services and amenities (remoteness).

States that the presence of social enterprises within the capital city (Dublin) is significantly higher compared to the national average and to other rural and urban areas of Ireland.

Previous research has also pointed towards the influence of the geographical context in the activities developed by social enterprises ( Mazzei, 2017 ; Smith and McColl, 2016 ). Looking at rural–urban differences and the sector of activities of social enterprises, research has highlighted the key role of social enterprises in community and local development in (remote) rural areas ( van Twuijver et al. , 2020 ; Olmedo et al. , 2023 ) and in providing services related to welfare objectives in urban centres ( Barraket et al. , 2019 ).

States that there is a significant relationship between the sectors of activities in which social enterprises operate and the type of rural–urban areas in which they are based.

States that there is a positive association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises in the sector of community and local development.

States that there is a negative association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises operating in sectors related to welfare objectives.

4. Methodology

Nationwide data on Irish social enterprises were obtained from a social enterprise baseline data collection exercise conducted in 2022. This baseline data collection exercise followed a bottom-up methodology in which a population of social enterprises for Ireland was built from social enterprises lists provided by 36 intermediary organisations and public institutions delivering social enterprise programmes [ 4 ]. The population of social enterprises included 4,335 organisations, geographical-location information was gathered for 4,234 social enterprises and data about their sector of activity was gathered for 4,329 organisations.

Location information of social enterprises was georeferenced using organisation’s Eircodes (postal code/zip code equivalent for Ireland), thus allowing for a precise geolocation. The Eircode was either provided by the social enterprises or when not available the address of the organisation was introduced on the website “Eircode finder” to obtain the Eircode. Geographical coordinates for each Eircode were obtained using ArcGIS Online. Once the geographical coordinates were obtained each social enterprise was mapped using the software QGIS [ 5 ].

Data related to the CSO rural–urban typology containing information about the type of area (six categories) and population [ 6 ] was obtained from the Ordnance Survey Ireland – Open Data Portal [ 7 ]. The rural–urban typology developed by the CSO (2019) used in this study was applied to small area levels. Small areas are the lowest level of geography for the compilation of statistics by the CSO in line with data protection guidelines and typically contain between 80 and 120 dwellings ( CSO, 2019 ). A shapefile with small areas ungeneralised – National Statistical Boundaries was used, this contains a subdivision of the territory of the Republic of Ireland into 18,641 small areas. Information of small areas was vectorised and mapped using QGIS. Information about the six rural–urban categories was joined to each small area within QGIS and a choropleth map was created to differentiate between the types of rural–urban areas. Colours from light green (rural areas with high urban influence) to dark green (highly rural/remote areas) were used for rural areas, whereas dark red was used for cities, light red for satellite urban towns and pink for independent urban towns (see Figure 1 ).

The statistical analysis of this study includes three variables: type of rural–urban area, presence of social enterprises and sector of activities of social enterprises. As the aforementioned six-tier typology combines population density with distance to services and amenities, the categories have been ordered according to their level of remoteness, creating a dummy ordinal variable in which cities are converted into 1 (less remote) and highly rural/remote areas into 6 (most remote). The presence of social enterprises was calculated by the ratio of social enterprises divided by 10,000 inhabitants, following international guidelines from previous social enterprises census/baseline studies (see, for example, CEIS and Social Value Lab, 2023 ). The activities of social enterprises were codified following sectoral categories from the Scottish social enterprise census. This decision was made given the similarities between the countries (Scotland and Ireland) and the long experience of Scotland in constructing this census.

Statistical analysis for this study was conducted using the software R, version 4.2.2, within RStudio. We conducted a descriptive analysis of the variables before undertaking bivariate analysis of the variables to test our hypotheses. Due to the (partially categorical) nature of our data, we used non-parametric statistical tests such as Kruskal–Wallis H test, including post hoc Dunn’s test, chi-square test and Jonckheere–Terpstra test to investigate our hypotheses. The specific tests used for testing each hypothesis are explained in the following section.

5.1 Descriptive statistics

Social enterprises are distributed across rural and urban areas of Ireland (see Figure 2 ). In terms of total number, social enterprises are often concentrated in counties with the most populated Irish cities, such as Dublin (17.9% of total social enterprises) and Cork (10.5%) (see Figure 3 ). However, when considering the ratio of social enterprises by population (social enterprises/10,000 inhabitants), higher ratios of social enterprises are found, namely, in the north and northwest of the country (see Figure 4 ) and in counties with a high density of rural areas, such as Leitrim (26.2 social enterprises per 10,000 inhabitants), Donegal (18.5), Monaghan (17.3) and Mayo (16.5).

The descriptive analysis of social enterprises in relation to the rural–urban typology (see Table 3 ), shows that rural areas present a higher ratio of social enterprises (10.8 social enterprises per 10,000 inhabitants) than urban areas (8.0). However, the ratios show important differences when analysing the rural and urban subcategories, with highly rural/remote areas having a ratio of 21.0 social enterprises per 10,000 inhabitants against the 5.9 social enterprises per 10,000 inhabitants of rural areas with high urban influence. Within urban areas, independent urban towns have a higher concentration of social enterprises (12.9), than cities (6.7) and satellite urban towns (4.9).

The descriptive statistical analysis of the sector of activities of Irish social enterprises also shows some differences between rural–urban areas (see Table 4 ). For example, over 20% of social enterprises within each type of rural areas focus on community infrastructure and local development, whereas only 7.9% of social enterprises in cities operate within this sector. On the other hand, approximately 20% of social enterprises in cities and satellite urban towns develop activities related to health, youth services and social care, whereas in rural areas less than 10% of social enterprises operate within this sector. Social enterprises in sectors such as training and work integration, and information and support services are more prominent in cities, approximately 10% of city-based social enterprises operate in these sectors, whereas these sectors represent less than 5% of the total social enterprises based in Irish rural areas.

5.2 Hypothesis testing

Based on previous literature we developed six hypotheses to be tested related to the distribution and sectors of activities of social enterprises in rural and urban areas in Ireland (see Appendix for the results of the statistical test conducted).

H1 stated that the presence of social enterprises (measured by the ratio of social enterprises per 10,000 inhabitants) is significantly associated with the type of rural–urban areas (operationalised following the six-tier typology developed by the Irish CSO). To analyse this hypothesis a Kruskal–Wallis H test, a non-parametric version of ANOVA suitable for assessing the differences among three or more groups of a categorical/ordinal variable (rural–urban typology) related to a non-normally distributed continuous variable (social enterprise ratio), was conducted ( Vargha and Delaney, 1998 ). The results from this test show a statistically significant relationship between the variables ( p < 0.01), supporting H1 . As the rural–urban areas typology is formed by six categories, a post hoc Dunn test (adjusted with Bonferroni) ( Dinno, 2022 ) was conducted to compare the relationship between each of the pair categories. The results from this test show a significant relationship between all categories except for “cities and satellite urban towns” and “cities and rural areas with high urban”.

H2 refers to the positive association between the presence (ratio) of social enterprises and areas with lower population density and greater distance to services and amenities (remoteness). The six rural–urban categories have been ordered into a dummy variable from 1 to 6 according to their degree of “remoteness”. To test the (positive) directional association between the ratio of social enterprises and the rural–urban areas according to their degree of “remoteness” a Jonckheere–Terpstra test, a non-parametric test similar to Kruskal–Wallis H test, but preferred when the groups are assumed to be arranged in order (ascendent or descendent), was conducted ( Ali et al. , 2015 ). The results show a significant positive association ( p < 0.01) between the remoteness of the rural–urban areas studied and the presence (ratio) of social enterprises, supporting H2 .

H3 refers to the significantly higher presence (ratio) of social enterprises within the capital city (Dublin) compared to the national average and to other rural–urban areas of Ireland. To test this hypothesis, first, we calculated the ratio of social enterprises for the specific SAPs belonging to the category “cities” within County Dublin which accounts for 6.2 social enterprises per 10,000 inhabitants. Although social enterprises based in the city of Dublin represent 16.4% of total Irish social enterprises, the ratio of social enterprises in the city of Dublin (6.2) is below the national average (9.0) and lower than in other urban areas, including other Irish cities of more than 50,000 inhabitants (8.3) and independent urban towns (12.9). The ratio of social enterprises in Dublin city is also lower than in rural areas with moderate urban influence (9.9) and highly rural/remote areas (21.0).

Alternatively, the ratio of social enterprises in Dublin city is higher than in satellite urban towns (4.9) and rural areas with high rural influence (5.9). To analyse the statistical significance between the ratios of Dublin city and the categories with lower ratios we used Welch’s two-sample t -test, suitable for comparing means of groups with unequal variances ( Lu and Yuan, 2010 ). The results show no statistically significant difference between these means ( p > 0.05), thus H3 was not supported.

H4 refers to the significant relationship between the sectors of activities in which social enterprises operate and the type of rural–urban areas in which they are based. Due to the categorical nature of both variables, a Pearson chi-square test (test of independence) was conducted ( Franke et al. , 2012 ). The results show a statistical significance relationship between the variables ( p < 0.01), supporting H4 .

H5 refers to a positive association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises in the community and local development sector and; H6 refers to a negative association between areas with lower population density and greater distance to services and amenities (remoteness) and the presence of social enterprises operating in sectors associated with welfare objectives such as “childcare” and “health, youth services and social care”. We followed the procedure explained in H2 of using a dummy variable to order the rural–urban categories according to their remoteness. Social enterprises within the category “community infrastructure and local development” were used to test H5 . Data of social enterprises from two categories, i.e. “childcare”, and “health, youth services and social care”, were used to test H6 .

To test the directional association between the ratio of social enterprises in community and local development ( H5 ) and in welfare services ( H6 ) with the rural–urban areas according to their degree of “remoteness” a Jonckheere–Terpstra test ( Ali et al. , 2015 ) was conducted. The results show a statistically significant relationship ( p < 0.05) for the variables of H5 , supporting this hypothesis. However, results for H6 were not statistically significant ( p > 0.05), thus this hypothesis was not supported.

In summary, our statistical analysis shows support for four of our six hypotheses (see Table 5 ). The hypothesis supported by our statistical analysis show a geographical rural–urban pattern in the distribution of social enterprises in Ireland ( H1 ) with a positive statistically significant association between the remoteness of the area and the ratio of social enterprises ( H2 ). However, our analysis suggests that there is not a capital effect that attracts a higher ratio of social enterprises to Dublin city ( H3 ). The statistical analysis also shows a geographical rural–urban pattern between the types of activities developed by social enterprises and the type of areas where they are based ( H4 ), with a positive association between the degree of remoteness of the area where social enterprises are based and the ratio of social enterprises in the community and local development sector ( H5 ). However, our analysis does not support a negative association between the degree of remoteness of the areas and the ratio of social enterprises in activities related to welfare services such as childcare and health, youth services and social care ( H6 ).

6. Discussion

The aim of this paper is to explore the distribution and type of activities developed by social enterprises in different rural and urban areas in Ireland. The results from our analysis show distinctive rural–urban patterns in the distribution of these organisations. Our research advances previous regional analysis of social enterprises ( Buckingham et al. , 2011 ) through the provision of fine-grained statistical data at subregional level and with a focus on heterogeneous rural and urban areas instead of following regional/county administrative divisions. The use of the six-tier rural–urban typology and the geo-localisation of social enterprises provides detailed evidence which can be used as a base by regional development actors and public authorities to develop targeted measures for social enterprises in geographically diverse areas ( Mazzei and Roy, 2017 ; Steiner and Teasdale, 2019 ).

Our results show the positive association between the presence of social enterprises and the degree of remoteness (low density of population and low access to services and amenities). These results align with previous studies that suggested rural areas and regions characterised by state and market failure as fertile grounds for social enterprises. ( Steiner and Steinerowska-Streb, 2012 ; Woo and Jung, 2023 ). Our study does not support the hypothesis that the capital city, in this case Dublin, with its greater entrepreneurial and innovation ecosystem acts as a significant area of social enterprises development – at least relative to its population. This result contradicts the analysis of Buckingham et al. (2011) which stressed the greater presence of social enterprises in London compared to other UK regions due to its capital effect.

Our results show the relevance of social enterprises in “lagged behind areas” and their aim to respond to unsatisfied needs, especially of marginalised people and territories ( Olmedo et al. , 2023 ). The great presence of social enterprises in these remote territories has meant the development of a wide range of services and community infrastructure which otherwise would have not been provided to the local population ( Aiken et al. , 2016 ; van Twuijver et al. , 2020 ). However, the presence of social enterprises cannot be automatically related to a greater capacity of these areas to overcome their challenges. Previous studies on rural social enterprises have shown their great potential to contribute to a socially inclusive and territorial integrated development when cooperating with other development actors including for-profit businesses and public institutions; however, these previous studies also show the incapacity of rural social enterprises to change, by themselves, structural-exogenous forces affecting marginalised territories ( Bock, 2016 ; Olmedo and O’Shaughnessy, 2022 ).

Our analysis of social enterprises by sectors of activities in different geographical areas does not show a relationship between social enterprises operating in urban areas and their greater focus on welfare objectives, contrary to the findings of Barraket et al. (2019) . It is important to note than in Ireland (community) childcares represent an important number of social enterprises (over 25%) and these are spread across the whole territory without a clear distinctive geographical pattern. Descriptive statistics by sectors of activity show that social enterprises focusing on activities related to health, youth services and social care represent over 10% in urban areas and only approximately 5% in rural areas which would be more in line with the results of Barraket et al. (2019) in Australia and Smith and McColl (2016) in Scotland when comparing urban and rural social enterprises.

Our results also show a significant focus of social enterprises on remote and rural areas in community and local development activities. This aligns with previous research on rural social enterprises that stress the relevance of community social entrepreneurship in rural territories ( Peredo and Chrisman, 2006 ) and the important role of rural (community-based) social enterprises in local development ( O’Shaughnessy et al. , 2011 ; Steiner and Teasdale, 2019 ; van Twuijver et al. , 2020 ). The significant developmental role of social enterprises in rural areas aligns with a key feature of rural social enterprises, which is their tendency to merge social, economic and/or environmental aims, contributing to an integrated territorial development ( Olmedo et al. , 2023 ). However, this significant focus of social enterprises in rural areas on community and local development activities often implies the development of basic infrastructure and services that are usually provided by public administrations in urban areas ( Bock, 2016 ). Thus social enterprises can, in this instance, be interpreted as a substitute arising from the absence and/or retrenchment of the state and public services ( Roy and Grant, 2019 ); this, in turn, can create an overburden to the citizens of these areas and increase the disparities between those better equipped and vulnerable social groups and territories ( Bock, 2016 ).

7. Conclusions, limitations and further research

This paper explored the distribution and sectors of activity of social enterprises in Ireland against a six-tier rural–urban typology that combines population density and access to services and amenities, adding a timely contribution to the body of geographical research on social enterprises. We suggest that the combination of national data of social enterprises with geographical tools and data at subregional level contributes to the methodological advancement of the field of social enterprises, through the provision of tools and frameworks for a nuanced and spatially sensitive analysis of these organisations. Moreover, this study contributes to testing, through a quantitative analysis, hypotheses developed from the findings of previous geographical research on social enterprises.

Our findings show geographical patterns in the distributions of social enterprises, such as their greater presence in highly rural/remote areas and the lack of a capital city effect in terms of density of social enterprises. Our analysis also shows a geographical rural–urban differentiation in terms of sectors of activity, with social enterprises in the community and local development sector being especially relevant in rural areas. Against this evidence, we conclude that social enterprise policies should incorporate territorially sensitive and place-based measures that account for the diversity of rural and urban areas. To this end, the alignment of social enterprises and rural development policies is a key aspect for harnessing the potential of these organisations in rural areas. However, we also conclude that there is great scope for the development of social enterprises in specific sectors in rural and remote areas, such as the creative industry, sustainable agri-food and the circular economy. The development of social enterprises within these sectors is linked to fostering a more socially and territorially inclusive society, but also to wider aspects related to the twin (digital and green) transitions.

This study is not absent of limitations. Social enterprises are context-specific, and the rural–urban typology used in this study was created by the Irish CSO with specific criteria. This makes international comparability difficult and any generalization of the results from this study to other contexts/countries should be taken with caution. Interestingly the Scottish Social Enterprise Census (latest version is of 2021) also follows a six-tier rural–urban typology, showing an important presence of social enterprises in remote rural areas; however, the use of different indicators for developing the Scottish rural–urban typology does not allow for a rigorous comparison with the data shown in this study. Recently developed methodologies such as the Global Human Settlement Layer by the Joint Research Centre of the European Commission ( Dijkstra et al. , 2021 ), which harmonise indicators for urban and rural areas to support consistent international comparisons across countries represent an interesting avenue for further research that compares geographical patterns of social enterprises in different countries. In this regard, the increasing amount of geolocation information and geographically sensitive data collection on social enterprises, and more generally on social economy organisations, can also represent an important advancement for future research.

A final suggestion for further research relates to the combination of geographical and institutional frameworks for the (quantitative) study of spatial patterns in social enterprises that can inform place-based social enterprise policies. This study can be further developed by isolating specific clusters of social enterprises at regional level and exploring their impact on the development of their areas and the critical factors supporting and/or hindering this impact.

Map rural–urban typology for the Republic of Ireland

Map of social enterprises by rural–urban typology

Map total number of social enterprises by county

Map ratio of social enterprises by county

Summary of literature on geographical research on social enterprises