meaning importance of case study

What Is The Purpose Of A Case Study?

A case study serves the purpose of deeply examining a specific subject or phenomenon within its real-life context. By meticulously analyzing a single case in detail, researchers aim to gain a comprehensive understanding of the complexities, nuances, and dynamics involved in the subject matter under investigation. Case studies offer a unique opportunity to explore and elucidate how various factors interact and influence outcomes, providing valuable insights that can inform theories, practices, and decision-making processes within academic disciplines and professional fields alike. They offer a means to investigate rare or exceptional situations, shed light on causal processes, and generate rich empirical data, all of which contribute to the advancement of knowledge and understanding.

Table of Contents

Definition of a case study

A case study is a research strategy that involves an in-depth examination of a particular individual, group, organization, or event. It aims to provide comprehensive and detailed insights into the chosen subject of study, delving into the complexities of real-life situations. A case study typically involves multiple data sources, including interviews, observations, documents, and other relevant materials. The findings of a case study can be used to generate insights, develop solutions, and inform decision-making processes.

Importance of case studies

Case studies hold significant importance in various academic fields, including psychology, sociology, business, and medicine, among others. They provide researchers with a unique opportunity to explore complex phenomena in their natural settings, allowing for a thorough understanding of real-life situations. Furthermore, case studies enable researchers to test and refine theories, explore new perspectives, and generate knowledge that can be applied in practical contexts. They also help bridge the gap between theory and practice, offering practitioners valuable insights and lessons learned.

Understanding the Problem

Identifying the research question.

Before embarking on a case study, it is crucial to identify a clear research question that will guide the investigation. The research question should be specific, focused, and relevant to the field of study. It should address an existing knowledge gap or seek to uncover new insights. The research question will serve as a compass throughout the case study, ensuring that the investigation remains focused and coherent.

Exploring the background and context

To fully understand the problem at hand, it is essential to explore the background and context surrounding the chosen case study subject. This involves gathering information about the historical, social, economic, and cultural factors that may influence the subject. By comprehensively examining the context, researchers can gain a deeper understanding of the complexities and dynamics involved, allowing for a more nuanced analysis of the case.

Research Design

Choosing the case study approach.

When designing a case study, researchers must choose the appropriate approach that aligns with the research question and objectives. There are several types of case study approaches, including exploratory, explanatory, and descriptive. Exploratory case studies aim to generate hypotheses and explore new areas of research. Explanatory case studies seek to determine cause-and-effect relationships. Descriptive case studies aim to provide a detailed account of a specific phenomenon. The chosen approach will shape the overall research design and methodology.

Selecting the appropriate case

Selecting the appropriate case for study is a critical decision that impacts the validity and generalizability of the findings. Researchers must consider various factors when selecting a case, such as relevance, uniqueness, and feasibility. The case should be relevant to the research question and offer valuable insights into the phenomena of interest. It should also possess unique characteristics or features that make it worthy of investigation. Additionally, the feasibility of accessing data and conducting the study should be carefully evaluated.

Data Collection

Determining data sources.

Data sources play a crucial role in case study research. These sources can include interviews, observations, documents, archival records, and other relevant materials. Typically, a combination of primary and secondary data sources is used to provide a comprehensive understanding of the case. Primary data sources involve firsthand information collected directly from participants or through direct observations. Secondary data sources involve pre-existing information that is analyzed in relation to the case study.

Collecting primary data

Collecting primary data involves engaging with participants or observing the case firsthand. This can be achieved through various methods, such as interviews, focus groups, surveys, or participant observation. Interviews allow researchers to gather detailed information and explore participants’ perspectives, experiences, and motivations. Focus groups provide a platform for participants to engage in group discussions and share insights. Surveys offer a structured way to collect data from a larger sample. Participant observation involves immersing oneself in the case study environment to directly observe and record behaviors and interactions.

Gathering secondary data

Secondary data sources complement primary data and enhance the richness of the case study. These sources include existing documents, archival records, scholarly articles, industry reports, and other relevant materials. Researchers must carefully select and analyze secondary data, ensuring it aligns with the research question and complements the primary data. A thorough examination of secondary data can contribute to a comprehensive understanding of the case and provide historical or background contextual information.

Data Analysis

Applying data analysis techniques.

Data analysis is a crucial step in case study research and involves transforming raw data into meaningful insights. Various data analysis techniques can be employed, including thematic analysis, content analysis, narrative analysis, and statistical analysis, among others. Thematic analysis involves identifying and categorizing themes or patterns within the data. Content analysis focuses on identifying and analyzing specific words, phrases, or concepts within the data. Narrative analysis seeks to uncover the underlying stories and narratives that emerge from the data. Statistical analysis involves quantifying and analyzing numerical data.

Identifying patterns and themes

During the data analysis process, researchers must carefully examine the data to identify patterns, themes, and relationships. This involves organizing and categorizing the data based on recurring ideas, concepts, or patterns that emerge. By identifying these patterns and themes, researchers can gain insights into the relationships and dynamics present in the case study. It allows for a deeper understanding of the complexities and nuances within the data and supports the generation of meaningful conclusions.

Generating Insights

Linking findings to research question.

The findings derived from the data analysis should be linked back to the research question and objectives of the case study. It is essential to establish the relevance and significance of the findings in relation to the original research question. By establishing this link, researchers can validate the findings and ensure their alignment with the objectives of the study. This step is crucial for generating insights that contribute to the existing knowledge base and address the research question effectively.

Drawing meaningful conclusions

Drawing meaningful conclusions from the case study involves synthesizing the key findings and deriving insights from the analysis. Researchers must critically evaluate the findings, considering their strengths and limitations, and interpret them in light of the research question and relevant literature. The conclusions should be justified and supported by empirical evidence. Meaningful conclusions will contribute to a deeper understanding of the case, provide practical implications, and pave the way for further research or the development of solutions.

Developing Solutions

Identifying potential solutions.

Based on the insights generated from the case study, researchers can identify potential solutions to the problem at hand. These solutions should be grounded in empirical evidence and address the key issues identified through the research. It is crucial to consider multiple perspectives and approaches when identifying potential solutions, evaluating their feasibility, effectiveness, and ethical implications. The solutions should align with the objectives of the case study and offer practical recommendations for addressing the problem.

Evaluating feasibility and effectiveness

After identifying potential solutions, it is important to evaluate their feasibility and effectiveness. This involves considering the resources, constraints, and practical implications associated with implementing the proposed solutions. Feasibility assessment involves evaluating whether the proposed solutions can be realistically implemented within the given context, timeframe, and available resources. Effectiveness evaluation involves assessing the potential impact of the solutions and their ability to address the identified problem.

Knowledge Application

Informing decision-making.

The findings and insights derived from a case study can be instrumental in informing decision-making processes. Decision-makers can draw upon the knowledge generated through the case study to make informed choices and develop strategies. The detailed analysis of the case, combined with the empirical evidence and practical implications, provides decision-makers with valuable insights and evidence-based recommendations. By utilizing the knowledge gained from case studies, decision-makers can optimize outcomes and enhance the effectiveness of their decisions.

Sharing lessons learned

Case studies also serve as a valuable source of knowledge dissemination. Sharing the lessons learned from a case study can benefit researchers, practitioners, academics, and other stakeholders in the field. By presenting the findings, insights, and recommendations, case studies contribute to the existing knowledge base, spark further discussions, and inspire new research. Sharing lessons learned facilitates the exchange of ideas, promotes collaboration, and supports ongoing learning and development within the respective field.

Strengths and Limitations

Highlighting advantages of case studies.

Case studies offer various advantages as a research method. They provide researchers with the opportunity to explore real-life phenomena in their natural context, offering a deep understanding of complex situations. Case studies can generate rich and detailed data, allowing for in-depth analysis and insights. They also provide a holistic perspective, considering multiple factors and variables. Case studies are particularly useful for exploring complex and dynamic phenomena that cannot be easily captured through quantitative methods.

Addressing potential biases

Like any research method, case studies are not without limitations. One potential limitation is the presence of biases in the data collection and analysis process. Researchers must be aware of their own biases and take steps to minimize their influence on the findings. To address this limitation, researchers can engage in reflexivity, seeking to critically evaluate their own perspectives and assumptions throughout the research process. Additionally, triangulation, the use of multiple data sources and perspectives, can help mitigate potential biases and enhance the validity of the findings.

Promoting Further Research

Building on existing knowledge.

Case studies often uncover new areas of research and generate additional questions for further investigation. Researchers can build on existing knowledge by exploring gaps identified through the case study and proposing new research avenues. The in-depth analysis and insights gained from the case study can inform the development of hypotheses or theories, which can then be tested through quantitative research methods. By building on existing knowledge, researchers contribute to the advancement of the field and foster ongoing exploration and discovery.

Exploring new perspectives

Case studies provide an opportunity to explore new perspectives and alternative approaches to understanding a phenomenon. Researchers can use the detailed analysis and insights gained from a case study to challenge existing theories or assumptions and propose new perspectives. This exploration of new perspectives can lead to innovative insights and alternative explanations for complex phenomena. By embracing diverse perspectives and exploring new avenues, researchers can push the boundaries of knowledge and stimulate new lines of inquiry.

In conclusion, case studies serve as a valuable research strategy for gaining an in-depth understanding of complex phenomena. By employing a systematic approach for each stage of the case study process, researchers can ensure rigor, validity, and relevance to the research question. Case studies have the potential to generate rich insights, inform decision-making, and contribute to the existing knowledge base within various academic fields. However, it is important to acknowledge the strengths and limitations of case studies and continually strive to promote further research and exploration of new perspectives.

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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

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

Table of Contents

What is a Case Study?  

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

When to do a Case Study?  

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

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

How to Create a Case Study – Step by Step  

Step 1: select a case  .

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

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

Step 2: Build a Theoretical Framework  

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

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

Step 3: Collect Your Data  

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

Step 4: Describe and Analyze the Case  

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

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

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

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

What Are the Benefits of a Case Study   

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

1. In-Depth Analysis  

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

2. Contextual Insight  

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

3. Flexibility  

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

4. Practical Solutions  

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

5. Unique Cases  

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

What Are the Limitations of a Case Study   

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

1. Limited Generalizability  

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

2. Subjectivity  

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

3. Time-Consuming  

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

4. Resource Intensive  

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

5. Replication Difficulties  

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

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

Key Takeaways  

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

Frequently Asked Questions  

Q: What is a case study in research?  

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

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

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

Q: Can case studies be used in quantitative research?  

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

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

A case study typically includes several key components:   

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

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

References  

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

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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• Introduction

The case study creation process

Types of case studies, benefits and limitations.

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

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

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

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

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

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

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

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

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

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

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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

While several factors make HBS Online unique —including a global Community and real-world outcomes —active learning through the case study method rises to the top.

In a 2023 City Square Associates survey, 74 percent of HBS Online learners who also took a course from another provider said HBS Online’s case method and real-world examples were better by comparison.

Here’s a primer on the case method, five benefits you could gain, and how to experience it for yourself.

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What Is the Harvard Business School Case Study Method?

The case study method , or case method , is a learning technique in which you’re presented with a real-world business challenge and asked how you’d solve it. After working through it yourself and with peers, you’re told how the scenario played out.

HBS pioneered the case method in 1922. Shortly before, in 1921, the first case was written.

“How do you go into an ambiguous situation and get to the bottom of it?” says HBS Professor Jan Rivkin, former senior associate dean and chair of HBS's master of business administration (MBA) program, in a video about the case method . “That skill—the skill of figuring out a course of inquiry to choose a course of action—that skill is as relevant today as it was in 1921.”

Originally developed for the in-person MBA classroom, HBS Online adapted the case method into an engaging, interactive online learning experience in 2014.

In HBS Online courses , you learn about each case from the business professional who experienced it. After reviewing their videos, you’re prompted to take their perspective and explain how you’d handle their situation.

You then get to read peers’ responses, “star” them, and comment to further the discussion. Afterward, you learn how the professional handled it and their key takeaways.

Learn more about HBS Online's approach to the case method in the video below, and subscribe to our YouTube channel for more.

HBS Online’s adaptation of the case method incorporates the famed HBS “cold call,” in which you’re called on at random to make a decision without time to prepare.

“Learning came to life!” said Sheneka Balogun , chief administration officer and chief of staff at LeMoyne-Owen College, of her experience taking the Credential of Readiness (CORe) program . “The videos from the professors, the interactive cold calls where you were randomly selected to participate, and the case studies that enhanced and often captured the essence of objectives and learning goals were all embedded in each module. This made learning fun, engaging, and student-friendly.”

If you’re considering taking a course that leverages the case study method, here are five benefits you could experience.

5 Benefits of Learning Through Case Studies

1. take new perspectives.

The case method prompts you to consider a scenario from another person’s perspective. To work through the situation and come up with a solution, you must consider their circumstances, limitations, risk tolerance, stakeholders, resources, and potential consequences to assess how to respond.

Taking on new perspectives not only can help you navigate your own challenges but also others’. Putting yourself in someone else’s situation to understand their motivations and needs can go a long way when collaborating with stakeholders.

2. Hone Your Decision-Making Skills

Another skill you can build is the ability to make decisions effectively . The case study method forces you to use limited information to decide how to handle a problem—just like in the real world.

Throughout your career, you’ll need to make difficult decisions with incomplete or imperfect information—and sometimes, you won’t feel qualified to do so. Learning through the case method allows you to practice this skill in a low-stakes environment. When facing a real challenge, you’ll be better prepared to think quickly, collaborate with others, and present and defend your solution.

3. Become More Open-Minded

As you collaborate with peers on responses, it becomes clear that not everyone solves problems the same way. Exposing yourself to various approaches and perspectives can help you become a more open-minded professional.

When you’re part of a diverse group of learners from around the world, your experiences, cultures, and backgrounds contribute to a range of opinions on each case.

On the HBS Online course platform, you’re prompted to view and comment on others’ responses, and discussion is encouraged. This practice of considering others’ perspectives can make you more receptive in your career.

“You’d be surprised at how much you can learn from your peers,” said Ratnaditya Jonnalagadda , a software engineer who took CORe.

In addition to interacting with peers in the course platform, Jonnalagadda was part of the HBS Online Community , where he networked with other professionals and continued discussions sparked by course content.

“You get to understand your peers better, and students share examples of businesses implementing a concept from a module you just learned,” Jonnalagadda said. “It’s a very good way to cement the concepts in one's mind.”

4. Enhance Your Curiosity

One byproduct of taking on different perspectives is that it enables you to picture yourself in various roles, industries, and business functions.

“Each case offers an opportunity for students to see what resonates with them, what excites them, what bores them, which role they could imagine inhabiting in their careers,” says former HBS Dean Nitin Nohria in the Harvard Business Review . “Cases stimulate curiosity about the range of opportunities in the world and the many ways that students can make a difference as leaders.”

Through the case method, you can “try on” roles you may not have considered and feel more prepared to change or advance your career .

5. Build Your Self-Confidence

Finally, learning through the case study method can build your confidence. Each time you assume a business leader’s perspective, aim to solve a new challenge, and express and defend your opinions and decisions to peers, you prepare to do the same in your career.

According to a 2022 City Square Associates survey , 84 percent of HBS Online learners report feeling more confident making business decisions after taking a course.

“Self-confidence is difficult to teach or coach, but the case study method seems to instill it in people,” Nohria says in the Harvard Business Review . “There may well be other ways of learning these meta-skills, such as the repeated experience gained through practice or guidance from a gifted coach. However, under the direction of a masterful teacher, the case method can engage students and help them develop powerful meta-skills like no other form of teaching.”

How to Experience the Case Study Method

If the case method seems like a good fit for your learning style, experience it for yourself by taking an HBS Online course. Offerings span eight subject areas, including:

• Business essentials
• Leadership and management
• Entrepreneurship and innovation
• Digital transformation
• Finance and accounting
• Business in society

No matter which course or credential program you choose, you’ll examine case studies from real business professionals, work through their challenges alongside peers, and gain valuable insights to apply to your career.

Are you interested in discovering how HBS Online can help advance your career? Explore our course catalog and download our free guide —complete with interactive workbook sections—to determine if online learning is right for you and which course to take.

About the Author

What Is a Case Study and Why You Should Use Them

Case studies can provide more insights into your business while helping you conduct further research with robust qualitative data analysis to learn more.

If you're in charge of running a company, then you're likely always looking for new ways to run your business more efficiently and increase your customer base while streamlining as many processes as possible.

Unfortunately, it can sometimes be difficult to determine how to go about implementing the proper program in order to be successful. This is why many business owners opt to conduct a case study, which can help significantly. Whether you've been struggling with brand consistency or some other problem, the right case study can identify why your problem exists as well as provide a way to rectify it.

A case study is a great tool that many businesses aren't even aware exists, and there are marketing experts like Mailchimp who can provide you with step-by-step assistance with implementing a plan with a case study. Many companies discover that not only do they need to start a blog in order to improve business, but they also need to create specific and relevant blog titles.

If your company already has a blog, then optimizing your blog posts may be helpful. Regardless of the obstacles that are preventing you from achieving all your professional goals, a case study can work wonders in helping you reverse this issue.

What is a case study?

A case study is a comprehensive report of the results of theory testing or examining emerging themes of a business in real life context. Case studies are also often used in the healthcare industry, conducting health services research with primary research interest around routinely collected healthcare data.

However, for businesses, the purpose of a case study is to help small business owners or company leaders identify the issues and conduct further research into what may be preventing success through information collection, client or customer interviews, and in-depth data analysis.

Knowing the case study definition is crucial for any business owner. By identifying the issues that are hindering a company from achieving all its goals, it's easier to make the necessary corrections to promote success through influenced data collection.

Why are case studies important?

Now that we've answered the questions, "what is a case study?" Why are case studies important? Some of the top reasons why case studies are important include:

• Understand complex issues: Even after you conduct a significant amount of market research , you might have a difficult time understanding exactly what it means. While you might have the basics down, conducting a case study can help you see how that information is applied. Then, when you see how the information can make a difference in business decisions, it could make it easier to understand complex issues.
• Collect data: A case study can also help with data tracking . A case study is a data collection method that can help you describe the information that you have available to you. Then, you can present that information in a way the reader can understand.
• Conduct evaluations: As you learn more about how to write a case study, remember that you can also use a case study to conduct evaluations of a specific situation. A case study is a great way to learn more about complex situations, and you can evaluate how various people responded in that situation. By conducting a case study evaluation, you can learn more about what has worked well, what has not, and what you might want to change in the future.
• Identify potential solutions: A case study can also help you identify solutions to potential problems. If you have an issue in your business that you are trying to solve, you may be able to take a look at a case study where someone has dealt with a similar situation in the past. For example, you may uncover data bias in a specific solution that you would like to address when you tackle the issue on your own. If you need help solving a difficult problem, a case study may be able to help you.

Remember that you can also use case studies to target your audience . If you want to show your audience that you have a significant level of expertise in a field, you may want to publish some case studies that you have handled in the past. Then, when your audience sees that you have had success in a specific area, they may be more likely to provide you with their business. In essence, case studies can be looked at as the original method of social proof, showcasing exactly how you can help someone solve their problems.

What are the benefits of writing a business case study?

Although writing a case study can seem like a tedious task, there are many benefits to conducting one through an in depth qualitative research process.

• Industry understanding: First of all, a case study can give you an in-depth understanding of your industry through a particular conceptual framework and help you identify hidden problems that are preventing you from transcending into the business world.
• Develop theories: If you decide to write a business case study, it provides you with an opportunity to develop new theories. You might have a theory about how to solve a specific problem, but you need to write a business case study to see exactly how that theory has unfolded in the past. Then, you can figure out if you want to apply your theory to a similar issue in the future.
• Evaluate interventions: When you write a business case study that focuses on a specific situation you have been through in the past, you can uncover whether that intervention was truly helpful. This can make it easier to figure out whether you want to use the same intervention in a similar situation in the future.
• Identify best practices: If you want to stay on top of the best practices in your field, conducting case studies can help by allowing you to identify patterns and trends and develop a new list of best practices that you can follow in the future.
• Versatility: Writing a case study also provides you with more versatility. If you want to expand your business applications, you need to figure out how you respond to various problems. When you run a business case study, you open the door to new opportunities, new applications, and new techniques that could help you make a difference in your business down the road.
• Solve problems: Writing a great case study can dramatically improve your chances of reversing your problem and improving your business.
• These are just a few of the biggest benefits you might experience if you decide to publish your case studies. They can be an effective tool for learning, showcasing your talents, and teaching some of your other employees. If you want to grow your audience , you may want to consider publishing some case studies.

What are the limitations of case studies?

Case studies can be a wonderful tool for any business of any size to use to gain an in-depth understanding of their clients, products, customers, or services, but there are limitations.

One limitation of case studies is the fact that, unless there are other recently published examples, there is nothing to compare them to since, most of the time, you are conducting a single, not multiple, case studies.

Another limitation is the fact that most case studies can lack scientific evidence.

Types of case studies

There are specific types of case studies to choose from, and each specific type will yield different results. Some case study types even overlap, which is sometimes more favorable, as they provide even more pertinent data.

Here are overviews of the different types of case studies, each with its own theoretical framework, so you can determine which type would be most effective for helping you meet your goals.

Explanatory case studies

Explanatory case studies are pretty straightforward, as they're not difficult to interpret. This type of case study is best if there aren't many variables involved because explanatory case studies can easily answer questions like "how" and "why" through theory development.

Exploratory case studies

An exploratory case study does exactly what its name implies: it goes into specific detail about the topic at hand in a natural, real-life context with qualitative research.

The benefits of exploratory case studies are limitless, with the main one being that it offers a great deal of flexibility. Having flexibility when writing a case study is important because you can't always predict what obstacles might arise during the qualitative research process.

Collective case studies

Collective case studies require you to study many different individuals in order to obtain usable data.

Case studies that involve an investigation of people will involve many different variables, all of which can't be predicted. Despite this fact, there are many benefits of collective case studies, including the fact that it allows an ongoing analysis of the data collected.

Intrinsic case studies

This type of study differs from the others as it focuses on the inquiry of one specific instance among many possibilities.

Many people prefer these types of case studies because it allows them to learn about the particular instance that they wish to investigate further.

Instrumental case studies

An instrumental case study is similar to an intrinsic one, as it focuses on a particular instance, whether it's a person, organization, or something different.

One thing that differentiates instrumental case studies from intrinsic ones is the fact that instrumental case studies aren't chosen merely because a person is interested in learning about a specific instance.

Tips for writing a case study

If you have decided to write case studies for your company, then you may be unsure of where to start or which type to conduct.

However, it doesn't have to be difficult or confusing to begin conducting a case study that will help you identify ways to improve your business.

Here are some helpful tips for writing your case studies:

1. Your case study must be written in the proper format

When writing a case study, the format that you should be similar to this:

Administrative summary

The executive summary is an overview of what your report will contain, written in a concise manner while providing real-life context.

Despite the fact that the executive summary should appear at the beginning of your case studies, it shouldn't be written until you've completed the entire report because if you write it before you finish the report, this summary may not be completely accurate.

Key problem statement

In this section of your case study, you will briefly describe the problem that you hope to solve by conducting the study. You will have the opportunity to elaborate on the problem that you're focusing on as you get into the breadth of the report.

Problem exploration

This part of the case study isn't as brief as the other two, and it goes into more detail about the problem at hand. Your problem exploration must include why the identified problem needs to be solved as well as the urgency of solving it.

Additionally, it must include justification for conducting the problem-solving, as the benefits must outweigh the efforts and costs.

Proposed resolution

This case study section will also be lengthier than the first two. It must include how you propose going about rectifying the problem. The "recommended solution" section must also include potential obstacles that you might experience, as well as how these will be managed.

Furthermore, you will need to list alternative solutions and explain the reason the chosen solution is best. Charts can enhance your report and make it easier to read, and provide as much proof to substantiate your claim as possible.

Overview of monetary consideration

An overview of monetary consideration is essential for all case studies, as it will be used to convince all involved parties why your project should be funded. You must successfully convince them that the cost is worth the investment it will require. It's important that you stress the necessity for this particular case study and explain the expected outcome.

Execution timeline

In the execution times of case studies, you explain how long you predict it will take to implement your study. The shorter the time it will take to implement your plan, the more apt it is to be approved. However, be sure to provide a reasonable timeline, taking into consideration any additional time that might be needed due to obstacles.

Always include a conclusion in your case study. This is where you will briefly wrap up your entire proposal, stressing the benefits of completing the data collection and data analysis in order to rectify your problem.

2. Make it clear and comprehensive

You want to write your case studies with as much clarity as possible so that every aspect of the report is understood. Be sure to double-check your grammar, spelling, punctuation, and more, as you don't want to submit a poorly-written document.

Not only would a poorly-written case study fail to prove that what you are trying to achieve is important, but it would also increase the chances that your report will be tossed aside and not taken seriously.

3. Don't rush through the process

Writing the perfect case study takes time and patience. Rushing could result in your forgetting to include information that is crucial to your entire study. Don't waste your time creating a study that simply isn't ready. Take the necessary time to perform all the research necessary to write the best case study possible.

Depending on the case study, conducting case study research could mean using qualitative methods, quantitative methods, or both. Qualitative research questions focus on non-numerical data, such as how people feel, their beliefs, their experiences, and so on.

Meanwhile, quantitative research questions focus on numerical or statistical data collection to explain causal links or get an in-depth picture.

It is also important to collect insightful and constructive feedback. This will help you better understand the outcome as well as any changes you need to make to future case studies. Consider using formal and informal ways to collect feedback to ensure that you get a range of opinions and perspectives.

4. Be confident in your theory development

While writing your case study or conducting your formal experimental investigation, you should have confidence in yourself and what you're proposing in your report. If you took the time to gather all the pertinent data collected to complete the report, don't second-guess yourself or doubt your abilities. If you believe your report will be amazing, then it likely will be.

5. Case studies and all qualitative research are long

It's expected that multiple case studies are going to be incredibly boring, and there is no way around this. However, it doesn't mean you can choose your language carefully in order to keep your audience as engaged as possible.

If your audience loses interest in your case study at the beginning, for whatever reason, then this increases the likelihood that your case study will not be funded.

Case study examples

If you want to learn more about how to write a case study, it might be beneficial to take a look at a few case study examples. Below are a few interesting case study examples you may want to take a closer look at.

• Phineas Gage by John Martin Marlow : One of the most famous case studies comes from the medical field, and it is about the story of Phineas Gage, a man who had a railroad spike driven through his head in 1848. As he was working on a railroad, an explosive charge went off prematurely, sending a railroad rod through his head. Even though he survived this incident, he lost his left eye. However, Phineas Gage was studied extensively over the years because his experiences had a significant, lasting impact on his personality. This served as a case study because his injury showed different parts of the brain have different functions.
• Kitty Genovese and the bystander effect : This is a tragic case study that discusses the murder of Kitty Genovese, a woman attacked and murdered in Queens, New York City. Shockingly, while numerous neighbors watched the scene, nobody called for help because they assumed someone else would. This case study helped to define the bystander effect, which is when a person fails to intervene during an emergency because other people are around.
• Henry Molaison and the study of memory : Henry Molaison lost his memory and suffered from debilitating amnesia. He suffered from childhood epilepsy, and medical professionals attempted to remove the part of his brain that was causing his seizures. He had a portion of his brain removed, but it completely took away his ability to hold memories. Even though he went on to live until the age of 82, he was always forced to live in the present moment, as he was completely unable to form new memories.

Case study FAQs

When should you do a case study.

There are several scenarios when conducting a case study can be beneficial. Case studies are often used when there's a "why" or "how" question that needs to be answered. Case studies are also beneficial when trying to understand a complex phenomenon, there's limited research on a topic, or when you're looking for practical solutions to a problem.

How can case study results be used to make business decisions?

You can use the results from a case study to make future business decisions if you find yourself in a similar situation. As you assess the results of a case study, you can identify best practices, evaluate the effectiveness of an intervention, generate new and creative ideas, or get a better understanding of customer needs.

How are case studies different from other research methodologies?

When compared to other research methodologies, such as experimental or qualitative research methodology, a case study does not require a representative sample. For example, if you are performing quantitative research, you have a lot of subjects that expand your sample size. If you are performing experimental research, you may have a random sample in front of you. A case study is usually designed to deliberately focus on unusual situations, which allows it to shed new light on a specific business research problem.

Writing multiple case studies for your business

If you're feeling overwhelmed by the idea of writing a case study and it seems completely foreign, then you aren't alone. Writing a case study for a business is a very big deal, but fortunately, there is help available because an example of a case study doesn't always help.

Mailchimp, a well-known marketing company that provides comprehensive marketing support for all sorts of businesses, can assist you with your case study, or you can review one of their own recently published examples.

Mailchimp can assist you with developing the most effective content strategy to increase your chances of being as successful as possible. Mailchimp's content studio is a great tool that can help your business immensely.

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

What is the definition of a case study.

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

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

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

Case Study Examples

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

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

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

Need Help with Writing a Case Study?

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

What Are the Types of Case Studies?

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

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

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

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

What Are the Methods of a Case Study?

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

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

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

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

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

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

Benefits and Limitations of Case Studies

The benefits of case studies are as follows:

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

Disadvantages of Case Studies

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

Importance of Case Study

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

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• What is a case study?
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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2017-102845

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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

• Gustafsson J
• Calanzaro M
• Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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What Is a Case Study?

Weighing the pros and cons of this method of research

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

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

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

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

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

At a Glance

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

What Are the Benefits and Limitations of Case Studies?

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

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

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

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

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

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

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

Case Study Examples

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

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

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

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

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

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

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

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

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

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

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

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

Section 1: A Case History

This section will have the following structure and content:

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

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

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

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

Section 2: Treatment Plan

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

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

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

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

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

Need More Tips?

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

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

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

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

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

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

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

Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

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

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

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

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

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

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

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

 Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

A case study research paper examines a person, place, event, condition, phenomenon, or other type of subject of analysis in order to extrapolate  key themes and results that help predict future trends, illuminate previously hidden issues that can be applied to practice, and/or provide a means for understanding an important research problem with greater clarity. A case study research paper usually examines a single subject of analysis, but case study papers can also be designed as a comparative investigation that shows relationships between two or more subjects. The methods used to study a case can rest within a quantitative, qualitative, or mixed-method investigative paradigm.

Case Studies. Writing@CSU. Colorado State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010 ; “What is a Case Study?” In Swanborn, Peter G. Case Study Research: What, Why and How? London: SAGE, 2010.

How to Approach Writing a Case Study Research Paper

General information about how to choose a topic to investigate can be found under the " Choosing a Research Problem " tab in the Organizing Your Social Sciences Research Paper writing guide. Review this page because it may help you identify a subject of analysis that can be investigated using a case study design.

However, identifying a case to investigate involves more than choosing the research problem . A case study encompasses a problem contextualized around the application of in-depth analysis, interpretation, and discussion, often resulting in specific recommendations for action or for improving existing conditions. As Seawright and Gerring note, practical considerations such as time and access to information can influence case selection, but these issues should not be the sole factors used in describing the methodological justification for identifying a particular case to study. Given this, selecting a case includes considering the following:

• The case represents an unusual or atypical example of a research problem that requires more in-depth analysis? Cases often represent a topic that rests on the fringes of prior investigations because the case may provide new ways of understanding the research problem. For example, if the research problem is to identify strategies to improve policies that support girl's access to secondary education in predominantly Muslim nations, you could consider using Azerbaijan as a case study rather than selecting a more obvious nation in the Middle East. Doing so may reveal important new insights into recommending how governments in other predominantly Muslim nations can formulate policies that support improved access to education for girls.
• The case provides important insight or illuminate a previously hidden problem? In-depth analysis of a case can be based on the hypothesis that the case study will reveal trends or issues that have not been exposed in prior research or will reveal new and important implications for practice. For example, anecdotal evidence may suggest drug use among homeless veterans is related to their patterns of travel throughout the day. Assuming prior studies have not looked at individual travel choices as a way to study access to illicit drug use, a case study that observes a homeless veteran could reveal how issues of personal mobility choices facilitate regular access to illicit drugs. Note that it is important to conduct a thorough literature review to ensure that your assumption about the need to reveal new insights or previously hidden problems is valid and evidence-based.
• The case challenges and offers a counter-point to prevailing assumptions? Over time, research on any given topic can fall into a trap of developing assumptions based on outdated studies that are still applied to new or changing conditions or the idea that something should simply be accepted as "common sense," even though the issue has not been thoroughly tested in current practice. A case study analysis may offer an opportunity to gather evidence that challenges prevailing assumptions about a research problem and provide a new set of recommendations applied to practice that have not been tested previously. For example, perhaps there has been a long practice among scholars to apply a particular theory in explaining the relationship between two subjects of analysis. Your case could challenge this assumption by applying an innovative theoretical framework [perhaps borrowed from another discipline] to explore whether this approach offers new ways of understanding the research problem. Taking a contrarian stance is one of the most important ways that new knowledge and understanding develops from existing literature.
• The case provides an opportunity to pursue action leading to the resolution of a problem? Another way to think about choosing a case to study is to consider how the results from investigating a particular case may result in findings that reveal ways in which to resolve an existing or emerging problem. For example, studying the case of an unforeseen incident, such as a fatal accident at a railroad crossing, can reveal hidden issues that could be applied to preventative measures that contribute to reducing the chance of accidents in the future. In this example, a case study investigating the accident could lead to a better understanding of where to strategically locate additional signals at other railroad crossings so as to better warn drivers of an approaching train, particularly when visibility is hindered by heavy rain, fog, or at night.
• The case offers a new direction in future research? A case study can be used as a tool for an exploratory investigation that highlights the need for further research about the problem. A case can be used when there are few studies that help predict an outcome or that establish a clear understanding about how best to proceed in addressing a problem. For example, after conducting a thorough literature review [very important!], you discover that little research exists showing the ways in which women contribute to promoting water conservation in rural communities of east central Africa. A case study of how women contribute to saving water in a rural village of Uganda can lay the foundation for understanding the need for more thorough research that documents how women in their roles as cooks and family caregivers think about water as a valuable resource within their community. This example of a case study could also point to the need for scholars to build new theoretical frameworks around the topic [e.g., applying feminist theories of work and family to the issue of water conservation].

Eisenhardt, Kathleen M. “Building Theories from Case Study Research.” Academy of Management Review 14 (October 1989): 532-550; Emmel, Nick. Sampling and Choosing Cases in Qualitative Research: A Realist Approach . Thousand Oaks, CA: SAGE Publications, 2013; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Seawright, Jason and John Gerring. "Case Selection Techniques in Case Study Research." Political Research Quarterly 61 (June 2008): 294-308.

Structure and Writing Style

The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case studies may also be used to reveal best practices, highlight key programs, or investigate interesting aspects of professional work.

In general, the structure of a case study research paper is not all that different from a standard college-level research paper. However, there are subtle differences you should be aware of. Here are the key elements to organizing and writing a case study research paper.

I.  Introduction

As with any research paper, your introduction should serve as a roadmap for your readers to ascertain the scope and purpose of your study . The introduction to a case study research paper, however, should not only describe the research problem and its significance, but you should also succinctly describe why the case is being used and how it relates to addressing the problem. The two elements should be linked. With this in mind, a good introduction answers these four questions:

• What is being studied? Describe the research problem and describe the subject of analysis [the case] you have chosen to address the problem. Explain how they are linked and what elements of the case will help to expand knowledge and understanding about the problem.
• Why is this topic important to investigate? Describe the significance of the research problem and state why a case study design and the subject of analysis that the paper is designed around is appropriate in addressing the problem.
• What did we know about this topic before I did this study? Provide background that helps lead the reader into the more in-depth literature review to follow. If applicable, summarize prior case study research applied to the research problem and why it fails to adequately address the problem. Describe why your case will be useful. If no prior case studies have been used to address the research problem, explain why you have selected this subject of analysis.
• How will this study advance new knowledge or new ways of understanding? Explain why your case study will be suitable in helping to expand knowledge and understanding about the research problem.

Each of these questions should be addressed in no more than a few paragraphs. Exceptions to this can be when you are addressing a complex research problem or subject of analysis that requires more in-depth background information.

II.  Literature Review

The literature review for a case study research paper is generally structured the same as it is for any college-level research paper. The difference, however, is that the literature review is focused on providing background information and  enabling historical interpretation of the subject of analysis in relation to the research problem the case is intended to address . This includes synthesizing studies that help to:

• Place relevant works in the context of their contribution to understanding the case study being investigated . This would involve summarizing studies that have used a similar subject of analysis to investigate the research problem. If there is literature using the same or a very similar case to study, you need to explain why duplicating past research is important [e.g., conditions have changed; prior studies were conducted long ago, etc.].
• Describe the relationship each work has to the others under consideration that informs the reader why this case is applicable . Your literature review should include a description of any works that support using the case to investigate the research problem and the underlying research questions.
• Identify new ways to interpret prior research using the case study . If applicable, review any research that has examined the research problem using a different research design. Explain how your use of a case study design may reveal new knowledge or a new perspective or that can redirect research in an important new direction.
• Resolve conflicts amongst seemingly contradictory previous studies . This refers to synthesizing any literature that points to unresolved issues of concern about the research problem and describing how the subject of analysis that forms the case study can help resolve these existing contradictions.
• Point the way in fulfilling a need for additional research . Your review should examine any literature that lays a foundation for understanding why your case study design and the subject of analysis around which you have designed your study may reveal a new way of approaching the research problem or offer a perspective that points to the need for additional research.
• Expose any gaps that exist in the literature that the case study could help to fill . Summarize any literature that not only shows how your subject of analysis contributes to understanding the research problem, but how your case contributes to a new way of understanding the problem that prior research has failed to do.
• Locate your own research within the context of existing literature [very important!] . Collectively, your literature review should always place your case study within the larger domain of prior research about the problem. The overarching purpose of reviewing pertinent literature in a case study paper is to demonstrate that you have thoroughly identified and synthesized prior studies in relation to explaining the relevance of the case in addressing the research problem.

III.  Method

In this section, you explain why you selected a particular case [i.e., subject of analysis] and the strategy you used to identify and ultimately decide that your case was appropriate in addressing the research problem. The way you describe the methods used varies depending on the type of subject of analysis that constitutes your case study.

If your subject of analysis is an incident or event . In the social and behavioral sciences, the event or incident that represents the case to be studied is usually bounded by time and place, with a clear beginning and end and with an identifiable location or position relative to its surroundings. The subject of analysis can be a rare or critical event or it can focus on a typical or regular event. The purpose of studying a rare event is to illuminate new ways of thinking about the broader research problem or to test a hypothesis. Critical incident case studies must describe the method by which you identified the event and explain the process by which you determined the validity of this case to inform broader perspectives about the research problem or to reveal new findings. However, the event does not have to be a rare or uniquely significant to support new thinking about the research problem or to challenge an existing hypothesis. For example, Walo, Bull, and Breen conducted a case study to identify and evaluate the direct and indirect economic benefits and costs of a local sports event in the City of Lismore, New South Wales, Australia. The purpose of their study was to provide new insights from measuring the impact of a typical local sports event that prior studies could not measure well because they focused on large "mega-events." Whether the event is rare or not, the methods section should include an explanation of the following characteristics of the event: a) when did it take place; b) what were the underlying circumstances leading to the event; and, c) what were the consequences of the event in relation to the research problem.

If your subject of analysis is a person. Explain why you selected this particular individual to be studied and describe what experiences they have had that provide an opportunity to advance new understandings about the research problem. Mention any background about this person which might help the reader understand the significance of their experiences that make them worthy of study. This includes describing the relationships this person has had with other people, institutions, and/or events that support using them as the subject for a case study research paper. It is particularly important to differentiate the person as the subject of analysis from others and to succinctly explain how the person relates to examining the research problem [e.g., why is one politician in a particular local election used to show an increase in voter turnout from any other candidate running in the election]. Note that these issues apply to a specific group of people used as a case study unit of analysis [e.g., a classroom of students].

If your subject of analysis is a place. In general, a case study that investigates a place suggests a subject of analysis that is unique or special in some way and that this uniqueness can be used to build new understanding or knowledge about the research problem. A case study of a place must not only describe its various attributes relevant to the research problem [e.g., physical, social, historical, cultural, economic, political], but you must state the method by which you determined that this place will illuminate new understandings about the research problem. It is also important to articulate why a particular place as the case for study is being used if similar places also exist [i.e., if you are studying patterns of homeless encampments of veterans in open spaces, explain why you are studying Echo Park in Los Angeles rather than Griffith Park?]. If applicable, describe what type of human activity involving this place makes it a good choice to study [e.g., prior research suggests Echo Park has more homeless veterans].

If your subject of analysis is a phenomenon. A phenomenon refers to a fact, occurrence, or circumstance that can be studied or observed but with the cause or explanation to be in question. In this sense, a phenomenon that forms your subject of analysis can encompass anything that can be observed or presumed to exist but is not fully understood. In the social and behavioral sciences, the case usually focuses on human interaction within a complex physical, social, economic, cultural, or political system. For example, the phenomenon could be the observation that many vehicles used by ISIS fighters are small trucks with English language advertisements on them. The research problem could be that ISIS fighters are difficult to combat because they are highly mobile. The research questions could be how and by what means are these vehicles used by ISIS being supplied to the militants and how might supply lines to these vehicles be cut off? How might knowing the suppliers of these trucks reveal larger networks of collaborators and financial support? A case study of a phenomenon most often encompasses an in-depth analysis of a cause and effect that is grounded in an interactive relationship between people and their environment in some way.

NOTE:   The choice of the case or set of cases to study cannot appear random. Evidence that supports the method by which you identified and chose your subject of analysis should clearly support investigation of the research problem and linked to key findings from your literature review. Be sure to cite any studies that helped you determine that the case you chose was appropriate for examining the problem.

IV.  Discussion

The main elements of your discussion section are generally the same as any research paper, but centered around interpreting and drawing conclusions about the key findings from your analysis of the case study. Note that a general social sciences research paper may contain a separate section to report findings. However, in a paper designed around a case study, it is common to combine a description of the results with the discussion about their implications. The objectives of your discussion section should include the following:

Reiterate the Research Problem/State the Major Findings Briefly reiterate the research problem you are investigating and explain why the subject of analysis around which you designed the case study were used. You should then describe the findings revealed from your study of the case using direct, declarative, and succinct proclamation of the study results. Highlight any findings that were unexpected or especially profound.

Explain the Meaning of the Findings and Why They are Important Systematically explain the meaning of your case study findings and why you believe they are important. Begin this part of the section by repeating what you consider to be your most important or surprising finding first, then systematically review each finding. Be sure to thoroughly extrapolate what your analysis of the case can tell the reader about situations or conditions beyond the actual case that was studied while, at the same time, being careful not to misconstrue or conflate a finding that undermines the external validity of your conclusions.

Relate the Findings to Similar Studies No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your case study results to those found in other studies, particularly if questions raised from prior studies served as the motivation for choosing your subject of analysis. This is important because comparing and contrasting the findings of other studies helps support the overall importance of your results and it highlights how and in what ways your case study design and the subject of analysis differs from prior research about the topic.

Consider Alternative Explanations of the Findings Remember that the purpose of social science research is to discover and not to prove. When writing the discussion section, you should carefully consider all possible explanations revealed by the case study results, rather than just those that fit your hypothesis or prior assumptions and biases. Be alert to what the in-depth analysis of the case may reveal about the research problem, including offering a contrarian perspective to what scholars have stated in prior research if that is how the findings can be interpreted from your case.

Acknowledge the Study's Limitations You can state the study's limitations in the conclusion section of your paper but describing the limitations of your subject of analysis in the discussion section provides an opportunity to identify the limitations and explain why they are not significant. This part of the discussion section should also note any unanswered questions or issues your case study could not address. More detailed information about how to document any limitations to your research can be found here .

Suggest Areas for Further Research Although your case study may offer important insights about the research problem, there are likely additional questions related to the problem that remain unanswered or findings that unexpectedly revealed themselves as a result of your in-depth analysis of the case. Be sure that the recommendations for further research are linked to the research problem and that you explain why your recommendations are valid in other contexts and based on the original assumptions of your study.

V.  Conclusion

As with any research paper, you should summarize your conclusion in clear, simple language; emphasize how the findings from your case study differs from or supports prior research and why. Do not simply reiterate the discussion section. Provide a synthesis of key findings presented in the paper to show how these converge to address the research problem. If you haven't already done so in the discussion section, be sure to document the limitations of your case study and any need for further research.

The function of your paper's conclusion is to: 1) reiterate the main argument supported by the findings from your case study; 2) state clearly the context, background, and necessity of pursuing the research problem using a case study design in relation to an issue, controversy, or a gap found from reviewing the literature; and, 3) provide a place to persuasively and succinctly restate the significance of your research problem, given that the reader has now been presented with in-depth information about the topic.

Consider the following points to help ensure your conclusion is appropriate:

• If the argument or purpose of your paper is complex, you may need to summarize these points for your reader.
• If prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the conclusion of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration of the case study's findings that returns the topic to the context provided by the introduction or within a new context that emerges from your case study findings.

Note that, depending on the discipline you are writing in or the preferences of your professor, the concluding paragraph may contain your final reflections on the evidence presented as it applies to practice or on the essay's central research problem. However, the nature of being introspective about the subject of analysis you have investigated will depend on whether you are explicitly asked to express your observations in this way.

Problems to Avoid

Overgeneralization One of the goals of a case study is to lay a foundation for understanding broader trends and issues applied to similar circumstances. However, be careful when drawing conclusions from your case study. They must be evidence-based and grounded in the results of the study; otherwise, it is merely speculation. Looking at a prior example, it would be incorrect to state that a factor in improving girls access to education in Azerbaijan and the policy implications this may have for improving access in other Muslim nations is due to girls access to social media if there is no documentary evidence from your case study to indicate this. There may be anecdotal evidence that retention rates were better for girls who were engaged with social media, but this observation would only point to the need for further research and would not be a definitive finding if this was not a part of your original research agenda.

Failure to Document Limitations No case is going to reveal all that needs to be understood about a research problem. Therefore, just as you have to clearly state the limitations of a general research study , you must describe the specific limitations inherent in the subject of analysis. For example, the case of studying how women conceptualize the need for water conservation in a village in Uganda could have limited application in other cultural contexts or in areas where fresh water from rivers or lakes is plentiful and, therefore, conservation is understood more in terms of managing access rather than preserving access to a scarce resource.

Failure to Extrapolate All Possible Implications Just as you don't want to over-generalize from your case study findings, you also have to be thorough in the consideration of all possible outcomes or recommendations derived from your findings. If you do not, your reader may question the validity of your analysis, particularly if you failed to document an obvious outcome from your case study research. For example, in the case of studying the accident at the railroad crossing to evaluate where and what types of warning signals should be located, you failed to take into consideration speed limit signage as well as warning signals. When designing your case study, be sure you have thoroughly addressed all aspects of the problem and do not leave gaps in your analysis that leave the reader questioning the results.

Case Studies. Writing@CSU. Colorado State University; Gerring, John. Case Study Research: Principles and Practices . New York: Cambridge University Press, 2007; Merriam, Sharan B. Qualitative Research and Case Study Applications in Education . Rev. ed. San Francisco, CA: Jossey-Bass, 1998; Miller, Lisa L. “The Use of Case Studies in Law and Social Science Research.” Annual Review of Law and Social Science 14 (2018): TBD; Mills, Albert J., Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Putney, LeAnn Grogan. "Case Study." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE Publications, 2010), pp. 116-120; Simons, Helen. Case Study Research in Practice . London: SAGE Publications, 2009;  Kratochwill,  Thomas R. and Joel R. Levin, editors. Single-Case Research Design and Analysis: New Development for Psychology and Education .  Hilldsale, NJ: Lawrence Erlbaum Associates, 1992; Swanborn, Peter G. Case Study Research: What, Why and How? London : SAGE, 2010; Yin, Robert K. Case Study Research: Design and Methods . 6th edition. Los Angeles, CA, SAGE Publications, 2014; Walo, Maree, Adrian Bull, and Helen Breen. “Achieving Economic Benefits at Local Events: A Case Study of a Local Sports Event.” Festival Management and Event Tourism 4 (1996): 95-106.

Writing Tip

At Least Five Misconceptions about Case Study Research

Social science case studies are often perceived as limited in their ability to create new knowledge because they are not randomly selected and findings cannot be generalized to larger populations. Flyvbjerg examines five misunderstandings about case study research and systematically "corrects" each one. To quote, these are:

Misunderstanding 1 :  General, theoretical [context-independent] knowledge is more valuable than concrete, practical [context-dependent] knowledge. Misunderstanding 2 :  One cannot generalize on the basis of an individual case; therefore, the case study cannot contribute to scientific development. Misunderstanding 3 :  The case study is most useful for generating hypotheses; that is, in the first stage of a total research process, whereas other methods are more suitable for hypotheses testing and theory building. Misunderstanding 4 :  The case study contains a bias toward verification, that is, a tendency to confirm the researcher’s preconceived notions. Misunderstanding 5 :  It is often difficult to summarize and develop general propositions and theories on the basis of specific case studies [p. 221].

While writing your paper, think introspectively about how you addressed these misconceptions because to do so can help you strengthen the validity and reliability of your research by clarifying issues of case selection, the testing and challenging of existing assumptions, the interpretation of key findings, and the summation of case outcomes. Think of a case study research paper as a complete, in-depth narrative about the specific properties and key characteristics of your subject of analysis applied to the research problem.

Flyvbjerg, Bent. “Five Misunderstandings About Case-Study Research.” Qualitative Inquiry 12 (April 2006): 219-245.

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What is the Case Study Method?

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

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

How the Case Method Creates Value

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

Case Discussion Preparation Details

In self-reflection.

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

In a small group setting

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

In the classroom

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

Beyond the classroom

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

How Cases Unfold In the Classroom

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

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

How to Prepare for Case Discussions

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

Preparation Guidelines

Read the professor's assignment or discussion questions.

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

Read the first few paragraphs and then skim the case

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

Reread the case, underline text, and make margin notes

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

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

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

Case Study Best Practices

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

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

Participate

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

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

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

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

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

Frequently Asked Questions

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

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Resources for research Part of series: Research Ethics Case Studies 2024

Research Ethics Case Studies 2024: Watch your language: Ethical issues when working with young people – meaning and power

This case study explores ethical issues that arise for researchers working with young people. This includes the right of young people to contribute their experiences and opinions in a way that enables them to express what they think or feel; respecting the role of parents and carers’ legal responsibilities in giving their informed consent; and thinking about whether the method being used is appropriate to ensure young people can be included in the research process.

Cassie’s research investigated the importance of clarifying the use of language when working with young people, and also the power dynamics between young people and adults. The research participants were young people, aged 10–18 years, who had a history of glue ear. This condition results in intermittent hearing loss and can delay development of pragmatic language skills, which may result in young people being excluded as active contributors to the research process. Two ethical issues were anticipated in the research: 1) how could the adult–young people power differentials be reduced to elicit young people’s considered views? and 2) what methods would legitimately encourage the active participation of young people? A third ethical issue also arose during the research when Cassie’s instructions inadvertently caused uncertainty for one participant. How did Cassie navigate these ethical dilemmas?

Drawing on BERA’s  Ethical Guidelines for Educational Research , this case study discusses key ethical issues, including:

• principles of consent, assent and transparency when conducting research with children and young people
• power differentials and the right to withdraw
• selecting appropriate methods that ensure the needs of the research are being properly addressed
• minimising harm arising from participation in research.

About this series

BERA’s  Research Ethics Case Studies , edited by Sin Wang Chong and Alison Fox, complement BERA’s  Ethical Guidelines for Educational Research , fifth edition (2024)  by giving concrete examples of how those guidelines can be applied during the research process. 

Annotations in the margins of each case study document indicate where, among the numbered paragraphs of BERA’s  Ethical Guidelines , readers can find full advice on the issues raised. The annotations include hyperlinks to the relevant passages of the guidelines.

For a full account of ethical best practice as recommended by BERA, researchers should refer to our  Ethical Guidelines , which these case studies are intended to illustrate without themselves offering guidance or recommendations.

Senior Lecturer at Oxford Brookes University

Carmel is a Lecturer in Early Years and Child Development at Oxford Brookes University. She has a strong interest in developing innovative research methods, particularly to encouraging the participation of young people in expressing and sharing...

Lecturer at University of Manchester

Dr Helen Hanna is a Lecturer in International Education at the University of Manchester. She is passionate about citizenship education, education rights, and educational inclusion, particularly of migrant learners and those from racial, ethnic...

Doctoral Researcher at University College London & Lecturer in ITE at Chulalongkorn University

Chawin Pongpajon is a doctoral researcher at University College London and a lecturer in ITE at Chulalongkorn University, Thailand.

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Making the case for artisanal and small-scale mining

• Morgan Sherburne

EXPERT Q&A

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

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

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

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

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

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

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

What is artisanal and small scale mining?

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

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

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

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

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

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

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

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

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

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

What are some solutions to these issues?

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

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

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

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

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

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

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

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

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

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

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Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea

• Open access
• Published: 04 September 2024

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• Dhahi Al-Shammari   ORCID: orcid.org/0000-0001-6608-8322 1 ,
• Yang Chen 2 ,
• Niranjan S. Wimalathunge 1 ,
• Chen Wang 3 ,
• Si Yang Han 1 &
• Thomas F. A. Bishop 1  

Introduction

Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness,  DDMs can be complemented by inputs derived from mechanistic models (MMs).

This study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC).

Results and conclusions

The validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha −1 . For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha −1 . However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha −1 , respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features.

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Data-driven models (DDMs) have emerged as powerful tools for agricultural applications, including crop yield prediction. These models utilise advanced statistical and machine learning techniques to analyse large amounts of data collected from multiple sources, such as weather stations, satellite imagery, soil sensors, and historical crop yield records. DDMs can capture the complex interactions between environmental factors and crop growth patterns by incorporating these diverse datasets, enabling accurate yield predictions. DDMs, such as Random Forest (Breiman, 2001 ) and Neural Networks (Abdi et al., 1999 ), are commonly used in these models to analyse large datasets and identify patterns that can help predict crop yields. The XGBOOST (eXtreme Gradient Boosting) is another one of these DDMs algorithms that has been shown to be successful in crop yield prediction (Al-Shammari, 2022 ; Huber et al., 2022 ; Jones et al., 2022 ; Kang et al., 2020 ). It excels at handling multicollinearity, which refers to strong correlations between features used for modelling. In agricultural datasets, features, such as remote sensing (RS), soil, and weather factors can exhibit interdependencies or collinearity. XGBOOST addresses this issue by using regularisation techniques, effectively reducing the impact of collinear of features in the final model.

Although DDMs require a minimum knowledge of the processes (Cao & Zhang, 2007 ), DDMs require representative features, which are assumed to help reveal hidden information about a specific phenomenon. One of the biggest challenges is to select features that allow a DDM to reveal hidden information or patterns to estimate the response variable more accurately.

In contrast to traditional DDMs, mechanistic models (MMs) require domain knowledge in a system to produce meaningful relationships between the inputs (features) and the outputs (response) (Fan et al., 2015 ). Unlike DDMs, MMs are governed by some processes defined by the experts who create the proper formulas to solve a specific issue. MMs can explain part of but not all processes in dynamic and complex systems, such as cropping systems. Consequently, many studies have employed DDMs to find empirical relationships rather than using mathematical formulas to describe a specific phenomenon, taking advantage of DDMs' flexibility in accepting a large number and variety of features and less of a need to understand complex mechanisms to provide predictions.

Many studies have used MMs to model processes in agricultural systems. Of relevance to this study is a processed-based model (C-Crop) that has been introduced based on simulating plant growth to predict crop yield (Donohue et al., 2018 ). The authors of C-Crop stated that this model is simple and effective for predicting wheat and canola yield. This model is based on calculating biomass from environmental variables (weather, remote sensing) to describe the processes using mathematical equations. In another study, a processed-based model for soil moisture prediction was developed by Wimalathunge and Bishop ( 2019 ). This model uses rainfall, evapotranspiration (ET), and some soil attributes, including clay, sand, and bulk density, to calculate soil moisture. The advantage of this model is that it can predict soil moisture for the whole profile, which is very important in dryland cropping systems since water availability is a yield-limiting factor in these systems. The advantage of these two models is that they can be easily constructed using readily and freely available geospatial data, capturing the within-field variability and benefiting precision agriculture operations.

Since DDMs are very flexible in dealing with various features, there is a need to select the best representative features, allowing these models to maximise the prediction quality. One way to do that is by combining MMs with DDMs to form what is known as domain-driven models (Cao & Zhang, 2007 ). Several studies have examined the advantages of combining DDMs and MMs (Džeroski & Todorovski, 2003 ; Fan et al., 2015 ; Todorovski & Džeroski, 2006 ). Fan et al. ( 2015 ) incorporated two sub-models derived from MMs and DDMs to build knowledge-and-data-driven modelling (KDDM) for tomato plant growth modelling. They found that the proposed KDDM has several advantages over the MM and the DDM approaches in predicting tomato yield.

Therefore, this study aimed to investigate the potential of incorporating MMs into DDMs for wheat and chickpeas yield prediction at the within-field scale for precision agriculture. Two outputs from MMs, namely biomass and soil moisture, are investigated in this study for use as features. The XGBOOST algorithm was selected, and four experiments are performed to explore scenarios involving different combinations of MM-based features and more general ones such as remote sensing. The results of the experiments are considered in terms of the prediction quality and the feature importance of the features in the models.

Methodology

The study area comprises a single site with 10 field-years of crop yield data near Moree, New South Wales, Australia. The total area of these fields is ~ 1 096 ha. In any season all 4 fields are sown to the same crop. Wheat yield maps were available for the four fields (Fields 1, 2, 3, and 4), whereas chickpea yield maps were unavailable for Field 2 (Fig.  1 and Table  1 ).

Map of Australia showing the location of the study area. Fields 1, 2, 3, and 4 are shown in yellow colours (Color figure online)

The area receives an average of 450 mm of rainfall annually, mostly occurring during summer. However, the area is also prone to drought and heat waves. The soils in the Moree region are predominantly heavy clays (Hunter & Earl, 1999 ), and clay loams, with some areas of sandy soils (Young & Schwenke, 2013 ). The soils are generally fertile and well-suited for crop production. Soil constraints, such as sodicity can be an issue in some areas (Filippi et al., 2018 ). The climate, topography, and soil characteristics of Moree make it a challenging yet productive agricultural region.

Yield monitor data of wheat and chickpeas was acquired from a private AgTech company and processed to remove anomalies by excluding data points outside the established range of 0.1 to 10 t ha −1 (Taylor et al., 2007 ). Subsequently, values exceeding 2.5 standard deviations from the field mean were also removed from the remaining data. Yield data was then kriged using block kriging (a spatial interpolation method) at a 10 m spatial support onto a 10 m grid. The total size of the yield dataset after interpolation was 259 318 observations.

Derived and processed features

To examine the potential of incorporating the MMs with the DDMs for wheat and chickpeas yield prediction, various sets of features were prepared from Tables  2 , 3 and shown in Fig.  2 . This study used the terms derived and processed to differentiate between the types of features. The difference between derived and processed features lies in the information extraction and refinement level applied to the raw data. Derived features refer to the conventional ones obtained from RS or other platforms that are used as is or after simple calculations. On the other hand, processed refers to the features that are the outputs of mechanistic models (MM).

Flowchart illustrating the process of creating the four datasets, models, and the evaluation processes for each experiment. Each coloured line (green, blue, black, and red) refers to a dataset and the features included in it (Color figure online)

Derived features

A set of temporal, elevation and soil-derived features were prepared as a space–time data cube (Tables  2 , 3 ). Google Earth Engine (GEE) (Gorelick et al., 2017 ) was used to obtain all the derived features (except rainfall). The time series of normalized difference vegetation index (NDVI) images (from 1st of May to 1st of September) were acquired from Sentinel-2 at 10 m resolution for each growing season and used to calculate the average of the seasonal NDVI for each pixel. The enhanced vegetation index (EVI) was used to calculate the leaf area index (LAI) for the 1st September (peak LAI) for each growing season, using an equation (Eq.  1 ) developed by (Boegh et al., 2002 ). The peak LAI was used in this study as it was reported to be highly correlated to the final yield (Cai et al., 2019 ).

The ET was derived from a dataset that provides accurate actual evapotranspiration (AET) for Australia using a model that has been developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) known as the CSIRO MODIS Reflectance-based Scaling EvapoTranspiration (CMRSET) model (Guerschman et al., 2022 ). The accumulated ET was calculated as the sum of ET from 1st of May to the 1st of September at 30 m resolution. Elevation is a spatially static data, which was accessed a digital elevation model (DEM) at 30 m resolution (Australia, 2015 ). Soil attributes were derived from the Soil and Landscape Grid of Australia (SLGA) at 90 m resolution (Grundy et al., 2015 ). These soil attributes were available as five layers from different soil depths (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, and 60–100 cm) for each attribute. Then, the weighted average of each attribute was calculated to extract the attribute as the root zone average (0–100 cm). Daily rainfall grids (~ 5 km) for each growing season have been downloaded from the Australian climate database known as (SILO) (Jeffrey et al., 2001 ). The rainfall was calculated as the accumulated rainfall from the start of the growing season 1st of May to the 1st of September.

In our study, spatial resolution harmonisation involved resampling the original 30 m, 90 m, and 5 km resolution layers to 10 m to ensure consistency across all features used in the final modelling. For data layers with spatial resolutions of 30 m and 90 m, bilinear interpolation was employed to resample the data, chosen for its effectiveness in preserving continuous spatial information without introducing significant artifacts. The rainfall data, was resampled using the nearest neighbour method as the difference from the target grid size of 10 m was too far from 5 km.

Biomass (processed feature)

The C-Crop model is an MM, which was developed to simulate some of the processes in the ecosystem using mathematical equations. This model can predict biomass using the fraction of photosynthetically active radiation ( f par ), which is derived from NDVI, and a carbon mass accumulation and turn-over model (Donohue et al., 2018 ). The Donahue et al. ( 2018 ) model used the 16-day NDVI product with 250 m resolution, the Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD13Q1: collection 5) (Justice et al., 1998 ). However, for this study, Landsat 8 data was used to derive the NDVI at 30 m resolution as the authors stated that this model is unrestricted to a specific source of data where the C-crop can use NDVI time series from any source (e.g., satellite) at any spatial resolution as long as the source provides 16 days or higher temporal resolution. Therefore, Landsat images at 16-day intervals were acquired to preserve the C-Crop structure unchanged. Moreover, the C-Crop model requires the NDVI time series to be prepared according to a nominal start and end of the growing season to calculate biomass. According to the crop calendar for wheat and chickpeas in the study area, wheat sowing begins in April and ends in July, while chickpeas sow in May and end in July, with the growing season for both crops ending in October to December. The nominal start and end of the growing season were determined as the 1st of May and the 1st of October, respectively. Besides the NDVI time series, C-Crop also uses the air temperature, which is a 5 km resolution and 1-day temporal resolution, which was readily available from (Jeffrey et al., 2001 ). Donohue et al. ( 2018 ) study provides more details about C-Crop. After preparing the inputs for the C-Crop model, biomass maps, which represent the seasonal biomass were generated for each field at 10 m resolution. The maximum biomass during the season was calculated from the time series and added to the space time data cube. Maximum biomass is valuable for assessing overall productivity and its impact on final yield.

Soil moisture (processed feature)

The water balance (WB) model, which has been developed by Wimalathunge and Bishop ( 2019 ), has been used to estimate the soil moisture to 100 cm depth (root zone). This WB is a multi-layer, knowledge-based model that better represents the vertical soil moisture variation. It is also an unsaturated model where water infiltrates through layers freely and continuously according to the soil properties. It requires rainfall, evapotranspiration and an estimate of the soil water bucket size. In this study the SLGA was used to represent the soil water bucket size. SLGA soil depth intervals are the WB model’s layer thickness. The corresponding clay, sand and bulk density values were used to calculate the bucket size which is represented by field capacity ( θ FC ) using a pedotransfer function (PTF) (Padarian et al., 2018 ).

For this study, the model used ET from MODIS (MOD16; Mu et al., 2011 ) which is a different source to ET used in the machine learning and shown in Tables  2 , 3 . This is because CMRSET ET provides the daily averages for each month and MODIS ET provides 8 day totals which is closer to the daily time step needed for the modelling. The MODIS ET was resampled using nearest neighbour to downscale the 8 day total to a daily total. The rainfall used was the same as presented in Tables  2 , 3 . The modelling depth was 100 cm, and the spatial resolution of the predictions was 90 m, as determined by the SLGA data. The model was run on each SLGA grid cell with the corresponding value for rainfall and ET. For example, the model uses the same ET value for each 90 m grid cell within the 500 m ET grid cell. The model was run on a daily time step from the 1st of January 2015 to the 31st of September 2023, and the output at 0–100 cm (root zone) was used as an input of the data-driven model. For this study, the sum of soil moisture from the 1st of May to the 1st of September for each growing season was calculated and used as a predictor. The hypothesis is that the soil moisture output can replace the rainfall because it provides a better representation of the available water for crops than the rainfall. Moreover, this output has a much higher resolution (~ 90 m) than the rainfall data, provided at a 5 km resolution.

Description of models

Figure  2 illustrates the main steps followed to evaluate each model with different sets of features. Four experiments were performed based on different datasets and tested individually in the XGBOOST (see next section). The base DDM model was built using all features, both derived and processed (Fig.  2 and Table  1 , Experiment 1). The biomass and soil moisture (processed) features were removed from the model (Experiment 2) to test the impact of removing these on the model's predictive power. Then, the derived temporal features were removed for Experiment 3 to test the potential of using soil moisture and biomass as features instead of their temporal inputs, e.g., rainfall, NDVI, ET. The aim was to create simpler and more interpretable models. The soil features were kept in the model even though some of these are used in the water balance model as inputs. The reason being that also relate to soil fertility which can impact on crop yield. Experiment 4 only uses features produced by the MMs (biomass and soil moisture) to test the possibility of the MMs replacing all the other derived features completely.

Wheat and chickpeas were modelled separately. The reason for this is that different crop types require specific models due to a variety of factors that are unique to each crop type. The factors include biological characteristics, environmental responses, and management practices. For each model, datasets were split randomly into 80 percent for training and 20 percent for validation. Therefore, other strategies such as leave-on-field-out (LOFO) and leave-one-season-out (LOSO) cross-validation were not employed. The models were evaluated individually using the concordance correlation coefficient (CCC), the root mean square error (RMSE) and feature importance to understand the most important features for the model.

These three metrics allow a fair evaluation of the benefits of the addition of processed features to the DDM model. The CCC is a measure that combines measures of both precision and accuracy to determine how well a pair of observations conform to a 1:1 correspondence with each other. The RMSE captures the amount of error (t ha −1 ) in the DDMs. The feature importance highlights the contribution of individual features to model prediction quality.

The XGBOOST is a highly efficient and powerful ML algorithm that has gained immense popularity (Chen & Guestrin, 2016 ). It is a decision-tree-based ensemble algorithm that applies boosting on weak learners (Fauzan & Murfi, 2018 ), such that the weak learners learn sequentially from the residual of the previous weak learner. The idea of learning from the trees sequentially can reduce the bias (Nielsen, 2016 ). L1 regularisation is used to handle the multicollinearity, as L1 adds a penalty term to the objective function during training, which helps control the complexity of the model and discourages large coefficients for correlated features.

The XGBOOST was used for all experiments. Predictions were repeated 100 times, and the mean of CCC, RMSE and feature importance were calculated. The feature importance was calculated for each experiment to evaluate the importance of derived and processed features and their contribution to the models. The feature importance in XGBOOST models is calculated based on the concept of gain, which measures the improvement in model performance resulting from splitting a particular feature. The gain is computed by considering the average loss reduction achieved by using that feature to split data across all trees in the ensemble. The higher the gain value, the more important the feature is considered. This calculation allows XGBOOST to identify features that contribute most significantly to predicting the response variable. According to Chen and Guestrin ( 2016 ), calculating feature importance in XGBOOST provides a robust measure of relevance for each feature. They explain that gain-based methods have advantages over other techniques, such as permutation-based or drop-column-importance approaches because they consider interactions between features and their contributions. Additionally, this method can handle missing values effectively without requiring imputation.

Tuning an XGBOOST model is important for maximising its predictive performance and generalisation capabilities. By fine-tuning hyperparameters, the model's predictive quality can be enhanced significantly, prevent overfitting, and improve the handling of diverse datasets. This study used a combination of grid search and internal cross-validation to tune hyperparameters for an XGBOOST model, aiming to optimise model performance by finding the best hyperparameter values for each model. For each combination of hyperparameters, each model for each experiment underwent a rigorous evaluation through fivefold cross-validation, implemented via the xgb.cv function. This function trains the model on different subsets of the data, ensuring that the evaluation is robust and not biased toward a specific portion of the data. The RMSEs were recorded for both the training and validation phases at the most effective iteration determined by early stopping (a technique used here to prevent overfitting by halting training if there is no improvement in validation error for three consecutive rounds). This systematic approach allowed for careful monitoring and adjusting the model based on its performance across the different data folds. Finally, the hyperparameter settings that yielded the lowest RMSE on the validation data were identified and selected for each model.

Exploratory data analysis

According to the coefficient of variation (CV) values (Tables 2 , 3 ), it is clear that there are notable differences in variability between wheat yield in 2017 and its features, such as biomass, SM, and LAI. Biomass, with a CV of 31.55%, shows greater variability compared to wheat yield, which has a CV of 25.30%. This suggests that biomass may better represent variability for predicting yield. In contrast, the SM showed significantly less variability (CV of 0.39%) compared to yield. This low variability might mean it is less useful for predicting yield than other features. LAI, with a CV close to that of yield (20.25%), might be better aligned as a feature, reflecting similar variability patterns without extreme fluctuations.

Furthermore, the variability of chickpea yield over the two seasons (2019 and 2022) was much higher (as reflected by the CV , where a noticeable trend can be noticed with a significant increase in yield variability from 2019 to 2022. The CV for chickpea yield in 2019 was around 99.95%, indicating considerable fluctuations in yield compared to wheat ( CV  = 25.30%). However, in 2022, this variability increased to 162.04%. This increase highlights a greater inconsistency in chickpea production, potentially due to more extreme environmental conditions or other factors affecting agricultural outputs.

When comparing the variability of other features across these two seasons, such as biomass, SM, LAI, and rainfall, distinct patterns are observed. Biomass, while still variable, shows a decrease in CV from 85.30% in 2019 to 31.89% in 2022, suggesting that the fluctuations in biomass production have become less pronounced, possibly due to more consistent growth conditions and better management practices. In contrast, SM and rainfall maintain consistently low variability across both seasons (with CVs under 1% for rainfall and around 1% for SM). LAI showed a reduction in variability, from a CV of 42.97% in 2019 to 18.72% in 2022, hinting at more consistent growth conditions between the seasons. The overall reduction in variability among these features, except for yield, raises important considerations for predictive modelling. It suggests that while environmental and growth conditions measured by SM, rainfall, and LAI have become more stable, the factors specifically affecting yield have diverged, becoming more volatile.

While many soil properties such as AWC, clay content, ECEC, PTO, and SOC are influenced by long-term processes like weathering, organic matter accumulation, and gradual shifts in mineral content and thus remain relatively stable over time, certain properties like BDW can exhibit more immediate changes. For example, BDW can be significantly impacted by soil compaction due to agricultural practices or the use of heavy machinery. These characteristics make soil a relatively static factor in short to medium term, especially when compared to more dynamic, temporal. Therefore, Tables 2 , 3 showed that the CV was much less compared to wheat and chickpeas yield.

The AWC showed little variability, with an average value of about 19% across the study area. The BDW remained relatively constant, averaging around 1.67 g/cm 3 . Clay content in the soil varied significantly between locations, ranging from 46 to 72%, with an average clay content of about 64%. The ECEC varies widely, ranging from 23.95 to 59.65 meq/100 g, indicating the soil's capacity to retain essential nutrients through cation exchange.

Figure  3 shows that the strongest positive correlation in all growing seasons was between ET and yield, and this was expected as ET reflects the amount of water used by a crop for growth and maintaining its physiological processes, and the spatial resolution of ET (30 m) is close to the yield’s resolution (10 m) as compared to other features. There was also strong positive correlation between NDVI and LAI, which is expected as the relationship between NDVI and LAI is generally positive (Smith et al., 2008 ). Similarly, there was a strong relationship between NDVI and ET and LAI and ET in all growing seasons. Yield-biomass and yield-SM correlation had very weak correlations. The yield-ET correlation was stronger in the 2017 growing season (wheat) than in the 2019 and 2022 growing seasons (chickpeas). The multicollinearity was also observed between other features, which can be an issue for the DDMs; however, using the XGBOOST with L1 regularisation can mitigate the multicollinearity.

Correlation matrix showing the Peason’s correlation between yield, processed, and derived features

The learning curves of XGBOOST are crucial in understanding the effectiveness and performance of the model, offering insight into how well XGBOOST generalises to new, unseen data. Typically, XGBOOST learning curves demonstrate rapid improvement in training performance with increasing data size, followed by a plateau as the model approaches its optimal capacity.

This study explored and reported the learning curves to examine the ability of the models to learn from the derived and the processed features (Figs.  4 and 5 ). Figure  4 shows the RMSE of training and validation sets over different iterations for the four wheat experiments (1, 2, 3, and 4). All experiments showed a similar initial high RMSE of around 2.3 t ha −1 , rapidly decreasing and stabilising around 0.32 t ha −1 , indicating effective model learning and minimal overfitting. Experiment 4 (Fig.  4 D) stands out with the fastest convergence, stabilising within 50 iterations, compared to the other datasets, which stabilise at around 200 iterations. However, the final RMSE values for Experiment 4 were higher, suggesting lower model performance on this dataset. The close alignment of training and validation RMSE values across all experiments indicates good model generalisation. However, this is not unexpected, as in each field, we have training and validation samples.

Learning curves for wheat models using XGBOOST. Panels A , B , C , and D represent models built for Experiments 1, 2, 3, and 4, respectively

Learning curves for chickpea models using XGBOOST. Panels A , B , C , and D represent models built for Experiments 1, 2, 3, and 4, respectively

The RMSE plots (Fig.  5 ) for the chickpea experiments (1, 2, 3, and 4) reveal distinct patterns in model performance and generalisation. Experiment 1 (Fig.  5 A) shows a gradual decrease in RMSE, stabilising around 0.3 t ha −1 for training and slightly higher for validation after about 2000 iterations, indicating slight overfitting. The learning curve for the model built using Experiment 2 converged quickly, with training RMSE dropping below 0.2 t ha −1 within 500 iterations, but the validation RMSE remains around 0.3 t ha −1 , suggesting significant overfitting. The model built using Experiment 3 followed a similar trend to that built using Experiment 1, with RMSE stabilising at 0.2 t ha −1 for training and above 0.3 t ha −1 for validation, showing moderate overfitting. The model built using Experiment 4 exhibited the best performance, with both training and validation RMSE stabilising around 0.48 t ha −1 within 500 iterations, indicating minimal overfitting and good generalisation. These results suggest that while the models developed in Experiments 1, 2, and 3 exhibited varying degrees of overfitting, the model that used processed data only achieved a balanced and robust fit, highlighting the need for potential regularisation and feature optimisation in the other features in the other experiments.

The results obtained from the validation set show that the predictive power of the XGBOOST for both crops was almost similar, where the CCC ranged from 0.89 to 0.91 and an RMSE ranged from 0.23 to 0.25 t ha −1 for chickpeas, and CCC ranged from 0.87 to 0.92 and an RMSE ranged from 0.29 to 0.35 t ha −1 for wheat, for Experiment 1, Experiment 2, and Experiment 3, respectively (Figs.  6 and 7 ). In the case of Experiment 4 there was a sharp degradation in the XGBOOST model's predictive power, where the CCCs declined to 0.47 and 0.36, with increased RMSEs to 0.46 and 0.65 for chickpeas and wheat, respectively.

Observed vs predicted yield of chickpeas obtained from XGBOOST model for Experiments 1, 2, 3, and 4, shown in Figures A , B , C , and D , respectively

Observed vs predicted yield of wheat obtained from XGBOOST model for Experiments 1, 2, 3, and 4, shown in Figures A , B , C , and D , respectively

Feature importance from XGBOOST was calculated (Figs.  8 and 9 ) to identify the features that have more impact on the model. In general, the temporal (processed and derived) features were more important than soil attributes for both crops, indicating that temporal features provide valuable insights into the temporal dynamics of a system and can significantly affect the outcome of predictive models. The importance of rainfall was evident for chickpeas but not for wheat, whereas the LAI was one of the most important features in Experiments 1 and 2 for both crops. The importance of biomass was not evident in Experiment 1 for both crops as the models seemed to learn from the derived features more than biomass; however, biomass importance was evident in Experiment 3 for both crops, which revealed the impact of biomass on the outcomes with the absence of other temporal features. The NDVI was at the top of the feature importance for wheat and the third most important feature for chickpeas, which is likely due to the correlation with yield (Fig.  3 ). The other soil properties were not important for both models (except ECEC and clay, which appeared to be more important for wheat in dataset 3). Soil moisture appeared to be more important than biomass in Experiment 4 for both crops, indicating the importance of soil moisture for predicting crop yield.

Feature importance obtained from XGBOOST for chickpeas. A , B , C and D correspond to Experiments 1, 2, 3, and 4, respectively

Feature importance obtained from XGBOOST for wheat. A , B , C and D correspond to Experiments 1, 2, 3, and 4, respectively

Although the model built for Experiment 4 converged better, its prediction performance was still lower than the other models. Figure  10 illustrates the difference between observed (top panel) and predicted yield of wheat for Experiment 3 (middle panel) and Experiment 4 (bottom panel). The model developed for Experiment 3 captured the within-field variability and could predict low and high yield values. However, the model that was built for Experiment 4 overpredicted yield and could not capture the within-field variability. This is likely due to the low variability in the soil moisture features.

The difference between observed (top panel) and predicted yield of wheat using Experiment 3 (middle panel) and Experiment 4 (bottom panel)

This study investigated the potential of using MMs outputs (biomass and soil moisture) as features in ML models. Four experiments (Experiments 1, 2, 3, and 4) were performed with different combinations of features and tested using the XGBOOST model in this study. The results obtained from all experiments showed the potential for using biomass and soil moisture as features in DDMs. However, several points must be considered when using MMs outputs as features in DDMs. For example, the MMs are usually restricted by expert knowledge, and the quality of the outputs from an MM depends on how these models are designed. Therefore, using these outputs in DDMs might increase/decrease in prediction quality depending on the MMs predictive quality. In this study, the XGBOOST could learn from the derived features;however, the need for a more real representation of the temporal processes or higher-resolution inputs might require using prior knowledge to model one or more inputs (Reichstein et al., 2019 ). For example, in the case of the WB model (Wimalathunge & Bishop, 2019 ), the soil moisture is represented by the root zone depth at a 90 m resolution. This can be considered an advantage for use in the DDMs as the rainfall (5 km) (Jeffrey et al., 2001 ) does not provide such representation. C-Crop also assumes that biomass can be predicted by empirically using the greenness of the vegetation cover as the response to light and temperature. Donohue et al. ( 2018 ) suggest biomass is directly related to yield through the harvest index. As shown in the results, using biomass and soil moisture along with the soil attributes and removing the other temporal features, XGBOOST could preserve the predictive power while reducing the number of features by removing the derived features. However, biomass and soil moisture could only provide reasonable predictions with soil features, which means that soil attributes play an important role in predicting crop yield. Instead, biomass and soil moisture could use multiple features because they provide a more stable and less noisy representation of the temporal features.

The feature importance plots helped us understand different features contribution towards predicting the target variable. It allowed us to gain insights into which features significantly impact the model's performance, aiding in feature selection, understanding relationships between features, and improving interpretability. According to the results in Fig.  8 A, rainfall was the most important feature for chickpeas but not for wheat. This is because chickpeas data were obtained for two seasons, and the rainfall variation in these two seasons was significantly different (Tables  2 , 3 ), which explains the impact of the rainfall on the outcomes. The LAI was also on the top of the feature importance list for both crops, and this is because LAI is a good indicator of final crop yield (Ziliani et al., 2022 ). Several studies have demonstrated the usefulness of LAI for predicting crop yield in various agricultural systems (Cao et al., 2021 ; Ma et al., 2022 ; Mokhtari et al., 2018 ; Tewes et al., 2020 ). The peak LAI contributed significantly to the model, which could be due to the high correlation with final yield that was found here (Report value) and has been reported in the past, e.g., Cai et al. ( 2019 ). The NDVI was also one of the most important features for both crops (Experiments 1 and 2). Research studies showed that NDVI can be a very important feature in DDM models of yield (Al-Shammari et al., 2021 ; Filippi et al., 2017 ; Johnson et al., 2016 ). The accumulated ET was also an important feature to the model for both crops (Figs.  8 .A and 8B and 9A and 8B). ET is a parameter that reflects the impact of various factors on crop growth and is highly correlated with crop growth and yield (Khan et al., 2019 ), and the ET obtained from the CMRSET model was a significant feature for crop yield.

Elevation did not have an impact on the outcomes of the models. Because the elevation in the study area did not vary significantly, the models (Figs.  8 and 9 A, B and C) could not identify the relationship between elevation and other features to discern its impact on the crop yield. This is because the variation will make a difference if it varies within a field. A significant change in elevation leads to variation in temperature frost incidence, and water flow, which leads to variation in yield (Dixit & Chen, 2011 ; Kelleher et al., 2001 ; Thornton et al., 2009 ). However, this was not the case in our study area, therefore, the elevation was not important to the model.

The low importance of soil properties in the XGBOOST model in all datasets was attributed to the uncertainty of these properties at the fine scale (10 m in this study). The SLGA is a valuable resource for understanding soil properties at a national scale. However, it is important to acknowledge some limitations associated with this dataset. Firstly, SLGA relies on predictive modelling techniques that use various environmental covariates to estimate soil properties. This can introduce uncertainty and potential errors in the predictions. These models depend on the availability and quality of input data, which may vary across different regions of Australia. Additionally, SLGA provides point estimates of soil properties at a spatial resolution of 90 m, which may not capture local-scale variability or fine-scale features, such as soil texture gradients within landscapes (Han et al., 2022 ; Kidd et al., 2020 ). Therefore, caution should be exercised when using SLGA for site-specific applications or detailed studies.

The learning curves (Figs.  4 and 5 ) revealed that the models would learn better from the processed data. The learning curves also showed that using data from different years would lead to overfitting the model using the derived features (Fig.  5 A–C). However, using the processed feature (Fig.  5 D) led to minimising the overfit, indicating the potential for modelling the other features (e.g., soil data). This would lead to improving the temporal extrapolation of predictions through time.

Conclusions

The potential of incorporating MMs and DDMs has been investigated. Two outputs from two mechanistic models (C-Crop and WB models) were used with the commonly used LAI, NDVI, weather and soil data. The results of this study indicated that these two outputs could be helpful to replace the other temporal features while preserving the model’s predictive power, reducing noise caused by using many temporal features in the model, predictions at higher resolution, and more interpretable models. Biomass and soil moisture cannot completely replace the soil features used in this study. This is especially important when the scale of a study is small. This study also highlighted the need for improving MMs to improve ML models that use MMs outputs as inputs, as shown in the Experiment 4 results.

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Acknowledgements

The corresponding author would like to gratefully acknowledge the financial support (CSIRO/Data61 Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture) from the Commonwealth Scientific and Industrial Research Organisation (CSIRO). This research was also funded by the University of Sydney, and partly funded by the Cotton Research and Development Corporation (CRDC).

Open Access funding enabled and organized by CAUL and its Member Institutions. The work was supported by CSIRO/Data61.

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Conceptualization & methodology: Dhahi Al-Shammari, Thomas F.A. Bishop; Material preparation & data collection: Dhahi Al-Shammari, Si Yang Han, Niranjan S. Wimalathunge; Statistical analysis: Dhahi Al-Shammari; Writing—original draft preparation: Dhahi Al-Shammari, Niranjan S. Wimalathunge, Si Yang Han; Review & editing: Dhahi Al-Shammari, Thomas F.A. Bishop, Si Yang Han; Supervision: Thomas F.A. Bishop, Chen Wang.

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Al-Shammari, D., Chen, Y., Wimalathunge, N.S. et al. Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea. Precision Agric (2024). https://doi.org/10.1007/s11119-024-10184-3

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Accepted : 23 August 2024

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DOI : https://doi.org/10.1007/s11119-024-10184-3

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