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Exploratory Research – Types, Methods and Examples

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Exploratory Research

Definition:

Exploratory research is a type of research design that is used to investigate a research question when the researcher has limited knowledge or understanding of the topic or phenomenon under study.

The primary objective of exploratory research is to gain insights and gather preliminary information that can help the researcher better define the research problem and develop hypotheses or research questions for further investigation.

Exploratory Research Methods

There are several types of exploratory research, including:

Literature Review

This involves conducting a comprehensive review of existing published research, scholarly articles, and other relevant literature on the research topic or problem. It helps to identify the gaps in the existing knowledge and to develop new research questions or hypotheses.

Pilot Study

A pilot study is a small-scale preliminary study that helps the researcher to test research procedures, instruments, and data collection methods. This type of research can be useful in identifying any potential problems or issues with the research design and refining the research procedures for a larger-scale study.

This involves an in-depth analysis of a particular case or situation to gain insights into the underlying causes, processes, and dynamics of the issue under investigation. It can be used to develop a more comprehensive understanding of a complex problem, and to identify potential research questions or hypotheses.

Focus Groups

Focus groups involve a group discussion that is conducted to gather opinions, attitudes, and perceptions from a small group of individuals about a particular topic. This type of research can be useful in exploring the range of opinions and attitudes towards a topic, identifying common themes or patterns, and generating ideas for further research.

Expert Opinion

This involves consulting with experts or professionals in the field to gain their insights, expertise, and opinions on the research topic. This type of research can be useful in identifying the key issues and concerns related to the topic, and in generating ideas for further research.

Observational Research

Observational research involves gathering data by observing people, events, or phenomena in their natural settings to gain insights into behavior and interactions. This type of research can be useful in identifying patterns of behavior and interactions, and in generating hypotheses or research questions for further investigation.

Open-ended Surveys

Open-ended surveys allow respondents to provide detailed and unrestricted responses to questions, providing valuable insights into their attitudes, opinions, and perceptions. This type of research can be useful in identifying common themes or patterns, and in generating ideas for further research.

Data Analysis Methods

Exploratory Research Data Analysis Methods are as follows:

Content Analysis

This method involves analyzing text or other forms of data to identify common themes, patterns, and trends. It can be useful in identifying patterns in the data and developing hypotheses or research questions. For example, if the researcher is analyzing social media posts related to a particular topic, content analysis can help identify the most frequently used words, hashtags, and topics.

Thematic Analysis

This method involves identifying and analyzing patterns or themes in qualitative data such as interviews or focus groups. The researcher identifies recurring themes or patterns in the data and then categorizes them into different themes. This can be helpful in identifying common patterns or themes in the data and developing hypotheses or research questions. For example, a thematic analysis of interviews with healthcare professionals about patient care may identify themes related to communication, patient satisfaction, and quality of care.

Cluster Analysis

This method involves grouping data points into clusters based on their similarities or differences. It can be useful in identifying patterns in large datasets and grouping similar data points together. For example, if the researcher is analyzing customer data to identify different customer segments, cluster analysis can be used to group similar customers together based on their demographic, purchasing behavior, or preferences.

Network Analysis

This method involves analyzing the relationships and connections between data points. It can be useful in identifying patterns in complex datasets with many interrelated variables. For example, if the researcher is analyzing social network data, network analysis can help identify the most influential users and their connections to other users.

Grounded Theory

This method involves developing a theory or explanation based on the data collected during the exploratory research process. The researcher develops a theory or explanation that is grounded in the data, rather than relying on pre-existing theories or assumptions. This can be helpful in developing new theories or explanations that are supported by the data.

Applications of Exploratory Research

Exploratory research has many practical applications across various fields. Here are a few examples:

• Marketing Research : In marketing research, exploratory research can be used to identify consumer needs, preferences, and behavior. It can also help businesses understand market trends and identify new market opportunities.
• Product Development: In product development, exploratory research can be used to identify customer needs and preferences, as well as potential design flaws or issues. This can help companies improve their product offerings and develop new products that better meet customer needs.
• Social Science Research: In social science research, exploratory research can be used to identify new areas of study, as well as develop new theories and hypotheses. It can also be used to identify potential research methods and approaches.
• Healthcare Research : In healthcare research, exploratory research can be used to identify new treatments, therapies, and interventions. It can also be used to identify potential risk factors or causes of health problems.
• Education Research: In education research, exploratory research can be used to identify new teaching methods and approaches, as well as identify potential areas of study for further research. It can also be used to identify potential barriers to learning or achievement.

Examples of Exploratory Research

Here are some more examples of exploratory research from different fields:

• Social Science : A researcher wants to study the experience of being a refugee, but there is limited existing research on this topic. The researcher conducts exploratory research by conducting in-depth interviews with refugees to better understand their experiences, challenges, and needs.
• Healthcare : A medical researcher wants to identify potential risk factors for a rare disease but there is limited information available. The researcher conducts exploratory research by reviewing medical records and interviewing patients and their families to identify potential risk factors.
• Education : A teacher wants to develop a new teaching method to improve student engagement, but there is limited information on effective teaching methods. The teacher conducts exploratory research by reviewing existing literature and interviewing other teachers to identify potential approaches.
• Technology : A software developer wants to develop a new app, but is unsure about the features that users would find most useful. The developer conducts exploratory research by conducting surveys and focus groups to identify user preferences and needs.
• Environmental Science : An environmental scientist wants to study the impact of a new industrial plant on the surrounding environment, but there is limited existing research. The scientist conducts exploratory research by collecting and analyzing soil and water samples, and conducting interviews with residents to better understand the impact of the plant on the environment and the community.

How to Conduct Exploratory Research

Here are the general steps to conduct exploratory research:

• Define the research problem: Identify the research problem or question that you want to explore. Be clear about the objective and scope of the research.
• Review existing literature: Conduct a review of existing literature and research on the topic to identify what is already known and where gaps in knowledge exist.
• Determine the research design : Decide on the appropriate research design, which will depend on the nature of the research problem and the available resources. Common exploratory research designs include case studies, focus groups, interviews, and surveys.
• Collect data: Collect data using the chosen research design. This may involve conducting interviews, surveys, or observations, or collecting data from existing sources such as archives or databases.
• Analyze data: Analyze the data collected using appropriate qualitative or quantitative techniques. This may include coding and categorizing qualitative data, or running descriptive statistics on quantitative data.
• I nterpret and report findings: Interpret the findings of the analysis and report them in a way that is clear and understandable. The report should summarize the findings, discuss their implications, and make recommendations for further research or action.
• Iterate : If necessary, refine the research question and repeat the process of data collection and analysis to further explore the topic.

When to use Exploratory Research

Exploratory research is appropriate in situations where there is limited existing knowledge or understanding of a topic, and where the goal is to generate insights and ideas that can guide further research. Here are some specific situations where exploratory research may be particularly useful:

• New product development: When developing a new product, exploratory research can be used to identify consumer needs and preferences, as well as potential design flaws or issues.
• Emerging technologies: When exploring emerging technologies, exploratory research can be used to identify potential uses and applications, as well as potential challenges or limitations.
• Developing research hypotheses: When developing research hypotheses, exploratory research can be used to identify potential relationships or patterns that can be further explored through more rigorous research methods.
• Understanding complex phenomena: When trying to understand complex phenomena, such as human behavior or societal trends, exploratory research can be used to identify underlying patterns or factors that may be influencing the phenomenon.
• Developing research methods : When developing new research methods, exploratory research can be used to identify potential issues or limitations with existing methods, and to develop new methods that better capture the phenomena of interest.

Purpose of Exploratory Research

The purpose of exploratory research is to gain insights and understanding of a research problem or question where there is limited existing knowledge or understanding. The objective is to explore and generate ideas that can guide further research, rather than to test specific hypotheses or make definitive conclusions.

Exploratory research can be used to:

• Identify new research questions: Exploratory research can help to identify new research questions and areas of inquiry, by providing initial insights and understanding of a topic.
• Develop hypotheses: Exploratory research can help to develop hypotheses and testable propositions that can be further explored through more rigorous research methods.
• Identify patterns and trends : Exploratory research can help to identify patterns and trends in data, which can be used to guide further research or decision-making.
• Understand complex phenomena: Exploratory research can help to provide a deeper understanding of complex phenomena, such as human behavior or societal trends, by identifying underlying patterns or factors that may be influencing the phenomena.
• Generate ideas: Exploratory research can help to generate new ideas and insights that can be used to guide further research, innovation, or decision-making.

Characteristics of Exploratory Research

The following are the main characteristics of exploratory research:

• Flexible and open-ended : Exploratory research is characterized by its flexible and open-ended nature, which allows researchers to explore a wide range of ideas and perspectives without being constrained by specific research questions or hypotheses.
• Qualitative in nature : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, or observation, to gather rich and detailed data on the research problem.
• Limited scope: Exploratory research is generally limited in scope, focusing on a specific research problem or question, rather than attempting to provide a comprehensive analysis of a broader phenomenon.
• Preliminary in nature : Exploratory research is preliminary in nature, providing initial insights and understanding of a research problem, rather than testing specific hypotheses or making definitive conclusions.
• I terative process : Exploratory research is often an iterative process, where the research design and methods may be refined and adjusted as new insights and understanding are gained.
• I nductive approach : Exploratory research typically takes an inductive approach to data analysis, seeking to identify patterns and relationships in the data that can guide further research or hypothesis development.

Advantages of Exploratory Research

The following are some advantages of exploratory research:

• Provides initial insights: Exploratory research is useful for providing initial insights and understanding of a research problem or question where there is limited existing knowledge or understanding. It can help to identify patterns, relationships, and potential hypotheses that can guide further research.
• Flexible and adaptable : Exploratory research is flexible and adaptable, allowing researchers to adjust their methods and approach as they gain new insights and understanding of the research problem.
• Qualitative methods : Exploratory research typically relies on qualitative methods, such as in-depth interviews, focus groups, and observation, which can provide rich and detailed data that is useful for gaining insights into complex phenomena.
• Cost-effective : Exploratory research is often less costly than other research methods, such as large-scale surveys or experiments. It is typically conducted on a smaller scale, using fewer resources and participants.
• Useful for hypothesis generation : Exploratory research can be useful for generating hypotheses and testable propositions that can be further explored through more rigorous research methods.
• Provides a foundation for further research: Exploratory research can provide a foundation for further research by identifying potential research questions and areas of inquiry, as well as providing initial insights and understanding of the research problem.

Limitations of Exploratory Research

The following are some limitations of exploratory research:

• Limited generalizability: Exploratory research is typically conducted on a small scale and uses non-random sampling techniques, which limits the generalizability of the findings to a broader population.
• Subjective nature: Exploratory research relies on qualitative methods and is therefore subject to researcher bias and interpretation. The findings may be influenced by the researcher’s own perceptions, beliefs, and assumptions.
• Lack of rigor: Exploratory research is often less rigorous than other research methods, such as experimental research, which can limit the validity and reliability of the findings.
• Limited ability to test hypotheses: Exploratory research is not designed to test specific hypotheses, but rather to generate initial insights and understanding of a research problem. It may not be suitable for testing well-defined research questions or hypotheses.
• Time-consuming : Exploratory research can be time-consuming and resource-intensive, particularly if the researcher needs to gather data from multiple sources or conduct multiple rounds of data collection.
• Difficulty in interpretation: The open-ended nature of exploratory research can make it difficult to interpret the findings, particularly if the researcher is unable to identify clear patterns or relationships in the data.

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• Methodology
• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on 6 May 2022 by Tegan George . Revised on 20 January 2023.

Exploratory research is a methodology approach that investigates topics and research questions that have not previously been studied in depth.

Exploratory research is often qualitative in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

When to use exploratory research, exploratory research questions, exploratory research data collection, step-by-step example of exploratory research, exploratory vs explanatory research, advantages and disadvantages of exploratory research, frequently asked questions about exploratory research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of primary schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis , as well as giving you the ‘lay of the land’ on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other universities done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesise a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualise your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyse data

Next, you proceed with collecting and analysing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalisable results.

It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with ‘why’ or ‘how’, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory research has its trade-offs: it provides a unique set of benefits but also comes with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalisable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labour-intensive.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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What Is a Case Study?

Weighing the pros and cons of this method of research

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

Cara Lustik is a fact-checker and copywriter.

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."

• How it works

A Quick Guide to Case Study with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On August 29, 2023

A case study is a documented history and detailed analysis of a situation concerning organisations, industries, and markets.

A case study:

• Focuses on discovering new facts of the situation under observation.
• Includes data collection from multiple sources over time.
• Widely used in social sciences to study the underlying information, organisation, community, or event.
• It does not provide any solution to the problem .

When to Use Case Study? 

You can use a case study in your research when:

• The focus of your study is to find answers to how and why questions .
• You don’t have enough time to conduct extensive research; case studies are convenient for completing your project successfully.
• You want to analyse real-world problems in-depth, then you can use the method of the case study.

You can consider a single case to gain in-depth knowledge about the subject, or you can choose multiple cases to know about various aspects of your  research problem .

What are the Aims of the Case Study?

• The case study aims at identifying weak areas that can be improved.
• This method is often used for idiographic research (focuses on individual cases or events).
• Another aim of the case study is nomothetic research (aims to discover new theories through data analysis of multiple cases).

Types of Case Studies

There are different types of case studies that can be categorised based on the purpose of the investigation.

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How to Conduct a Case Study?

• Select the Case to Investigate
• Formulate the Research Question
• Review of Literature
• Choose the Precise Case to Use in your Study
• Select Data Collection and Analysis Techniques
• Collect the Data
• Analyse the Data
• Prepare the Report

Step1: Select the Case to Investigate

The first step is to select a case to conduct your investigation. You should remember the following points.

• Make sure that you perform the study in the available timeframe.
• There should not be too much information available about the organisation.
• You should be able to get access to the organisation.
• There should be enough information available about the subject to conduct further research.

Step2: Formulate the Research Question

It’s necessary to  formulate a research question  to proceed with your case study. Most of the research questions begin with  how, why, what, or what can . 

You can also use a research statement instead of a research question to conduct your research which can be conditional or non-conditional. 

Step 3: Review of Literature

Once you formulate your research statement or question, you need to extensively  review the documentation about the existing discoveries related to your research question or statement.

Step 4: Choose the Precise Case to Use in your Study

You need to select a specific case or multiple cases related to your research. It would help if you treated each case individually while using multiple cases. The outcomes of each case can be used as contributors to the outcomes of the entire study.  You can select the following cases. 

• Representing various geographic regions
• Cases with various size parameters
• Explaining the existing theories or assumptions
• Leading to discoveries
• Providing a base for future research.

Step 5: Select Data Collection and Analysis Techniques

You can choose both  qualitative or quantitative approaches  for  collecting the data . You can use  interviews ,  surveys , artifacts, documentation, newspapers, and photographs, etc. To avoid biased observation, you can triangulate  your research to provide different views of your case. Even if you are focusing on a single case, you need to observe various case angles. It would help if you constructed validity, internal and external validity, as well as reliability.

Example: Identifying the impacts of contaminated water on people’s health and the factors responsible for it. You need to gather the data using qualitative and quantitative approaches to understand the case in such cases.

Construct validity:  You should select the most suitable measurement tool for your research. 

Internal validity:   You should use various methodological tools to  triangulate  the data. Try different methods to study the same hypothesis.

External validity:  You need to effectively apply the data beyond the case’s circumstances to more general issues.

Reliability:   You need to be confident enough to formulate the new direction for future studies based on your findings.

Also Read:  Reliability and Validity

Step 6: Collect the Data

Beware of the following when collecting data:

• Information should be gathered systematically, and the collected evidence from various sources should contribute to your research objectives.
• Don’t collect your data randomly.
• Recheck your research questions to avoid mistakes.
• You should save the collected data in any popular format for clear understanding.
• While making any changes to collecting information, make sure to record the changes in a document.
• You should maintain a case diary and note your opinions and thoughts evolved throughout the study.

Step 7: Analyse the Data

The research data identifies the relationship between the objects of study and the research questions or statements. You need to reconfirm the collected information and tabulate it correctly for better understanding. 

Step 8: Prepare the Report

It’s essential to prepare a report for your case study. You can write your case study in the form of a scientific paper or thesis discussing its detail with supporting evidence. 

A case study can be represented by incorporating  quotations,  stories, anecdotes,  interview transcripts , etc., with empirical data in the result section. 

You can also write it in narrative styles using  textual analysis  or   discourse analysis . Your report should also include evidence from published literature, and you can put it in the discussion section.

Advantages and Disadvantages of Case Study

Frequently asked questions, what is the case study.

A case study is a research method where a specific instance, event, or situation is deeply examined to gain insights into real-world complexities. It involves detailed analysis of context, data, and variables to understand patterns, causes, and effects, often used in various disciplines for in-depth exploration.

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Case study research for better evaluations of complex interventions: rationale and challenges

• Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
• Judith Green 2 ,
• Chrysanthi Papoutsi 1 ,
• Jamie Murdoch 3 ,
• Mark Petticrew 4 ,
• Trish Greenhalgh 1 ,
• Benjamin Hanckel 5 &
• Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

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The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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Writing A Case Study

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Case studies are effective research methods that focus on one specific case over time. This gives a detailed view that's great for learning.

Writing a case study is a very useful form of study in the educational process. With real-life examples, students can learn more effectively. 

A case study also has different types and forms. As a rule of thumb, all of them require a detailed and convincing answer based on a thorough analysis.

In this blog, we are going to discuss the different types of case study research methods in detail.

So, let’s dive right in!

• 1. Understanding Case Studies
• 2. What are the Types of Case Study?
• 3. Types of Subjects of Case Study 
• 4. Benefits of Case Study for Students

Understanding Case Studies

Case studies are a type of research methodology. Case study research designs examine subjects, projects, or organizations to provide an analysis based on the evidence.

It allows you to get insight into what causes any subject’s decisions and actions. This makes case studies a great way for students to develop their research skills.

A case study focuses on a single project for an extended period, which allows students to explore the topic in depth.

What are the Types of Case Study?

Multiple case studies are used for different purposes. The main purpose of case studies is to analyze problems within the boundaries of a specific organization, environment, or situation. 

Many aspects of a case study such as data collection and analysis, qualitative research questions, etc. are dependent on the researcher and what the study is looking to address. 

Case studies can be divided into the following categories:

Illustrative Case Study

Exploratory case study, cumulative case study, critical instance case study, descriptive case study, intrinsic case study, instrumental case study.

Let’s take a look at the detailed description of each type of case study with examples. 

An illustrative case study is used to examine a familiar case to help others understand it. It is one of the main types of case studies in research methodology and is primarily descriptive. 

In this type of case study, usually, one or two instances are used to explain what a situation is like. 

Here is an example to help you understand it better:

Illustrative Case Study Example

An exploratory case study is usually done before a larger-scale research. These types of case studies are very popular in the social sciences like political science and primarily focus on real-life contexts and situations.

This method is useful in identifying research questions and methods for a large and complex study. 

Let’s take a look at this example to help you have a better understanding:

Exploratory Case Study Example

A cumulative case study is one of the main types of case studies in qualitative research. It is used to collect information from different sources at different times.

This case study aims to summarize the past studies without spending additional cost and time on new investigations. 

Let’s take a look at the example below:

Cumulative Case Study Example

Critical instances case studies are used to determine the cause and consequence of an event. 

The main reason for this type of case study is to investigate one or more sources with unique interests and sometimes with no interest in general. 

Take a look at this example below:

Critical Instance Case Study Example

When you have a hypothesis, you can design a descriptive study. It aims to find connections between the subject being studied and a theory.

After making these connections, the study can be concluded. The results of the descriptive case study will usually suggest how to develop a theory further.

This example can help you understand the concept better:

Descriptive Case Study Example

Intrinsic studies are more commonly used in psychology, healthcare, or social work. So, if you were looking for types of case studies in sociology, or types of case studies in social research, this is it.

The focus of intrinsic studies is on the individual. The aim of such studies is not only to understand the subject better but also their history and how they interact with their environment.

Here is an example to help you understand;

Intrinsic Case Study Example

This type of case study is mostly used in qualitative research. In an instrumental case study, the specific case is selected to provide information about the research question.

It offers a lens through which researchers can explore complex concepts, theories, or generalizations.

Take a look at the example below to have a better understanding of the concepts:

Instrumental Case Study Example

Review some case study examples to help you understand how a specific case study is conducted.

Types of Subjects of Case Study 

In general, there are 5 types of subjects that case studies address. Every case study fits into the following subject categories. 

• Person: This type of study focuses on one subject or individual and can use several research methods to determine the outcome. 
• Group: This type of study takes into account a group of individuals. This could be a group of friends, coworkers, or family. 
• Location: The main focus of this type of study is the place. It also takes into account how and why people use the place. 
• Organization: This study focuses on an organization or company. This could also include the company employees or people who work in an event at the organization. 
• Event: This type of study focuses on a specific event. It could be societal or cultural and examines how it affects the surroundings. 

Benefits of Case Study for Students

Here's a closer look at the multitude of benefits students can have with case studies:

Real-world Application

Case studies serve as a crucial link between theory and practice. By immersing themselves in real-world scenarios, students can apply theoretical knowledge to practical situations.

Critical Thinking Skills

Analyzing case studies demands critical thinking and informed decision-making. Students cultivate the ability to evaluate information, identify key factors, and develop well-reasoned solutions – essential skills in both academic and professional contexts.

Enhanced Problem-solving Abilities

Case studies often present complex problems that require creative and strategic solutions. Engaging with these challenges refines students' problem-solving skills, encouraging them to think innovatively and develop effective approaches.

Holistic Understanding

Going beyond theoretical concepts, case studies provide a holistic view of a subject. Students gain insights into the multifaceted aspects of a situation, helping them connect the dots and understand the broader context.

Exposure to Diverse Perspectives

Case studies often encompass a variety of industries, cultures, and situations. This exposure broadens students' perspectives, fostering a more comprehensive understanding of the world and the challenges faced by different entities.

So there you have it!

We have explored different types of case studies and their examples. Case studies act as the tools to understand and deal with the many challenges and opportunities around us.

Case studies are being used more and more in colleges and universities to help students understand how a hypothetical event can influence a person, group, or organization in real life. 

Not everyone can handle the case study writing assignment easily. It is even scary to think that your time and work could be wasted if you don't do the case study paper right. 

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The exploratory case study approach in research aims to provide a comprehensive understanding of answers to a specific research question, employing qualitative methods and multiple data collection techniques such as surveys, questionnaires, and focus group interviews. Rooted in the qualitative paradigm, particularly the case study method, the objective is to delve deeply into a particular case, offering perspectives on a previously uninvestigated field or issue. This approach, distinct from theory building or understanding abstract constructs, focuses on the nuanced exploration of a specific context, potentially guiding future research directions.

According to Merriam (1998), Patton (1990), Stake (1995, 2005), and Yin (2009), the case study approach investigates a contemporary phenomenon within its real-life context, especially when the boundaries between the phenomenon and its context are not clearly evident.

The researchers aim to describe the case, analyze themes present in the description, and make interpretations from the data. This multi-faceted approach involves incorporating data from various sources to attain a comprehensive understanding of the phenomenon.

The study's driving force is the research question, and the purpose is to generate evidence-based answers. By consulting multiple data sources (by employing, for example, surveys, artefacts, and focus group interviews), the researchers triangulate data to offer a robust and multifaceted interpretation. Unlike studies aiming for broad theoretical implications, the exploratory case study prioritizes a detailed understanding of a specific case, potentially serving as a foundation for future research endeavors.

In essence, the researchers use the case study method to navigate the complexities of the real-life context in which a phenomenon occurs. The utilization of multiple data sources enhances the study's richness and validity, providing a nuanced exploration of the research question, seeking to contribute new insights and perspectives by closely examining a particular case and generating evidence-based answers to the research question.

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Methodology

• Explanatory Research | Definition, Guide, & Examples

Explanatory Research | Definition, Guide, & Examples

Published on December 3, 2021 by Tegan George and Julia Merkus. Revised on November 20, 2023.

Explanatory research is a research method that explores why something occurs when limited information is available. It can help you increase your understanding of a given topic, ascertain how or why a particular phenomenon is occurring, and predict future occurrences.

Explanatory research can also be explained as a “cause and effect” model, investigating patterns and trends in existing data that haven’t been previously investigated. For this reason, it is often considered a type of causal research .

Table of contents

When to use explanatory research, explanatory research questions, explanatory research data collection, explanatory research data analysis, step-by-step example of explanatory research, explanatory vs. exploratory research, advantages and disadvantages of explanatory research, other interesting articles, frequently asked questions about explanatory research.

Explanatory research is used to investigate how or why a phenomenon takes place. Therefore, this type of research is often one of the first stages in the research process, serving as a jumping-off point for future research. While there is often data available about your topic, it’s possible the particular causal relationship you are interested in has not been robustly studied.

Explanatory research helps you analyze these patterns, formulating hypotheses that can guide future endeavors. If you are seeking a more complete understanding of a relationship between variables, explanatory research is a great place to start. However, keep in mind that it will likely not yield conclusive results.

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Explanatory research answers “why” and “how” questions, leading to an improved understanding of a previously unresolved problem or providing clarity for related future research initiatives.

Here are a few examples:

• Why do undergraduate students obtain higher average grades in the first semester than in the second semester?
• How does marital status affect labor market participation?
• Why do multilingual individuals show more risky behavior during business negotiations than monolingual individuals?
• How does a child’s ability to delay immediate gratification predict success later in life?
• Why are teens more likely to litter in a highly littered area than in a clean area?

After choosing your research question, there is a variety of options for research and data collection methods to choose from.

A few of the most common research methods include:

• Literature reviews
• Interviews and focus groups
• Pilot studies
• Observations
• Experiments

The method you choose depends on several factors, including your timeline, budget, and the structure of your question. If there is already a body of research on your topic, a literature review is a great place to start. If you are interested in opinions and behavior, consider an interview or focus group format. If you have more time or funding available, an experiment or pilot study may be a good fit for you.

In order to ensure you are conducting your explanatory research correctly, be sure your analysis is definitively causal in nature, and not just correlated.

Always remember the phrase “correlation doesn’t mean causation.” Correlated variables are merely associated with one another: when one variable changes, so does the other. However, this isn’t necessarily due to a direct or indirect causal link.

Causation means that changes in the independent variable bring about changes in the dependent variable. In other words, there is a direct cause-and-effect relationship between variables.

Causal evidence must meet three criteria:

• Temporal : What you define as the “cause” must precede what you define as the “effect.”
• Variation : Intervention must be systematic between your independent variable and dependent variable.
• Non-spurious : Be careful that there are no mitigating factors or hidden third variables that confound your results.

Correlation doesn’t imply causation, but causation always implies correlation. In order to get conclusive causal results, you’ll need to conduct a full experimental design .

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Your explanatory research design depends on the research method you choose to collect your data . In most cases, you’ll use an experiment to investigate potential causal relationships. We’ll walk you through the steps using an example.

Step 1: Develop the research question

The first step in conducting explanatory research is getting familiar with the topic you’re interested in, so that you can develop a research question .

Let’s say you’re interested in language retention rates in adults.

You are interested in finding out how the duration of exposure to language influences language retention ability later in life.

Step 2: Formulate a hypothesis

The next step is to address your expectations. In some cases, there is literature available on your subject or on a closely related topic that you can use as a foundation for your hypothesis . In other cases, the topic isn’t well studied, and you’ll have to develop your hypothesis based on your instincts or on existing literature on more distant topics.

You phrase your expectations in terms of a null (H 0 ) and alternative hypothesis (H 1 ):

• H 0 : The duration of exposure to a language in infancy does not influence language retention in adults who were adopted from abroad as children.
• H 1 : The duration of exposure to a language in infancy has a positive effect on language retention in adults who were adopted from abroad as children.

Step 3: Design your methodology and collect your data

Next, decide what data collection and data analysis methods you will use and write them up. After carefully designing your research, you can begin to collect your data.

You compare:

• Adults who were adopted from Colombia between 0 and 6 months of age.
• Adults who were adopted from Colombia between 6 and 12 months of age.
• Adults who were adopted from Colombia between 12 and 18 months of age.
• Monolingual adults who have not been exposed to a different language.

During the study, you test their Spanish language proficiency twice in a research design that has three stages:

• Pre-test : You conduct several language proficiency tests to establish any differences between groups pre-intervention.
• Intervention : You provide all groups with 8 hours of Spanish class.
• Post-test : You again conduct several language proficiency tests to establish any differences between groups post-intervention.

You made sure to control for any confounding variables , such as age, gender, proficiency in other languages, etc.

Step 4: Analyze your data and report results

After data collection is complete, proceed to analyze your data and report the results.

You notice that:

• The pre-exposed adults showed higher language proficiency in Spanish than those who had not been pre-exposed. The difference is even greater for the post-test.
• The adults who were adopted between 12 and 18 months of age had a higher Spanish language proficiency level than those who were adopted between 0 and 6 months or 6 and 12 months of age, but there was no difference found between the latter two groups.

To determine whether these differences are significant, you conduct a mixed ANOVA. The ANOVA shows that all differences are not significant for the pre-test, but they are significant for the post-test.

Step 5: Interpret your results and provide suggestions for future research

As you interpret the results, try to come up with explanations for the results that you did not expect. In most cases, you want to provide suggestions for future research.

However, this difference is only significant after the intervention (the Spanish class.)

You decide it’s worth it to further research the matter, and propose a few additional research ideas:

• Replicate the study with a larger sample
• Replicate the study for other maternal languages (e.g. Korean, Lingala, Arabic)
• Replicate the study for other language aspects, such as nativeness of the accent

It can be easy to confuse explanatory research with exploratory research. If you’re in doubt about the relationship between exploratory and explanatory research, just remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research questions often begin with “what”. They are designed to guide future research and do not usually have conclusive results. Exploratory research is often utilized as a first step in your research process, to help you focus your research question and fine-tune your hypotheses.

Explanatory research questions often start with “why” or “how”. They help you study why and how a previously studied phenomenon takes place.

Like any other research design , explanatory research has its trade-offs: while it provides a unique set of benefits, it also has significant downsides:

• It gives more meaning to previous research. It helps fill in the gaps in existing analyses and provides information on the reasons behind phenomena.
• It is very flexible and often replicable , since the internal validity tends to be high when done correctly.
• As you can often use secondary research, explanatory research is often very cost- and time-effective, allowing you to utilize pre-existing resources to guide your research prior to committing to heavier analyses.

Disadvantages

• While explanatory research does help you solidify your theories and hypotheses, it usually lacks conclusive results.
• Results can be biased or inadmissible to a larger body of work and are not generally externally valid . You will likely have to conduct more robust (often quantitative ) research later to bolster any possible findings gleaned from explanatory research.
• Coincidences can be mistaken for causal relationships , and it can sometimes be challenging to ascertain which is the causal variable and which is the effect.

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

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

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

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

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Increased CX3CL1 in cerebrospinal fluid and ictal serum t-tau elevations in migraine: results from a cross-sectional exploratory case-control study

• Marie Süße   ORCID: orcid.org/0000-0002-6167-4354 1 ,
• Christine Kloetzer 1 ,
• Sebastian Strauß 1 ,
• Johanna Ruhnau 1 ,
• Lucas Hendrik Overeem 2 , 3 ,
• Merle Bendig 1 ,
• Juliane Schulze 1 ,
• Uwe Reuter 1 , 2 ,
• Antje Vogelgesang 1   na1 &
• Robert Fleischmann 1   na1  

The Journal of Headache and Pain volume  25 , Article number:  46 ( 2024 ) Cite this article

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To date, migraine is diagnosed exclusively based on clinical criteria, but fluid biomarkers are desirable to gain insight into pathophysiological processes and inform clinical management. We investigated the state-dependent profile of fluid biomarkers for neuroaxonal damage and microglial activation as two potentially relevant aspects in human migraine pathophysiology.

This exploratory study included serum and cerebrospinal fluid (CSF) samples of patients with migraine during the headache phase (ictally) ( n  = 23), between attacks (interictally) ( n  = 16), and age/sex-matched controls ( n  = 19). Total Tau (t-Tau) protein, glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and neurofilament light chain (NfL) were measured with the Neurology 4-plex kit on a Single Molecule Array SR-X Analyzer (Simoa® SR-X, Quanterix Corp., Lexington, MA). Markers of microglial activation, C-X3-C motif chemokine ligand 1 (CX3CL1) and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), were assessed using an immunoassay.

Concentrations of CX3CL1 but not sTREM2 were significantly increased both ictally and interictally in CSF but not in serum in comparison to the control cohort ( p  = 0.039). ROC curve analysis provided an AUC of 0.699 (95% CI 0.563 to 0.813, p  = 0.007). T-Tau in serum but not in CSF was significantly increased in samples from patients taken during the headache phase, but not interictally (effect size: η 2  = 0.121, p  = 0.038). ROC analysis of t-Tau protein in serum between ictal and interictal collected samples provided an AUC of 0.729 (95% CI 0.558 to 0.861, p  = 0.006). The other determined biomarkers for axonal damage were not significantly different between the cohorts in either serum or CSF.

CX3CL1 in CSF is a novel potential fluid biomarker of migraine that is unrelated to the headache status. Serum t-Tau is linked to the headache phase but not interictal migraine. These data need to be confirmed in a larger hypothesis-driven prospective study.

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Migraine is among the most prevalent neurological disorders, with a one-year prevalence of about 15% [ 1 , 2 ]. Currently, migraine is defined and diagnosed exclusively based on clinical criteria [ 3 ]. There are no clinical predictors or biomarkers to inform clinical management once the diagnosis of migraine is established, limiting personalized medicine strategies [ 4 ]. Fluid biomarkers are desirable to assist clinical decision-making in several instances, e.g., to monitor disease activity, potentially predict and stratify therapeutic approaches, and inform novel treatment targets [ 5 ]. Potential biomarkers of neuroinflammation are of interest, as migraine headaches, at least in part, reflect neurogenic inflammation along trigeminal afferents, including meningeal branches [ 6 ]. Overeem et al. recently described serum total Tau (t-Tau) as a potential novel biomarker possibly reflecting peripheral trigeminal neuroinflammation in migraine [ 7 ]. The state-dependent pattern of t-Tau release, however, remains to be determined. It is currently unknown if t-Tau is exclusively elevated in serum but not cerebrospinal fluid (CSF) during the headache phase and in neither compartment interictally. The latter would support the notion that its release is due to an activation of the trigeminovascular system and peripheral neuroinflammatory mechanisms. Biomarkers for neural destruction were not elevated in this or in previous migraine studies [ 7 , 8 ]. Imaging studies using radioactive tracers and mouse models furthermore point towards a critical role of microglia in migraine pathophysiology [ 9 , 10 ]. Microglia are essential mediators of neurogenic inflammation and promote the production of inflammatory and cytotoxic mediators [ 6 ]. A potential biomarker to mirror microglial activity is C-X3-C motif chemokine ligand 1 (CX3CL1), which is a chemokine released by neurons and glia. Its receptor, CX3CR1, is primarily expressed on microglia. CX3CL1 acts as a regulator of microglial activation within the central nervous system (CNS) [ 11 ]. Another biomarker for microglia activation is the triggering receptor expressed on myeloid cells 2 (TREM2). TREM2 is involved more generally in microglial activation in the brain [ 12 , 13 ]. In CSF, sTREM2 is strongly correlated with CSF biomarkers of Tau pathology that track closely with the neurodegenerative processes in Alzheimer’s disease (AD) [ 14 ]. Neurodegenerative processes in migraine patients were discussed in view of white matter lesions and volumetric changes in white and grey matter on magnetic resonance imaging (MRI) [ 15 ].

In summary, there is an evolving set of candidate fluid biomarkers in migraine. Markers of microglial activation in CSF have not been tested to date in humans with migraine. This is not surprising since biomarker analyses in the CSF of migraine patients are rarely available as CSF is not routinely collected from migraine patients. The situation is further complicated by the necessity to determine the state-dependent profile of microglial activation, i.e., whether changes are found in the headache phase or interictally, in serum or CSF. For this purpose, we assembled a unique cohort of combined CSF and serum samples from migraine patients in their headache and interictal states, as well as age-matched controls. Based on the results of the study by Overeem et al. [ 7 ] data from these cohorts will furthermore be used to characterize the state-dependent profile of t-Tau release in migraine, which is critical to advance the understanding of its role in migraine pathophysiology.

Ethical approval and study registration

All procedures adhered to the Helsinki Declaration in its latest revision and were conducted in line with current guidelines for good clinical practice . This retrospective, single-center cohort study was based on the use of coded surplus material. The use of coded surplus material without additional informed consent was based on the local ethics research committee statement (ID: III UV 39/03 and BB 161/18, University Medicine Greifswald, Germany). All participants provided their consent that surplus biological samples may be used along with clinical data for research purposes.

Study design and participant selection

This is a retrospective exploratory case-control study with blood and CSF samples of episodic migraineurs serving as cases and samples of patients without neurological diseases serving as matched controls. Matching was done for age (±5 years) and sex. There were several indications for CSF analysis. In patients with interictal migraine, suspected diagnoses were transient symptoms later diagnosed as aura ( n  = 6), migraine as a concomitant disease ( n  = 3), headache symptoms not previously classified as migraine ( n  = 7). Patients with ictal migraine received lumbar puncture because of headache characteristics different form previous presentations ( n  = 9), exclusion of infections in headache disorder not previously diagnosed as migraine ( n  = 13) and unclear aura symptoms ( n  = 1).

Control subjects had nonspecific symptoms and a neurological disorder was eventually excluded. Discharge reports were required not to include any evidence of a headache disorder (defined as a headache frequency of < 1/year and a negative medical history). Cerebrospinal fluid analysis in these cases was done to rule-out neuroinflammatory disorders that may have been undetected on MRI, although suspicion was generally low. But thorough work-up is warranted before diagnosing a patient with a functional disorder. The diagnosis of migraine was retrieved from discharge reports, re-reviewed, and confirmed by certified specialists from the University Headache Center according to international classification of headache disorders, 3rd revision (ICHD-3) criteria [ 3 ]. Importantly, evidence of recurrent attacks was required to confirm that patients suffer migraine as a headache disorder and not only a first-time migrainous headache. Insufficient clinical information following chart review or any doubt about the diagnosis led to the exclusion of the patient. Further exclusion criteria were the presence of any neurodegenerative, neuro-, or systemic inflammatory condition or any lesional defect in cerebral imaging. Concerning the detailed selection process of the patient samples see supplementary Fig.  1 .

Minor parts of the analyses were previously used to assist in the interpretation of another data set; however, they were not the main focus of the current publication [ 7 ].

Laboratory analyses

All samples were acquired from patients treated at the Department of Neurology, University Medicine of Greifswald (Greifswald, Germany). Lumbar CSF was collected according to local standards. CSF supernatant was extracted and aliquoted in 0.5 mL polypropylene tubes. Blood and CSF samples were stored at − 80 °C until measurement. All standard laboratory CSF analyses were performed as described previously [ 16 ].

We quantified t-Tau protein, glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and neurofilament light chain (NfL) with a Single Molecule Array (Simoa®, Neurology 4-plex A kit Lot No.: 503223) on the SR-X platform according to the manufacturer’s instructions (Quanterix, Billerica, MA, USA). Samples were analysed in duplicates following the manufacturer’s instructions and standard procedures at the department of neurology, University Medicine Greifswald. Serum samples were measured at a dilution of 1:4, CSF 1:100. The assays’ lower limit of quantification lies at 0.317 pg/mL (NfL), 0.933 pg/ml (GFAP), 9.6 pg/ml (UCH-L1), 0.114 pg/ml (t-Tau). The limit of detection (LOD) lies at 0.136 pg/mL (NfL), 0.276 pg/ml (GFAP), 4.03 pg/ml (UCH-L1), 0.0298 pg/ml (t-Tau). Three serum Tau samples were measured below the LOD. All other measurements were above these limits. Comparability was assured by including two plate controls (Lot Nr. 131,802) in all runs according to the manufacturer’s instructions. CX3CL1 was quantified with the QuantikineTM ELISA Human CX3CL1/Fractalkine Immunoassay (R&D Systems) according to manufacturer’s instructions. The limit of detection lies at 0.072 ng/mL (assay range: 0.2–10 ng/mL). s-TREM was quantified with the Human TREM2 SimpleStep ELISA Kit (abcam) according to the manufacturer’s instructions. The limit of detection lies at 10.5 pg/mL (assay range: 78.1 pg/ml - 5000 pg/ml). One patient sample presented values of CX3CL1 and sTREM2 below the LOD. Samples below the LOD were excluded from analysis.

Data evaluation and statistics

Primary endpoint was the difference in absolute concentration in pg/ml of parameters microglial markers CX3CL1 and sTREM in serum and CSF samples between patients with migraine either collected during the acute migraine attack (ictal collection) or between attacks (interical collection) and healthy controls. Secondary parameters were markers for neuroaxonal destruction (NfL, GFAP, UCHL-1, t-Tau protein). Statistical analyses were carried out with SPSS (v28.0, IBM, Armonk, NY, USA) and GraphPad Software (version 7, La Jolla, CA). Normally distributed group data are presented as mean ± standard deviation; non-normally distributed data are presented as median and their interquartile range. Continuous data were analyzed for normal distribution using the Kolmogorov-Smirnov test. All biomarkers were non-normally distributed. Inferential comparisons within and between group means were carried out using the Mann-Whitney U test for pairwise comparisons and the Kruskal-Wallis test for more than two groups. Single parameters were post-hoc compared pairwise and corrected for multiple comparisons using the Bonferroni method. Frequencies are reported numerically. Classification properties of significant between-group differences of t-Tau protein (ictal vs. interictal state) and CX3CL1 (migraine vs. control) were assessed using receiver operating characteristic (ROC) analyses, the performance of which is reported by their area under the curve (AUC) and 95% confidence intervals (CI). ROC curve analysis was calculated with MedCalc® Statistical Software version 20.115 (MedCalc Software Ltd., Ostend, Belgium). P -values below 0.05 were considered significant.

Data availability

The data that supports the findings of this study are available from the corresponding author, upon reasonable request.

Demographics

We included blood and CSF samples of 39 different patients with episodic migraine (31 female, 35.4 ± 4.2 years of age), of which 16 were acquired interictal and 23 in acute migraine attacks. No patient was receiving preventive medication at the time of the lumbar puncture. 19 age-matched patients (12 female, 32.68 ± 10.29 years of age) were used as controls (Table  1 ). Age, sex, and albumin quotient did not differ between groups (all p  > 0.05).

Total tau protein in serum but not in CSF is elevated exclusively in the headache phase

As seen in Fig. 1 , there was a significant global effect comparing all 3 groups (effect size: η 2  = 0.121, p  = 0.038). Post-hoc tests yielded that this was due to a significant difference between samples obtained in the headache phase and interictally ( p  = 0.037) (Fig. 1 A). In contrast, this effect cannot be detected in CSF, where irrespective of the migraine state concentrations correspond to the level of the control cohort ( p  = 0.42, Fig. 1 B). ROC analysis between migraine samples obtained in the headache and interictal phase yielded an AUC of 0.729 [AUC = 0.729 (95% CI 0.558 to 0.861, p  = 0.006)] (Fig. 1 C).

Box plots of the temporospatial profile of t-Tau. The median and IQR of t-Tau protein concentration in pg/ml in serum and CSF are presented. There is a significant difference in t-Tau protein concentration in serum between the interictal collected sample and the ictal collected sample of migraine patients (* p  < 0.05). ROC curve analysis between the ictal and interictal collected migraine samples provided an AUC of 0.729 [AUC = 0.729 (95% CI 0.558 to 0.861, p  = 0.006)]

Biomarkers for axonal damage are not elevated in migraine patients

Other biomarkers for axonal damage such as GFAP, UCHL-1 and NfL concentrations were not significantly different in either serum (Fig.  2 , upper row) or CSF (Fig. 2 , lower row) between the 3 different cohorts (serum GFAP p  = 0.341; CSF GFAP p  = 0.993; serum NfL p  = 0.579; NfL CSF p  = 0.98; serum UCHL-1: 0.431; UCHL-1 CSF: 0.579).

Box plots of markers of axonal damage. The median and IQR of NfL, GFAP, and UCHL1 concentrations in pg/ml in CSF (upper row) and serum (lower row) of the 3 different cohorts are presented. There is no significant difference in any protein concentration, neither in serum nor in CSF, between the interictal, the ictal-collected sample of migraine patients, or the control cohort of non-migraine patients (for all p  > 0.05)

Microglial activation marker CX3CL1, but not sTREM2, is elevated in CSF only in migraine patients

Concentrations of CX3CL1 were significantly increased both in the headache phase and interictally in CSF but not in serum compared with the non-migraine cohort ( p  = 0.014) (Fig.  3 ). The AUC to differentiate between samples of patients with migraine, both in the headache phase and interictally, and non-migraine samples was 0.699 (95% CI 0.563 to 0.813, p  = 0.007) (Fig. 3 ). There was no significant difference between the concentration of CX3CL1 in the headache phase and interictally ( p  = 0.442). There was no significant difference in the concentrations of sTREM2 as an additional biomarker for microglial activation, neither in serum nor in CSF, between the 3 cohorts (Fig.  4 ).

Box plots of the temporospatial profile of CX3CL1. The median and IQR of CX3CL1 concentration in pg/ml in serum and CSF of the 3 different cohorts are presented. A significant difference was found in the amount of CX3CL1 in the cerebrospinal fluid (CSF) between the sample of migraine patients who had an ictal headache and the control group and of migraine patients who did not have an ictal headache. Right: ROC curve analysis of CX3CL1 between migraine patients (ictally and interictally collected samples) and healthy controls, AUC of 0.699 (95% CI 0,563 to 0,813, p  = 0.007)

Box plots of the temporospatial profile of sTREM2. The median and IQR of sTREM2 concentration in pg/ml in serum and CSF of the 3 different cohorts are presented. STREM2 levels did neither differ between controls and patients with migraine nor between different stages of migraine

This is the first study to demonstrate that microglial activation can be detected in patients with migraines, irrespective of their headache status. CX3CL1 in CSF thus provides a novel biomarker to study migraine pathophysiology in humans. We were furthermore able to clarify the state-dependent effects of serum t-Tau in the migraine cycle and found that its concentrations were elevated in the headache phase but not interictally, rendering it a potential biomarker of migraine attacks. T-tau elevations were not accompanied by an increase in other biomarkers for neurodegeneration, which argues against significant neuroaxonal deterioration in migraine patients. In line, the absence of an increase in GFAP in migraine patients, as well as the lack of an increase in NFL speaks against astrocytic or neuroaxonal damage in the CNS in migraine patients.

In contrast, CX3CL1 and t-Tau pose potential fluid biomarkers to study the pathophysiology of migraine and possibly inform clinical management.

Serum t-tau as a potential biomarker for disease activity in migraine patients

Tau protein in peripheral blood samples has been tested as a marker of neuroaxonal damage in a variety of diseases [ 17 ]. Only one study investigated the significance of t-Tau in the serum of migraine patients in the interictal phase of the disease. Overeem et al. found increased concentrations of t-Tau in migraine patients compared with healthy controls [ 7 ]. Based on our data, we demonstrated that this difference is driven primarily by elevated concentrations in the headache phase of the disease. However, according to the paper by Overeem et al., there is no dependence of t-Tau release on the presence of aura symptoms [ 7 ]. The authors conclude that it is not cortical activity but neuronal pain-associated activity, e.g. in the trigeminal ganglion, that could be responsible for t-Tau release. Our finding of serum t-Tau elevation in the ictal phase of the disease supports this hypothesis. In conclusion, serum t-Tau could be a potential biomarker for disease activity, as it is already used in peripheral neuroinflammatory disorders such as Guillain-Barré syndrome [ 18 ]. Further studies are needed to evaluate the relationship between attack frequency, distance to migraine attacks, and dependence on specific migraine therapeutics and concentration of t-Tau protein in serum.

We demonstrated that t-Tau concentration is detectable only in serum and not in CSF. This offers room for pathophysiologically relevant speculation in the context of migraine attacks. It was speculated that t-Tau elevation in migraine patients represents functional changes in the trigeminal nervous system [ 7 ], but without further elucidation of a specific mechanism. Regardless of why t-Tau protein elevation occurs in migraine patients, the lack of difference in concentrations of other biomarkers of neuroaxonal degeneration such as NfL, GFAP and UCHL1 needs to be emphasized and refutes major neurodegenerative processes in migraine.

CX3CL1 in CSF as a biomarker for microglial activity in migraine patients

CX3CL1 in CSF but not in serum was significantly higher in migraine patients regardless of sample collection in the headache phase or interictally versus healthy controls. Even though there are some investigations in mouse models suggesting microglial activation in migraine, evidence in human subjects is lacking [ 19 ]. Most murine studies investigated purinergic receptors (e.g., P2X/P2Y), which is plausible given the metabolic aspects of migraine pathophysiology, including neuronal energy deficiency [ 20 ]. The involvement of the fraktalkine pathway was only recently suggested in another mouse model of migraine and was also associated with impaired neuronal signalling in thalamo-cortical networks [ 21 ]. We found that CX3CL1, the ligand to the fractalkine receptor, is increased in humans irrespective of the headache status. Based on the current evidence, it can only be speculated that increased microglial activity could represent a pro-neuroinflammatory trait, with implications for neuronal transmission in regions with dense microglial concentration such as the thalamus [ 21 ]. Additionally, microglia are thought to play an important role in the modulation of migraine, and this process may be responsible for the progression toward chronification [ 19 ]. Also, microglia activation has been associated with cortical spreading depolarization [ 22 ], a potential cause of microgliosis in migraine patients. Supporting this notion, patients undergoing CSF examination in the interictal phase of migraine in this study mostly presented with transient neurological symptoms and white matter abnormalities on MRI, which were eventually attributed to migraine with aura. It remains to be clarified whether CX3CL1 concentrations differ between patients with migraines with and without aura. Finally, neuron-microglia interactions play a role in pain generation associated with migraine [ 23 , 24 ]. In our study, we detected an increase in CX3CL1 in comparison with healthy controls solely in the CSF, which is indicative of a central origin of CX3CL1.

Interestingly, we could not detect a difference in the levels of another microglial marker, sTREM2, between the groups studied. To date, no studies exist on the significance of sTREM2 in the context of migraine. Given that there are no indications of neurodegeneration in migraine, neither in neuroimaging nor in biomarker studies, a lack of elevations of sTREM2, which is associated with microglial activation in Alzheimer’s and Parkinson’s disease, is plausible [ 25 ]. It must be noted that micoglial activation is indeed not a simple binary state but rather a fine-tuned homeostatic process that may differ between the site of micoglial activation and its triggering receptor [ 26 ].

Limitations

A main limitation of this study is the retrospective design, so that no more detailed information on further migraine characteristics could be collected, such as duration of the disease, or acute or preventive medication. On the other hand, CSF samples were specifically acquired in order to rule out other neurological disorders. Performing a lumbar puncture is nowadays rather unusual in migraine patients making it almost impossible to perform prospective CSF studies in this population. For the same reason, a higher number of cases is very difficult to achieve, even if this would certainly be desirable for a biomarker analysis, particularly with regard to the statistical significance between the individual groups. Body mass index (BMI) may be a covariate for at least NFL and GFAP; no values were available for this because of the retrospective design. For other covariates, such as age and gender, we controlled by choosing an age- and gender-matched control group.

Another limiting factor is the sometimes very long storage time of the samples before the above-mentioned biomarkers have been measured. On the other hand, it has been described that the measurement of t-Tau is reliable even after years of storage [ 27 ].

To assess the levels of the above biomarkers as diagnostic, other control groups would need to be studied. Since this study was exploratory in nature, larger studies with other patient cohorts would be desirable, specifically for t-Tau protein in serum.

To assess disease activity by a biomarker, longitudinal sample collections from the same patients would be desirable. This is also a goal of future studies.

CX3CL1 in CSF and serum t-Tau are novel potential fluid biomarkers in migraine, which may advance the investigation of migraine pathophysiology and inform clinical management. These data need to be confirmed on larger hypothesis-driven prospective study cohorts.

Availability of data and materials

The datasets generated and/or analysed in the current study are not publicly available due to data protection regulations that impede unconditional distribution. The data that supports the findings of this study are, however, available from the corresponding author upon reasonable request.

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Antje Vogelgesang and Robert Fleischmann contributed equally to this work.

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Department of Neurology, University Medicine Greifswald, Ferdinand-Sauerbruch-Str. 1, 17475, Greifswald, Germany

Marie Süße, Christine Kloetzer, Sebastian Strauß, Johanna Ruhnau, Merle Bendig, Juliane Schulze, Uwe Reuter, Antje Vogelgesang & Robert Fleischmann

Department of Neurology With Experimental Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany

Lucas Hendrik Overeem & Uwe Reuter

International Graduate Program Medical Neurosciences, Humboldt Graduate School, 10117, Berlin, Germany

Lucas Hendrik Overeem

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Conception and design of the study: RF, MS, AV. Data acquisition and pre-processing: MS, AV. Clinical data acquisition: RF, SS, MS, CK, MB. Interpretation of results: MS, RF, AV, BR, LO, UR. Drafting the manuscript: MS. All authors reviewed and approved the final version of manuscript.

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Additional file 1: suppl. fig..

  1 Flow chart of patient sample selection.

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Süße, M., Kloetzer, C., Strauß, S. et al. Increased CX3CL1 in cerebrospinal fluid and ictal serum t-tau elevations in migraine: results from a cross-sectional exploratory case-control study. J Headache Pain 25 , 46 (2024). https://doi.org/10.1186/s10194-024-01757-8

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Received : 21 December 2023

Accepted : 24 March 2024

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DOI : https://doi.org/10.1186/s10194-024-01757-8

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