case study analysis model

Organizing Your Social Sciences Research Assignments

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

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is Case Study Analysis? (Explained With Examples)

Oct 11, 2023

Case Study Analysis is a widely used research method that examines in-depth information about a particular individual, group, organization, or event. It is a comprehensive investigative approach that aims to understand the intricacies and complexities of the subject under study. Through the analysis of real-life scenarios and inquiry into various data sources, Case Study Analysis provides valuable insights and knowledge that can be used to inform decision-making and problem-solving strategies.

1°) What is Case Study Analysis?

Case Study Analysis is a research methodology that involves the systematic investigation of a specific case or cases to gain a deep understanding of the subject matter. This analysis encompasses collecting and analyzing various types of data, including qualitative and quantitative information. By examining multiple aspects of the case, such as its context, background, influences, and outcomes, researchers can draw meaningful conclusions and provide valuable insights for various fields of study.

When conducting a Case Study Analysis, researchers typically begin by selecting a case or multiple cases that are relevant to their research question or area of interest. This can involve choosing a specific organization, individual, event, or phenomenon to study. Once the case is selected, researchers gather relevant data through various methods, such as interviews, observations, document analysis, and artifact examination.

The data collected during a Case Study Analysis is then carefully analyzed and interpreted. Researchers use different analytical frameworks and techniques to make sense of the information and identify patterns, themes, and relationships within the data. This process involves coding and categorizing the data, conducting comparative analysis, and drawing conclusions based on the findings.

One of the key strengths of Case Study Analysis is its ability to provide a rich and detailed understanding of a specific case. This method allows researchers to delve deep into the complexities and nuances of the subject matter, uncovering insights that may not be captured through other research methods. By examining the case in its natural context, researchers can gain a holistic perspective and explore the various factors and variables that contribute to the case.

1.1 - Definition of Case Study Analysis

Case Study Analysis can be defined as an in-depth examination and exploration of a particular case or cases to unravel relevant details and complexities associated with the subject being studied. It involves a comprehensive and detailed analysis of various factors and variables that contribute to the case, aiming to answer research questions and uncover insights that can be applied in real-world scenarios.

When conducting a Case Study Analysis, researchers employ a range of research methods and techniques to collect and analyze data. These methods can include interviews, surveys, observations, document analysis, and experiments, among others. By using multiple sources of data, researchers can triangulate their findings and ensure the validity and reliability of their analysis.

Furthermore, Case Study Analysis often involves the use of theoretical frameworks and models to guide the research process. These frameworks provide a structured approach to analyzing the case and help researchers make sense of the data collected. By applying relevant theories and concepts, researchers can gain a deeper understanding of the underlying factors and dynamics at play in the case.

1.2 - Advantages of Case Study Analysis

Case Study Analysis offers numerous advantages that make it a popular research method across different disciplines. One significant advantage is its ability to provide rich and detailed information about a specific case, allowing researchers to gain a holistic understanding of the subject matter. Additionally, Case Study Analysis enables researchers to explore complex issues and phenomena in their natural context, capturing the intricacies and nuances that may not be captured through other research methods.

Moreover, Case Study Analysis allows researchers to investigate rare or unique cases that may not be easily replicated or studied through experimental methods. This method is particularly useful when studying phenomena that are complex, multifaceted, or involve multiple variables. By examining real-world cases, researchers can gain insights that can be applied to similar situations or inform future research and practice.

Furthermore, this research method allows for the analysis of multiple sources of data, such as interviews, observations, documents, and artifacts, which can contribute to a comprehensive and well-rounded examination of the case. Case Study Analysis also facilitates the exploration and identification of patterns, trends, and relationships within the data, generating valuable insights and knowledge for future reference and application.

1.3 - Disadvantages of Case Study Analysis

While Case Study Analysis offers various advantages, it also comes with certain limitations and challenges. One major limitation is the potential for researcher bias, as the interpretation of data and findings can be influenced by preconceived notions and personal perspectives. Researchers must be aware of their own biases and take steps to minimize their impact on the analysis.

Additionally, Case Study Analysis may suffer from limited generalizability, as it focuses on specific cases and contexts, which might not be applicable or representative of broader populations or situations. The findings of a case study may not be easily generalized to other settings or individuals, and caution should be exercised when applying the results to different contexts.

Moreover, Case Study Analysis can require significant time and resources due to its in-depth nature and the need for meticulous data collection and analysis. This can pose challenges for researchers working with limited budgets or tight deadlines. However, the thoroughness and depth of the analysis often outweigh the resource constraints, as the insights gained from a well-conducted case study can be highly valuable.

Finally, ethical considerations also play a crucial role in Case Study Analysis, as researchers must ensure the protection of participant confidentiality and privacy. Researchers must obtain informed consent from participants and take measures to safeguard their identities and personal information. Ethical guidelines and protocols should be followed to ensure the rights and well-being of the individuals involved in the case study.

2°) Examples of Case Study Analysis

Real-world examples of Case Study Analysis demonstrate the method's practical application and showcase its usefulness across various fields. The following examples provide insights into different scenarios where Case Study Analysis has been employed successfully.

2.1 - Example in a Startup Context

In a startup context, a Case Study Analysis might explore the factors that contributed to the success of a particular startup company. It would involve examining the organization's background, strategies, market conditions, and key decision-making processes. This analysis could reveal valuable lessons and insights for aspiring entrepreneurs and those interested in understanding the intricacies of startup success.

2.2 - Example in a Consulting Context

In the consulting industry, Case Study Analysis is often utilized to understand and develop solutions for complex business problems. For instance, a consulting firm might conduct a Case Study Analysis on a company facing challenges in its supply chain management. This analysis would involve identifying the underlying issues, evaluating different options, and proposing recommendations based on the findings. This approach enables consultants to apply their expertise and provide practical solutions to their clients.

2.3 - Example in a Digital Marketing Agency Context

Within a digital marketing agency, Case Study Analysis can be used to examine successful marketing campaigns. By analyzing various factors such as target audience, message effectiveness, channel selection, and campaign metrics, this analysis can provide valuable insights into the strategies and tactics that contribute to successful marketing initiatives. Digital marketers can then apply these insights to optimize future campaigns and drive better results for their clients.

2.4 - Example with Analogies

Case Study Analysis can also be utilized with analogies to investigate specific scenarios and draw parallels to similar situations. For instance, a Case Study Analysis could explore the response of different countries to natural disasters and draw analogies to inform disaster management strategies in other regions. These analogies can help policymakers and researchers develop more effective approaches to mitigate the impact of disasters and protect vulnerable populations.

In conclusion, Case Study Analysis is a powerful research method that provides a comprehensive understanding of a particular individual, group, organization, or event. By analyzing real-life cases and exploring various data sources, researchers can unravel complexities, generate valuable insights, and inform decision-making processes. With its advantages and limitations, Case Study Analysis offers a unique approach to gaining in-depth knowledge and practical application across numerous fields.

About the author

Arnaud Belinga

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Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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Case Study Analysis: Examples + How-to Guide & Writing Tips

A case study analysis is a typical assignment in business management courses. The task aims to show high school and college students how to analyze a current situation, determine what problems exist, and develop the best possible strategy to achieve the desired outcome.

Many students feel anxious about writing case analyses because being told to analyze a case study and provide a solution can seem like a big task. That is especially so when working with real-life scenarios. However, you can rest assured writing a case analysis paper is easier than you think. Just keep reading this article and you will find case study examples for students and the advice provided by Custom-writing experts!

• 👣 Main Steps
• 🕵 Preparing the Case

🔬 Analyzing the Case

• 📑 Format & Structure
• 🙅 Things to Avoid
• 🏁 Conclusion

🔗 References

👣 writing a case study analysis: main steps.

Business management is built on case analysis. Every single economic result shows that the methods and instruments employed were either well-timed and expedient, in the event of success, or not, in case of failure. These two options indicate whether the strategy is efficient (and should be followed) or requires corrections (or complete change). Such an approach to the case study will make your writing piece more proficient and valuable for the reader. The following steps will direct your plan for writing a case study analysis.

Step 1: Preliminary work

• Make notes and highlight the numbers and ideas that could be quoted.
• Single out as many problems as you can, and briefly mark their underlying issues. Then make a note of those responsible. In the report, you will use two to five of the problems, so you will have a selection to choose from.
• Outline a possible solution to each of the problems you found. Course readings and outside research shall be used here. Highlight your best and worst solution for further reference.

Step 2: Drafting the Case

• Provide a general description of the situation and its history.
• Name all the problems you are going to discuss.
• Specify the theory used for the analysis.
• Present the assumptions that emerged during the analysis, if any.
• Describe the detected problems in more detail.
• Indicate their link to, and effect on, the general situation.
• Explain why the problems emerged and persist.
• List realistic and feasible solutions to the problems you outlined, in the order of importance.
• Specify your predicted results of such changes.
• Support your choice with reliable evidence (i.e., textbook readings, the experience of famous companies, and other external research).
• Define the strategies required to fulfill your proposed solution.
• Indicate the responsible people and the realistic terms for its implementation.
• Recommend the issues for further analysis and supervision.

Step 3: Finalizing the Case

Like any other piece of writing, a case analysis requires post-editing. Carefully read it through, looking for inconsistencies and gaps in meaning. Your purpose is to make it look complete, precise, and convincing.

🕵 Preparing a Case for Analysis

Your professor might give you various case study examples from which to choose, or they may just assign you a particular case study. To conduct a thorough data analysis, you must first read the case study. This might appear to be obvious. However, you’d be surprised at how many students don’t take adequate time to complete this part.

Read the case study very thoroughly, preferably several times. Highlight, underline, flag key information, and make notes to refer to later when you are writing your analysis report.

If you don’t have a complete knowledge of the case study your professor has assigned, you won’t conduct a proper analysis of it. Even if you make use of a business case study template or refer to a sample analysis, it won’t help if you aren’t intimately familiar with your case study.

You will also have to conduct research. When it comes to research, you will need to do the following:

• Gather hard, quantitative data (e.g. 67% of the staff participated in the meeting).
• Design research tools , such as questionnaires and surveys (this will aid in gathering data).
• Determine and suggest the best specific, workable solutions.

It would be best if you also learned how to analyze a case study. Once you have read through the case study, you need to determine the focus of your analysis. You can do this by doing the following:

Compare your chosen solutions to the solutions offered by the experts who analyzed the case study you were given or to online assignments for students who were dealing with a similar task. The experts’ solutions will probably be more advanced than yours simply because these people are more experienced. However, don’t let this discourage you; the whole point of doing this analysis is to learn. Use the opportunity to learn from others’ valuable experience, and your results will be better next time.

If you are still in doubt, the University of South Carolina offers a great guide on forming a case study analysis.

📑 Case Analysis Format & Structure

When you are learning how to write a case study analysis, it is important to get the format of your analysis right. Understanding the case study format is vital for both the professor and the student. The person planning and handing out such an assignment should ensure that the student doesn’t have to use any external sources .

In turn, students have to remember that a well-written case analysis provides all the data, making it unnecessary for the reader to go elsewhere for information.

Regardless of whether you use a case study template, you will need to follow a clear and concise format when writing your analysis report. There are some possible case study frameworks available. Still, a case study should contain eight sections laid out in the following format:

• Describe the purpose of the current case study;
• Provide a summary of the company;
• Briefly introduce the problems and issues found in the case study
• Discuss the theory you will be using in the analysis;
• Present the key points of the study and present any assumptions made during the analysis.
• Present each problem you have singled out;
• Justify your inclusion of each problem by providing supporting evidence from the case study and by discussing relevant theory and what you have learned from your course content;
• Divide the section (and following sections) into subsections, one for each of your selected problems.
• Present a summary of each problem you have identified;
• Present plausible solutions for each of the problems, keeping in mind that each problem will likely have more than one possible solution;
• Provide the pros and cons of each solution in a way that is practical.
• Conclusion . This is a summary of your findings and discussion.
• Decide which solution best fits each of the issues you identified;
• Explain why you chose this solution and how it will effectively solve the problem;
• Be persuasive when you write this section so that you can drive your point home;
• Be sure to bring together theory and what you have learned throughout your course to support your recommendations.
• Provide an explanation of what must be done, who should take action, and when the solution should be carried out;
• Where relevant, you should provide an estimate of the cost in implementing the solution, including both the financial investment and the cost in terms of time.
• References. While you generally do not need to refer to many external sources when writing a case study analysis, you might use a few. When you do, you will need to properly reference these sources, which is most often done in one of the main citation styles, including APA, MLA, or Harvard. There is plenty of help when citing references, and you can follow these APA guidelines , these MLA guidelines , or these Harvard guidelines .
• Appendices. This is the section you include after your case study analysis if you used any original data in the report. These data, presented as charts, graphs, and tables, are included here because to present them in the main body of the analysis would be disruptive to the reader. The University of Southern California provides a great description of appendices and when to make use of them.

When you’ve finished your first draft, be sure to proofread it. Look not only for potential grammar and spelling errors but also for discrepancies or holes in your argument.

You should also know what you need to avoid when writing your analysis.

🙅 Things to Avoid in Case Analysis

Whenever you deal with a case study, remember that there are some pitfalls to avoid! Beware of the following mistakes:

• Excessive use of colloquial language . Even though it is a study of an actual case, it should sound formal.
• Lack of statistical data . Give all the important data, both in percentages and in numbers.
• Excessive details. State only the most significant facts, rather than drowning the reader in every fact you find.
• Inconsistency in the methods you have used . In a case study, theory plays a relatively small part, so you must develop a specific case study research methodology.
• Trivial means of research . It is critical that you design your own case study research method in whatever form best suits your analysis, such as questionnaires and surveys.

It is useful to see a few examples of case analysis papers. After all, a sample case study report can provide you with some context so you can see how to approach each aspect of your paper.

👀 Case Study Examples for Students

It might be easier to understand how a case study analysis works if you have an example to look at. Fortunately, examples of case studies are easy to come by. Take a look at this video for a sample case study analysis for the Coca-Cola Company.

If you want another example, then take a look at the one below!

Business Case Analysis: Example

CRM’s primary focus is customers and customer perception of the brand or the company. The focus may shift depending on customers’ needs. The main points that Center Parcs should consider are an increase in customer satisfaction and its market share. Both of these points will enhance customer perception of the product as a product of value. Increased customer satisfaction will indicate that the company provides quality services, and increased market share can reduce the number of switching (or leaving) customers, thus fostering customer loyalty.

Case Study Topics

• Equifax case study: the importance of cybersecurity measures. 
• Study a case illustrating ethical issues of medical research.
• Examine the case describing the complications connected with nursing and residential care.
• Analyze the competitive strategy of Delta Airlines .
• Present a case study of an ethical dilemma showing the conflict between the spirit and the letter of the law.  
• Explore the aspects of Starbucks’ marketing strategyin a case study.  
• Research a case of community-based clinic organization and development.
• Customer service of United Airlines: a case study .
• Analyze a specific schizophrenia case and provide your recommendations.
• Provide a case study of a patient with hyperglycemia.
• Examine the growth strategy of United Healthcare.
• Present a case study demonstrating ethical issues in business.
• Study a case of the 5% shareholding rule application and its impact on the company.
• Case study of post-traumatic stress disorder .
• Analyze a case examining the issues of cross-cultural management .
• Write a case study exploring the ethical issues the finance manager of a long-term care facility can face and the possible reaction to them.
• Write a case study analyzing the aspects of a new president of a firm election.
• Discuss the specifics of supply chain management in the case of Tehindo company.
• Study a case of a life crisis in a family and the ways to cope with it.
• Case study of Tea Leaves and More: supply chain issues.   
• Explore the case of ketogenic diet implementation among sportspeople.  
• Analyze the case of Webster Jewelry shop and suggest some changes.  
• Examine the unique aspects of Tea and More brand management.  
• Adidas case study: an ethical dilemma .
• Research the challenges of Brazos Valley Food Bank and suggest possible solutions.  
• Describe the case of dark web monitoring for business.  
• Study a case of permissive parenting style .
• Case study of Starbucks employees.
• Analyze a case of workplace discrimination and suggest a strategy to avoid it.
• Examine a case of the consumer decision-making process and define the factors that influence it.
• Present a case study of Netflix illustrating the crucial role of management innovation for company development.  
• Discuss a case describing a workplace ethical issue and propose ways to resolve it.
• Case study of the 2008 financial crisis: Graham’s value investing principles in the modern economic climate.
• Write a case study analyzing the harmful consequences of communication issues in a virtual team.
• Analyze a case that highlights the importance of a proper functional currency choice. 
• Examine the case of Hitachi Power Systems management.  
• Present a case study of medication research in a healthcare facility.
• Study the case of Fiji Water and the challenges the brand faces.  
• Research a social problem case and suggest a solution.
• Analyze a case that reveals the connection between alcohol use and borderline personality disorder.
• Transglobal Airline case study: break-even analysis.
• Examine the case of Chiquita Brands International from the moral and business ethics points of view.
• Present a case study of applying for Social Security benefits. 
• Study the case of a mass hacker attack on Microsoft clients and suggest possible ways to prevent future attacks.
• Case study of leadership effectiveness. 
• Analyze a case presenting a clinical moral dilemma and propose ways to resolve it. 
• Describe the case of Cowbell Brewing Company and discuss the strategy that made them successful.
• Write a case study of WeWork company and analyze the strengths and weaknesses of its strategy.
• Case study of medical ethical decision-making.
• Study the case of The Georges hotel and suggest ways to overcome its managerial issues.

🏁 Concluding Remarks

Writing a case study analysis can seem incredibly overwhelming, especially if you have never done it before. Just remember, you can do it provided you follow a plan, keep to the format described here, and study at least one case analysis example.

If you still need help analyzing a case study, your professor is always available to answer your questions and point you in the right direction. You can also get help with any aspect of the project from a custom writing company. Just tackle the research and hand over the writing, write a rough draft and have it checked by a professional, or completely hand the project off to an expert writer.

Regardless of the path you choose, you will turn in something of which you can be proud!

✏️ Case Study Analysis FAQ

Students (especially those who study business) often need to write a case study analysis. It is a kind of report that describes a business case. It includes multiple aspects, for example, the problems that exist, possible solutions, forecasts, etc.

There should be 3 main points covered in a case study analysis:

• The challenge(s) description,
• Possible solutions,
• Outcomes (real and/or foreseen).

Firstly, study some examples available online and in the library. Case study analysis should be a well-structured paper with all the integral components in place. Thus, you might want to use a template and/or an outline to start correctly.

A case study analysis is a popular task for business students. They typically hand it in the format of a paper with several integral components:

• Description of the problem
• Possible ways out
• Results and/or forecasts

Students sometimes tell about the outcome of their research within an oral presentation.

• Case Study: Academia
• Windows of vulnerability: a case study analysis (IEEE)
• A (Very) Brief Refresher on the Case Study Method: SAGE
• The case study approach: Medical Research Methodology
• Strengths and Limitations of Case Studies: Stanford University
• A Sample APA Paper: Radford University
• How to Write a Case Study APA Style: Seattle PI
• The Case Analysis: GVSU
• How to Outline: Purdue OWL
• Incorporating Interview Data: UW-Madison Writing Center
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The Ultimate Guide to Qualitative Research - Part 1: The Basics

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Writing a Case Study

What is a case study?

A Case study is: 

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

What are the different types of case studies?

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

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

Who are your case study participants?

What is triangulation ? 

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

How to write a Case Study?

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

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How to Write a Case Study Analysis

Step-By-Step Instructions

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When writing a business case study analysis , you must first have a good understanding of the case study . Before you begin the steps below, read the business case carefully, taking notes all the while. It may be necessary to read the case several times to get all of the details and fully grasp the issues facing the group, company, or industry.

As you are reading, do your best to identify key issues, key players, and the most pertinent facts. After you are comfortable with the information, use the following step-by-step instructions (geared toward a single-company analysis) to write your report. To write about an industry, just adapt the steps listed here to discuss the segment as a whole.

Step 1: Investigate the Company’s History and Growth

A company’s past can greatly affect the present and future state of the organization. To begin, investigate the company’s founding, critical incidents, structure, and growth. Create a timeline of events, issues, and achievements. This timeline will come in handy for the next step. 

Step 2: Identify Strengths and Weaknesses

Using the information you gathered in step one, continue by examining and making a list of the value creation functions of the company. For example, the company may be weak in product development but strong in marketing. Make a list of problems that have occurred and note the effects they have had on the company. You should also list areas where the company has excelled. Note the effects of these incidents as well.

You're essentially conducting a partial SWOT analysis to get a better understanding of the company's strengths and weaknesses. A SWOT analysis involves documenting things like internal strengths (S) and weaknesses (W) and external opportunities (O) and threats (T). 

Step 3: Examine the External Environment

The third step involves identifying opportunities and threats within the company’s external environment. This is where the second part of the SWOT analysis (the O and the T) comes into play. Special items to note include competition within the industry, bargaining powers, and the threat of substitute products. Some examples of opportunities include expansion into new markets or new technology. Some examples of threats include increasing competition and higher interest rates.

Step 4: Analyze Your Findings

Using the information in steps 2 and 3, create an evaluation for this portion of your case study analysis. Compare the strengths and weaknesses within the company to the external threats and opportunities. Determine if the company is in a strong competitive position, and decide if it can continue at its current pace successfully.

Step 5: Identify Corporate-Level Strategy

To identify a company’s corporate-level strategy, identify and evaluate the company’s mission , goals, and actions toward those goals. Analyze the company’s line of business and its subsidiaries and acquisitions. You also want to debate the pros and cons of the company strategy to determine whether or not a change might benefit the company in the short or long term.​

Step 6: Identify Business-Level Strategy

Thus far, your case study analysis has identified the company’s corporate-level strategy. To perform a complete analysis, you will need to identify the company’s business-level strategy. (Note: If it is a single business, without multiple companies under one umbrella, and not an industry-wide review, the corporate strategy and the business-level strategy are the same.) For this part, you should identify and analyze each company’s competitive strategy, marketing strategy, costs, and general focus.

Step 7: Analyze Implementations

This portion requires that you identify and analyze the structure and control systems that the company is using to implement its business strategies. Evaluate organizational change, levels of hierarchy, employee rewards, conflicts, and other issues that are important to the company you are analyzing.

Step 8: Make Recommendations

The final part of your case study analysis should include your recommendations for the company. Every recommendation you make should be based on and supported by the context of your analysis. Never share hunches or make a baseless recommendation.

You also want to make sure that your suggested solutions are actually realistic. If the solutions cannot be implemented due to some sort of restraint, they are not realistic enough to make the final cut.

Finally, consider some of the alternative solutions that you considered and rejected. Write down the reasons why these solutions were rejected. 

Step 9: Review

Look over your analysis when you have finished writing. Critique your work to make sure every step has been covered. Look for grammatical errors , poor sentence structure, or other things that can be improved. It should be clear, accurate, and professional.

Business Case Study Analysis Tips

Keep these strategic tips in mind:

• Know the case study ​backward and forward before you begin your case study analysis.
• Give yourself enough time to write the case study analysis. You don't want to rush through it.
• Be honest in your evaluations. Don't let personal issues and opinions cloud your judgment.
• Be analytical, not descriptive.
• Proofread your work, and even let a test reader give it a once-over for dropped words or typos that you no longer can see.
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How the Case Method Works

• Read and analyze the case. Each case is a 10-20 page document written from the viewpoint of a real person leading a real organization. In addition to background information on the situation, each case ends in a key decision to be made. Your job is to sift through the information, incomplete by design, and decide what you would do.
• Discuss the case. Each morning, you’ll bring your ideas to a small team of classmates from diverse professional backgrounds, your discussion group, to share your findings and listen to theirs. Together, you begin to see the case from different perspectives, better preparing you for class.
• Engage in class. Be prepared to change the way you think as you debate with classmates the best path forward for this organization. The highly engaged conversation is facilitated by the faculty member, but it’s driven by your classmates’ comments and experiences. HBS brings together amazingly talented people from diverse backgrounds and puts that experience front and center. Students do the majority of the talking (and lots of active listening), and your job is to better understand the decision at hand, what you would do in the case protagonist’s shoes, and why. You will not leave a class thinking about the case the same way you thought about it coming in! In addition to learning more about many businesses, in the case method you will develop communication, listening, analysis, and leadership skills. It is a truly dynamic and immersive learning environment.
• Reflect. The case method prepares you to be in leadership positions where you will face time-sensitive decisions with limited information. Reflecting on each class discussion will prepare you to face these situations in your future roles.

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15 Real-Life Case Study Examples & Best Practices

Written by: Oghale Olori

Case studies are more than just success stories.

They are powerful tools that demonstrate the practical value of your product or service. Case studies help attract attention to your products, build trust with potential customers and ultimately drive sales.

It’s no wonder that 73% of successful content marketers utilize case studies as part of their content strategy. Plus, buyers spend 54% of their time reviewing case studies before they make a buying decision.

To ensure you’re making the most of your case studies, we’ve put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We’ve also included best practices, design tips and templates to inspire you.

Let’s dive in!

Table of Contents

What is a case study, 15 real-life case study examples, sales case study examples, saas case study examples, product case study examples, marketing case study examples, business case study examples, case study faqs.

• A case study is a compelling narrative that showcases how your product or service has positively impacted a real business or individual. 
• Case studies delve into your customer's challenges, how your solution addressed them and the quantifiable results they achieved.
• Your case study should have an attention-grabbing headline, great visuals and a relevant call to action. Other key elements include an introduction, problems and result section.
• Visme provides easy-to-use tools, professionally designed templates and features for creating attractive and engaging case studies.

A case study is a real-life scenario where your company helped a person or business solve their unique challenges. It provides a detailed analysis of the positive outcomes achieved as a result of implementing your solution.

Case studies are an effective way to showcase the value of your product or service to potential customers without overt selling. By sharing how your company transformed a business, you can attract customers seeking similar solutions and results.

Case studies are not only about your company's capabilities; they are primarily about the benefits customers and clients have experienced from using your product.

Every great case study is made up of key elements. They are;

• Attention-grabbing headline: Write a compelling headline that grabs attention and tells your reader what the case study is about. For example, "How a CRM System Helped a B2B Company Increase Revenue by 225%.
• Introduction/Executive Summary: Include a brief overview of your case study, including your customer’s problem, the solution they implemented and the results they achieved.
• Problem/Challenge: Case studies with solutions offer a powerful way to connect with potential customers. In this section, explain how your product or service specifically addressed your customer's challenges.
• Solution: Explain how your product or service specifically addressed your customer's challenges.
• Results/Achievements : Give a detailed account of the positive impact of your product. Quantify the benefits achieved using metrics such as increased sales, improved efficiency, reduced costs or enhanced customer satisfaction.
• Graphics/Visuals: Include professional designs, high-quality photos and videos to make your case study more engaging and visually appealing.
• Quotes/Testimonials: Incorporate written or video quotes from your clients to boost your credibility.
• Relevant CTA: Insert a call to action (CTA) that encourages the reader to take action. For example, visiting your website or contacting you for more information. Your CTA can be a link to a landing page, a contact form or your social media handle and should be related to the product or service you highlighted in your case study.

Now that you understand what a case study is, let’s look at real-life case study examples. Among these, you'll find some simple case study examples that break down complex ideas into easily understandable solutions.

In this section, we’ll explore SaaS, marketing, sales, product and business case study examples with solutions. Take note of how these companies structured their case studies and included the key elements.

We’ve also included professionally designed case study templates to inspire you.

1. Georgia Tech Athletics Increase Season Ticket Sales by 80%

Georgia Tech Athletics, with its 8,000 football season ticket holders, sought for a way to increase efficiency and customer engagement.

Their initial sales process involved making multiple outbound phone calls per day with no real targeting or guidelines. Georgia Tech believed that targeting communications will enable them to reach more people in real time.

Salesloft improved Georgia Tech’s sales process with an inbound structure. This enabled sales reps to connect with their customers on a more targeted level. The use of dynamic fields and filters when importing lists ensured prospects received the right information, while communication with existing fans became faster with automation.

As a result, Georgia Tech Athletics recorded an 80% increase in season ticket sales as relationships with season ticket holders significantly improved. Employee engagement increased as employees became more energized to connect and communicate with fans.

Why Does This Case Study Work?

In this case study example , Salesloft utilized the key elements of a good case study. Their introduction gave an overview of their customers' challenges and the results they enjoyed after using them. After which they categorized the case study into three main sections: challenge, solution and result.

Salesloft utilized a case study video to increase engagement and invoke human connection.

Incorporating videos in your case study has a lot of benefits. Wyzol’s 2023 state of video marketing report showed a direct correlation between videos and an 87% increase in sales.

The beautiful thing is that creating videos for your case study doesn’t have to be daunting.

With an easy-to-use platform like Visme, you can create top-notch testimonial videos that will connect with your audience. Within the Visme editor, you can access over 1 million stock photos , video templates, animated graphics and more. These tools and resources will significantly improve the design and engagement of your case study.

Simplify content creation and brand management for your team

• Collaborate on designs , mockups and wireframes with your non-design colleagues
• Lock down your branding to maintain brand consistency throughout your designs
• Why start from scratch? Save time with 1000s of professional branded templates

Sign up. It’s free.

2. WeightWatchers Completely Revamped their Enterprise Sales Process with HubSpot

WeightWatchers, a 60-year-old wellness company, sought a CRM solution that increased the efficiency of their sales process. With their previous system, Weightwatchers had limited automation. They would copy-paste message templates from word documents or recreate one email for a batch of customers.

This required a huge effort from sales reps, account managers and leadership, as they were unable to track leads or pull customized reports for planning and growth.

WeightWatchers transformed their B2B sales strategy by leveraging HubSpot's robust marketing and sales workflows. They utilized HubSpot’s deal pipeline and automation features to streamline lead qualification. And the customized dashboard gave leadership valuable insights.

As a result, WeightWatchers generated seven figures in annual contract value and boosted recurring revenue. Hubspot’s impact resulted in 100% adoption across all sales, marketing, client success and operations teams.

Hubspot structured its case study into separate sections, demonstrating the specific benefits of their products to various aspects of the customer's business. Additionally, they integrated direct customer quotes in each section to boost credibility, resulting in a more compelling case study.

Getting insight from your customer about their challenges is one thing. But writing about their process and achievements in a concise and relatable way is another. If you find yourself constantly experiencing writer’s block, Visme’s AI writer is perfect for you.

Visme created this AI text generator tool to take your ideas and transform them into a great draft. So whether you need help writing your first draft or editing your final case study, Visme is ready for you.

3. Immi’s Ram Fam Helps to Drive Over $200k in Sales

Immi embarked on a mission to recreate healthier ramen recipes that were nutritious and delicious. After 2 years of tireless trials, Immi finally found the perfect ramen recipe. However, they envisioned a community of passionate ramen enthusiasts to fuel their business growth.

This vision propelled them to partner with Shopify Collabs. Shopify Collabs successfully cultivated and managed Immi’s Ramen community of ambassadors and creators.

As a result of their partnership, Immi’s community grew to more than 400 dedicated members, generating over $200,000 in total affiliate sales.

The power of data-driven headlines cannot be overemphasized. Chili Piper strategically incorporates quantifiable results in their headlines. This instantly sparks curiosity and interest in readers.

While not every customer success story may boast headline-grabbing figures, quantifying achievements in percentages is still effective. For example, you can highlight a 50% revenue increase with the implementation of your product.

Take a look at the beautiful case study template below. Just like in the example above, the figures in the headline instantly grab attention and entice your reader to click through.

Having a case study document is a key factor in boosting engagement. This makes it easy to promote your case study in multiple ways. With Visme, you can easily publish, download and share your case study with your customers in a variety of formats, including PDF, PPTX, JPG and more!

4. How WOW! is Saving Nearly 79% in Time and Cost With Visme

This case study discusses how Visme helped WOW! save time and money by providing user-friendly tools to create interactive and quality training materials for their employees. Find out what your team can do with Visme. Request a Demo

WOW!'s learning and development team creates high-quality training materials for new and existing employees. Previous tools and platforms they used had plain templates, little to no interactivity features, and limited flexibility—that is, until they discovered Visme.

Now, the learning and development team at WOW! use Visme to create engaging infographics, training videos, slide decks and other training materials.

This has directly reduced the company's turnover rate, saving them money spent on recruiting and training new employees. It has also saved them a significant amount of time, which they can now allocate to other important tasks.

Visme's customer testimonials spark an emotional connection with the reader, leaving a profound impact. Upon reading this case study, prospective customers will be blown away by the remarkable efficiency achieved by Visme's clients after switching from PowerPoint.

Visme’s interactivity feature was a game changer for WOW! and one of the primary reasons they chose Visme.

“Previously we were using PowerPoint, which is fine, but the interactivity you can get with Visme is so much more robust that we’ve all steered away from PowerPoint.” - Kendra, L&D team, Wow!

Visme’s interactive feature allowed them to animate their infographics, include clickable links on their PowerPoint designs and even embed polls and quizzes their employees could interact with.

By embedding the slide decks, infographics and other training materials WOW! created with Visme, potential customers get a taste of what they can create with the tool. This is much more effective than describing the features of Visme because it allows potential customers to see the tool in action.

To top it all off, this case study utilized relevant data and figures. For example, one part of the case study said, “In Visme, where Kendra’s team has access to hundreds of templates, a brand kit, and millions of design assets at their disposal, their team can create presentations in 80% less time.”

Who wouldn't want that?

Including relevant figures and graphics in your case study is a sure way to convince your potential customers why you’re a great fit for their brand. The case study template below is a great example of integrating relevant figures and data.

This colorful template begins with a captivating headline. But that is not the best part; this template extensively showcases the results their customer had using relevant figures.

The arrangement of the results makes it fun and attractive. Instead of just putting figures in a plain table, you can find interesting shapes in your Visme editor to take your case study to the next level.

5. Lyte Reduces Customer Churn To Just 3% With Hubspot CRM

While Lyte was redefining the ticketing industry, it had no definite CRM system . Lyte utilized 12–15 different SaaS solutions across various departments, which led to a lack of alignment between teams, duplication of work and overlapping tasks.

Customer data was spread across these platforms, making it difficult to effectively track their customer journey. As a result, their churn rate increased along with customer dissatisfaction.

Through Fuelius , Lyte founded and implemented Hubspot CRM. Lyte's productivity skyrocketed after incorporating Hubspot's all-in-one CRM tool. With improved efficiency, better teamwork and stronger client relationships, sales figures soared.

The case study title page and executive summary act as compelling entry points for both existing and potential customers. This overview provides a clear understanding of the case study and also strategically incorporates key details like the client's industry, location and relevant background information.

Having a good summary of your case study can prompt your readers to engage further. You can achieve this with a simple but effective case study one-pager that highlights your customer’s problems, process and achievements, just like this case study did in the beginning.

Moreover, you can easily distribute your case study one-pager and use it as a lead magnet to draw prospective customers to your company.

Take a look at this case study one-pager template below.

This template includes key aspects of your case study, such as the introduction, key findings, conclusion and more, without overcrowding the page. The use of multiple shades of blue gives it a clean and dynamic layout.

Our favorite part of this template is where the age group is visualized.

With Visme’s data visualization tool , you can present your data in tables, graphs, progress bars, maps and so much more. All you need to do is choose your preferred data visualization widget, input or import your data and click enter!

6. How Workato Converts 75% of Their Qualified Leads

Workato wanted to improve their inbound leads and increase their conversion rate, which ranged from 40-55%.

At first, Workato searched for a simple scheduling tool. They soon discovered that they needed a tool that provided advanced routing capabilities based on zip code and other criteria. Luckily, they found and implemented Chili Piper.

As a result of implementing Chili Piper, Workato achieved a remarkable 75–80% conversion rate and improved show rates. This led to a substantial revenue boost, with a 10-15% increase in revenue attributed to Chili Piper's impact on lead conversion.

This case study example utilizes the power of video testimonials to drive the impact of their product.

Chili Piper incorporates screenshots and clips of their tool in use. This is a great strategy because it helps your viewers become familiar with how your product works, making onboarding new customers much easier.

In this case study example, we see the importance of efficient Workflow Management Systems (WMS). Without a WMS, you manually assign tasks to your team members and engage in multiple emails for regular updates on progress.

However, when crafting and designing your case study, you should prioritize having a good WMS.

Visme has an outstanding Workflow Management System feature that keeps you on top of all your projects and designs. This feature makes it much easier to assign roles, ensure accuracy across documents, and track progress and deadlines.

Visme’s WMS feature allows you to limit access to your entire document by assigning specific slides or pages to individual members of your team. At the end of the day, your team members are not overwhelmed or distracted by the whole document but can focus on their tasks.

7. Rush Order Helps Vogmask Scale-Up During a Pandemic

Vomask's reliance on third-party fulfillment companies became a challenge as demand for their masks grew. Seeking a reliable fulfillment partner, they found Rush Order and entrusted them with their entire inventory.

Vomask's partnership with Rush Order proved to be a lifesaver during the COVID-19 pandemic. Rush Order's agility, efficiency and commitment to customer satisfaction helped Vogmask navigate the unprecedented demand and maintain its reputation for quality and service.

Rush Order’s comprehensive support enabled Vogmask to scale up its order processing by a staggering 900% while maintaining a remarkable customer satisfaction rate of 92%.

Rush Order chose one event where their impact mattered the most to their customer and shared that story.

While pandemics don't happen every day, you can look through your customer’s journey and highlight a specific time or scenario where your product or service saved their business.

The story of Vogmask and Rush Order is compelling, but it simply is not enough. The case study format and design attract readers' attention and make them want to know more. Rush Order uses consistent colors throughout the case study, starting with the logo, bold square blocks, pictures, and even headers.

Take a look at this product case study template below.

Just like our example, this case study template utilizes bold colors and large squares to attract and maintain the reader’s attention. It provides enough room for you to write about your customers' backgrounds/introductions, challenges, goals and results.

The right combination of shapes and colors adds a level of professionalism to this case study template.

8. AMR Hair & Beauty leverages B2B functionality to boost sales by 200%

With limits on website customization, slow page loading and multiple website crashes during peak events, it wasn't long before AMR Hair & Beauty began looking for a new e-commerce solution.

Their existing platform lacked effective search and filtering options, a seamless checkout process and the data analytics capabilities needed for informed decision-making. This led to a significant number of abandoned carts.

Upon switching to Shopify Plus, AMR immediately saw improvements in page loading speed and average session duration. They added better search and filtering options for their wholesale customers and customized their checkout process.

Due to this, AMR witnessed a 200% increase in sales and a 77% rise in B2B average order value. AMR Hair & Beauty is now poised for further expansion and growth.

This case study example showcases the power of a concise and impactful narrative.

To make their case analysis more effective, Shopify focused on the most relevant aspects of the customer's journey. While there may have been other challenges the customer faced, they only included those that directly related to their solutions.

Take a look at this case study template below. It is perfect if you want to create a concise but effective case study. Without including unnecessary details, you can outline the challenges, solutions and results your customers experienced from using your product.

Don’t forget to include a strong CTA within your case study. By incorporating a link, sidebar pop-up or an exit pop-up into your case study, you can prompt your readers and prospective clients to connect with you.

9. How a Marketing Agency Uses Visme to Create Engaging Content With Infographics

SmartBox Dental , a marketing agency specializing in dental practices, sought ways to make dental advice more interesting and easier to read. However, they lacked the design skills to do so effectively.

Visme's wide range of templates and features made it easy for the team to create high-quality content quickly and efficiently. SmartBox Dental enjoyed creating infographics in as little as 10-15 minutes, compared to one hour before Visme was implemented.

By leveraging Visme, SmartBox Dental successfully transformed dental content into a more enjoyable and informative experience for their clients' patients. Therefore enhancing its reputation as a marketing partner that goes the extra mile to deliver value to its clients.

Visme creatively incorporates testimonials In this case study example.

By showcasing infographics and designs created by their clients, they leverage the power of social proof in a visually compelling way. This way, potential customers gain immediate insight into the creative possibilities Visme offers as a design tool.

This example effectively showcases a product's versatility and impact, and we can learn a lot about writing a case study from it. Instead of focusing on one tool or feature per customer, Visme took a more comprehensive approach.

Within each section of their case study, Visme explained how a particular tool or feature played a key role in solving the customer's challenges.

For example, this case study highlighted Visme’s collaboration tool . With Visme’s tool, the SmartBox Dental content team fostered teamwork, accountability and effective supervision.

Visme also achieved a versatile case study by including relevant quotes to showcase each tool or feature. Take a look at some examples;

Visme’s collaboration tool: “We really like the collaboration tool. Being able to see what a co-worker is working on and borrow their ideas or collaborate on a project to make sure we get the best end result really helps us out.”

Visme’s library of stock photos and animated characters: “I really love the images and the look those give to an infographic. I also really like the animated little guys and the animated pictures. That’s added a lot of fun to our designs.”

Visme’s interactivity feature: “You can add URLs and phone number links directly into the infographic so they can just click and call or go to another page on the website and I really like adding those hyperlinks in.”

You can ask your customers to talk about the different products or features that helped them achieve their business success and draw quotes from each one.

10. Jasper Grows Blog Organic Sessions 810% and Blog-Attributed User Signups 400X

Jasper, an AI writing tool, lacked a scalable content strategy to drive organic traffic and user growth. They needed help creating content that converted visitors into users. Especially when a looming domain migration threatened organic traffic.

To address these challenges, Jasper partnered with Omniscient Digital. Their goal was to turn their content into a growth channel and drive organic growth. Omniscient Digital developed a full content strategy for Jasper AI, which included a content audit, competitive analysis, and keyword discovery.

Through their collaboration, Jasper’s organic blog sessions increased by 810%, despite the domain migration. They also witnessed a 400X increase in blog-attributed signups. And more importantly, the content program contributed to over $4 million in annual recurring revenue.

The combination of storytelling and video testimonials within the case study example makes this a real winner. But there’s a twist to it. Omniscient segmented the video testimonials and placed them in different sections of the case study.

Video marketing , especially in case studies, works wonders. Research shows us that 42% of people prefer video testimonials because they show real customers with real success stories. So if you haven't thought of it before, incorporate video testimonials into your case study.

Take a look at this stunning video testimonial template. With its simple design, you can input the picture, name and quote of your customer within your case study in a fun and engaging way.

Try it yourself! Customize this template with your customer’s testimonial and add it to your case study!

11. How Meliá Became One of the Most Influential Hotel Chains on Social Media

Meliá Hotels needed help managing their growing social media customer service needs. Despite having over 500 social accounts, they lacked a unified response protocol and detailed reporting. This largely hindered efficiency and brand consistency.

Meliá partnered with Hootsuite to build an in-house social customer care team. Implementing Hootsuite's tools enabled Meliá to decrease response times from 24 hours to 12.4 hours while also leveraging smart automation.

In addition to that, Meliá resolved over 133,000 conversations, booking 330 inquiries per week through Hootsuite Inbox. They significantly improved brand consistency, response time and customer satisfaction.

The need for a good case study design cannot be over-emphasized.

As soon as anyone lands on this case study example, they are mesmerized by a beautiful case study design. This alone raises the interest of readers and keeps them engaged till the end.

If you’re currently saying to yourself, “ I can write great case studies, but I don’t have the time or skill to turn it into a beautiful document.” Say no more.

Visme’s amazing AI document generator can take your text and transform it into a stunning and professional document in minutes! Not only do you save time, but you also get inspired by the design.

With Visme’s document generator, you can create PDFs, case study presentations , infographics and more!

Take a look at this case study template below. Just like our case study example, it captures readers' attention with its beautiful design. Its dynamic blend of colors and fonts helps to segment each element of the case study beautifully.

12. Tea’s Me Cafe: Tamika Catchings is Brewing Glory

Tamika's journey began when she purchased Tea's Me Cafe in 2017, saving it from closure. She recognized the potential of the cafe as a community hub and hosted regular events centered on social issues and youth empowerment.

One of Tamika’s business goals was to automate her business. She sought to streamline business processes across various aspects of her business. One of the ways she achieves this goal is through Constant Contact.

Constant Contact became an integral part of Tamika's marketing strategy. They provided an automated and centralized platform for managing email newsletters, event registrations, social media scheduling and more.

This allowed Tamika and her team to collaborate efficiently and focus on engaging with their audience. They effectively utilized features like WooCommerce integration, text-to-join and the survey builder to grow their email list, segment their audience and gather valuable feedback.

The case study example utilizes the power of storytelling to form a connection with readers. Constant Contact takes a humble approach in this case study. They spotlight their customers' efforts as the reason for their achievements and growth, establishing trust and credibility.

This case study is also visually appealing, filled with high-quality photos of their customer. While this is a great way to foster originality, it can prove challenging if your customer sends you blurry or low-quality photos.

If you find yourself in that dilemma, you can use Visme’s AI image edit tool to touch up your photos. With Visme’s AI tool, you can remove unwanted backgrounds, erase unwanted objects, unblur low-quality pictures and upscale any photo without losing the quality.

Constant Contact offers its readers various formats to engage with their case study. Including an audio podcast and PDF.

In its PDF version, Constant Contact utilized its brand colors to create a stunning case study design.  With this, they increase brand awareness and, in turn, brand recognition with anyone who comes across their case study.

With Visme’s brand wizard tool , you can seamlessly incorporate your brand assets into any design or document you create. By inputting your URL, Visme’s AI integration will take note of your brand colors, brand fonts and more and create branded templates for you automatically.

You don't need to worry about spending hours customizing templates to fit your brand anymore. You can focus on writing amazing case studies that promote your company.

13. How Breakwater Kitchens Achieved a 7% Growth in Sales With Thryv

Breakwater Kitchens struggled with managing their business operations efficiently. They spent a lot of time on manual tasks, such as scheduling appointments and managing client communication. This made it difficult for them to grow their business and provide the best possible service to their customers.

David, the owner, discovered Thryv. With Thryv, Breakwater Kitchens was able to automate many of their manual tasks. Additionally, Thryv integrated social media management. This enabled Breakwater Kitchens to deliver a consistent brand message, captivate its audience and foster online growth.

As a result, Breakwater Kitchens achieved increased efficiency, reduced missed appointments and a 7% growth in sales.

This case study example uses a concise format and strong verbs, which make it easy for readers to absorb the information.

At the top of the case study, Thryv immediately builds trust by presenting their customer's complete profile, including their name, company details and website. This allows potential customers to verify the case study's legitimacy, making them more likely to believe in Thryv's services.

However, manually copying and pasting customer information across multiple pages of your case study can be time-consuming.

To save time and effort, you can utilize Visme's dynamic field feature . Dynamic fields automatically insert reusable information into your designs.  So you don’t have to type it out multiple times.

14. Zoom’s Creative Team Saves Over 4,000 Hours With Brandfolder

Zoom experienced rapid growth with the advent of remote work and the rise of the COVID-19 pandemic. Such growth called for agility and resilience to scale through.

At the time, Zoom’s assets were disorganized which made retrieving brand information a burden. Zoom’s creative manager spent no less than 10 hours per week finding and retrieving brand assets for internal teams.

Zoom needed a more sustainable approach to organizing and retrieving brand information and came across Brandfolder. Brandfolder simplified and accelerated Zoom’s email localization and webpage development. It also enhanced the creation and storage of Zoom virtual backgrounds.

With Brandfolder, Zoom now saves 4,000+ hours every year. The company also centralized its assets in Brandfolder, which allowed 6,800+ employees and 20-30 vendors to quickly access them.

Brandfolder infused its case study with compelling data and backed it up with verifiable sources. This data-driven approach boosts credibility and increases the impact of their story.

Bradfolder's case study goes the extra mile by providing a downloadable PDF version, making it convenient for readers to access the information on their own time. Their dedication to crafting stunning visuals is evident in every aspect of the project.

From the vibrant colors to the seamless navigation, everything has been meticulously designed to leave a lasting impression on the viewer. And with clickable links that make exploring the content a breeze, the user experience is guaranteed to be nothing short of exceptional.

The thing is, your case study presentation won’t always sit on your website. There are instances where you may need to do a case study presentation for clients, partners or potential investors.

Visme has a rich library of templates you can tap into. But if you’re racing against the clock, Visme’s AI presentation maker is your best ally.

15. How Cents of Style Made $1.7M+ in Affiliate Sales with LeadDyno

Cents of Style had a successful affiliate and influencer marketing strategy. However, their existing affiliate marketing platform was not intuitive, customizable or transparent enough to meet the needs of their influencers.

Cents of Styles needed an easy-to-use affiliate marketing platform that gave them more freedom to customize their program and implement a multi-tier commission program.

After exploring their options, Cents of Style decided on LeadDyno.

LeadDyno provided more flexibility, allowing them to customize commission rates and implement their multi-tier commission structure, switching from monthly to weekly payouts.

Also, integrations with PayPal made payments smoother And features like newsletters and leaderboards added to the platform's success by keeping things transparent and engaging.

As a result, Cents of Style witnessed an impressive $1.7 million in revenue from affiliate sales with a substantial increase in web sales by 80%.

LeadDyno strategically placed a compelling CTA in the middle of their case study layout, maximizing its impact. At this point, readers are already invested in the customer's story and may be considering implementing similar strategies.

A well-placed CTA offers them a direct path to learn more and take action.

LeadDyno also utilized the power of quotes to strengthen their case study. They didn't just embed these quotes seamlessly into the text; instead, they emphasized each one with distinct blocks.

Are you looking for an easier and quicker solution to create a case study and other business documents? Try Visme's AI designer ! This powerful tool allows you to generate complete documents, such as case studies, reports, whitepapers and more, just by providing text prompts. Simply explain your requirements to the tool, and it will produce the document for you, complete with text, images, design assets and more.

Still have more questions about case studies? Let's look at some frequently asked questions.

How to Write a Case Study?

• Choose a compelling story: Not all case studies are created equal. Pick one that is relevant to your target audience and demonstrates the specific benefits of your product or service.
• Outline your case study: Create a case study outline and highlight how you will structure your case study to include the introduction, problem, solution and achievements of your customer.
• Choose a case study template: After you outline your case study, choose a case study template . Visme has stunning templates that can inspire your case study design.
• Craft a compelling headline: Include figures or percentages that draw attention to your case study.
• Work on the first draft: Your case study should be easy to read and understand. Use clear and concise language and avoid jargon.
• Include high-quality visual aids: Visuals can help to make your case study more engaging and easier to read. Consider adding high-quality photos, screenshots or videos.
• Include a relevant CTA: Tell prospective customers how to reach you for questions or sign-ups.

What Are the Stages of a Case Study?

The stages of a case study are;

• Planning & Preparation: Highlight your goals for writing the case study. Plan the case study format, length and audience you wish to target.
• Interview the Client: Reach out to the company you want to showcase and ask relevant questions about their journey and achievements.
• Revision & Editing: Review your case study and ask for feedback. Include relevant quotes and CTAs to your case study.
• Publication & Distribution: Publish and share your case study on your website, social media channels and email list!
• Marketing & Repurposing: Turn your case study into a podcast, PDF, case study presentation and more. Share these materials with your sales and marketing team.

What Are the Advantages and Disadvantages of a Case Study?

Advantages of a case study:

• Case studies showcase a specific solution and outcome for specific customer challenges.
• It attracts potential customers with similar challenges.
• It builds trust and credibility with potential customers.
• It provides an in-depth analysis of your company’s problem-solving process.

Disadvantages of a case study:

• Limited applicability. Case studies are tailored to specific cases and may not apply to other businesses.
• It relies heavily on customer cooperation and willingness to share information.
• It stands a risk of becoming outdated as industries and customer needs evolve.

What Are the Types of Case Studies?

There are 7 main types of case studies. They include;

• Illustrative case study.
• Instrumental case study.
• Intrinsic case study.
• Descriptive case study.
• Explanatory case study.
• Exploratory case study.
• Collective case study.

How Long Should a Case Study Be?

The ideal length of your case study is between 500 - 1500 words or 1-3 pages. Certain factors like your target audience, goal or the amount of detail you want to share may influence the length of your case study. This infographic has powerful tips for designing winning case studies

What Is the Difference Between a Case Study and an Example?

Case studies provide a detailed narrative of how your product or service was used to solve a problem. Examples are general illustrations and are not necessarily real-life scenarios.

Case studies are often used for marketing purposes, attracting potential customers and building trust. Examples, on the other hand, are primarily used to simplify or clarify complex concepts.

Where Can I Find Case Study Examples?

You can easily find many case study examples online and in industry publications. Many companies, including Visme, share case studies on their websites to showcase how their products or services have helped clients achieve success. You can also search online libraries and professional organizations for case studies related to your specific industry or field.

If you need professionally-designed, customizable case study templates to create your own, Visme's template library is one of the best places to look. These templates include all the essential sections of a case study and high-quality content to help you create case studies that position your business as an industry leader.

Get More Out Of Your Case Studies With Visme

Case studies are an essential tool for converting potential customers into paying customers. By following the tips in this article, you can create compelling case studies that will help you build trust, establish credibility and drive sales.

Visme can help you create stunning case studies and other relevant marketing materials. With our easy-to-use platform, interactive features and analytics tools , you can increase your content creation game in no time.

There is no limit to what you can achieve with Visme. Connect with Sales to discover how Visme can boost your business goals.

Easily create beautiful case studies and more with Visme

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• Boston University Libraries

Business Case Studies

• Case Analysis
• Getting Started
• Harvard Business School Cases
• Diverse Business Cases
• Databases with Cases
• Journals with Cases
• Books with Cases
• Open Access Cases

Online Resources

• Case Interviews
• Case Method (Teaching)
• Writing Case Studies
• Citing Business Sources

Once you have identified a case study that you wish to analyze, the sources listed below can help you analyze the case materials.

• Cengage Learning - Case Studies Explains how to effectively analyze cases and write a case study analysis. Provides a checklist and explanation of areas to consider, suggested research tools, and tips on financial analysis.
• Guide to case analysis From the publisher McGraw Hill. Includes sections on objectives of case analysis, preparing a case for class discussion, preparing a written case analysis and the Ten Commandments of Case Analysis.
• How to Analyze a Case Study From the Simmons University Writing Center.
• Writing a Case Analysis From the University of New South Wales Business School.
• Writing a Case Study Analysis From The University of Arizona, Global Campus, Writing Center.

• << Previous: Open Access Cases
• Next: Case Interviews >>
• Last Updated: Jul 16, 2024 9:04 AM
• URL: https://library.bu.edu/business-case-studies

The Ultimate Guide on Writing an A+ Case Study Analysis + 15 Examples

Struggle with writing a case study analysis? You are in the right place! Below, we will show you nuts and bolts of this type of paper, how to write it, and share 15 distinct essay examples. Plus, you will find the case study checklist to keep your writing on track.

What is a Case Study Analysis?

Case study analysis topics.

• Important Aspects

You might ask, what is a case study analysis ? With this type of work, you take an actual situation from a specific discipline, such as business or education. The goal is to find a solution, analyze the outcomes of the situation, or evaluate it.

Case study analysis does not target one specific theory or piece of knowledge. It requires a universal application of several theories and methods for research and review. Hence, it can be helpful for many disciplines at once. If you need to look at some examples, head over to our essay database .

There are several steps you need to take for a successful analysis depending on the type of your case study. Here are the most critical universal points:

• Analyze the problem from different perspectives. Use the theories and methods you have learned about in the classroom.
• Devise a series of solutions or outcomes. You need to analyze their advantages and weaknesses.
• Provide the best solution according to your analysis. You must present solid arguments for why you have suggested it.
• Demonstrate well-grounded research in your case study analysis You should not make claims without proof.
• Provide credible references for any theory that you mention in your analysis. You do not want your work to be discarded because of plagiarism.

If you want to start the business in the future, case study analysis is essential for your education. It can give you a taste of what your career is might include.

Are you panicking because you have never written anything like this before? Don’t worry! After going through the guidelines in this article, you will get a better sense of what is required from you. It will not seem as scary anymore. 😊

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15 Case Study Analysis Examples

We have prepared 15 examples of case study analysis, so you can get an idea of how they should look. The disciplines are broad and there is something here for everyone. Check out the table below!

Let’s concentrate on the format for case study analysis!

Case Study Analysis: Format

In this section, we will get you acquainted with different types of case studies. We will focus on the difference between multiple and single case study analysis. Additionally, we will show you how to organize all of your ideas into an outline. Hence, your work will be understandable and complete.

• Types of Case Studies

There are several distinct types of case studies , each with its nuances. The choice will depend on the needs of your investigation. We will focus on Illustrative, Exploratory, Cumulative, and Critical Instance studies. Let’s explore each of them one by one.

• Illustrative Case Studies Illustrative case studies are the most common type. They are very descriptive, and the main goal is to help you understand the situation. You are typically given one or two instances of an event. Illustrative case studies answer two questions: What is happening? Why is it happening? The case study is explained in great detail, including location, key players, roles, influence, and involvement. The focus of illustrative case studies is to maintain the reader’s interest. Hence, the language should be understandable, but it should not be oversimplified. It is not preferred to quote more than two instances as the case study might become too complicated.
• Exploratory Case Studies Exploratory case studies are mainly used in Social Sciences. They tend to focus on real-life contexts for situations. They are implemented before a large-scale investigation to help develop the case for more advanced research processes. Exploratory case studies aim to research a specific topic in detail to help you reach a complete understanding of it. You need to identify questions that can later be answered as part of a more extensive examination. Your initial research might reveal very persuasive details. However, it is crucial to remember that concluding before the large-scale investigation is counterproductive.
• Cumulative Case Studies The idea of a cumulative case study is to gather information and details from many data sources to claim a general phenomenon. Cumulative case studies eliminate the need for additional and expensive new studies, which are simply repetitions of the old ones. If you properly analyze all of the case study data that exists, you will realize that everything you are looking for is already there. However, it is essential to look at the existing research from a new perspective to ensure it fits the current challenges and needs.
• Critical Instance Case Studies Critical instance case studies are similar to cumulative but work oppositely. Instead of defining a general phenomenon based on little research, it tries to understand a specific case based on generalized findings. Critical instance case studies help answer cause and effect questions. The adequate specification of your evaluation question is the most crucial part of your analysis.
• Single vs. Multiple Case Study

Your case study can either include a single case or multiple cases . In this section, we will discuss the benefits of both:

• Single Case Study Single case studies are less expensive and do not take as much time. When examining one case, it is easier to put all of your energy into it and get a deeper understanding of the subject. Since the study is more careful, you can look at one thing from many different perspectives. Usually, in a single case study, the case is more critical and unique , and it is possible to focus on a more longitudinal research.
• Multiple Case Study When studying several cases, you can understand the critical similarities and differences among them. If you base your research on many cases, it will be more robust and reliable. Such analysis allows you to form broader research questions. Hence, you can end up with a more convincing theory.

Now, let’s talk about the backbone of every study: the outline!

Case Study Outline

Before writing any paper, you should first prepare an outline. It not only makes your job easier but also enhances the organization of your paper. You should put all of your ideas down and try to understand which order and format will best suit your analysis.

We have tried to simplify the process by preparing a sample outline for your case study analysis. It will help you understand what your paper should include.

• Introduction Firstly, you should introduce your reader to the case, assuming they have no prior knowledge. Describe all the challenges and mention the most important details.
• Answers to the Questions There will probably be some questions about your case. It would be best if you answered all of them in an organized manner. Make sure not to make it too obvious. Answer them in such a way that it seems like a part of your analysis.
• Challenges Though you talk about the challenges in your introduction a little bit, it is essential to go into more detail within the main paragraphs to make sure your readers understand them. It will improve the comprehension of solutions later.
• Solutions Introduce each solution you have devised. Evaluate those based on different theories and mechanisms. All of your propositions need to be solid and well-designed. Your case study evaluation should be informative and engaging.
• Final Statement In this part of your paper, you should state which solution best fits the case according to your broader analysis. You should compare it with the others and explain why you have chosen it.
• Conclusion The conclusion is the last thing you need to write. There is nothing specific that should be included. Make sure that your paper comes to a logical end.
• References Of course, you need to reference all of the theories and practices mentioned in your paper for your analysis to be solid and well-grounded. The style of your references will depend on the format assigned by your instructor.

This is how a typical case study analysis should look like. We mentioned this format for you to imagine the standard thought process that goes into making outlines. First, organize your research and divide it into parts to achieve an exciting and compelling paper. The type of case study analysis with which you are dealing also matters.

Now, here’s how you start writing!

How to Write Case Study Analysis: Important Aspects

In this section, you will find everything you need to kickstart writing your paper. Firstly, we will go through the things you need to have ready before you start. Then, we will show you how to do adequate research. Finally, we will give you a case study checklist to make it easier to complete the tasks throughout your analysis.

Before You Start

It is vital to choose an appropriate case study topic first. It should be exciting and relevant to your area of study. Once you select your topic, you need to choose an applicable case study . There are several criteria that you need to keep in mind in your search of a case study:

• The case study should complement your topic of research For example, if the topic you have chosen is related to marketing, it would be weird if your case study was about banking. However, a case study about how a famous company handles its marketing would be very acceptable.
• The case study should apply to the phenomena that you have chosen to research Make sure that the way the company handles its marketing can be generalized to benefit other companies. It needs to be universally reusable.
• The case study cannot be outdated You need to know that you can apply the outcome of your research to the modern world. Everything needs to be analyzed from the perspective of today.
• Decide whether you need a single or multiple case study You can go through the comparison above once again. Try to understand which one suits your topic best.

Do you already have the topic? Let’s get your researching skills up to date!

Case Study Research

When doing case study research, simply googling something rains down tons of sources full of information. However, most of those sources cannot be trusted, especially when writing an academic paper. The references are the most crucial part of such papers. If they are poor, then your essay has no reliable basis. Hence, you need to look for official sources, such as university websites or scholarly articles. Remember that Wikipedia is not a valid source !

Throughout the process, have your research question in mind. It is easy to get carried away and read every exciting article on the topic. Nonetheless, it would be best to have a specific goal to ensure your research is practical and not wasting your time.

Finally, keep your research up to date! Remember that you are looking for information that applies to the current world and its challenges. You need to look for modern solutions to the given problem.

We are almost there! Let’s make sure you can tick off every item in this checklist !

Checklist for Case Study Analysis

Go through this checklist. It will help you keep your case study research and writing on track.

• Choose the topic.
• Decide whether you are going to do single or multiple case study.
• Identify the type of your case study.
• They are relevant to the topic.
• Their outcome can be generalized to fit other cases within the same area of research.
• They are not outdated.
• Define a clear case study research question.
• Make sure that the sources of your research are credible and up to date.
• Identify the theories and methodologies you are going to use to analyze the case.
• Use more than one point of view to examine the case, and look at it from different perspectives.
• Have a clear outline of what you are going to include in your paper.
• Write and proofread your paper.

Have you completed every item on this list? Congratulations! You are done with your case study analysis!

❓ What is the difference between case study and case analysis?

Case study and case analysis both provide you with a topic and require extensive research. However, a case study must be taken from real life. For example, a student might analyze Coca-Cola’s financial results and come up with brilliant results that can significantly impact the company. If you send this kind of case study analysis to the company, you might even get a reward. Case analysis focuses more on problems and solutions, whereas case studies can include general research and evaluation.

❓ What are the stages of a case study?

There are four main stages of a case study:

• analyzing the case,
• identifying its challenges,
• devising a set of possible solutions or outcomes,
• evaluating those outcomes.

To ensure the success of your analysis, you should go over all of these stages with equal diligence.

❓ What is the purpose of a case study?

The purpose of a case study analysis is to describe a case in detail and identify the main issues with it. Afterwards, these issues need to be analyzed based on appropriate theories from the discipline that you have learned about in class. Finally, you need to recommend a list of actions that should be performed for that case.

❓ What are the qualities of a good case study?

Here are some qualities of a good case study:

• It is written in a formal, academic language with good grammar and coherent structure.
• All of the claims made in the case study have a reasonable basis and can be proven.
• The case study does not overload the readers with unnecessary information. It is clear and to the point.
• The information is passed to the reader in an organized manner. It has flow, and it is easy to keep up with the extensive academic research.
• What is a Case Study Analysis
• Illustrative Case Studies
• Exploratory Case Study Example
• Definition of Cumulative Case Studies
• Definition of Critical Instance Case Studies
• A Comparative Study of Single and Multiple Case Studies
• How to Choose an Applicable Study
• Case Study Checklist
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A Model for Case Analysis and Problem Solving

ABOUT THIS CONTENT

Table of Contents

Why the Case Approach

The most effective way for learning to take place is to actually be in real situations, make decisions, deal with the consequences of those decisions, and learn from our real mistakes. Nothing will ever replace learning from experience. Cases (which involve real situations although names may be changed) allow us to "simulate" real life situations when we don’t have the luxury of having years of experience. Cases allow us (to some degree) to live with real situations, make decisions, and feel the consequences. Like scientists in a laboratory, students of management use case problems and experiential exercises as "laboratory" opportunities to experiment with real organizations in the classroom setting.

Cases attempt to reflect the various pressures and considerations managers confront in everyday organizational life. By using complex real world problems as a focus, cases are designed to challenge you to develop and practice skills that will be appropriate to the practical problems you will face in your career.

The case method is based on the learning principle that learning occurs most when people teach themselves, through their own struggles. You will gain greater understanding and improved skills in judgment when you work through a problem than if you listened passively to a lecture. Similarly, there will be greater learning if you "use" a theory than if you just heard about it. Therefore cases have two basic uses:

• Helping us learn how to apply theories to real situations
• Helping us learn how to solve real problems

Like real situations cases center around an array of partially-ordered, ambiguous, seemingly contradictory and reasonably unstructured facts, opinions, inferences and bits of information, data, and incidents out of which you must provide order by selectively choosing which bits to use and which to ignore. In real life others won’t do this for us. As in real life situations, it is unlikely that any two people would assemble the data or make inferences identically. You will have to work within the limitations inherent in evidence and arrive at internally consistent interpretations. Experiencing the process of learning this way may be frustrating and confusing, but it is also practical and realistic.

Cases, as in real management situations, require you to work with the "as is" of reality, not the "should be" of theory. Like managers you will have to exercise judgment which can be improved by discussion and consultation with others. However, note that like the manager, you will seldom be sure before your decision is made and often after it is made, that you have made the right or "best" decision. Like any manager, you will approach cases under time pressure, on the basis of limited facts, and in the face of many unknowns. You will approach cases along with other people who like you have idiosyncrasies and limitations, and different opinions.

In summary, cases have a number of benefits:

• They allow us to develop skill in thinking clearly about ambiguous, unstructured situations using incomplete information;
• They help us to develop skills at recognizing what information is important and what is missing
• They help us to develop concise, reasonable, and consistent action plans;
• Help you to identify implicit models and assumptions, values and goals you use every day
• They provide an opportunity to develop skills in presenting (written and oral) our ideas to people and to groups; to influence and persuade others
• Improve your ability to predict behavioral outcomes-yours and others

Your Responsibilities

Little can be learned from a case without preparing it carefully and discussing it with others. Cases are not designed to present you with a right answer which you can memorize in the hopes that you will remember it if you ever encounter a similar situation. Similarly you won’t gain much from listening to what others think is the right answer. The learning comes from actively participating in the search for solutions. Cases are the raw materials that permit simulation in the classroom of actual discussions carried on informally among managers.

Preparation : Cases require more preparation and active participation than most class activities. How much you get out of a case discussion depends heavily on how much effort you put into preparing it before class. Many students confronting cases for the first time are overwhelmed; they see so many factors that come into play. Facts are confusing and ambiguous and often incomplete. This guide is intended to help you walk through the critical steps.

Informal Discussion Groups: After preparing a case by yourself, it can really help to meet with a group of other students to talk about a case before class. This will give you a chance to test your ideas on others and learn about other perspectives about the case.

Participating in Class Discussions: The purpose of the class discussion is to test others ideas so that together students can reach a richer and deeper understanding of the case. The role of the discussion is to moderate and create an environment in which contributions of individual students build on one another to understand the problem more fully. The instructor’s role is not to answer. The instructor may highlight, synthesize the issues and help shape the discussion.

The quality of the class discussion depends on the quality of the students’ preparation and participation in class. The class should be considered a team of colleagues that has been asked to work together to solve a challenging problem. This requires good team members to push ideas and support them. Good class also requires an emphasis on listening; others will raise ideas you hadn’t thought of and you should be prepared to change your mind and incorporate new ideas when you find them persuasive.

Try to have your ideas build on the comments of others. Don’t be afraid to be challenged or to be wrong. Sometimes students leave a class discussion discouraged because many issues and arguments that were raised that they had not considered before class. Remember that no case would be worth discussing if it were simple and straightforward enough for you to have figured it out on your own.

The classroom should be a place where you can test ideas and learn from each other. Finally enjoy yourself. There should be a lot of satisfaction in struggling with a complex problem and through your efforts, coming to a better understanding of it.

Preparing a Case: Six Steps for Problem Analysis

The checklist is presented as a framework for diagnosis, problem-solving, and managerial action taking. Note that few if any situations that you will experience will require that you consider every element listed here. Management is a dynamic, ongoing process that never takes place as sequentially or rationally as this list would imply. In most real-world situations, as opposed to case discussions in class, you already know a great deal about the people and prior experiences that are relevant. In addition, events never turn out exactly as you anticipate them.

Step 1: Comprehend the Case Situation: Data Collection, Identify Relevant Facts

Most cases require at least two readings, sometimes more; the first time through should involve familiarizing yourself with the basic situation; you may be given some guide questions to help you and you also might think about why the case was assigned now. There are some standard questions that you might keep in mind as you read the case:

• What are the key issues in the case; who is the decision maker in the case; is there a critical decision?
• What is the environment in which the key people operate; what are the constraints on their actions; what demands are imposed by the situation?
• Are solutions called for?
• If you had the chance to talk to critical people in the company, what would you want to know?
• What are the actual outcomes of the current situation-productivity, satisfaction, etc; how stable are present conditions?
• What are the "ideal" outcomes; what is an ideal "future" condition?
• What information is lacking; what are the sources of the available information?

Managers and students rarely have complete information and must rely on inferences. Be prepared to make creative assumptions; good analysis goes beyond identifying the relevant facts in the case. If some facts aren’t given, figure out what you can assume they are.

Rereading: After the first reading, try to formulate several plausible courses of action and explanation for the data in the case. Imagine yourself as various key people in the case and figure out why you (as the person in the case) might have acted as he/she did, or what you would do. Think about the consequences if you are wrong.

Using evidence and numbers: One of the most difficult problems in preparing a case is sorting through the mass of information and evidence. Often cases involve considerable background information of varying relevance to the decision at hand. Often cases involve conflict with different actors providing selective information and courses of action to support their claims. As in real life, you must decide what information is important and what isn’t and evaluate apparently conflicting evidence.

As in real life, you will be faced with a lot of information but perhaps not exactly the information you need. It is not uncommon to feel paralyzed by all the available information; it is difficult to identify the key information after the first reading. You should be slightly skeptical about the information presented or the interpretation placed on it by various actors in the case. You won’t have time to question all evidence in the case but if the evidence is critical, you might ask yourself what it really implies and whether it is as compelling as it seems.

As you read the case keep in mind:

• remember that all behavior is caused, motivated, and goal-directed; behavior may see strange, or "irrational" but you can assume it makes sense to the actor
• separate fact from opinion; distinguish between what people say vs. do
• it might be possible to get more information about the case (eg. the industry) but for the most part you will be asked to do your best with the information available
• separate symptoms from underlying causes
• avoid judgments; avoid premature solutions

Step 2: Defining the Problem

What is the critical issue or problems to be solved? This is probably the most crucial part of the analysis and sometimes the hardest thing to do in the whole analysis. Perhaps the most common problem in case analysis (and in real life management) is that we fail to identify the real problem and hence solve the wrong problem. What we at first think is the real problem often isn’t the real problem .

To help in this stage here are some questions to ask in trying to identify the real problem:

• where is the problem (individual, group, situation) why is it a problem; is there a "gap" between actual performance and desired performance; for whom is it a problem and why
• explicitly state the problem; are you sure it is a problem; is it important; what would happen if the "problem" were left alone"; could doing something about the "problem" have unintended consequences?
• what standard is violated; where is the deviation from standard
• what are the actual outcomes in terms of productivity and job satisfaction; what are the ideal outcomes
• how do key people feel about the problem and current outcomes
• what type of problem is it ?(individual, relationships, group, intergroup, leadership/motivation/power, total system)
• how urgent is the problem? How important is the problem relative to other problems?
• assess the present conditions:
• What are the consequences; how high are the stakes; what factors must and can change?
• for the organization (costs and profits; meeting obligations; productivity)
• for the people (personal and financial rewards; careers; satisfaction and growth)
• How stable are present conditions?
• What information is lacking?
• What are the sources of the available information?

Traps in this stage :

• suggesting a solution prematurely-stating a problem while implying a solution
• stating problems in behavioral (personal) terms, not situational terms
• not explicitly stating the problem-assuming "your" problem is "the" problem
• blindly applying stereotypes to problems; accepting all information at face value; making premature judgments; multiple causality
• most crucial at this step is to avoid suggesting a solution
• confusing symptoms with causes; differentiating fact from opinion; prematurely judging people and actions
• stating the problem as a disguised solution (eg. Hardesty’s failure is due to his not visiting purchasing agents)

Step 3: Causes

Once you have identified the key problem(s), try to find the causes here. Most critical here is avoiding solutions, and avoiding blaming or judging people. Also

• don’t quit at the most obvious answer-try playing devil’s advocate; put yourself in the other person’s shoes
• accept the multiple causality of events
• there may be a number of viable ways to fit the data together; explore as many as you can; go past the obvious
• there is a great tendency to evaluate behavior as good or bad; I care about why it occurred; judgments leads to a poor analysis focusing on justification for the evaluation
• the concern is not whether behavior is good or bad but why it occurred and its consequences
• be careful about hindsight; actors in the case usually don’t have access to outcomes when they act so avoid "Monday Morning Quarterbacking"-consider what actors in the case are reasonably likely to know or do
• as before, avoid premature solutions and premature judgments

Step 4: Generating Alternative Solutions (not all assignments will call for this)

In thinking about a context for generating alternatives, think about:

• what are the decision-maker’s sources of power in the situation? (legitimate, reward, punishment, expert, referent)
• what are possible leverage points (changing technology such as machines, processes, product designs; changing organizational structure; changing reward systems, job descriptions education, changing personnel, changing culture)
• can individual behavior be changed (education, training, reward systems, job description, etc.)
• what are the constraints on the solution? (time, money, organizational traditions, prior commitments, external realities, legal etc)
• what are the available resources (time, money, people, existing relationships, power)
• should others be involved (in problem definition, data collection, generating alternatives, implementing solutions, monitoring and assessing realities)
• In this stage it is important to avoid reaching for a solution too quickly; be creative here and put yourself in the case. Try living with various alternatives that you are thinking about; what would be the impact on you and on others. Be sure to think about the costs and benefits of each alternative.

Step 5: Decision (note that not all assignments will call for a solution)

In considering the alternatives generated above you need to be clear on the criteria you will use to evaluate them. Some possible criteria include:

• does the alternative address the critical aspect of the problem? What are your objective? Be specific.
• what are the intended consequences; what are some unintended possible consequences; how will your decision improve the situation
• what is the probability of success; what are the risks; what happens if the plan fails
• what does the plan depend on? What are the costs? What power and control is needed?
• who would be the "change agent" Does he/she have the power, skills, knowledge to be successful
• is the "solution" consistent with organizational realities

Remember that there is no one "elegant" solution; all solutions have costs and benefits ; identify pros and cons of each alternative; evaluate relative to goals; look at main and side effects you may have to make inferences and judgments; do this as long as you have good reasons for your inferences Choose alternative which best meets the criteria. The decision might not be accepted by those involved so you may have to choose a more acceptable one. You might want to rank order your alternatives according to how well they meet the criteria used. as you think about action, put yourself into the case; try to project living with the consequences

Step 6: Taking Action and Following Up

In thinking about implementation you want to think about these areas:

• what are leverage points for change-technology, reward systems, work relationships, reporting relationships, personnel changes
• what are the decision maker’s sources of power: legitimate, reward, expert, referent, etc?
• what are the constraints on a solution: time, money, organizational policies, traditions, prior commitments, external realities
• does culture have to change; what historical relationships must be respected
• implementation-will people resist change; is change being reinforced; is a new stability developing
• monitoring changes-are further changes necessary; are costs and benefits of changes as expected
• make sure you have thought about the ramifications of implementing the plan; how will you address them

Action Plans : provide options for meeting specific objectives should include: a brief description of the plan, costs, benefits, drawbacks

Some simple models are helpful in thinking about implementation. One involves thinking about implementation as involving three stages:

• Unfreezing: Making sure those affected feel the need for change
• Change: introducing the change
• Refreezing: Reinforcing the new behaviors

General Reminders/Check List

• remember you will never have enough information!
• the most critical aspect of case analysis may be "identifying the problem"
• you will never be sure you have identified the real problem
• there is rarely one "right" answer-different answers may be somewhat right

Accept that cases and managerial situations involve:

• ambiguous situations multiple causality inadequate information
• no elegant solution
• acknowledge that personal values play a role in case analysis
• no one (including the instructors) can "solve" the case
• try to imagine "living" with the problem and your recommendations

Try to avoid:

• blindly applying stereotypes to problems accepting information at face value
• confusing symptoms with problems making premature evaluations
• judging behavior-we assume no one is "good" or "bad"; labelling people as such is an easy way to dispense with problems of trying to figure out why someone does what he does
• don’t assume you are so much smarter or better informed than managers you observe or read about that you can readily solve problems they have been dealing with for years
• managers involved may understand their problems better than you do and act the way they do for reasons that are sound to themselves

Writing Tips

• while it is critical to follow the above advice on case analysis, much of this analysis may not appear in your paper. The analysis is required to generate material for your memo but may not necessarily appear in it
• think carefully in your writing who your audience is
• assume your reader is a little dense; write in a form that is easy to digest-good introduction, subheadings, manageable paragraphs, clear topic sentences, clear transitions
• provide a strong introduction; give your reader a reason to read the analysis; give the reader the "benefits"
• in a memo, you can only convey one or two main points; make sure the reader knows what they are; make sure your introduction provides a clear "road map" for where you are going; reinforce this in the conclusion
• use models/theories in your analysis, but you may not necessarily "leave" these "tools" in your document.

Final Comments

Case teaching is a lab experience. It is low risk and participative. It does not provide "how to" or surefire techniques. Students sometimes express dissatisfaction with cases. "Information is ambiguous, redundant, irrelevant; the issue isn’t stated clearly; the instructor isn’t directive enough; we never know the "right" answer; the instructor should lecture more."

These comments are legitimate. But for the most part the difficulties associated with case teaching stem from real situations themselves. These are the same dilemmas you will face as managers.

More Related Posts

• Case Hints – The Case Method
• Case Analysis Template
• 5 Forces Framework
• Case Hints – Some Guidelines for Case Analysis & Number Crunching
• Case Hints – Marketing Management Case Analysis

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Open Access

Peer-reviewed

Research Article

Association of IL13 polymorphisms with susceptibility to myocardial infarction: A case-control study in Chinese population

Roles Formal analysis, Methodology, Resources, Writing – original draft

Affiliation Department of Cardiology, Qinghai Province Cardiovascular and Cerebrovascular Disease Specialist Hospital, Xining, Qinghai, China

Roles Formal analysis, Investigation, Writing – original draft

Affiliation Department of Coronary Heart Disease, Qinghai Province Cardiovascular and Cerebrovascular Disease Specialist Hospital, Xining, Qinghai, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected]

• Rong Chen, 
• Qiaoling Bao, 
• Xiaofeng Ma

• Published: August 1, 2024
• https://doi.org/10.1371/journal.pone.0308081
• Peer Review
• Reader Comments

Inflammatory cytokines play a major role in the pathogenesis of myocardial infarction (MI). Although information on the importance of interleukin 13 (IL13) in human MI is limited, it has been well documented in the mouse model. Genetic variation in the IL13 gene has been associated with the structure and expression of the IL13. In the present study, we hypothesized that IL13 common genetic variants would be associated with a predisposition to the development of MI.

Materials and methods

The present study enrolled 305 MI patients and 310 matched healthy controls. Common genetic polymorphisms in the IL13 gene (rs20541, rs1881457, and rs1800925) were genotyped using the TaqMan SNP genotyping method. Plasma levels of IL13 were measured using an enzyme-linked immunosorbent assay (ELISA).

In MI patients, minor alleles of the IL13 rs1881457 and rs1800925 polymorphisms were less common than in healthy controls [rs1881457: AC (P = 0.004, OR = 0.61), C (P = 0.001, OR = 0.66); rs1800925: CT (P = 0.006, OR = 0.59)]. Further haplotype analysis of three studied SNPs revealed a significant association with predisposition to MI. Interestingly, IL13 rs1881457 and rs1800925 were linked to plasma levels of IL13: the reference genotype had higher levels, heterozygotes were intermediate, and the alternate genotype had the lowest levels.

Conclusions

In the Chinese population, IL13 (rs1881457 and rs180092) variants are associated with different plasma IL13 levels and offer protection against MI development. However, additional research is required to validate our findings in different populations, including descent samples.

Citation: Chen R, Bao Q, Ma X (2024) Association of IL13 polymorphisms with susceptibility to myocardial infarction: A case-control study in Chinese population. PLoS ONE 19(8): e0308081. https://doi.org/10.1371/journal.pone.0308081

Editor: Andrea Da Porto, University of Udine, ITALY

Received: April 24, 2024; Accepted: July 16, 2024; Published: August 1, 2024

Copyright: © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All minimal data are in the manuscript. if further info is required, request can be directed to the corresponding author.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviation: MI, Myocardial infarction; SNP, Single nucleotide polymorphism; HWE, Hardy-Weinberg equilibrium; ELISA, Enzyme-linked immunosorbent assay; IL, interleukin; TNF, tumor necrosis factor; STEMI, ST-Elevation Myocardial Infarction; NSTEMI, Non-ST-Elevation Myocardial Infarction; ECG, Electrocardiogram

Introduction

Myocardial infarction (MI) is a serious form of coronary artery disease (CAD) that results from the occlusion of a coronary artery, impairing blood supply to the heart muscle. Despite significant advances in the understanding and treatment of cardiovascular diseases, MI remains a major public health concern worldwide. A recent meta-analysis revealed the global prevalence of MI is around 3.8% below 60 years old individual and the prevalence is 9.5% in older age group (>60 years) [ 1 ]. The mortality rate has increased in Chinese populations both in rural and urban areas in between 2002 to 2016 [ 2 ]. MI can be categorized as either STEMI or NSTEMI based on ECG changes. STEMI involves a complete obstruction of a coronary artery, which is indicated by ST-segment elevation on an ECG, necessitating immediate reperfusion therapy. On the other hand, NSTEMI results from a partial blockage and presents without ST elevation but with other ECG changes, which are managed with medications and, in some cases, percutaneous coronary intervention. In the context of MI, STEMI is more commonly observed than NSTEMI [ 3 ]. Both types of MI require prompt medical attention to prevent heart damage and improve patient outcomes. The multifaceted aetiology o MI includes complex interactions between genetic predisposition and environmental factors [ 4 ]. Numerous environmental factors and host genetic factors have been demonstrated to impact cytokines and the immune response in humans [ 5 ]. Cytokines are critical in the pathophysiology of MI, as they mediate the inflammatory response that follows cardiac tissue injury [ 6 ]. These small, signaling proteins are secreted by immune cells, endothelial cells, and other cell types in the heart, orchestrating a complex series of events aimed at limiting damage and initiating repair [ 7 ]. During an MI, cytokines such as interleukins, tumor necrosis factor-alpha (TNF-α), and chemokines are rapidly increased, contributing to both protective and detrimental effects [ 8 ] and facilitate leukocyte infiltration, modulate cell survival and apoptosis, and influence myocardial remodelling [ 9 ]. A recent study found differences in inflammatory molecules between ST elevation myocardial infraction (STEMI) and non STEMI clinical phenotypes, which are distinguished by elevated methylene diphosphonate (MDP), macrophage inflammatory protein-1β (MIP-1β), and TNF-α [ 6 ]. Additionally, cytokine levels were related to blood flow through the infract related artery [ 6 ]. Interleukin-13 (IL-13), is a pro-fibrotic cytokine that is secreted by various immune cells, including T cells, B cells, and macrophages. This cytokine plays a crucial role in the Th2 immune response, a process that is triggered by the activation of T helper 2 cells. These cells release a range of cytokines, such as IL-4, IL-5, and IL-13, which promote B cell activation, class switching to IgE production, and the recruitment of eosinophils [ 10 ]. Although IL-13 was initially thought to play a role in allergic responses, it has gained increasing attention due to its involvement in a variety of immune processes and diseases. The cytokine exerts multiple and diverse biological effects on different cell types or tissues and is responsible for immune cell differentiation, proliferation, and inflammatory responses [ 10 ]. In the MI mouse model, dynamic expression of IL13 has been reported; after the incidence of MI, the levels significantly increased, reaching a peak on day three and then declining until day seven, when they increased again [ 11 ]. However, another research group found that upregulation began on day 7 and continued until day fourteen [ 12 ]. Surprisingly, investigations on the role of IL13 in MI patients are very limited and in a study lower levels have been reported [ 13 ].

Genetic variations can influence gene expression, protein function, and susceptibility to different disease risks [ 14 ]. Functional variations in the IL13 gene have been linked with different types of cardiovascular diseases [ 15 ]. Although various genome-wide association studies have been carried out in diverse populations, no notable genetic association between the IL13 gene and susceptibility to develop MI has been identified [ 16 , 17 ]. Given that differential IL13 levels have been observed in MI patients and functional genetic variants play a role in regulating IL13 levels, we hypothesized that common genetic variants affecting plasma IL13 levels would be linked to the pathogenesis of MI in the Chinese population. Till date several single nucleotide polymorphisms (SNP) in IL13 gene have been reported to be linked with wide range of clinical diseases ( https://www.ncbi.nlm.nih.gov/clinvar/?term=IL13[gene ]). However, three SNPs (rs20541, rs1881457 and rs1800925) have been studied most frequently based on their functional relevance. The human IL13 gene, which consists of 5 exons and 4 introns, is located in the long arm of the fifth chromosome (q31.1). The IL13 rs20541 polymorphism is found in the fourth exon, which is a result of a transition mutation (A>G) that alters the amino acid sequence of the IL13 protein at the 130 th codon (Glycine to Arginine). The other two genetic variants are situated in the promoter region of IL13 gene (rs1881457: A-1512C; rs1800925: C-1111T) and are presumed to alter the expression of IL13 [ 18 ]. In addition, the significance of IL13 polymorphisms has been documented in clinical phenotypes that serve as risk factors for MI, such as type-1 diabetes mellitus, hypertension, and dyslipidemia. Specifically, in Kuwaiti children, the rs20541 variant was associated with susceptibility to T1DM [ 19 ]. Conversely, genetic variants in the IL13 gene failed to demonstrate an association with T1DM in Filipino [ 20 ], British [ 21 ], and Thai populations [ 22 ]. Moreover, the rs1800925 and rs1881457 polymorphisms have been shown to be linked to hypertension in the Korean population [ 23 ]. Based on the functional significance of these genetic polymorphisms, case-control studies in various populations have been conducted to investigate their association with MI susceptibility. In the Iranian population, rs1881457 heterozygotes provided protection against the development of MI, but no significant link between MI susceptibility and the rs20541 polymorphism was found [ 24 ]. In contrast, the IL13 rs20541 variant increased the risk of MI in Greek Cypriot males [ 25 ].

Given the importance of IL13 in the pathogenesis of MI and the possibility of genetic variants influencing IL13 levels, we hypothesized that the common IL13 genetic variant would be associated with susceptibility/resistance to MI. We conducted a hospital-based case-control study in the Chinese population to investigate the possibility of an association between IL13 variants and MI.

Study design

The study used a hospital-based case-control design to investigate the genetic association between common variations of the IL13 gene and the susceptibility or resistance to MI in a Chinese cohort. The report of this study adhered to the STREGA guidelines.

The present study was conducted on the patients enrolled in the Department of Cardiology, Qinghai Province Cardiovascular and Cerebrovascular Disease Specialist Hospital, from January 2017 to December 2022. Other baseline parameters such as hypertension, hypercholesterolemia, hyperglycemia, and coronary artery disease were also explored in the included MI patients. Age, sex and gender-matched healthy controls hailing from similar geographical locations were considered healthy controls without any history of heart-related issues. All healthy controls were subjected to blood pressure measurement and different biochemical tests such as cholesterol and blood sugar levels, and subjects with higher BP (more than 120 mm Hg for systolic and 80 mm Hg for diastolic), cholesterol (total >200 mg/dL, LDL >100 mg/dL, HDL<40 mg/dL for eman and 50 mg/dL for women, triglycerides > 150 mg/dL), or sugar levels (fasting blood glucose > 99 mg/dL and HbA1c >5.7%) were not included in the present study. The study protocol was approved by the institutional ethical committee of Qinghai Province Cardiovascular and Cerebrovascular Disease Specialist Hospital, and informed written consent was obtained from each participant. In situations where patients were unable to provide informed consent due to their medical condition, consent was obtained from legally authorized representatives, including family members or legal guardians. The IEC reviewed and approved the study protocol, including this consent process (IEC/2016/156). If patients subsequently regained capacity, they were informed and requested to provide direct consent. About 5 ml of intravenous blood was collected from MI (within six hours of the MI onset) and healthy control subjects with anticoagulants.

Sample size calculation

The sample size for the study was calculated a priori to ensure that an adequate number of cases and controls were enrolled. The GPower v3.1.9.7 software was utilized for the sample size calculation. To achieve a power of 90% with an effect size of 0.15 and an alpha error probability of 0.05, the analysis indicated that 630 subjects were needed. Consequently, the study was designed to include 315 cases and an equal number of healthy controls in the investigation of the genetic association.

IL13 genotyping

Whole genomic DNA was isolated from the whole blood by QIAamp Blood mini kit according to the manufacturer’s instructions (QIAGEN, Germany). DNA quantity and quality were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis. Genotyping of common IL13 gene polymorphisms (rs20541, rs1881457 and rs1800925) were carried out by TaqMan Genotyping assays kit (Thermo Fisher Scientific, Catalog number: 4351379) ( https://tools.thermofisher.com/content/sfs/manuals/TaqMan_SNP_Genotyping_Assays_man.pdf ). Details of the probe are given below: rs20541, C___2259921_20 , Context Sequence [VIC/FAM] TTAAAGAAACTTTTTCGCGAGGGAC[A/G]GTTCAACTGAAACTTCGAAAGCATC , rs1881457: C__11740467_10 , Context Sequence [VIC/FAM]TACAGATTAGGAAACAGGCCCGTAG[A/C]GGGGTCACACGGCCAAGTAGCGGCA , rs1800925, C___8932056_10 , Context Sequence [VIC/FAM]GGTTTCTGGAGGACTTCTAGGAAAA[C/T]GAGGGAAGAGCAGGAAAAGGCGACA . The genotyping reactions comprised the genomic DNA template, TaqMan Genotyping Master Mix, and the specific primer/probe combination. These reactions were performed using a real-time PCR thermal cycler, with an initial denaturation step at 95°C for 10 minutes, followed by 40 cycles of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 1 minute. The fluorescence signals were detected and analyzed. To ensure the accuracy and reliability of the genotyping results, positive and negative controls were included in each run. Genotype calling was accomplished based on the allelic discrimination plot. Each sample was considered for genotyping in duplicate and considered for analysis only in case of concordant observation. Subjects who were unsuccessful in genotyping were excluded from the present investigation.

Plasma IL13 quantification

Plasma levels of IL-13 were measured by enzyme-linked immunosorbent assays (ELISA) according to the manufacturer’s instructions (Invitrogen, Catalog no: BMS231-3, https://www.thermofisher.com/document-connect/document-connect.html?url=https://assets.thermofisher.com/TFS-Assets%2FLSG%2Fmanuals%2FMAN0016602_231–3_HuIL-13ELISA_UG.pdf ). The plasma samples, which were collected and stored at -80°C until analysis, were thawed on ice before use. To prepare a standard curve, the standards were reconstituted with distilled water and serially diluted (1:2) on seven tubes, such that the highest concentration of the standard remained at 100 pg/mL and the lowest was 1.6 pg/mL. The precoated microplate wells were properly washed, and the pre-prepared standards were applied in duplicate wells. Two wells were used as blank wells and were only filled with assay buffer. 50 microliters of assay buffer were applied to all sample wells, and an equal amount of samples were applied to the wells in duplicate. Fifty microliters of conjugate mixture were added to all wells, and the plate was incubated at room temperature for two hours. After incubation, the plate was washed thrice properly and proceeded to the color development step using TMB as a substrate solution. The reaction stopped ten minutes later, and absorbance was measured at 450 nm. The concentrations of IL-13 in the samples were determined by interpolation from the standard curve.

Statistical analysis

All statistical analysis was performed by GraphPad Prism v9 (GraphPad Software, Boston) using the default setting. The allele and genotype frequency was calculated by manual counting. The distribution of genotypes with references to Hardy Weinberg equilibrium (HWE) was explored using the Microsoft Excel program. The Fisher exact test compared the prevalence of genotype and allele in different clinical categories. The Fisher exact test is a reliable method for obtaining precise test results in small sample sizes. It calculates the exact probability value and the confidence interval of the observed data for measuring the association [ 26 ]. Bonferroni correction is a technique used to correct the significance level for multiple comparisons, aiming to reduce the likelihood of false positives. It entails dividing the desired significance level (α) by the number of comparisons (n), thereby creating a more stringent threshold (α/n) [ 27 ]. In the current analysis, three SNPs were examined, and the significance levels were adjusted using the Bonferroni correction method. A P value less than 0.016 (0.05 divided by 3) was considered statistically significant. The haplotype construction and comparison of their distribution among MI patients and healthy controls were executed by SNPAlyze software Version 8.1.1 employing the default setting (DYNACOM Co. Ltd. Japan). The software uses a permutation test for the comparison of haplotype frequencies among two groups. The mean plasma IL13 levels in healthy controls and MI patients were compared by student’s t-test. The association of IL13 polymorphism with the plasma levels of IL13 was explored by one-way analysis of variances (ANOVA). For the comparison of cytokine among cases and controls or within different genotypes of IL13 polymorphisms, a P value less than 0.05 was considered statistically significant.

Baseline characteristics of patients and controls

The baseline characteristics of patients and controls are shown in Table 1 . In the present study, a total of 305 MI patients (Male: 189, Female: 116) were enrolled. In addition, 310 age and gender-matched (male: 195, female: 115) healthy controls were considered. The mean age of patients was 56.65 years, and healthy controls were 55.35 (p = 0.31). Hypercholesterolemia (58%) was more frequent among MI patients, followed by hyperglycemia (53%) and hypertension (52%). About one-third of the total MI patients had coronary artery diseases. Interestingly, the family history of cardiovascular diseases or smoking habit was more frequent in the MI patients compared to the healthy controls. While the excessive alcohol consumption rate was comparable among MI patients and controls.

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https://doi.org/10.1371/journal.pone.0308081.t001

MI patients displayed higher levels of IL-13 compared to healthy controls

Based on the availability of plasma samples, a total of 224 plasma samples were quantified for plasma levels of IL13, including each of 112 patients and controls, by ELISA. As shown in Fig 1 , the MI patients displayed significantly higher mean levels of IL13 compared to healthy controls (p<0.0001).

Levels of IL-13 were quantified by ELISA in plasma of MI patients (n = 112) and healthy controls (n = 112). The mean IL13 levels were compared by student’s t test. The MI patients displayed higher levels of IL13 compared to the healthy controls. A P value less than 0.05 considered as significant.

https://doi.org/10.1371/journal.pone.0308081.g001

Distribution of IL13 polymorphisms in healthy controls

In the present investigation, we genotyped three common gene polymorphisms in the IL13 gene (rs20541, rs1881457, and rs1800925) by TaqMan genotyping assays. Although we have enrolled 328 MI patients and 322 healthy controls for the present study, 310 cases and 305 healthy controls were successfully genotyped for IL13 polymorphisms. The prevalence of these polymorphisms is demonstrated in Table 2 . GG genotype (56%) of rs20541 polymorphism was more frequent than heterozygotes (34%) and AA type (10%). The genotype distributions did not match HWE (χ 2 = 6.03, p = 0.01). For the other two gene polymorphisms (rs1881457 and rs1800925), the reference genotypes (AA: 47%, CC: 63%) were more frequent than heterozygotes (AC: 44% and CT: 32%) and alternative genotypes (CC: 7% and TT: 5%), respectively. The distribution of genotypes for rs1881457 (χ 2 = 0.21, p = 0.64) and rs1800925 (χ 2 = 0.54, p = 0.46) polymorphisms were in line with the HWE. The HWE holds substantial implications in population genetics, serving as a foundational model for comprehending the genetic composition of populations. It posits that allele and genotype frequencies in a population remain constant from generation to generation in the absence of evolutionary influences, such as selection, mutation, migration, genetic drift, and non-random mating. HWE offers a null hypothesis for detecting evolutionary forces acting on a population, and any departure from it can indicate the presence of these forces, thereby providing insights into the evolutionary dynamics at play [ 28 , 29 ].

https://doi.org/10.1371/journal.pone.0308081.t002

IL13 polymorphisms (rs1881457 and rs1800925) are associated with MI

The distribution of IL13 polymorphisms (rs20541, rs1881457, and rs1800925) was explored in MI patients and healthy controls. Details are depicted in Table 2 . Genotypes and allele distributions were comparable in patients and controls for IL13 rs20541 polymorphism. However, the heterozygotes of rs1881457 and rs1800925 polymorphisms were more frequent in healthy controls than the MI patients (rs1881457: p = 0.004, OR = 0.61; rs1800925: p = 0.006, OR = 0.59). Similarly, the alternate allele of rs1881457 polymorphism (C) was also highly prevalent in healthy controls compared to MI patients (p = 0.001, OR = 0.66), indicating the alternate allele’s protective nature against the development of MI in the Chinese population. However, the prevalence of the alternate genotypes for rs1881457 and rs1800925 polymorphisms was comparable among MI patients and healthy controls.

Association of rs20541, rs1881457, and rs180092 haplotype with MI

Haplotype frequency was calculated and compared among the MI patients and healthy controls by SNPAlyze software, and the results are shown in Table 3 . Haplotype rs20541-rs1881457-rs1800925, G-A-C (p = 0.02), and A-A-C (p = 0.01) were more frequent in MI patients than healthy controls. In contrast, the haplotypes G-C-C (p = 1E-3), A-C-C (p = 9E-3), G-A-T (p = 0), and A-A-T (p = 2E-3) were more prevalent in controls than in MI patients.

https://doi.org/10.1371/journal.pone.0308081.t003

Plasma levels of IL13 linked with IL13 polymorphisms

To explore the functional relevance of the studied SNPs in the IL13 gene, plasma levels of IL13 were quantified by ELISA, and possible association with different genotypes of IL13 was explored. As shown in Fig 2 , the rs1881457 and rs1800925 polymorphisms were linked with plasma levels of IL13. The wildtype of rs1881457 and rs1800925 polymorphisms displayed significantly higher levels of IL13 compared to their respective heterozygotes and homozygous mutants. Heterozygotes had intermediate levels of IL13 ( Fig 2A and 2B ). Interestingly, a similar pattern of association between IL13 polymorphisms and plasma levels of IL13 remained valid still after separating MI patients ( Fig 2C and 2D ) and healthy controls ( Fig 2E and 2F )

Plasma levels of IL13 were quantified in MI patients (n = 112) and healthy controls (n = 112). Mean IL13 levels in different genotypes of rs1881457 (a) and rs1800925 (b) in the pooled clinical categories (MI patients and healthy controls) were compared by ANOVA. Further, an association of rs1881457 and rs1800925 polymorphism with IL13 was explored in MI patients (c and d) and healthy controls (e and f). A P value <0.05 is taken as statistically significant.

https://doi.org/10.1371/journal.pone.0308081.g002

Discussions

The current hospital-based case-control study found a significant link between IL13 common gene polymorphisms (rs1881457 and rs180092) and MI risk. Furthermore, a substantial relationship was found between plasma IL13 levels and IL13 promoter variants (rs1881457 and rs180092). To the best of our knowledge, this is the first study to investigate the possible association of IL13 polymorphism with the development of MI in the Chinese population.

The role of IL13 in MI has been widely investigated in experimental model systems, but data are limited from human subjects. Dynamic IL13 levels have been demonstrated in mice models in relation to the time period after the incidence of the MI. The levels significantly increased and reached the highest levels on the third day and, after that, started declining until day seven, and then further increased [ 11 ]. However, other reports showed an upregulation of IL13 after the seventh day and continued until day fourteen [ 12 ]. In the present study, we observed about four folds of elevated IL13 in MI patients compared to healthy controls. Most of the patients enrolled in the present study just after the incidence of MI (within six hours), possibly that could be the reason for higher levels of IL13 in the plasma levels. Further, it has been well documented that elevated IL13 is essential for the cardio-protective effect [ 11 ].

There have been limited investigations into the role of IL13 common variants in the development of MI. The homozygous mutant of the rs20541 polymorphism has been linked to MI susceptibility in Greek Cypriot males [ 25 ]. In contrast, the Iranian population rs20541 failed to show such an association [ 24 ], which is consistent with the findings of the current study. In the Iranian population [ 24 ] and in the current study, heterozygotes of IL13 rs1881457 polymorphisms are associated with protection against MI development. Furthermore, the other polymorphism, rs1800925, demonstrated the protective nature of heterozygotes, which were highly prevalent in healthy controls. The precise mechanism by which the variant protects against the development of MI is unknown. Both IL13 (rs1800925 and rs1881457) polymorphisms are located in the intron region. Although these regions are typically not translated into proteins but can affect gene expression by altering splicing patterns, transcription factor binding, or RNA stability. Intronic variants can lead to alternative splicing, exon skipping, or the inclusion of aberrant exons, which may disrupt protein function and contribute to disease [ 30 – 32 ]. The two IL13 polymorphisms (rs1800925 and rs1881457) were possibly linked to plasma levels of IL13, and the intermediate production of IL13 cytokines may protect against the development of MI. The current study has several advantages over previous studies, including i) a larger number of participants and ii) genotyping and quantification of plasma IL13 in each sample.

HWE provides a theoretical framework for predicting the expected frequencies of genotypes in a large, randomly mating population where no evolutionary forces are at play. Any departure from this equilibrium can signal the presence of evolutionary forces, such as natural selection, mutation, migration, genetic drift, or non-random mating. We found a deviation of IL13 rs20541 polymorphism genotypes from HWE in this study. HWE deviation has been linked to factors such as population stratification, genotyping error, and selection pressure [ 33 ]. We recruited healthy controls from a similar ethnic group and geographical area, reducing the possibility of population stratification. Using stringent genotyping technology, we eliminated the possibility of error in the genotyping method. The deviation could be attributed to the selection pressure provided by different infections [ 34 – 37 ]. Importantly, IL13 rs20541 polymorphism has been linked with susceptibility to pulmonary tuberculosis [ 38 ], Schistosoma mansoni infection [ 39 ], hand, foot, and mouth diseases [ 40 ], further strengthening its possibility of the beneficial selection of genetic variants in the studied population.

The current study has revealed a significant role played by IL13 in the pathogenesis of MI. The genetic variations within the IL13 gene, specifically rs1881457 and rs180092, were observed to have varying levels of plasma IL13 and were found to offer protection against the development of MI. Although the precise mechanism linking the genetic mutants to plasma levels of IL13 is not yet fully understood, understanding the potential mechanism could provide valuable information on the underlying mechanisms of atherosclerosis and plaque instability, which are key factors contributing to MI. Identifying specific IL-13 polymorphisms associated with increased MI risk or severity could aid in risk stratification and the early detection of at-risk individuals. Furthermore, IL-13 genetic profiling may help personalize treatment strategies, such as anti-inflammatory therapies or targeted interventions to modulate immune responses, to improve outcomes and prevent recurrent cardiovascular events. Overall, the study of IL-13 gene polymorphism holds promise for enhancing risk assessment, prognosis prediction, and therapeutic interventions in the context of MI and cardiovascular disease management. Although the current study successfully demonstrated the significance of the IL13 genetic variant in MI, it is essential to acknowledge the limitations of the investigation. One limitation is that the Chinese population is a mixed group comprised of various ethnic and sub-population groups. The sample size used in the study may not be sufficient, which warrants further investigation with a larger sample size. Additionally, the study only examined three SNPs in the IL13 gene, so the role of other variants in the predisposition to MI remains unknown. Moreover, the current case-control study did not explore the mechanism of how the genetic variations in the IL13 gene alter the expression of mRNA or respective protein levels. Furthermore, the frequency distribution of IL13 rs20541 genotypes did not conform to the expectations of the HWE, thus rendering the interpretation of the relationship between haplotype and MI cautious.

Based on the observations of this study, future research should concentrate on elucidating the molecular mechanisms underlying the protective effect of IL13 genetic mutants and exploring how IL13 variants modulate gene expression and inflammatory pathways in cardiovascular tissues. Employing transgenic animal models and cellular systems can provide deeper insights into the functional impact of these polymorphisms. Large-scale epidemiological studies are necessary to confirm these findings across diverse populations and understand the gene-environment interactions that influence MI risk. Furthermore, investigating the potential of IL13 polymorphisms as biomarkers for risk stratification and personalized medicine could significantly impact cardiovascular care, leading to targeted prevention and therapeutic strategies.

The IL13 gene variants rs1881457 and rs180092 have been linked to varying plasma levels of IL13 and offer protection against the development of MI in the Chinese population. This finding adds to the existing body of research on genetic factors influencing MI risk, and it sheds light on the possible role of IL13 in cardiovascular health. To better comprehend the importance and applicability of these gene variants, further investigations are needed in diverse populations, including those with different ethnic backgrounds. These studies are crucial for validating the findings and determining the broader relevance of IL13 variants in the global context of MI prevention and treatment.

Acknowledgments

Authors would like to thanks all participants of the study for their voluntary contribution and involvement in the research.

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• 10. de Vries JE, Carballido JM. Interleukin-13. In: Henry HL, Norman AW, editors. Encyclopedia of Hormones. New York: Academic Press; 2003. p. 470–8.

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• Published: 05 August 2024

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study

• Dimitrios Andrikopoulos 1 ,
• Georgia Vassiliou 2 ,
• Panagiotis Fatouros 1 ,
• Charalampos Tsirmpas 1 ,
• Artemios Pehlivanidis 2 &
• Charalabos Papageorgiou 2 , 3  

BMC Psychiatry volume  24 , Article number:  547 ( 2024 ) Cite this article

Metrics details

Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals’ functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored.

The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection.

Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD.

Conclusions

This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Peer Review reports

Introduction

Attention Deficit Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric disorder typically emerging during childhood. Individuals with ADHD exhibit distinct personality traits and cognitive characteristics that impede their ability to regulate attention, behavior, and emotional responses [ 1 ]. The symptoms often manifest themselves as a persistent challenge to maintain focus, accompanied by hyperactivity or impulsivity, resulting in impairments in cognition and various life domains, including social, educational, and vocational among others [ 2 , 3 ]. Although ADHD primarily arises early in childhood, in most cases, it often persists into adulthood, exerting long-term effects on individuals’ functioning, productivity, and overall quality of life [ 4 , 5 ]. Although extensive research efforts have focused on childhood ADHD, where approximately 8% of children and 6% of adolescents worldwide are affected [ 6 ], researchers have only recently started investigating the disorder in adults [ 7 , 8 , 9 , 10 , 11 ], with an estimated global prevalence of 6.76% as reported by a 2022 study [ 12 ].

Although increased self-control in adulthood can help mitigate some of the symptoms associated with hyperactivity, difficulties in regulating emotional responses, maintaining focus, inferiority and feelings of impulsivity often persist beyond childhood [ 5 , 13 ]. Inattention in adults with ADHD may lead to a slower pace of thinking and decision-making, as they often become entangled in irrelevant details [ 14 ]. Meanwhile, hyperactivity is linked to an inner sense of restlessness, with symptoms such as talking too much or too loudly, pacing up and down, or even experiencing muscle strain when seated [ 14 ]. Overall, adults with ADHD encounter several challenges in their personal, social, academic and vocational lives [ 2 , 14 , 15 ]. Their struggle to maintain focus and complete tasks renders them less productive at work [ 16 ], leading to work loss and substantial economic repercussions [ 17 ]. Furthermore, adults with ADHD are more prone to addictive behaviors (e.g. substance abuse disorders) [ 18 , 19 ], while they also have a higher risk of injury and serious accidents, particularly stemming from risky driving behavior [ 20 , 21 ].

In light of the profound impact ADHD has on individuals throughout their lives, diagnosing ADHD in adults is a crucial step towards managing and treating the disorder, ultimately enhancing their overall well-being and quality of life. The current standard for diagnosing ADHD in adults relies on a combination of psychometric questionnaires, in-person interviews with the individual and/or their parents to gather a comprehensive clinical history, and clinical assessments to identify the presence of specific symptoms [ 22 ]. Although widely used, this process has several limitations that can significantly affect its overall effectiveness [ 1 ]. Firstly, the current diagnostic process primarily relies on face-to-face assessments, which presents a significant limitation in terms of accessibility. Additionally, it can be time-consuming and economically burdensome, as multiple sessions may be required [ 22 ]. Furthermore, obtaining ancillary information to establish ADHD onset in childhood, which in most cases extends to adulthood, relies on subjective recall and may not always be available [ 23 ]. Moreover, recognizing the occurrence of symptoms is subject to the clinician’s interpretation, introducing potential bias into the diagnosis [ 24 , 25 ]. Finally, ADHD symptomatology does not uniquely correlate with an ADHD condition and can overlap with symptoms of other psychiatric conditions [ 26 ]. It is estimated that approximately three out of four adults with ADHD suffer from at least one additional mental disorder such as depression, anxiety, personality disorders, or substance abuse [ 14 , 24 , 25 ]. The non-distinct nature of ADHD symptoms and the presence of psychiatric comorbidities introduce an additional level of uncertainty and complexity into the current diagnostic process [ 24 , 26 , 27 ].

In an effort to address some of the limitations associated with the standard of care interview-based diagnostic approaches, clinical experts working with ADHD patients have sought additional tools to provide objective data and facilitate more informed decisions [ 28 , 29 ]. In this context, evaluations of the neuropsychological and neurophysiological aspects of the disorder have emerged as promising tools, though they have attracted varying levels of scientific interest. On the one hand, neuropsychological evaluations, which are more popular, have been employed to uncover impairments in various cognitive functions [ 29 ], serving as objective indicators of ADHD [ 28 ]. One such evaluation method is the Continuous Performance Tests (CPTs), which measure sustained attention and vigilance in a task-oriented computerized setting [ 1 , 28 , 30 , 31 ]. Additionally, Stroop tests are commonly utilized to assess selective attention, interference and inhibitory control [ 31 , 32 , 33 , 34 ]. While the value of CPTs has been demonstrated in child populations [ 35 , 36 ] and several studies [ 37 , 38 , 39 ] have highlighted the potential of Stroop tests for adults with ADHD, the utility of neuropsychological evaluations for diagnosing ADHD in adults is limited, as conclusive results cannot always be obtained [ 28 , 29 , 40 , 41 , 42 , 43 ]. The primary drawback of this approach is that it targets specific cognitive deficits, which may fail to capture impairments when multiple cognitive domains are affected, as is often the case for ADHD [ 28 , 44 ].

On the other hand, clinicians have recently focused on the neurophysiological impact of the disorder, including brain and Autonomic Nervous System (ANS) functioning [ 5 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Research has shown that ADHD is associated with alterations in brain function that may lead to cognitive impairment [ 49 ]. These alterations can be captured by imaging techniques capable of measuring functional brain activity (e.g. Electroencephalography (EEG), Magnetoencephalography, and Magnetic Resonance Imaging (MRI)) [ 52 , 53 , 54 , 55 , 56 , 57 ]. Recent studies [ 52 , 53 , 54 , 55 , 56 , 57 ] have underscored the correlation between functional brain activity, primarily captured by Functional Magnetic Resonance Imaging and EEG methods, and an underlying ADHD condition. However, such methods can not be used for large-scale testing and deployment, as MRI and EEG setups tend to be expensive, time-consuming, and obtrusive data collection approaches. Consequently, integrating these methods into current clinical practice may prove inefficient and impractical [ 58 ].

Besides its impact on brain function, studies have demonstrated that ADHD also affects the functioning of the Autonomic Nervous System (ANS) [ 50 ], which controls involuntary physiological processes [ 59 , 60 ]. Within the context of ADHD, several studies [ 5 , 46 , 47 , 48 , 50 ] have suggested dysregulation in the part of ANS responsible for controlling arousal. This dysregulation may be linked to the behavioral challenges experienced by individuals with ADHD [ 47 , 50 ]. Arousal of the ANS can be gauged in real-time by measuring physiological data [ 61 ], such as electrodermal activity (EDA), heart rate (HR), heart rate variability (HRV) and skin temperature (ST) [ 13 , 62 , 63 , 64 , 65 , 66 ]. Several studies have proposed that these data modalities [ 45 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ] may carry useful information and offer valuable insights into an underlying ADHD condition. However, there has been limited focus on adult ADHD [ 45 , 67 , 68 , 69 , 70 , 72 ], with most studies involving relatively complicated setups [ 45 , 68 , 71 , 72 , 73 , 74 , 75 ] that hinder scalability and fail to leverage wearable sensors that enable multimodal data capturing in an unobtrusive and continuous manner [ 58 ].

In summary, neuropsychological and neurophysiological evaluations provide complementary and valuable information, offering different insights into ADHD conditions. Both approaches appear beneficial for clinicians and have the potential to address the limitations and complement the current symptom-based diagnostic process for adults with ADHD. Given that that research has demonstrated that ADHD is characterized by dysregulation in controlling physiological arousal, exploring the distinct physiological expressions of adult ADHD patients during potentially arousing conditions, such as neuropsychological tests, is of particular interest. Neuropsychological tests can reliably elicit ANS responses related to attention, cognitive control, and emotional regulation [ 76 , 77 , 78 , 79 , 80 ]. Therefore, the main focus of this study is to assess the potential capability of physiological data (i.e. EDA, HRV, ST) to distinguish ADHD adults, when collected during a series of neuropsychological evaluations (i.e. Stroop tests).

The selection of these specific data modalities serves multiple purposes. Firstly, we aim to explore the potential of physiological markers as standalone indicators for ADHD. To the best of our knowledge, there is limited research exploring the connection between physiological data and adult ADHD compared to neuropsychological assessments. Secondly, we seek to assess the utility of unobtrusively and continuously collected data, using scalable wearable technology. Such approaches would be more feasible for real-world applications and more likely to be integrated into current clinical practice compared to brain activity evaluations. Lastly, even though including additional data modalities (e.g. metrics from neuropsychological evaluations) in our analysis would be interesting, at the same time, it would significantly increase the complexity and dimensionality of our models, potentially compromising the robustness of the results and necessitating a more extensive dataset.

Therefore, this study employs EDA, HRV and ST data collected from a wrist-worn sensor during neuropsychological evaluations to investigate their potential utility in the diagnostic process for adult ADHD. For this purpose, we leverage the Feel Digital Precision Medicine Platform (DPMP), which enables continuous, real-time, and unobtrusive data capturing through a single wearable device. Furthermore, the platform offers signal processing and feature extraction capabilities, which are also utilized in this work. The main aims of this work are to: (i) evaluate the feasibility and utility of integrating physiological data, captured through a cost-effective and unobtrusive wearable sensor, into the current diagnostic framework for adult ADHD, (ii) develop and validate the performance and diagnostic capability of a machine learning (ML)-based pipeline in distinguishing ADHD adults, leveraging our DPMP and (iii) assess the complementary value of physiological data in providing a comprehensive understanding of ADHD symptoms.

Study design and participant recruitment

This was an observational, case-control study that included two groups of participants, an experimental and a control one. Participants for this study were recruited in collaboration with the First Department of Psychiatry of the Athens School of Medicine. The experimental group (EG) consisted of adult patients diagnosed with ADHD who were evaluated by healthcare professionals of the clinic. The assessment process for potential candidates in the EG included completing a questionnaire that consisted of questions collecting demographic, educational, occupational, and clinical data. This was followed by a battery of screening instruments, including a modified version of the Barkley Adult ADHD Rating Scale (BAARS) [ 81 ], the Autism-Spectrum Quotient (AQ) [ 82 ] and the Empathy Quotient (EQ) [ 83 , 84 ]. Additionally, the semi-structured Diagnostic Interview for ADHD in Adults (DIVA) was administered to all patients [ 85 , 86 ]. Complementary information was also collected from relatives. The second step of the assessment process entailed a comprehensive two-hour psychiatric examination by an experienced psychiatrist in the Psychiatry Department. This examination aimed to explore the presence of lifetime psychopathology using the Mini-International Neuropsychiatric Interview (M.I.N.I.) Greek version [ 87 ], which consists of a short structured interview assessing patient symptoms and signs against diagnostic criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [ 3 ]. Overall, the main inclusion criteria for the EG candidates were: (i) ADHD diagnosis; (ii) age \(\ge\) 18 years; (iii) IQ>70; (iv) proficient in Greek; and (v) willing and able to give informed consent for participation. On the other hand, the main exclusion criteria were: (i) major psychiatric disorder (e.g., schizophrenia, bipolar disorder); (ii) significant neurological conditions (e.g., epilepsy, traumatic brain injury); and (iii) severe learning disabilities. The final diagnosis regarding the presence of ADHD was based on DSM-5 criteria. Furthermore, candidates for the control group (CG) included neurotypical adults who had not undergone any screening for the presence of ADHD. Control participants were required to meet the same inclusion and exclusion criteria with EG participants, apart from the ADHD diagnosis.

Eligible candidates for both groups were then informed about the study scope, aims, and experimental process and were invited to participate. Those who expressed interest in joining had to select their preferred time slot and schedule an in-person experimental session. During this session, held at the First Department of Psychiatry of the Athens School of Medicine, participants were provided the Feel physiological data monitoring device (see Data acquisition, preprocessing and feature extraction infrastructure  section). This device unobtrusively and continuously collected participants’ data, as they progressed through the various steps of experimental protocol. The flowchart of the participant screening and recruitment process is illustrated in Fig.  1 .

Flowchart for participant screening and recruitment

Experimental setup

The experimental protocol for both the EG and CG, consisted of a series of six computerized Stroop tests during which participants were required to answer as quickly and accurately as possible: three color-word Stroop tests, two number Stroop tests, and one emotion Stroop test. At the same time, their physiological data was collected via the Feel Monitoring Device. The Stroop tasks were developed in Python programming language [ 88 ] and were presented using a Windows personal computer and a 17-inch flat screen. Participants provided their responses for the number and emotion Stroop tests using the computer keyboard, while the number of correct and wrong responses for the color-word Stroop tests were manually recorded by the researcher executing the experiment. The start and end times for each test were manually input by the researcher through the data collection mobile application (see Data acquisition, preprocessing and feature extraction infrastructure  section). This ensured that the physiological data recorded by the wearable device was synchronized with the task events. In more detail, each test was designed as follows:

Color-Word Stroop : This step consisted of three different tests labeled as C1, C2 and C3. In C1, participants were instructed to name a color word depicted in a neutral color (e.g. the word “red” presented in a black-colored font). In C2, participants were presented with differently colored symbols and asked to identify the color of the font (e.g. symbol XXX presented in a blue-colored font). In C3, color words were depicted in a different color, which the subjects were asked to identify, ignoring the word itself (e.g. the word “red” presented in a blue-colored font). Each of the color-word Stroop tests lasted approximately one minute, during which participants were prompted to give as many answers as possible from a list of 200 words. The total duration of the session was 3 minutes.

Number Stroop : This part consisted of two tests labeled as N-S (Number-Size) and N-V (Number-Value). In both tests, participants were presented with pairs of numbers, each one displayed in a different font size. In the congruent condition, the number with the higher value was displayed in the larger font size, while in the incongruent condition, the number with the higher value was displayed in the smaller font size. During the N-S test, participants were asked to identify the number with the larger font size, while in N-V, they were asked to identify the number with the larger value. Each test included 100 pairs of numbers. This series of tests did not have a fixed duration and test duration depended on the time required by each participant to respond to all pairs of numbers. The average duration for each test was 3.9 minutes, with the total duration of this session being on average 7.8 minutes.

Emotion Stroop : This part consisted of a single test labeled as E. In the Emotion Stroop task, participants were instructed to name as quickly and accurately as possible the color of the presented words, while ignoring their meaning, which could be either neutral (e.g. tower, fork, etc.) or negative (e.g. crime, traitor, annoying, etc.). This session included 30 neutral and 30 negative words, presented in 4 different colors. The total number of trials was 240. The number of words displayed was fixed, and like the Number Stroop step, the total test time varied among participants. The duration of this session was on average 4.3 minutes.

Data acquisition, preprocessing and feature extraction infrastructure

For the purposes of this study, we have leveraged the capabilities and functionalities of our proprietary DPMP. The DPMP is a remote patient monitoring platform designed for unobtrusive, passive, and continuous monitoring and analysis of neurological and psychiatric patient data. The platform facilitates the collection, curation, and processing of multimodal data, with a focus on the discovery, extraction, and validation of metrics and biomarkers for various use cases in the fields of neurology and psychiatry. In this study, the following components of the platform were employed:

Feel Monitoring Device : The wrist-worn device features five embedded sensors and connects to the user’s smartphone via Bluetooth to accommodate continuous and passive data collection and transmission (Fig.  2 A). The device collects psychophysiological, activity, and ambient conditions data, which have proven to be relevant and highly valuable for many neurological and psychiatric applications [ 89 , 90 , 91 ]. More specifically, the following data modalities are captured by the wearable device: EDA, HR, HRV, ST, 9-axis Inertial Measurement Unit, ambient temperature, and humidity. The focus of this work is on the first three data modalities.

Feel Mobile app : The Mobile app (Fig.  2 B) connects to the Feel Monitoring Device, collects data, and transfers it from the device to the secure cloud-based processing infrastructure. The app allows users to control the start/stop of the data acquisition session and annotate specific timestamps during the acquisition with custom labels, indicating the start and end of each session step. These labels are used to identify data segments associated with each Stroop test. The Feel Mobile App is available for Android and iOS platforms.

Digital Endpoints Development & Biomarker Discovery Infrastructure : This sophisticated infrastructure integrates data curation, signal processing, time series analysis, and pattern recognition tools and frameworks for noisy artifact detection and denoising, signal annotation and segmentation, and feature extraction purposes. The processing pipeline for collected time series commences with the denoising step, where noisy signal parts are identified and the impact of noise artifacts is appropriately mitigated. Subsequently, time segments of the collected signals corresponding to each of the six Stroop tests are identified, using the timestamp annotations that have been inserted from the Feel mobile app. Finally, a wide variety of proprietary features are calculated for each segment of the acquired time series. These features range from simple statistical metrics to more complex and highly nonlinear ones reflecting the morphological, frequency, repeatability, and predictability characteristics of the signals. Specifically, 103 features have been extracted from the EDA signals, 77 from the HRV signals, and 11 from the ST signals, resulting in a feature set of 191 features available for our analysis.

The feel monitoring device ( A ) and the feel mobile app ( B )

Statistical analysis and decision pipeline

We employed descriptive statistics to analyze the demographic characteristics of the two groups. Additionally, we compared the age distribution of the two groups using an independent t-test [ 92 ] and assessed the gender ratio using the \(\chi ^2\) test [ 93 ]. Leveraging the features extracted by the Biomarker Discovery infrastructure, we conducted a two-step univariate feature analysis to identify the best subset of the collected data that, when fed into an ML pipeline, would be able to distinguish between the experimental and control groups. An overview of the data processing and decision pipeline is shown in Fig.  3 .

Overview of the data processing and decision pipeline

Firstly, within each group (i.e. EG and CG), we investigated the variance of each feature at the individual level. To accomplish this, we performed a series of nonparametric pairwise statistical tests for every pair of Stroop tests using the Wilcoxon signed-rank test [ 94 ]. Utilizing this method, a univariate analysis was performed in order to determine the proportion of total features showing a statistically significant difference between pairs of Stroop tests. A low ratio suggests minimal impact at the individual level for the specific combination of tests, allowing paired data to be treated as uncorrelated. Consequently, the feature data from two Stroop tests could be combined and analyzed together under a common test label. The second step of our statistical analysis involved identifying the data subsets that yielded more profound differences across features between the two groups (CG and EG). To achieve this, for each type of test (either the original tests or the bundled ones determined by the first level of our analysis), we conducted a univariate feature analysis using Kolmogorov-Smirnov statistical tests [ 95 ]. Subsequently, the subset of our dataset corresponding to the Stroop tests that yielded a higher percentage of features with statistically significant differences between the two groups were selected as inputs to the learning algorithms. For both levels of the statistical analysis, the cutoff P -value used for statistical significance was set at 0.1. For both statistical tests, we utilized the corresponding implementations in the SciPy Python package [ 96 ].

Having built a more informative data subset, the decision pipeline consists of three main parts: i) informative point selection, ii) feature selection, and iii) classification. The input of this pipeline is a dataset that includes the features identified as the most informative from the previously discussed statistical analysis. Separate datasets were constructed both for each of the three signals (i.e. EDA, HRV, and ST) and for their fusion. In the latter case, the feature space is constructed by concatenating the features from all signals.

Informative point selection

We enhance the separability of the two groups by identifying and discarding the least informative points [ 97 ]. Towards this, we utilize a modified version of the k-Nearest Neighbors algorithm (KNN), namely the Informative KNN (i-KNN) [ 98 ]. The least informative points are defined as data points that are (isolated) instances of one class in a n -dimensional feature space that reside in a neighborhood with a high density of points from the opposite class. For each data point \(\textbf{x}_i\) with class \(y_i\) we select k nearest neighbors and calculate for each neighbor \(\textbf{x}_j\) , the associated informativeness given by Eq. ( 1 ) [ 98 ]:

where d is a distance function defined as \(d(\textbf{x}_j,\textbf{x}_i)=e^{-|\textbf{x}_j-\textbf{x}_i|^2}\) , \(\eta\) represents the ratio of neighbors that have the same class \(y_j\) as \(\textbf{x}_j\) and \(C_i\) is a normalization factor such that \(\sum \nolimits _j P(\textbf{x}_j|\textbf{x}_i)=1\) . The function \(\Lambda (\textbf{x}_j)\) can be interpreted as a weighting parameter, which quantifies how far apart the point \(\textbf{x}_j\) lies from the rest of the \(k-1\) neighbors of \(\textbf{x}_i\) which have a different label than \(y_j\) . This weighting parameter is defined in Eq. ( 2 ).

with \(\delta _{y_j,y_n}\) being the Kronecker delta which equals 1 only if \(y_j=y_n\) and 0 otherwise. We rank the k neighbors according to their informativeness (Eq. ( 1 )) and classify \(\textbf{x}_i\) using the majority vote of the labels from the M neighbors with the highest informativeness. This classification decision is denoted as \(\tilde{y}_i\) . We perform this process for \(k\in \{3,5,7,10\}\) and for each k , we use \(M\in [1,k]\) (25 possible combinations in total). For each value l for k and m for M , we compare the real label \(y_i\) with the decision \(\tilde{y}_i\) from the most informative neighbors and assign a binary score \(s(\textbf{x}_i,l,m) = 1-\delta _{y_i,\tilde{y}_i}\) . We rank every vector \(\textbf{x}_i\) using the average \(\bar{s}(\textbf{x}_i)\) of the scores \(s(\textbf{x}_i,l,m)\) , which is defined as shown in Eq. ( 3 ). A higher value of \(\bar{s}(\textbf{x}_i)\) implies that \(\textbf{x}_i\) is less informative for its corresponding class \(y_i\) and could thus be discarded. In this study, we examined two input datasets to the decision pipeline: i) the full dataset and ii) the reduced dataset after discarding the worst 5% of the data points, as described above. The implementation of this algorithm was performed in Python utilizing the respective packages [ 99 ].

Feature selection

Regarding feature selection, we utilize the relative Median Absolute Deviation (rMAD, Eq. ( 4 )) and the relative Interquartile Range (rIQR, Eq. ( 5 )) as lower bounds to discard low-variance features that have minimal impact on the separability of the two groups. We have selected these two quantities as thresholds since they are robust to outlier values.

In Eqs. ( 4 ) and ( 5 ), \(\textbf{f}_i\) refers to a feature vector, i.e. a list of the values of the feature i . Furthermore, \(\text {med}(\textbf{f}_i)\) in Eq. ( 4 ) is the median and \(p(\textbf{f}_i,q)\) in Eq. ( 5 ) represents the q -th percentile of \(\textbf{f}_i\) .

Both rMAD and rIQR have been normalized respectively. For rIQR if \(p(\textbf{f}_i,0.25)=0\) , then \(p(\textbf{f}_i,0.75)\) is used in the denominator.

Classification

For the classification step, we first utilize Principal Component Analysis (PCA) to construct linear combinations of the features that best capture the variability, and thus the useful information, of the dataset. Then, we employ four widely used classification algorithms: Logistic Regression (LR), KNN, Random Forests (RF) and Support Vector Machines (SVM) [ 99 ]. For each algorithm, a set of classifier-specific hyperparameters along with the rMAD and rIQR thresholds are tuned to achieve the best accuracy. This tuning process is conducted using a 100-fold cross-validation scheme, where we split the data into a 70%-30% train-test stratified split. It is noteworthy that whether the full or reduced dataset was used was not treated as a hyperparameter, ensuring that each algorithm utilizes the same data points across all validation folds. For each input dataset, we have the option to keep all input data, or discard the least informative points and evaluate the output of the decision pipeline on the obtained accuracy, sensitivity and specificity of the test sets.

Ethical considerations

This observational study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the National and Kapodistrian University of Athens, 1st Department of Psychiatry, Eginition Hospital. Prior to their participation in the study, all individuals were fully informed about the study scope, objectives, methodology, and components, and they provided written informed consent. They were also informed that their participation was voluntary and they could withdraw from the study at any time. Each participant was assigned a unique identifier upon providing informed consent. The collected data was pseudonymized, and no personal identifiers were used during data processing and reporting of results. Furthermore, participants did not receive any monetary compensation for their involvement in the study.

Sample characteristics

A total of 95 individuals participated in this study, with 58 in the control group and 37 in the ADHD group. There were no difference in the age ( \(35.18\pm 11.14\) vs \(32.58\pm 11.39\) , \(t(93)=1.06\) , \(P=.29\) ) and gender ratio (Male/Female, 32/26 vs. 24/13, \(\chi ^2(1, 95)=0.5221\) , \(P=.46\) ) between the two groups. Out of the 95 participants who joined the study, 4 did not complete the experimental process due to time limitations or personal discomfort issues. Additionally, physiological data was not retrievable for 15 participants, mainly due to protocol execution errors, mobile app malfunctions, and internet connectivity issues. The remaining participants (i.e. 44 CG and 32 EG participants) successfully completed the experimental process. For this group of participants, there were no significant differences in the age distributions (36.23 ± 9.36 vs. 33.26 ± 12.18, \(t(74)=1.18\) , \(P=.24\) ), as well as for the gender ratio (Male/Female 29/15 vs. 21/11, \(\chi ^{2}(1,76)<10^{-4}\) , \(P=.97\) ). Finally, 23 out of 32 EG participants were receiving ADHD medication.

Feature analysis

Firstly, we explore the variability of the extracted features within the CG and EG for all combinations of Stroop tests in the experimental process, employing the Wilcoxon signed-rank test. In Fig.  4 , we showcase the ratio of the total features that show a statistically significant change between every pair of tests for the CG and EG in the left and right plots, respectively. The x and y -axis in each plot indicate the two tests involved in the comparison. For example, the element in the first row and second column corresponds to the output of the comparison between the C1 and C2 Stroop tests. Accordingly, the elements on the diagonal are all equal to 0, since these correspond to the output of the comparison of a Stroop test with itself. It becomes evident that a clear pattern exists involving Stroop tests of the same type (i.e. Color-Word, Number, or Emotion). For such cases, the percentage of features showing a statistically significant change between the corresponding Stroop tests within both CG and EG is much lower (light-colored regions) than for cases with Stroop tests of different types being compared (dark-colored regions). Therefore, data from tests C1, C2, and C3, as well as N-Size and N-Value, can be bundled together under the new label “C” and “N”, respectively.

Percentage of features that differ significantly ( \(P\le .1\) ) within the control (CG; left) and experimental (EG; right) groups, per every couple of Stroop Tests

After bundling together the Color-Word and Number Stroop tests, we now have three types of tests: Color (C), Number (N), and Emotion (E). As the next step of the analysis, we explore how much the extracted features differ between the CG and EG for the three types of tests. For each case, the percentage of total features that showed a statistically significant difference between the CG and EG is illustrated in Fig.  5 A. Moreover, the respective percentages per data modality (i.e. EDA, HRV and ST) are shown in Fig.  5 B. As can be seen, the dataset consisting of N tests shows a much larger percentage of features differing between the two groups compared to the C and E tests, with more than half of the features being significantly different. Therefore, in the rest of this work, we focus solely on the subset of our dataset that includes features only from the N test.

Percentage of features showing statistically significant differences ( \(P\le .1\) ) between the control (CG) and experimental (EG) groups for the Emotion (E), Color-Word (C), and Number (N) Stroop tests in total ( A ) and per data modality ( B )

Decision pipeline

For the evaluation of the performance of the decision pipeline, three metrics have been utilized: accuracy, sensitivity, and specificity. The former corresponds to the ratio of the correctly identified instances (true CG + true EG) to the total number of instances and reflects how many instances were correctly classified in total. Sensitivity assesses the capability of the model to correctly identify positive (true EG) instances and is calculated as the ratio of correctly identified EG instances to their total number. Similarly, specificity evaluates the same aspects for the CG instances and is extracted as the ratio of correctly identified CG instances to their total number. Tables  1 , 2 , 3  and 4 present these metrics for the different classification models employed for each physiological signal (i.e. EDA, HRV, ST) and their fusion, utilizing data captured during the Number Stroop tests. In all cases, the results for both the full and the reduced datasets are provided.

Electrodermal activity (EDA)

Table 1 displays the accuracy, sensitivity, and specificity of different classification models using both the original and reduced feature sets from EDA data. It should be noted that in most cases, all metrics improve when the least informative points are discarded, and the reduced dataset is utilized. More specifically, all performance metrics improve for the LR and RF algorithms, while only accuracy and sensitivity are enhanced for KNN and SVM. Notably, for SVM, the capability to identify CG instances is significantly reduced (specificity=40%). Interestingly, noisy data points appear to play a crucial role in constructing the support vectors, as the performance of the SVM model in terms of sensitivity and specificity is reversed when these points are discarded. The LR model achieves the highest accuracy and sensitivity but performs relatively poorly in identifying CG data points, with specificity below 60%. On the other hand, the RF classifier achieves a more balanced performance, with all performance metrics ranging from 70% to 74%.

Heart rate variability (HRV)

Similar to the previous case, the performance of all models is generally enhanced when the most informative data points are retained (Table 2 ). This becomes also evident for the SVM case, at least with regard to the accuracy and sensitivity metrics, which are significantly improved. This time, the KNN algorithm achieves the most balanced performance, with all metrics hovering around 78%. The RF algorithm also demonstrates a balanced performance, albeit with slightly lower metrics ranging from 73% to 77%. On the other hand, the capability of the LR model to identify the CG instances is the best among all models with a specificity of 82.2%, while also showing similar performance to the RF and KNN models with respect to the other metrics. Finally, the SVM achieves the best sensitivity, which is close to that of the KNN, but shows poorer performance in identifying CG data points. Similar to the EDA case, the SVM is notably influenced by the presence of the least informative data points.

Skin temperature (ST)

In the case of the ST signal (Table 3 ), the LR algorithm achieves an accuracy of 72% with a sensitivity of 89%. However, there is a notable increase in misclassification of CG participants, with a specificity close to 55%. The KNN and RF models exhibit similar performance, with more balanced metrics compared to the other models. On the other hand, the SVM algorithm demonstrates the least balanced performance, successfully identifying most of the EG data (sensitivity: 85%) but misclassifying the majority of CG data (specificity: 40%). Retaining the most informative data points affects the performance of all algorithms, with the SVM being particularly impacted.

Data modalities fusion

Finally, when fusing data from all physiological signals together, we effectively expand the feature space and utilize the information from each signal simultaneously. As shown in Table 4 , this leads to improved performance for all classifiers. Interestingly, the SVM classifier, which exhibited the least balanced performance when each signal was utilized separately, now shows almost identical capability to identify both the CG and EG data well, with sensitivity and specificity close to 82%. The rest of the classifiers demonstrate similarly balanced performance, but with slightly inferior performance metrics, except for the sensitivity of the LR model, which reaches sensitivity values up to 82.4%. Finally, discarding some of the noisy data points results in a boost of the performance for all classifiers, with the respective performance metrics for the SVM model ranging from 81.4%-81.9%.

In this study, we investigated the potential of utilizing physiological data for the detection of adult ADHD. Specifically, we focused on three physiological signals: EDA, HRV, and ST, acquired using a wrist-worn sensor during a series of Stroop tests. Our main hypothesis was that these tests would elicit ANS responses that differ between neurotypical adults and adults with an ADHD condition. One of our primary objectives was to evaluate the performance of unimodal models using each physiological signal separately. Additionally, we explored the potential of the complementarity of information, provided by these data modalities and constructed a multimodal model combining them simultaneously, to enhance the overall model capabilities. Using our proprietary data processing infrastructure, we extracted a series of features for each physiological signal and employed them in ML algorithms for a classification task aimed at identifying neurotypical and ADHD populations. Our results supported that these physiological signals carry significant information to be correlated with underlying ADHD conditions. Specifically, we developed multimodal ML models that achieved up to approximately 82% sensitivity and specificity. In the following, we will delve into the interpretation and implications of these results.

Early in our investigation, we explored the ANS responses, as captured by the available physiological data, across the six different Stroop tests, and between the two participant groups. The aim was to identify the subset of our data that could yield optimal separability between the ADHD and the neurotypical populations. Initially, we analyzed each of the two participant groups separately. Through the use of Wilcoxon signed-rank tests, we found that each type of Stroop test (C, N and E) yields sufficiently distinct responses. More specifically, within the three Color-Word and the two Number Stroop tests, the average variation of the feature distributions was significantly lower than comparing feature distributions from different types of Stroop tests (i.e. Color-Word vs Number, Number vs Emotion and Color-Word vs Emotion). Therefore, for the Color-Word and Number Stroop tests, the impact of the specific test at the individual level was minimal, and thus, we merged the corresponding data under a single unique test label. Utilizing these new groupings, we gained a second insight regarding the efficiency of the Stroop tests in triggering ANS responses sufficient to distinguish between the two groups. Our results suggest that the Number Stroop tests yield far more features showing a statistically significant ( \(P\le .1\) ) difference between the CG and EG, than Color-Word or Emotion Stroop tests. In more detail, for the Number Stroop, approximately 52% of the total features exhibit a significant difference, while for the Color-Word and the Emotion Stroop, approximately only 10.4% and 3.6% of the total features, respectively, show significant differences. What is more, the percentage of statistically significant features is similar across the different data modalities, indicating their potential value in distinguishing between the CG and the EG. Specifically, statistically significant differences were primarily observed in the statistical time domain and morphological features of the EDA (e.g. mean, standard deviation, first difference, number of EDA responses, EDA responses characteristics, etc.), time and frequency domain features of the HRV (e.g. SDNN, RMSSD, pNN50, power in low and high frequency bands, etc.) and the time domain features of the ST (e.g. standard deviation, first and second difference, etc.).

The sensitivity of the Number Stroop test to group differences in ANS response compared to other Stroop tasks is an intriguing finding that warrants further discussion. This phenomenon can potentially be attributed to the inherent complexity of the numerical Stroop task, which involves comparing two stimuli, the numerical value, and the physical size, exhibiting higher cognitive load and complexity than the color-word Stroop task [ 100 ]. Therefore, higher ANS activation is expected during numerical tasks since they require substantial working memory and executive function resources [ 101 ]. Additionally, previous research suggests that the processing of numerical and physical magnitudes relies on a semantic abstract and non-verbal magnitude representation, which may facilitate or interfere with cognitive processes without the confounding effects of reading skill and articulatory speed commonly associated with ADHD [ 102 ]. Consequently, numerical Stroop tasks may be more effective in triggering cognitive challenges associated with ADHD conditions. Finally, the nature of the stimuli in the numerical Stroop task, where both relevant and irrelevant tasks involve the comparison of magnitudes, may lead to similar brain activations and require constant active control mechanisms to inhibit the irrelevant task [ 103 , 104 , 105 ]. As a result, it may be more difficult to modulate the task conflict, leading to increased ANS activation.

A later step of our analysis identified data points carrying significant information for their respective classes. This data mining procedure [ 106 ] has become essential when dealing with real-world data [ 106 ] and aims to filter out the least informative data points, thereby improving input quality for prediction models [ 106 ]. In this study, we specifically considered outlier data points from the neurotypical population. Given that participants in the CG were not previously assessed for potential ADHD symptomatology, ADHD-like characteristics in their physiological data might have emerged during the Stroop tests, despite their labeling as CG. In contrast, participants in the EG underwent assessment by a clinical expert, during which their inclusion in the EG group was verified, and, thus, their physiological expressions were representative of an ADHD condition. Among various methods for data filtering [ 106 ], we employed an extension of the k-Nearest Neighbors algorithm (i-KNN), which incorporates a distance metric that considers the class labels of the neighboring points [ 97 , 98 ]. The impact of this filtering method was assessed by comparing the performance of prediction algorithms separately for the full and the reduced dataset. Based on the nature of the i-KNN algorithm, we anticipated a larger impact on prediction algorithms exploiting the geometrical properties of data [ 106 ]. To demonstrate the effect on the prediction capabilities of the different models, we utilized balanced accuracy, defined as the mean value of sensitivity and specificity. Our results indicate a consistently positive mean relative change in balanced accuracy across all signals separately and their fusion when using the reduced dataset. Notably, the relative change is more pronounced for the RF (15.21%), KNN (14.23%), and SVM (17.72%) models, which leverage the high-dimensional structure of the data. In contrast, the LR models, which rely on statistical properties, exhibit a smaller mean relative change (9.57%). Therefore, the rest of this section focuses on the reduced dataset.

The primary focus of this work was to explore the performance of models leveraging each physiological signal separately (i.e., unimodal), as well as the fusion of them (i.e. multimodal). In the following, we discuss the findings regarding the performance of these models and compare their effectiveness. Starting from the unimodal models, significant variations in performance were observed across different physiological signals and performance metrics. Figure  6 illustrates the accuracy, sensitivity, and specificity of each physiological signal and their fusion for each tested algorithm, focusing on the reduced dataset. Notably, EDA and ST exhibit equal or better sensitivity compared to HRV, while HRV shows higher specificity. The most interesting observation occurs when the fusion of signals is utilized. In this case, more complex models can be built by exploiting information from all signals simultaneously. Therefore, multimodal models demonstrate more balanced performance, also achieving higher sensitivity and specificity across all algorithms except for KNN (for the HRV dataset), compared to the unimodal ones, which exhibited either high sensitivity or high specificity. This suggests that combining information from multiple signals simultaneously improves model performance, highlighting the importance of considering multimodal approaches in such studies. Independent of the classification algorithm, we can argue that multimodal information from EDA, HRV and ST yields at least equal or better performance than unimodal for both neurotypical and ADHD adults. This could be attributed to the fact that the selected data modalities convey information about various aspects of the ANS [ 5 , 13 , 61 , 63 , 64 , 65 , 66 ] and better capture the multifaceted effect of ADHD in autonomic functioning [ 107 ]. Further investigation into the underlying mechanisms driving these observations could provide valuable insights into the pathophysiology of ADHD and inform the development of more effective diagnostic and therapeutic strategies.

Test accuracy, sensitivity and specificity of the LR (top left), RF (top right), KNN (bottom left) and SVM (bottom right) models for EDA (orange line), HRV (light blue line), ST (light green line) and Fusion (black line), when the reduced dataset is utilized

Focusing on the multimodal models, a comparison across different algorithms was conducted to evaluate their performance. In Fig.  7 , we present the accuracy, sensitivity, and specificity obtained for the LR, RF, KNN, and SVM algorithms for the multimodal case. Our analysis reveals that the SVM algorithm consistently outperforms the others, particularly in terms of accuracy and specificity. Although it is only slightly worse than LR in terms of sensitivity, SVM achieves the most balanced result overall. The superior performance of SVM in this context can be attributed to its ability to effectively utilize multimodal data and learn from its complex structure. By optimizing the utilization of multimodal information, SVM demonstrates proficiency in identifying both neurotypical and ADHD adults.

Test accuracy, sensitivity and specificity for the LR (red line), RF (green line), KNN (blue line) and SVM (orange line) models, when using all data modalities

Overall, the findings of this study validate our hypothesis regarding ANS responses and their association with an underlying ADHD condition. Specifically, we demonstrated that ANS responses triggered by neuropsychological evaluations and captured by the EDA, HRV and ST physiological signals can effectively distinguish between ADHD and neurotypical adults. The most promising results were observed when following a multimodal data approach combining all signals captured during the execution of the Number Stroop tests. The SVM classification model exhibited the most balanced performance with regards to sensitivity and specificity (81.4% and 81.9%, respectively) highlighting the potential to recognize both ADHD and neurotypical adults. Future studies could further explore the potential of integrating additional sensor-collected data modalities (e.g. ambient conditions, accelerometer data, etc.), along with participants’ performance metrics from the Stroop tests (e.g. correct and wrong responses, response times, etc.) in the ADHD detection process. Academic research has recognized the potential contribution of these data sources to improve the efficiency of early ADHD diagnosis, but very limited clinical evidence is available [ 58 ].

Our findings contribute to addressing the lack of biomarkers for the detection of complex conditions like ADHD [ 24 , 108 ]. Even though the current diagnostic process for adult ADHD has been widely used for decades, several limitations including accessibility, subjectiveness of the diagnosis, comorbidities, time and financial requirements, may hinder its efficiency and effectiveness. By leveraging passively collected multimodal physiological data, our study provides valuable insights into the pathophysiology of ADHD and presents a scalable solution for ADHD screening. The unobtrusive data collection method combined with the low cost of associated equipment render our platform a promising tool for assisting specialists during ADHD screening, supplementing traditional and resource-intensive methods. Furthermore, the use of physiological data obtained from wrist-worn devices opens avenues for continuous evaluation, enabling clinicians to monitor symptoms progression in near-real-time and obtain a more accurate clinical image of patients. This continuous monitoring could also lead to timely interventions and improved patient outcomes.

Limitations

While this study provides valuable insights into ADHD detection using multimodal physiological data, a few study limitations should be acknowledged. The relatively small sample size restricts the complexity of the ML models built and the robustness of results obtained, highlighting the need for larger samples to enable more comprehensive analyses. This work relies on in-sample model validation methods (i.e. cross-validation), which may lead to over-optimistic performance estimates. Larger sample sizes will enable the utilization of out-of-sample validation methods, fortifying the generalizability and robustness of the study outcomes. Additionally, the study did not control for co-occurring disorders and medication usage among participants, potentially confounding the observed ANS responses and ADHD detection accuracy. Future research should address these confounding factors to improve the validity of findings. Moreover, the study focused on detecting ADHD as a general condition and did not differentiate between ADHD subtypes, warranting exploration of subtype-specific detection methods. Furthermore, incorporating a wider range of continuous performance tests beyond Stroop tests targeting other ADHD-related characteristics could enhance performance, while also improving the generalizability of findings. Finally, confirming the profiles of the control group participants through clinical evaluations, in order to ensure that only neurotypical individuals are included, would further enhance our understanding of the potential of ADHD detection using physiological data.

In conclusion, this study has shed light on the potential of utilizing physiological data, including EDA, HRV, and ST for the detection of adult ADHD. By investigating ANS responses elicited during Stroop tests, we have demonstrated significant differences between neurotypical adults and those with ADHD, supporting the feasibility of using physiological signals as biomarkers for ADHD detection. Our analysis revealed that multimodal models, combining information from all physiological signals, outperformed unimodal ones, highlighting the importance of considering multimodal approaches in ADHD research. The SVM classification model emerged as the most effective in distinguishing between ADHD and neurotypical adults, achieving a balanced performance in terms of sensitivity and specificity (81.4% and 81.9%, respectively).

However, the relatively small sample size and the use of in-sample model validation methods pose challenges to the generalizability of our findings. Future research should aim to address these limitations by incorporating larger and more diverse samples, as well as employing out-of-sample validation methods to enhance the robustness of the results. Despite these limitations, our findings contribute to the growing body of literature on ADHD detection and underscore the potential of physiological data as valuable tools in clinical practice. Moving forward, further investigation into the underlying mechanisms driving ANS responses in ADHD and the integration of additional data modalities could provide deeper insights into the pathophysiology of the disorder and inform the development of more effective diagnostic and therapeutic strategies.

Availability of data and materials

The original contributions presented in the study are included in the article; Further inquiries can be directed to the corresponding authors.

Abbreviations

• Attention-deficit/hyperactivity disorder

Autonomic nervous system

Autism-spectrum quotient

Barkley adult ADHD rating scale–IV

Control group

Continuous performance test

Diagnostic interview for ADHD in adults

Digital precision medicine platform

Diagnostic and statistical manual of mental disorders

• Electrodermal activity

Electroencephalography

Experimental group

Empathy quotient

Heart rate variability

Informative KNN

k-Nearest neighbors

Logistic regression

Mini-international neuropsychiatric interview

Magnetic resonance imaging

Principal component analysis

Random forests

Relative interquartile range

Relative median absolute deviation

• Skin temperature

Support vector machines

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AP, CP, CT and GV contributed to the conceptualization and methodology of the study, and AP, CP, DA, and GV contributed to the coordination, monitoring and execution of the study. CT, DA, and PF designed and performed the data curation, processing and statistical analyses. The original draft was prepared by CT, DA, GV, and PF and all authors provided feedback, edited multiple drafts and reviewed the final manuscript.

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Andrikopoulos, D., Vassiliou, G., Fatouros, P. et al. Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry 24 , 547 (2024). https://doi.org/10.1186/s12888-024-05987-7

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Integrating geoinformatics and numerical modelling for landslide back-analysis and forecasting: a proactive mitigation study of the Shiv Bawri landslide

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In the monsoon season of 2023, Himachal Pradesh witnessed the catastrophic Shiv Bawri landslide, underscoring the vulnerability of mountainous regions to natural disasters. This study employs advanced geoinformatics and numerical modelling to provide a comprehensive back-analysis and forecasting of landslide dynamics. A detailed methodology encompassing field investigations, drone surveys, and data compilation for rainfall and satellite imagery forms the basis of the analysis. A multi-phase mass flow model and the TRIGRS-derived factors of safety for pre-event and post-event analysis, considering vegetation’s influence through root reinforcement models, are employed. The findings reveal a high correspondence between modelled and actual landslide events, with the models effectively predicting the landslide’s volume, flow height, and velocity. The multi-phase mass flow calculations yield a volume estimate of 4.12 \(\times\) 10 4 m 3 (post-event) and 2.92 \(\times\) 10 4 m 3 (pre-event), with respective validation success rates of 88.99% and 93.9%. The analysis indicates maximum flow height and velocity of 14.2 m and 16.2 m/s for post-event and 12.1 m and 12.6 m/s for pre-event analysis. The study emphasises the necessity of integrating detailed terrain analysis and numerical modelling for effective landslide risk mitigation and preparedness. By providing insights into the complex interplay of natural factors leading to landslides, this research advances the proactive management of landslide risks in susceptible mountainous regions.

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Sajwan, A., Ramana, G.V. Integrating geoinformatics and numerical modelling for landslide back-analysis and forecasting: a proactive mitigation study of the Shiv Bawri landslide. Landslides (2024). https://doi.org/10.1007/s10346-024-02321-w

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The importance of topographical recognition of pulmonary arteries in diagnostics and treatment of cteph, based on an analysis of a dissected case model—a pilot study.

1. Introduction

2. materials and methods, 2.1. study design, 2.2. dissection procedure, 2.3. evaluation of the arterial branching, measurements and comparisons, 2.4. statistical methods, 3.1. branching variant of the right lung, 3.2. branching variant of the left lung, 3.3. morphological parameters of the dissected model, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Zicans, M.; Kazoka, D.; Pilmane, M.; Skride, A. The Importance of Topographical Recognition of Pulmonary Arteries in Diagnostics and Treatment of CTEPH, Based on an Analysis of a Dissected Case Model—A Pilot Study. Diagnostics 2024 , 14 , 1684. https://doi.org/10.3390/diagnostics14151684

Zicans M, Kazoka D, Pilmane M, Skride A. The Importance of Topographical Recognition of Pulmonary Arteries in Diagnostics and Treatment of CTEPH, Based on an Analysis of a Dissected Case Model—A Pilot Study. Diagnostics . 2024; 14(15):1684. https://doi.org/10.3390/diagnostics14151684

Zicans, Matiss, Dzintra Kazoka, Mara Pilmane, and Andris Skride. 2024. "The Importance of Topographical Recognition of Pulmonary Arteries in Diagnostics and Treatment of CTEPH, Based on an Analysis of a Dissected Case Model—A Pilot Study" Diagnostics 14, no. 15: 1684. https://doi.org/10.3390/diagnostics14151684

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