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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.

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Arnaud Belinga

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

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

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

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

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

Case studies

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

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

Definition of a case study

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

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

Characteristics of case studies

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

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

The role of case studies in research

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

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

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

What is the purpose of a case study?

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

Why use case studies in qualitative research?

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

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

The explanatory, exploratory, and descriptive roles of case studies

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

The impact of case studies on knowledge development

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

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

Types of case studies

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

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

Exploratory case studies

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

Descriptive case studies

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

Explanatory case studies

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

Intrinsic, instrumental, and collective case studies

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

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

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

Critical information systems research

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

Health research

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

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

Asthma research studies

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

Other fields

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

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

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

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

Propositions

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

Units of analysis

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

Argumentation

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

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

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

Defining the research question

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

Selecting and defining the case

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

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

Developing a detailed case study protocol

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

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

Collecting data

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

Analyzing and interpreting data

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

Writing the case study report

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

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

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

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

Observations

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

Documents and artifacts

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

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

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

Ensuring the quality of data collection

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

Data analysis

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

Organizing the data

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

Categorizing and coding the data

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

Identifying patterns and themes

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

Interpreting the data

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

Verification of the data

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

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

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

Benefits include the following:

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

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

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

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

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Acknowledgments

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

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

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

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

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

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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What Is a Case Study?

Weighing the pros and cons of this method of research

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

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

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

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

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

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

At a Glance

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

What Are the Benefits and Limitations of Case Studies?

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

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

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

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

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

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

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

Case Study Examples

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

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

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

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

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

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

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

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

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

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

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

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

Section 1: A Case History

This section will have the following structure and content:

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

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

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

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

Section 2: Treatment Plan

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

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

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

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

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

Need More Tips?

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

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

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

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

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

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

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

Research Writing and Analysis

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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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Blog Beginner Guides What is a Case Study? [+6 Types of Case Studies]

What is a Case Study? [+6 Types of Case Studies]

Written by: Ronita Mohan Sep 20, 2021

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

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

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

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

CREATE A CASE STUDY

Click to jump ahead:

What is a case study, what is the case study method, benefits of case studies, limitations of case studies, types of case studies, faqs about case studies.

Case studies are research methodologies. They examine subjects, projects, or organizations to tell a story.

USE THIS TEMPLATE

Numerous sectors use case analyses. The social sciences, social work, and psychology create studies regularly.

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

CREATE THIS REPORT TEMPLATE

Now that you know what a case study is, we explain how case reports are used in three different industries.

What is a business case study?

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

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

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

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

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

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

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

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

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

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

What is a case study in psychology?

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

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

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

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

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

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

What is a case study in research?

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

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

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

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

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

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

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

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A case analysis is a deep dive into a subject. To facilitate this case studies are built on interviews and observations. The below example would have been created after numerous interviews.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Descriptive case studies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Related: 10+ Case Study Infographic Templates That Convert

What makes a case study a case study?

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

How long should a case study be?

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

What should you ask in a case study?

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

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

How to present a case study?

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

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

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

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What Is a Case Study and Why You Should Use Them

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

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

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

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

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

What is a case study?

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

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

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

Why are case studies important?

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

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

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

What are the benefits of writing a business case study?

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

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

What are the limitations of case studies?

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

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

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

Types of case studies

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

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

Explanatory case studies

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

Exploratory case studies

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

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

Collective case studies

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

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

Intrinsic case studies

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

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

Instrumental case studies

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

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

Tips for writing a case study

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

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

Here are some helpful tips for writing your case studies:

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

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

Administrative summary

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

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

Key problem statement

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

Problem exploration

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

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

Proposed resolution

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

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

Overview of monetary consideration

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

Execution timeline

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

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

2. Make it clear and comprehensive

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

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

3. Don't rush through the process

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

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

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

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

4. Be confident in your theory development

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

5. Case studies and all qualitative research are long

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

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

Case study examples

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

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

Case study FAQs

When should you do a case study.

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

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

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

How are case studies different from other research methodologies?

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

Writing multiple case studies for your business

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

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

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

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What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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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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Do Your Students Know How to Analyze a Case—Really?

Explore more.

• Case Teaching
• Student Engagement

J ust as actors, athletes, and musicians spend thousands of hours practicing their craft, business students benefit from practicing their critical-thinking and decision-making skills. Students, however, often have limited exposure to real-world problem-solving scenarios; they need more opportunities to practice tackling tough business problems and deciding on—and executing—the best solutions.

To ensure students have ample opportunity to develop these critical-thinking and decision-making skills, we believe business faculty should shift from teaching mostly principles and ideas to mostly applications and practices. And in doing so, they should emphasize the case method, which simulates real-world management challenges and opportunities for students.

To help educators facilitate this shift and help students get the most out of case-based learning, we have developed a framework for analyzing cases. We call it PACADI (Problem, Alternatives, Criteria, Analysis, Decision, Implementation); it can improve learning outcomes by helping students better solve and analyze business problems, make decisions, and develop and implement strategy. Here, we’ll explain why we developed this framework, how it works, and what makes it an effective learning tool.

The Case for Cases: Helping Students Think Critically

Business students must develop critical-thinking and analytical skills, which are essential to their ability to make good decisions in functional areas such as marketing, finance, operations, and information technology, as well as to understand the relationships among these functions. For example, the decisions a marketing manager must make include strategic planning (segments, products, and channels); execution (digital messaging, media, branding, budgets, and pricing); and operations (integrated communications and technologies), as well as how to implement decisions across functional areas.

Faculty can use many types of cases to help students develop these skills. These include the prototypical “paper cases”; live cases , which feature guest lecturers such as entrepreneurs or corporate leaders and on-site visits; and multimedia cases , which immerse students into real situations. Most cases feature an explicit or implicit decision that a protagonist—whether it is an individual, a group, or an organization—must make.

For students new to learning by the case method—and even for those with case experience—some common issues can emerge; these issues can sometimes be a barrier for educators looking to ensure the best possible outcomes in their case classrooms. Unsure of how to dig into case analysis on their own, students may turn to the internet or rely on former students for “answers” to assigned cases. Or, when assigned to provide answers to assignment questions in teams, students might take a divide-and-conquer approach but not take the time to regroup and provide answers that are consistent with one other.

To help address these issues, which we commonly experienced in our classes, we wanted to provide our students with a more structured approach for how they analyze cases—and to really think about making decisions from the protagonists’ point of view. We developed the PACADI framework to address this need.

PACADI: A Six-Step Decision-Making Approach

The PACADI framework is a six-step decision-making approach that can be used in lieu of traditional end-of-case questions. It offers a structured, integrated, and iterative process that requires students to analyze case information, apply business concepts to derive valuable insights, and develop recommendations based on these insights.

Prior to beginning a PACADI assessment, which we’ll outline here, students should first prepare a two-paragraph summary—a situation analysis—that highlights the key case facts. Then, we task students with providing a five-page PACADI case analysis (excluding appendices) based on the following six steps.

Step 1: Problem definition. What is the major challenge, problem, opportunity, or decision that has to be made? If there is more than one problem, choose the most important one. Often when solving the key problem, other issues will surface and be addressed. The problem statement may be framed as a question; for example, How can brand X improve market share among millennials in Canada? Usually the problem statement has to be re-written several times during the analysis of a case as students peel back the layers of symptoms or causation.

Step 2: Alternatives. Identify in detail the strategic alternatives to address the problem; three to five options generally work best. Alternatives should be mutually exclusive, realistic, creative, and feasible given the constraints of the situation. Doing nothing or delaying the decision to a later date are not considered acceptable alternatives.

Step 3: Criteria. What are the key decision criteria that will guide decision-making? In a marketing course, for example, these may include relevant marketing criteria such as segmentation, positioning, advertising and sales, distribution, and pricing. Financial criteria useful in evaluating the alternatives should be included—for example, income statement variables, customer lifetime value, payback, etc. Students must discuss their rationale for selecting the decision criteria and the weights and importance for each factor.

Step 4: Analysis. Provide an in-depth analysis of each alternative based on the criteria chosen in step three. Decision tables using criteria as columns and alternatives as rows can be helpful. The pros and cons of the various choices as well as the short- and long-term implications of each may be evaluated. Best, worst, and most likely scenarios can also be insightful.

Step 5: Decision. Students propose their solution to the problem. This decision is justified based on an in-depth analysis. Explain why the recommendation made is the best fit for the criteria.

Step 6: Implementation plan. Sound business decisions may fail due to poor execution. To enhance the likeliness of a successful project outcome, students describe the key steps (activities) to implement the recommendation, timetable, projected costs, expected competitive reaction, success metrics, and risks in the plan.

“Students note that using the PACADI framework yields ‘aha moments’—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.”

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

The PACADI framework covers all of the major elements of business decision-making, including implementation, which is often overlooked. By stepping through the whole framework, students apply relevant business concepts and solve management problems via a systematic, comprehensive approach; they’re far less likely to surface piecemeal responses.

As students explore each part of the framework, they may realize that they need to make changes to a previous step. For instance, when working on implementation, students may realize that the alternative they selected cannot be executed or will not be profitable, and thus need to rethink their decision. Or, they may discover that the criteria need to be revised since the list of decision factors they identified is incomplete (for example, the factors may explain key marketing concerns but fail to address relevant financial considerations) or is unrealistic (for example, they suggest a 25 percent increase in revenues without proposing an increased promotional budget).

In addition, the PACADI framework can be used alongside quantitative assignments, in-class exercises, and business and management simulations. The structured, multi-step decision framework encourages careful and sequential analysis to solve business problems. Incorporating PACADI as an overarching decision-making method across different projects will ultimately help students achieve desired learning outcomes. As a practical “beyond-the-classroom” tool, the PACADI framework is not a contrived course assignment; it reflects the decision-making approach that managers, executives, and entrepreneurs exercise daily. Case analysis introduces students to the real-world process of making business decisions quickly and correctly, often with limited information. This framework supplies an organized and disciplined process that students can readily defend in writing and in class discussions.

PACADI in Action: An Example

Here’s an example of how students used the PACADI framework for a recent case analysis on CVS, a large North American drugstore chain.

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

How should CVS Health evolve from the “drugstore of your neighborhood” to the “drugstore of your future”?

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

C1. Customer value: service, quality, image, and price (40%)

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

C4. Customer retention and customer lifetime value (20%)

Each alternative was analyzed by each criterion using a Customer Value Assessment Tool

Alternative 4 (A4): Personalization was selected. This is operationalized via: segmentation—move toward segment-of-1 marketing; geodemographics and lifestyle emphasis; predictive data analysis; relationship marketing; people, principles, and supply chain management; and exceptional customer service.

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

*Source: A. Weinstein, Y. Rodriguez, K. Sims, R. Vergara, “The CVS Prescription for Superior Customer Value—A Case Study,” Back to the Future: Revisiting the Foundations of Marketing from Society for Marketing Advances, West Palm Beach, FL (November 2, 2018).

Results of Using the PACADI Framework

When faculty members at our respective institutions at Nova Southeastern University (NSU) and the University of North Carolina Wilmington have used the PACADI framework, our classes have been more structured and engaging. Students vigorously debate each element of their decision and note that this framework yields an “aha moment”—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.

These lively discussions enhance individual and collective learning. As one external metric of this improvement, we have observed a 2.5 percent increase in student case grade performance at NSU since this framework was introduced.

Tips to Get Started

The PACADI approach works well in in-person, online, and hybrid courses. This is particularly important as more universities have moved to remote learning options. Because students have varied educational and cultural backgrounds, work experience, and familiarity with case analysis, we recommend that faculty members have students work on their first case using this new framework in small teams (two or three students). Additional analyses should then be solo efforts.

To use PACADI effectively in your classroom, we suggest the following:

Advise your students that your course will stress critical thinking and decision-making skills, not just course concepts and theory.

Use a varied mix of case studies. As marketing professors, we often address consumer and business markets; goods, services, and digital commerce; domestic and global business; and small and large companies in a single MBA course.

As a starting point, provide a short explanation (about 20 to 30 minutes) of the PACADI framework with a focus on the conceptual elements. You can deliver this face to face or through videoconferencing.

Give students an opportunity to practice the case analysis methodology via an ungraded sample case study. Designate groups of five to seven students to discuss the case and the six steps in breakout sessions (in class or via Zoom).

Ensure case analyses are weighted heavily as a grading component. We suggest 30–50 percent of the overall course grade.

Once cases are graded, debrief with the class on what they did right and areas needing improvement (30- to 40-minute in-person or Zoom session).

Encourage faculty teams that teach common courses to build appropriate instructional materials, grading rubrics, videos, sample cases, and teaching notes.

When selecting case studies, we have found that the best ones for PACADI analyses are about 15 pages long and revolve around a focal management decision. This length provides adequate depth yet is not protracted. Some of our tested and favorite marketing cases include Brand W , Hubspot , Kraft Foods Canada , TRSB(A) , and Whiskey & Cheddar .

Art Weinstein , Ph.D., is a professor of marketing at Nova Southeastern University, Fort Lauderdale, Florida. He has published more than 80 scholarly articles and papers and eight books on customer-focused marketing strategy. His latest book is Superior Customer Value—Finding and Keeping Customers in the Now Economy . Dr. Weinstein has consulted for many leading technology and service companies.

Herbert V. Brotspies , D.B.A., is an adjunct professor of marketing at Nova Southeastern University. He has over 30 years’ experience as a vice president in marketing, strategic planning, and acquisitions for Fortune 50 consumer products companies working in the United States and internationally. His research interests include return on marketing investment, consumer behavior, business-to-business strategy, and strategic planning.

John T. Gironda , Ph.D., is an assistant professor of marketing at the University of North Carolina Wilmington. His research has been published in Industrial Marketing Management, Psychology & Marketing , and Journal of Marketing Management . He has also presented at major marketing conferences including the American Marketing Association, Academy of Marketing Science, and Society for Marketing Advances.

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Comparison of data processing strategies using commercial vs . open-source software in GC-Orbitrap-HRMS untargeted metabolomics analysis for food authentication: thyme geographical differentiation and marker identification as a case study

• Paper in Forefront
• Open access
• Published: 28 May 2024

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• Araceli Rivera-Pérez   ORCID: orcid.org/0000-0003-1099-7185 1 &
• Antonia Garrido Frenich   ORCID: orcid.org/0000-0002-7904-7842 1  

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Untargeted analysis of gas chromatography–high-resolution mass spectrometry (GC-HRMS) data is a key and time-consuming challenge for identifying metabolite markers in food authentication applications. Few studies have been performed to evaluate the capability of untargeted data processing tools for feature extraction, metabolite annotation, and marker selection from untargeted GC-HRMS data since most of them are focused on liquid chromatography (LC) analysis. In this framework, this study provides a comprehensive evaluation of data analysis tools for GC-Orbitrap-HRMS plant metabolomics data, including the open-source MS-DIAL software and commercial Compound Discoverer™ software (designed for Orbitrap data processing), applied for the geographical discrimination and search for thyme markers (Spanish vs . Polish differentiation) as the case study. Both approaches showed that the feature detection process is highly affected by unknown metabolites (Levels 4–5 of identification confidence), background signals, and duplicate features that must be carefully assessed before further multivariate data analysis for reliable putative identification of markers. As a result, Compound Discoverer™ and MS-DIAL putatively annotated 52 and 115 compounds at Level 2, respectively. Further multivariate data analysis allowed the identification of differential compounds, showing that the putative identification of markers, especially in challenging untargeted analysis, heavily depends on the data processing parameters, including available databases used during compound annotation. Overall, this method comparison pointed out both approaches as good options for untargeted analysis of GC-Orbitrap-HRMS data, and it is presented as a useful guide for users to implement these data processing approaches in food authenticity applications depending on their availability.

Graphical Abstract

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Avoid common mistakes on your manuscript.

Introduction

Nowadays, there is increasing interest in moving toward the consumption of natural food products with beneficial properties such as herbs and spices [ 1 ]. Particularly, the current demand for spices and herbs is growing in Europe due to interest in new flavors, more healthy dietary habits, and the rise of the so-called ethnic gastronomy in Western civilizations. According to the Observatory of Economic Complexity (OEC), the global herb and spice trade has increased by 82.3% in the last decade (from 1.98 to 3.61 billion dollars between 2010 and 2020), with China and India as the main exporters [ 2 , 3 ]. Nevertheless, herbs and spices are vulnerable to food fraud for a high number of reasons, including complex and long supply chain systems, high demand and limited production, and the fact that they are mostly supplied as ground powder, increasing their fraud vulnerability [ 4 , 5 ]. This fact has led to the continuous demand for analytical methods to detect potential fraud practices.

The chemical composition of plant-derived food products is directly linked to their quality, being an indicator of the product's genuineness, including their origin, quality, processing, and authenticity (or adulteration) characteristics [ 6 ]. The metabolomics composition is highly dependent on diverse factors (e.g., growing conditions, production system, post-harvest processing such as drying or milling processes, or storage conditions), leading to variations in metabolite levels [ 7 ]. For these reasons, assessing alterations in the phytochemical composition of plant-derived foodstuffs through metabolomics approaches is one of the strategies of choice to monitor food authenticity [ 6 ].

Recently, metabalomics approaches based on chromatography coupled to high-resolution mass spectrometry (HRMS) have gained attention as one of the most reliable analytical tools for food authenticity assessment of complex matrices, such as herbs and spices, including geographical discrimination [ 8 , 9 ], quality control [ 10 ], and adulteration [ 11 ] studies, being most of them based on ultra-high performance liquid chromatography (UHPLC)-HRMS analysis. A remarkable strength of HRMS is that accurate mass acquisition (by Orbitrap and time-of-flight, TOF, analyzers) greatly reduces the number of candidates per detected metabolite leading to a highly confident compound identification in metabolomics studies [ 12 ]. In contrast, up to now, only few gas chromatography (GC)-HRMS-based metabolomics applications are described in the herb and spice authentication field, including HRMS (Orbitrap)-based studies for geographical differentiation and processing traceability of black pepper [ 13 ] and thyme [ 14 ] by volatile fingerprinting.

As a result of the high number of detected metabolites (features) arising from raw GC-HRMS acquisition, automatic data analysis tools are necessary to manage untargeted omics data. In the field of HRMS-based metabolomics, there are different data processing options, including open-source tools such as MS-DIAL [ 15 ] and commercial packages such as Compound Discoverer™ (the most widely used data analysis tool for Thermo Fisher Scientific instruments). The capabilities of data processing tools may be compared depending on the tested approach, i.e., targeted or untargeted data analysis. The first one is usually focused on the analysis of a reference standard mixture where the method performance is assessed through the detection, identification, and quantification of expected features, being limited to these targeted compounds that often can not reflect complex situations found in the routine analysis [ 16 ]. On the other side, data processing performance is investigated in untargeted data analysis approaches by considering real samples. It constitutes a great challenge compared with targeted strategies, especially considering the limited knowledge of the features present in the samples due to the untargeted nature of the analysis, and that compound identification is greatly dependent on the availability of well-updated databases used for feature annotation, leading to considerable different detection and annotation findings from the same dataset [ 16 , 17 ]. In this context, few studies have compared the performance of available data analysis tools for HRMS data processing. For instance, Li et al. [ 17 ] evaluated the performance of five software (open-source ones, namely MS-DIAL, MZmine 2, and XCMS; and commercial MarkerView and Compound Discoverer™ software) by considering a benchmark sample set consisting of 1100 compounds (drugs and metabolites) acquired using two advanced HRMS platforms (TOF and Q-Orbitrap analyzers). In another study, six data processing tools (including MS-DIAL and Compound Discoverer™, among others) were comprehensively evaluated for UHPLC-Q-Orbitrap-HRMS targeted and untargeted plant metabolomics analysis (licorice, tea, and tobacco), revealing that the feature extraction performance may vary in untargeted analysis of real samples, and as a result, only a few detected features were common within the tested processing methods for the same dataset [ 16 ]. Similarly, open-source MS-DIAL and patRoon platforms were used for the UHPLC-Q-Orbitrap-HRMS untargeted analysis of biological samples (plasma and feces) to discover biomarkers from the Parkinson’s disease metabolome [ 18 ]. As a result of the current trend towards “open science” workflows, some open-source tools (e.g., MS-DIAL) are gaining special attention to manage UHPLC-HRMS data, including different applications such as untargeted analysis for water contamination assessment [ 19 ], phytochemical profiling of herbal medicines [ 20 ], and metabolomics approaches to reveal the post-harvest processing influence on the chemical composition of plant matrices [ 21 ]. Notably, most previous studies were focused on UHPLC-HRMS data processing, and only a few applications of the MS-DIAL software for GC–MS data analysis have been reported in the literature, all of them dealing with untargeted low-resolution mass spectrometry (LRMS) information [ 22 , 23 ]. Bearing in mind that limited attention had been paid to GC-HRMS data processing options, this study not only provides for the first time the application of an open-source data processing workflow based on the MS-DIAL software to manage GC-Orbitrap-HRMS untargeted plant metabolomics data, but also a comprehensive performance comparison with the commercial Compound Discoverer™ software, the tool of choice for Thermo Fisher Scientific instruments’ users. Both untargeted approaches were applied for the geographical differentiation (Spain and Poland) of thyme samples as the case study to notice how data processing strategies may influence the metabolome overview and the identification of differential compounds (markers) highlighted by further multivariate data analysis tools. Thus, this study offers guidance on MS-DIAL and Compound Discoverer™ suitable processing parameters for users who worked in GC-HRMS-based plant metabolomics for food authenticity and traceability applications.

Materials and methods

GC–MS grade ethyl acetate (AcOEt) of purity ≥ 99.5% was supplied by Honeywell Riedel-de-Haën (Seelze, Germany). Kovats retention indices (KI) were estimated considering a certified reference material of C 7 -C 40 saturated alkanes (1000 μg/mL of each compound in n -hexane) obtained from Sigma-Aldrich (St. Louis, MO, USA).

Samples of the study and sample pretreatment

Thyme samples were supplied by Sabater Spices (Murcia, Spain). Two different geographical origins were assessed as the case study: Poland (Lublin, n  = 25) and Spain (Castilla-La Mancha, n  = 25). Polish thyme samples were collected in August 2020 and Spanish ones were in July 2020. Before sample extraction and analysis, thyme samples were ground (final particle size of 0.2 mm) using an ultra centrifugal mill (ZM200, Retsch GmbH, Germany) for 10 min at 8000 rpm (3584 rcf, relative centrifugal force). All the samples were stored in airtight packaging at room temperature until further GC-Orbitrap-HRMS analysis.

Sample preparation for GC-Orbitrap-HRMS metabolomics analysis

Thyme samples were extracted as described in previous research using simple and effective ultrasound-assisted extraction (UAE) [ 14 ]. Briefly, 200.00 ± 0.01 mg of sample was weighed in a 15 mL-polypropylene tube using a research-grade analytical balance (Ohaus ® , Nänikon, Switzerland), 4 mL of AcOEt was added, and the samples were introduced in an Elmasonic S 80 H ultrasonic bath (Elma Schmidbauer, Germany) for 30 min (at 37 kHz) at room temperature. Then, the sample extracts were centrifuged (at 5500 rpm or 4400 ×  g for 10 min) using a Frontier™ 5816 centrifuge (Ohaus ® , Nänikon, Switzerland) and filtered through 0.45 µm nylon filters. Thyme extracts were stored at − 21 °C until further analysis. All the samples were randomly extracted and analyzed to avoid analysis bias. Procedure blanks were also prepared for the instrument clean-up and to remove potential background signals during further data processing.

GC-Orbitrap-HRMS instrumental analysis

GC-Orbitrap-HRMS metabolomics analysis of thyme extracts was carried out using a Trace 1310 GC chromatograph equipped with a TriPlus RSH autosampler coupled to a Q-Exactive Orbitrap mass analyzer (Thermo Fisher Scientific, Waltham, MA). Metabolite separation was performed using a BP5MS capillary column (30 mm × 0.25 i.d., 0.25 µm particle size) from SGE Analytical Science (Victoria, Australia). The GC oven temperature was programmed as follows: initial oven temperature of 60 °C (held for 2 min), increased from 60 to 180 °C (at 20 °C/min) and increased to 310 °C (at 10 °C/min, held for 10 min). The total running time was 31 min. Helium (99.999%) was used as the carrier gas and its flow rate was 1 mL/min. The injector temperature was set at 250 °C and the volume injection was 1 µL using the splitless mode (splitless time of 3 min). HRMS data acquisition was performed using the full scan-MS mode (profile mode) with electron ionization (EI at 70 eV) within the m/z range of 50–500 (scan time of 200 ms) at a resolution power of 60,000 full width at half maximum (FWHM) at m/z 200.

Data processing strategies tested for GC-Orbitrap-HRMS metabolomics data

Dataset under study: polish vs. spanish thyme geographical differentiation.

As previously described, the dataset consisted of a total of 50 thyme GC-Orbitrap-HRMS raw files, split into 25 Polish thyme samples and 25 Spanish ones. Additionally, a total of 6 GC-Orbitrap-HRMS raw files corresponding to procedure blanks analyzed at the beginning, every 10 analyzed samples, as well as at the end of the analysis batch, were considered during data processing to remove background signals. This dataset was specifically designed to evaluate the performance of data processing software in plant untargeted metabolomics analysis for applications focused on food authentication and the corresponding search for metabolite markers.

Data processing workflow using commercial Compound Discoverer™ software

Compound Discoverer™ software allows untargeted data processing of GC/LC-HRMS raw data specifically acquired from Thermo Fisher Scientific instruments. The software streamlines compound identification in metabolomics applications since it provides pre-defined workflow templates, as well as the possibility of flexible and fully customizable data processing workflows designed by the users. A further advantage of Compound Discoverer™ software for metabolomics applications is that the platform includes univariate data analysis to notice key compounds between sample groups (e.g., two-sample t -tests, fold change (FC) analysis). In this study, Compound Discoverer™ software version 3.3 was used.

A customized metabolomics workflow was designed for the untargeted analysis of thyme samples using the Compound Discoverer™ software. A comparison of the data analysis workflows using Compound Discoverer™ and MS-DIAL software is shown in Fig.  1 . Despite the various functionalities within each data processing software, data analysis parameters were kept as similar as possible between tested workflows to provide a comprehensive and fair comparison of current data processing methods.

Overview of data processing workflows for the geographical differentiation of thyme using commercial Compound Discoverer™ (left) and open-source MS-DIAL software (right). Abbreviations: EI electron ionization, KI Kovats retention index, m/z mass-to-charge ratio, NIST National Institute of Standards and Technology, RI retention index, RT retention time, S/N signal-to-noise ratio

First, GC-Orbitrap-HRMS raw data were imported into the platform and two sample groups were defined regarding the geographical origin factor: Poland and Spain. Additionally, procedure blanks were imported into the software for further background signal removal. For that, procedure blank files were denoted as “blanks” within the sample type categories. All comprehensive Compound Discoverer™ parameters of the designed workflow are shown in Table S1 . Feature extraction was performed within the RT and m/z ranges of 5.0–31.0 min and 50–500 Da, respectively, selecting the positive polarity mode (corresponding to EI-MS data acquisition). Then, data deconvolution was carried out considering the following main parameters: mass tolerance of 5 ppm, spectral and peak signal-to-noise (S/N) thresholds of 3, and total ion chromatogram (TIC) threshold of 10 6 . The compounds found in the dataset were grouped across the samples using a RT tolerance of 5 s. KI estimation of deconvoluted features was achieved considering a semi-standard non-polar column (as the one used in this study) by including in the software the list of RT-number of carbons obtained for the C 7 -C 40 n -alkane reference sample analyzed within the sample batch (Table S1 ). Then, feature identification was done by library search using the NIST libraries included in the Compound Discoverer™ software. The following library search parameters were considered for reliable compound identification: match or reverse match factor (SI/RSI) threshold of 700 and a maximum retention index delta (ΔKI) of 20 between library and experimental data (Fig.  1 ). Moreover, (reverse) high-resolution filtering, (R)HRF, scores were inspected as an additional parameter for accurate compound identification. For peak area refinement, missing value imputation, background filtering, and data normalization were performed. Missing value imputation was carried out by the “automatic selection” option of the software and procedure blanks were used to identify potential background features by setting in 5 the maximum allowed ratio of sample vs . blank to be considered as blank. The Compound Discoverer™ software offers three types of normalization: constant sum, constant median, and constant mean. In this study, data were normalized by constant sum (samples normalized to the maximum peak area sum of all samples) to avoid the potential dilution effects within the samples. The basis of this choice was providing a fair comparison between Compound Discoverer™ and MS-DIAL data processing results since constant median or constant mean normalizations were not contemplated in the MS-DIAL platform. Finally, differential analysis between sample groups was performed in the platform by considering the Poland vs . Spain condition, providing the corresponding p -values calculated by a two-tailed student’s t -test with Benjamini–Hochberg correction, as well as fold change analysis (FC) expressed as Log2(FC) values. Once data processing was finished, the normalized peak area data matrix was exported in .csv format for further multivariate data analysis.

Data processing workflow using the open-source MS-DIAL software

MS-DIAL was launched as a universal software for untargeted metabolomics analysis which is suitable for GC–MS and LC–MS data processing from different commercial platforms (Agilent Technologies, Bruker, and Thermo Fisher Scientific, among others). Moreover, MS-DIAL supports all data processing steps from raw data import to feature identification providing different public compound libraries (for EI-MS or positive/negative MS/MS data) in.msp formats. In this study, the MS-DIAL software version 4.9.221218 was used.

Regarding the data processing workflow using the MS-DIAL software, the raw data from Thermo Fisher Scientific (.raw files) were initially converted into .abf files using the Reifycs Abf Converter. Then, .abf files were imported to the MS-DIAL platform (Fig.  1 ). All the specific parameters used for GC-Orbitrap-HRMS data processing using the MS-DIAL software are detailed in Table S2 . Briefly, during the creation of the MS-DIAL project, the following parameters were set: hard ionization (for GC–MS data), chromatography as separation type, profile for data type, and positive ion mode (for EI-MS data). Two sample groups were defined: Poland and Spain. As previously described, procedure blanks were also considered during data processing and labeled as “blank” within sample type categories. Feature extraction was performed within the m/z range of 50–500 Da, the RT range of 5.0–31.0 min, considering a minimum peak height of 10 6 for Orbitrap instruments (as previously defined for the TIC threshold parameter in the Compound Discoverer™ software). Moreover, the “accurate MS” option was selected in the platform, setting 0.001 Da for the mass accuracy for centroiding and a mass slice width of 0.05 Da (suitable for accurate GC–MS data). The comparability between MS-DIAL and Compound Discoverer™ software mass accuracy criteria (0.001 Da and 5 ppm, respectively) was ensured since 0.001 Da tolerance is equivalent to 5 ppm at m/z 200 for Orbitrap analyzers. Peak smoothing was performed considering the following parameters: linear weighted moving average as the smoothing method (smoothing Level 2 scans and average peak width of 20 scans, according to developer recommendations). Data deconvolution was achieved using default parameters (using a sigma window value of 0.5 and an amplitude EI spectra cut-off of 10). For peak alignment considering retention indexes, the “together with alignment” option was ticked and one representative sample from the dataset was set as the reference file, setting a maximum retention index tolerance of 20 together with an EI similarity tolerance of 70%. Compound identification was performed considering retention indexes by importing into the platform the list of RT-number of carbons for the C 7 -C 40 n -alkane reference file (Table S2 ). A retention index tolerance of 20 (as previously defined for the Compound Discoverer™-based workflow), an m/z tolerance of 0.5 Da (developer recommendations), an EI similarity cut-off of 70%, and an identification score cut-off of 70% were set for confident feature identification. Compound identification was done by library matching using a publicly available MS-DIAL library, i.e., “all records with Kovats RI” database (.msp format) containing EI-MS and KI data for 9062 known unique compounds (Fig.  1 ). Moreover, gall filling by compulsion was carried out. Finally, data filtering was done considering a peak count filter of 50% (calculated as the quotient of the number of biological replicates per group or condition and the total number of samples, i.e., 25/50). Moreover, a feature must be detected in 80% of the samples in at least one sample group to be annotated. Procedure blank files were considered to remove background features by setting in 5 the maximum allowed sample vs . blank fold change to be considered as blank (Fig.  1 ). The option “keep removable features and assign the tag” was selected for a further overview of background features identified within the dataset. Once the data processing was finished, data were normalized using the TIC option of the MS-DIAL software as the normalization factor for reliable comparison with Compound Discoverer™ constant sum normalization. The alignment result was exported as normalized peak heights into a .txt data file by setting “filtering by the ion abundance of blank sample” and “replace zero values with 1/10 of minimum peak height over all samples” options.

Multivariate data analysis

For multivariate data analysis, commercial (SIMCA ® version 17 software, Umetrics, Umeå, Sweden) and open-source (MetaboAnalyst 5.0 platform available at http://www.metaboanalyst.ca ) platforms were used.

Normalized peak areas (from Compound Discoverer™) and normalized peak heights (from MS-DIAL) were uploaded to SIMCA ® 17 or MetaboAnalyst 5.0 for statistical analysis. The common preliminary processing step was Pareto scaling. As the final aim of this authentication study was the search for Polish/Spanish thyme markers using commercial or open-source data processing tools as a case study, supervised PLS-DA modelling was employed. For that, the dataset was split into two groups: a training set consisting of 80% of the total thyme files for model-building, and a prediction set (remaining 20% of thyme samples) which was used for external model validation. An advantage of SIMCA ® 17 over MetaboAnalyst 5.0 is that statistical models may be externally validated by considering the correct classification rate (CCR%) obtained for the blind prediction of the samples included in the prediction set. The performance of PLS-DA models was assessed by the goodness-of-fit (R 2 Y) and the goodness-of-prediction (Q 2 ) parameters. The higher the R 2 Y and Q 2 parameters, the higher the model performance, accepting a Q 2 cut-off > 0.5 as good predictability. PLS-DA model from SIMCA ® 17 was validated by internal k -fold cross-validation (CV) (default k  = 7) when a CV-ANOVA p -value < 0.05 is obtained, by permutation tests ( n  = 100 permutations) to exclude model overfitting, and considering the CCR% achieved for the prediction set. Similarly, the PLS-DA model from the MetaboAnalyst 5.0 platform was validated by default internal 5-fold CV, which was also used to estimate the optimum number of latent variables, as well as by performing a permutation test ( n  = 100) that excludes model overfitting when an empirical p -value < 0.05 is achieved.

Finally, the variable importance in the projection (VIP) approach was used to determine which compounds were the most discriminant to distinguish between Polish and Spanish thyme samples. For univariate analysis of markers, Log2(FC) (FC cut-off > 1.2) and t -test p -values ( p -values < 0.05 indicate significant differences among sample groups) were calculated using the Compound Discover™ software. However, the MS-DIAL platform did not contemplate univariate analysis and these values were mandatory obtained from the open-source MetaboAnalyst 5.0 platform.

Thus, all annotated compounds, as well as the differential ones (markers) extracted from the dataset via multivariate data analysis, were identified in good agreement with the NIST (included in the Compound Discoverer™ workflow) or the open-source EI-MS MS-DIAL library with a maximum ΔKI of 20, indicated for reliable GC-based putative identification [ 24 ]. Therefore, Level 2 of identification confidence (i.e., putatively annotated compounds or probable structures) was fulfilled for all the detected features according to established identification confidence levels using HRMS-based approaches [ 25 , 26 ].

Evaluation of commercial vs. open-source data processing performances

It is worth noting that the data processing parameters were kept as similar as possible in both tested workflows for the fairest method evaluation. However, due to the differences in functionalities of each data analysis software, this challenging comparison was not only focused on the total number of initially extracted features, since it is highly dependent on the data analysis parameters, especially those related to the feature extraction process (e.g., parameters set for the peak detection or peak alignment processes) and the further identification of extracted features (which is highly dependent on the internally available libraries). Regarding the identification of extracted features, both workflows were based on their corresponding internally available GC–MS databases, namely, NIST for Compound Discoverer™ and the “all records with Kovats RI” database (.msp format with 9062 known unique compounds) for the MS-DIAL workflow.

Therefore, for an equitable method comparison, the data processing capabilities of the two workflow strategies under study (Compound Discoverer™ and MS-DIAL software, Fig.  1 ) were compared in terms of the number of features extracted from each processing method, including a deeper evaluation of the number of unknown or non-library referenced, background, and duplicate features registered using each platform. Moreover, the capabilities for compound identification were assessed by comparing the final number of Level 2–identified features using available spectral libraries offered by the Compound Discoverer™ or the MS-DIAL software. Finally, reliable marker identification via SIMCA ® 17 or MetaboAnalyst 5.0–based multivariate data analysis was performed from Compound Discoverer™ and MS-DIAL curated data matrices, and overlap between highlighted markers was also explored as the final step of this case study.

Results and discussion

Results of feature extraction from untargeted gc-orbitrap-hrms data analysis.

Representative total ion chromatograms (TICs) of Polish and Spanish thyme samples are provided in Fig. S1 . TICs were characterized by a considerably high number of peaks, indicating that the compound identification process in high-complexity matrices, such as spices and herbs, may be challenging work.

Compound Discoverer™ and MS-DIAL performances were comprehensively evaluated considering the following aspects (Fig.  2 ): (i) initial extracted features from the dataset, (ii) the number of unknown or non-annotated features, (iii) the number of background features, (iv) duplicate features within the dataset, and (v) the final number of annotated features at Level 2 of identification confidence (by library and KI matching), which were further considered for multivariate data analysis. The initially annotated features were manually curated for further multivariate data analysis for marker searching, and thus, only Level 2–identified features were kept for reliable identification of Polish vs . Spanish thyme marker compounds. The final number of Level 2–identified features by each different approach are included in Table S3 (Compound Discoverer™ dataset) and Table S4 (MS-DIAL dataset), and they are comprehensively described below.

Performance comparison of data processing tools for GC-Orbitrap-HRMS untargeted metabolomics analysis using A Compound Discoverer™ and B MS-DIAL software

Results of untargeted data processing using commercial Compound Discoverer™ software

For the untargeted data processing using Compound Discoverer™ workflow (Fig.  2 A), a total of 2034 features were initially annotated for Polish vs . Spanish thyme differentiation. This high preliminary number of detected features was in good agreement with the complex sample analysis observed by qualitative inspection of thyme TICs. Most of the initial features (1654) remained unknown without library match results (Level 4 was obtained for features with unequivocal molecular formula or Level 5 if only the exact mass of interest was known [ 26 ]), with insufficient available information for their chemical annotation via NIST library matching (Fig.  2 A). Since the final aim of this study was to notice the applicability of different data processing workflows for authentication studies, and thus, for the identification of thyme marker compounds, these non-library-matched features (Levels 4 and 5) were not further considered in multivariate statistical analysis. False positive feature detection should be also evaluated in data processing workflows, especially considering that background or interference compounds may be originated during the sample preparation or from instrument noise. The analysis of the procedure blanks allowed the assessment of any source of contamination introduced during any step of the measurement procedure, including from the extraction process (e.g., solvent, polypropylene, or nylon filter interferences) to the instrumental process (background features arising from mobile phases or instrument noise), as previously reported [ 16 ]. Pointing out background features by manual checking would be time-demanding bearing in mind the complexity of the matrix of study and the number of analyzed samples. In this context, the designed Compound Discoverer™ workflow allowed the automatized assessment of background features considering procedure blanks included in the analysis batch. For that, the maximum allowed ratio of sample vs . blank to be considered as a background compound was set at 5. As a result, the Compound Discoverer™ software marked 324 compounds as background features which were further discarded to reduce interferences and non-differential false positive features to distinguish between Polish and Spanish thyme (Fig.  2 A).

Once unknown and background features were discarded, the remaining features were inspected to notice the presence of potential duplicate compounds. For duplicate identification, the remaining features were compared via “name” and corresponding InChIKey across the compound list (Table S3 ). It is worth noting that Compound Discoverer™ automatically provided the CAS numbers of annotated compounds (when available) retrieved from the NIST database used during compound identification. However, InChIKey should be manually consulted in the NIST library by introducing the feature CAS number. Only 4 duplicates were discarded, maintaining the feature with the highest total identification score (%) and the lowest ΔKI as the selection criteria within the same duplicates (Fig.  2 A).

At this point, the Compound Discoverer™ curated data matrix was manually completed by adding the compound classes of final features and their potential occurrence in herbs and spices using the Human Metabolome Database (HMDB) [ 27 ]. Thus, only 52 unique features annotated at a high confidence level (Level 2) were finally considered (Fig.  2 A and Table S3 ), with ΔKI ≤ 20, and SI or RSI values ≥ 700. Additionally, annotated features were characterized by satisfactory HRF/RHRF scores close to 100%, and total identification scores > 90% were obtained in all the cases (Table S3 ). The comprehensive list of annotated compounds via Compound Discoverer™ software also included compound name, RT (min), CAS numbers, InChIKey identifiers, compound class taken from HMDB, and abundance values (expressed as constant sum normalized peak areas), among other identification parameters (Table S3 ). A broad compound diversity could be observed in thyme samples according to Level 2–identified features (Fig.  3 A), with a clear predominance of monoterpenoid compounds (e.g., eucalyptol, sabinene hydrate, linalool) followed by sesquiterpenoids (e.g., trans -calamenene or epicubenol), which accounted for almost half of total Level 2–identified features. The annotation results also highlighted the presence of other miscellaneous compounds, as well as diterpenoids (e.g., ferruginol or sugiol) and alkenylbenzenes (e.g., estragole, eugenol, myristicin) in thyme extracts (Fig.  3 A and Table S3 ). Three compounds belonging to the vitamin E and derivative group were also annotated (e.g., δ -tocopherol). In addition, the 10 remaining identified compounds belonged to different chemical classes, including tyrosol derivatives (e.g., tyrosol, acetate), fatty acid esters (e.g., octanoic acid, methyl ester), methoxyphenols, alkanes (e.g., heptacosane), and fatty acids and derivatives, among others (Fig.  3 A and Table S3 ). According to the annotation results (Table S3 ), the Compound Discoverer™ strategy allowed the reliable identification of some metabolites, such as eugenol, 4-hydroxy-benzeneethanol, and β -tocopherol, in line with thyme phytochemical composition, and other detected metabolites were usually found in herbs and spices such as α -phellandrene, estragole, bicyclosesquiphellandrene, epicubenol, and heptacosane [ 27 ].

Pie charts showing the chemical classes of Level 2–annotated features used for geographical differentiation of thyme samples according to A Compound Discoverer™ and B MS-DIAL data processing results

Results of untargeted data processing using open-source MS-DIAL software

As shown in Fig.  2 B, the MS-DIAL data processing strategy led to a lower initial number of total features (833 extracted features) compared with the Compound Discoverer™ one (Fig.  2 A). However, it would not be equitable to evaluate the processing workflow performance by the total number of initially extracted features, since it is highly dependent on the data analysis parameters. Furthermore, it should be taken into account that the open-source MS-DIAL workflow begins with the raw data conversion from .raw (GC-Orbitrap-HRMS data from Thermo Fisher Scientific) to .abf files. Indeed, this additional step makes MS-DIAL an alternative and universal processing tool for data from different instruments in comparison with commercial ones such as Compound Discoverer™, in which the workflow does not require data extension conversion but, therefore, is limited to .raw files. This conversion step might be another source of variability to explain the difference between the initial number of features extracted following both tested workflows.

Consequently, all these features should be further critically inspected by considering the annotation confidence level (if they are unknown features), background compounds, and duplicate features among all extracted metabolites for real knowledge of the data processing performance. The results in Fig.  2 B also revealed that the number of library-matched features only took a small part of the total features, as previously obtained using the Compound Discoverer™–based strategy, since 519 features remained unknown after library matching using the public MS-DIAL library file. In this case, only the exact masses and EI spectrum of unknown features were provided using the MS-DIAL strategy (molecular formulas were not unequivocally assigned), achieving Level 5 of identification [ 26 ]. As previously described, unknown features were not further considered. Then, background interferences were automatically inspected by the MS-DIAL data processing workflow by considering the fold change of thyme samples vs . procedure blanks (set at a maximum of 5 to be considered a background feature). In this context, a lower number of background features (191) were annotated compared with the Compound Discoverer™ strategy, which was in line with the lower number of features initially extracted from the dataset (Fig.  2 B). The number of duplicates was also investigated by considering the feature name or InChIKey. An advantage of the MS-DIAL software is that it directly provides different feature identifiers, including InChIKey and SMILES, as well as the compound “ontology” corresponding to the chemical class (e.g., monoterpenoids, sesquiterpenoids, fatty alcohols) (Table S4 ). In this case, although MS-DIAL counted with a lower number of initially extracted compounds, a slightly higher number of duplicates (8 discarded duplicate features) were highlighted in comparison with the Compound Discoverer™ data analysis. As previously indicated for the Compound Discoverer™ approach, duplicate features were compared and the one with the highest total identification score (%) and the lowest ΔKI was kept in the dataset. As a result of the manually curated MS-DIAL dataset, matching of deconvoluted EI-MS spectra to the MS-DIAL library resulted in 115 confident annotations at Level 2 (Fig.  2 B and Table S4 ), with ΔKI ≤ 20, and total identification scores > 70%, which were further considered for multivariate data analysis and searching of thyme markers. Different factors might explain the differences observed in the final Level 2–annotated compounds within tested workflows. For instance, it is worth noting that the MS-DIAL workflow presented an additional peak area refinement filter compared to the Compound Discoverer™ one, which might contribute to the difference between the number of features extracted using both tools. As previously described, data filtering was done in both cases by removing blank features using a maximum sample-to-blank fold change of 5. Moreover, the data analysis by the MS-DIAL software was done by considering a peak count filter of 50% (calculated as the quotient of the number of biological replicates per group or condition and the total number of samples), and considering that a feature must be detected in 80% of the samples in at least one sample group to be annotated. This peak filter was not contemplated as an available customizable node in version 3.3. Compound Discoverer™ software.

Table S4 describes the 115 Level 2–identified features together with their compound names, RT (min), molecular formulas, compound classes, InChIKey identifiers, total identification scores (%), EI spectrum, and abundance values (expressed as TIC normalized peak heights), among other parameters considered during the feature identification process. An overview of compound classes of Level 2–identified features by MS-DIAL is shown in Fig.  3 B. Similar to the compound overview offered by the Compound Discoverer™ software (Fig.  3 A), sesquiterpenoid (e.g., α -cubebene or γ -muurolene) and monoterpenoid components (e.g., geraniol, terpinolene, fenchyl alcohol) majorly represented thyme extracts according to MS-DIAL annotation results (Table S4 ). The MS-DIAL strategy also revealed other 23 metabolites classified as miscellaneous compounds (e.g., 2-allyl-2-methylcyclohexanone or cumacrene) (Fig.  3 B and Table S4 ). Tetradecane, dodecane, octane, and tetracosane were identified in thyme extracts, among other 9 alkane compounds (Fig.  3 B). The MS-DIAL strategy pointed out 13 different fatty acid–related metabolites including octanoic and linolenic acids, fatty acid esters (e.g., benzylmalonic acid methyl ester and 2-methylbutyl 2-methylbutyrate), fatty alcohols (e.g., ( Z )-9-hexadecen-1-ol), and fatty alcohol esters (e.g., citronellyl hexanoate) (Fig.  3 B and Table S4 ). According to the results shown in Fig.  3 B, diterpenoids (such as abietic acid) and triterpenoids (e.g., lanosterol and squalene) were found in thyme samples (Table S4 ). Other chemical classes of compounds annotated by MS-DIAL included methoxyphenols (e.g., 4-vinylguaiacol), terpene lactones (such as deoxysericealactone), and ergosterol, steroid, and vitamin E derivatives (Fig.  3 B and Table S4 ). Some compounds such as octanoic acid, thymyl acetate, 5-isopropyl-2-methylphenol, terpinolene, β -ionone, and lanosterol were detected in good agreement with thyme phytochemical composition while other identified metabolites, including cedrol, hexacosane, n -pentadecanol, and γ -muurolene, are widespread among spices and herbs [ 27 ].

The comparison results from feature annotation via Compound Discoverer™ or MS-DIAL approaches (Fig.  2 ) revealed that, despite the apparent higher number of initial features extracted using Compound Discoverer™ software, MS-DIAL provided a higher number of final Level 2–putatively annotated compounds (52 features vs . 115 features). The higher number of Level 2–identified features by MS-DIAL could be explained by the different criteria and spectral libraries employed for compound identification (even though the parameters between the tested strategies were kept as similar as possible). Finally, a comparison of Level 2–identified features using Compound Discoverer™ and MS-DIAL revealed a low overlap of candidate compounds between data processing strategies (Table S3 and Table S4 ), as reported in other previous studies assessing the performance of data processing tools for challenging untargeted data analysis in plant metabolomics [ 16 ]. These results showed that only a small percentage of the features can be simultaneously identified by the two methods (although both workflows were designed as similar as possible). This indicates that untargeted analysis of real samples is challenging and highly dependent on the feature extraction capability of each method, including peak detection algorithms used in each case, peak filtering processing, and peak alignment (among others), resulting in differences in feature RT annotation and areas. Furthermore, additional differences may arise from the second main step in untargeted analysis, i.e., feature identification, because each workflow depends on the internal availability of GC–MS libraries.

Particularly, the corresponding Venn diagram [ 28 ] performed considering feature InChIKey (since compound name may vary depending on the data library nomenclature) highlighted only four common compounds between Compound Discoverer™ and MS-DIAL datasets, namely eucalyptol (RT = 5.63 min), sabinene hydrate (RT = 5.98 min), cis -2-menthenol (RT = 6.25 min), and thymohydroquinone (RT = 9.65 min) (Fig. S2 ). Thus, the data processing results highly encourage the evaluation not only of the initial extracted features but also the distribution of unknown, background, and duplicate features as a sign of real data processing performance.

Multivariate data analysis results: PLS-DA supervised modeling

Supervised PLS-DA modeling built considering the training sets was used to extract information about differential compounds to discriminate within Polish and Spanish thyme. The Compound Discoverer™ data matrix further used for multivariate data analysis was made of 80% of the total observations and 52 variables (constant sum normalized peak areas of Level 2–identified features, Table S3 ) which was Pareto-scaled before multivariate data analysis. The PLS-DA model (built considering 80% of total observations as the training set) performed by the SIMCA ® software resulted in 4 latent variables (LVs) explaining 89.3% of the dataset variance considering the first two LVs (LV1 = 65.1% and LV2 = 24.2%) (Fig.  4 A), with high-quality parameters (R 2 Y = 0.987, Q 2  = 0.978, and CV-ANOVA p -value < 0.05, Table S5 ). Moreover, the permutations tests ( n  = 100 permutations) excluded model overfitting ( R 2 , Q 2 intercepts for Poland class: 0.163, − 0.400; for Spain class: 0.152, − 0.420) (Table S5 ). Additionally, the high model predictability indicated by the Q 2 value was externally verified considering the maximum CCR = 100% obtained for the observations included in the prediction set. The PLS-DA score plot pointed out satisfactory sample clustering to discriminate between Polish and Spanish thyme samples along the LV1 (Fig.  4 A).

PLS-DA score plots built considering 80% of total thyme samples (training sets) showing discrimination of samples according to the geographical origin (Poland and Spain). PLS-DA score plots obtained from the SIMCA ® software considering A Compound Discoverer™ and B MS-DIAL datasets. PLS-DA score plots obtained from the MetaboAnalyst platform considering C Compound Discoverer™ and D MS-DIAL datasets

Similarly, multivariate data analysis was performed considering the Pareto-scaled MS-DIAL data matrix consisting of 80% of total observations and 115 Level 2–identified features (TIC normalized peak heights, Table S4 ). The PLS-DA model (built considering the training set) was formed by 3 LVs, explaining 75.7% of the total variance by the first two LVs (LV1 = 71.2% and LV2 4.5%) (Fig.  4 B). In this case, high model performance was also obtained (R 2 Y = 0.987, Q 2  = 0.908, and CV-ANOVA p -value < 0.05, Table S5 ), but the MS-DIAL-based model presented slightly lower predictive ability (Q 2  = 0.908) compared to the Compound Discoverer™-based model (Q 2  = 0.978). Nevertheless, the high model predictability of the MS-DIAL-based model was also confirmed by the external model validation since a maximum correct classification rate (CCR = 100%) was achieved for thyme samples blindly predicted by the model (Table S5 ). Besides, model overfitting was discarded by permutations tests (R 2 , Q 2 intercepts for Poland class: 0.310, − 0.274; for Spain class: 0.300, − 0.274, Table S5 ). The resulting PLS-DA score plot built considering the first 2 LVs showed clear discrimination between thyme samples from the two geographical locations (Poland and Spain) (Fig.  4 B). MS-DIAL platform also contemplates multivariate statistical analysis (i.e., PLS-DA modeling and the VIP approach as offered by the SIMCA ® software to select marker compounds). Multivariate statistical analysis in MS-DIAL could be performed by choosing the following metabolites to be considered as variables (features) from the alignment result dataset: “Ref. matched” (previously defined as Level 2–identified metabolites by library matching) or “Unknown” (Level 5–identified features) tagged compounds. Nevertheless, as described during the discussion of the MS-DIAL annotation results, the MS-DIAL data matrix resulting from the alignment result needed further improvement since it contained duplicate and background features. Thus, compared to the highly curated data matrix considered for further multivariate statistical analysis by the SIMCA ® software, the possibility of manual selection of reliable compounds within “Ref. matched” features to be considered in statistical analysis by the MS-DIAL platform would be of interest in further software improvements.

Multivariate data analysis was also performed using the open-source MetaboAnalyst 5.0 platform. The SIMCA ® and MetaboAnalyst statistical results from Compound Discoverer™ and MS-DIAL data matrices were compared in terms of sample clustering and the model performance parameters (goodness-of-fit by R 2 Y and goodness-of-prediction by Q 2 ). When datasets resulting from Compound Discoverer™ (Table S3 ) or MS-DIAL (Table S4 ) data processing were imported to the MetaboAnalyst platform, no normalization nor data filtering was performed since these steps were previously done during the untargeted data analysis using Compound Discoverer™/MS-DIAL software. Thus, similar to the SIMCA ® approach, the data matrices were only Pareto-scaled for reliable comparison between SIMCA ® and MetaboAnalyst statistical results. The PLS-DA model performed from the Compound Discoverer™ data matrix (52 variables) using the MetaboAnalyst platform was characterized by high-performance parameters (R 2 Y = 0.997 and Q 2  = 0.983), explaining 89.3% of total variance considering the first 2 LVs (LV1 = 65.1% and LV2 = 24.2%), with reliable Polish vs . Spanish sample differentiation (Fig.  4 C). As can be seen, the PLS-DA results using the SIMCA ® or the MetaboAnlyst approaches were highly similar (Fig.  4 A and Fig.  4 C), demonstrating that the platform used for multivariate data analysis was not as critical as the software used during the data processing of untargeted metabolomics data, and thus, the use of commercial or open-source statistical software depends on user’s availability. However, it is worth mentioning that the SIMCA ® software allows the external model validation (i.e., a prediction set may be specified and the software blindly classifies these observations into sample classes providing the result into a misclassification table), whose assessment is especially recommended in metabolomics applications focused on food authentification. This option is not currently attempted in the MetaboAnalyst 5.0 platform.

Regarding the MS-DIAL data matrix (made of 115 variables), the resulting PLS-DA model from the MetaboAnalyst platform presented R 2 Y and Q 2 parameters of 0.996 and 0.964, respectively, explaining 75.7% of total variance considering the first 2 LVs (LV1 = 71.2% and LV2 = 4.5%), with reliable sample clustering (Fig.  4 D). In this case, the MetaboAnalyst platform would notice a higher predictive ability of the model (Q 2  = 0.964) compared with SIMCA ® results (Q 2  = 0.908).

Evaluation of significant features to differentiate Polish vs. Spanish thyme

As the final step in food metabolomics applications, once PLS-DA models were built for geographical differentiation of thyme samples, the VIP approach was used to investigate potential markers contributing to the discrimination between Polish and Spanish thyme samples. As a result, the VIP analysis identified 4 differential markers (with VIP scores > 1.00) from the Compound Discoverer™-based PLS-DA model (Fig.  4 A–C), whereas a slightly higher number of differential features (5 markers) were highlighted by the VIP approach considering the MS-DIAL-based PLS-DA model (Fig.  4 B–D). All VIP markers are shown in Table  1 , together with their retention times (min), VIP scores (a comparison of VIP scores obtained from SIMCA ® software and the MetaboAnalyst platform is displayed), p -values (from two-samples t -tests performed by Compound Discoverer™ software or taken from MetaboAnalyst 5.0 platform for MS-DIAL markers), the results from fold change analysis (expressed as Log2(FC) values for Poland vs . Spain class comparison), and supporting literature. Moreover, further information on selected features, including ΔKI, InChIKey identifiers, and compound classes is provided in Table S6 . Interestingly, most of the markers were exclusively annotated from Compound Discoverer™ or MS-DIAL approaches since only one marker (thymohydroquinone) was common between the two tested data processing strategies (Table  1 ).

Thyme markers revealed by the Compound Discoverer™–based approach

Four markers were highlighted for thyme geographical differentiation using the Compound Discoverer™ dataset, namely p -cymene, thymohydroquinone, vitamin E (or α -tocopherol), and 2-methoxy-4-methyl-1-(1-methylethyl)-benzene (or thymol methyl ether) (Table  1 ). The results revealed that most of the markers belong to the monoterpenoid family (Table S6 ).

The PLS-DA model followed by the VIP approach revealed the feature with RT = 5.55 min (VIP scores of 5.47–5.74) as the most differential one to discriminate between Polish and Spanish thyme (Table  1 ). This feature was putatively identified as p -cymene (ΔKI = 7 with a total identification score of 97.2%, Table S3 ) and it was found significantly up-accumulated in Polish thyme (Log2(FC) value of 1.72 and p -value of 8.64 × 10 −12 ) (Table  1 ). In line with these results, p -cymene was described in previous literature as a major constituent of commercial Thymus vulgaris L. [ 14 , 29 ].

Thymohydroquinone (RT of 9.65 min, ΔKI = 1, and annotated with a total identification score of 96.7%), a monoterpenoid reported within the phytochemical composition of thyme [ 14 , 30 ], was highlighted as a differential compound (VIP scores of 2.82–2.87) to discriminate Polish from Spanish thyme (Table  1 ). Fold change analysis (Log2(FC) = 1.33) and the two-sample t -test results ( p -value of 1.93 × 10 −18 ) showed that thymohydroquinone may be used as a marker of thyme cultivated in Poland since this metabolite was found up-accumulated compared to Spanish thyme extracts (Table  1 ). Figure  5 describes a comparison of the Compound Discoverer™ and MS-DIAL workflow for the identification of thyme markers, showing the annotation overview of thymohydroquinone as an example. Firstly, all chromatograms of the feature were properly aligned by the RT or the KI (Fig.  5 A–B). Then, putative annotation was performed by comparing the acquired mass spectra with the NIST or the MS-DIAL libraries (Fig.  5 C–D), obtaining in both cases high total identification scores (> 85%). Additionally, an acceptable difference between the experimental KI and the library KI (ΔKI < 20) was obtained using both platforms. Finally, box plots revealed significant enrichment of thymoquinone in Polish thyme compared to Spanish one ( p -value < 0.05) (Fig.  5 E–F).

Overview of compound identification workflow by Compound Discoverer™ (left) and MS-DIAL software (right) showing the Level 2–identification of thymohydroquinone in thyme as an example. A , B Aligned chromatograms showing feature peaks at RT = 9.65 min detected within the thyme samples. C , D EI-MS spectrum of thymohydroquinone in thyme samples (measurement in black) compared to the reference spectrum from the NIST or public MS-DIAL library (reference in red). E , F Boxplot analysis displaying significant differences in relative levels of thymohydroquinone between Polish and Spanish thyme (significant differences are indicated by an asterisk using a two-sample t -test, p -value < 0.05)

Vitamin E (RT of 21.96 min, ΔKI = 16, and associated with a total identification score of 97.7%), with VIP scores of 1.63–1.67, was reported for the first time as a potential geographical marker of thyme due to the enhanced content in Polish thyme compared with lower concentrations found in Spanish samples (Log2(FC) of 1.67), but in this case, despite being highlighted as a high-contribution compound for Polish vs . Spanish thyme differentiation by the PLS-DA and the VIP analysis, these content differences were not significant ( p -value > 0.05) according to the t -test findings (Table  1 ). Vitamin E (so-called α -tocopherol) has been identified in other related matrices such as saffron [ 33 ]. Furthermore, an α -tocopherol derivative (i.e., α -tocopherol succinate) was recently revealed as a potential marker of thyme submitted to post-harvest sterilization practices [ 8 ].

The remaining metabolite (RT = 7.27 min and VIP scores of 1.49–1.50) was Level 2–identified as thymol methyl ether (ΔKI = 1 with a total identification score of 97.4%) (Table  1 and Table S3 ). Thymol methyl ether was also found at significantly higher relative concentrations in Polish thyme (Log2(FC) of 2.88 and p -value of 9.55 × 10 −28 ) (Table  1 ). Although the occurrence of thymol methyl ether was reported in common thyme ( Thymus vulgaris L.) [ 29 ] and other thyme species [ 31 ], this study highlighted for the first time its novelty as a thyme marker for Polish origin discrimination.

Overall, the univariate data analysis performed for markers annotated via Compound Discoverer™ software and further selected using the VIP approach revealed the following outcome: all differential metabolites (mainly belonging to the monoterpenoid class) may be used as markers of the Polish cultivation area as a result of their enrichment compared to thyme produced in Spain. In line with these findings, previous thyme studies revealed that Polish thyme was mainly characterized by monoterpenoids, representing almost 90% of its total volatile phytochemical composition [ 34 ].

Thyme markers revealed by the MS-DIAL-based approach

Five discriminant metabolites were extracted from the MS-DIAL dataset to differentiate between Polish and Spanish thyme samples (Table  1 ). Compared with Compound Discoverer™ markers, a slightly larger variety of compound classes was found within the MS-DIAL differential compounds, mainly represented by monoterpenoids, followed by sesquiterpenoids, and other miscellaneous compounds (Table S6 ).

In detail, most of the annotated marker metabolites were monoterpenoids, namely 3-acetyl-2,6-dimethyl-2,5-heptadiene and thymohydroquinone (Table  1 ). In fact, 3-acetyl-2,6-dimethyl-2,5-heptadiene (VIP scores of 5.58–5.56) and thymohydroquinone (VIP scores of 2.49–2.51) were the most discriminant variables according to the VIP findings (associated with the highest VIP scores) (Table  1 ). Particularly, the occurrence of thymohydroquinone is reported in common thyme [ 14 , 30 ], whereas 3-acetyl-2,6-dimethyl-2,5-heptadiene occurrence in thyme was firstly reported by this study, revealing its novelty as a thyme marker for Polish origin discrimination (Table  1 ). Considering the fold change results, all the monoterpenoid markers were significantly ( p -values < 0.05) up-accumulated in Polish thyme (positive Log2(FC) values between 1.13 and 1.55 were obtained) (Table  1 ).

Two markers belonging to the sesquiterpenoid family were annotated, namely 6,9-guaiadiene and cis -calamenene (Table  1 ). 6,9-guaiadiene (RT = 8.81 min and VIP scores 1.89–1.97) was Level 2–identified by considering a ΔKI = 12 and a total identification score of 86.5% (Table S4 and Table S6 ). This compound has not been described in thyme but it is found in other condiments such as ginger essential oil ( Z. officinale L.) [ 35 ]. cis -calamenene (RT = 9.52 min, VIP scores 1.14–1.16, ΔKI = 10, and total identification score of 87.9%) (Table S4 and Table S6 ) was found in line with previous literature on thyme phytochemical composition [ 32 ] (Table  1 ). The inspection of fold change results obtained for sesquiterpenoid markers revealed the same up-accumulation trend in Polish thyme (Log2(FC) values ranging from 0.81 to 1.26) (Table  1 ).

The remaining marker was identified as a miscellaneous compound: 5-methyl-1-nonyl-6,8-dioxabicyclo(3.2.1)octan-3-one (Table  1 ). This metabolite was putatively annotated for the first time in this GC-Orbitrap-HRMS study. Interestingly, 5-methyl-1-nonyl-6,8-dioxabicyclo(3.2.1)octan-3-one (VIP score of 1.97) was found at significantly higher levels in Spanish thyme extracts compared to Polish ones (Log2(FC) of − 1.18) (Table  1 ), and thus, it was revealed as a marker of Spanish production area.

Thus, compared to the Compound Discoverer™ approach possibilities, the MS-DIAL data processing strategy revealed not only markers found significantly up-accumulated in thyme produced in Poland but also key metabolites to distinguish the Spanish cultivation area.

Conclusions

In the present study, two different GC-Orbitrap-HRMS data processing approaches (Compound Discoverer™ and MS-DIAL) were comprehensively evaluated for their use in plant-derived food authenticity applications by considering the Polish vs . Spanish thyme marker searching as a case study.

In terms of compound detection, the simultaneous comparison of both data processing strategies has revealed that resulting raw data matrices should be properly curated considering the background, unknown, and potential duplicate features before performing multivariate data analysis to extract differential metabolites, being a time-consuming but essential step for unbiased results. In fact, among the total 2034 (Compound Discoverer™) or 833 (MS-DIAL) extracted features, only 52 or 115 ones, respectively, were Level 2–annotated and further considered, leaving the structures of most detected features as unknown (corresponding to Level 4 or Level 5 of identification confidence), and thus, representing one of the main limitations of current data processing options for untargeted data analysis. In detail, only four Level 2–identified features were common between both datasets, demonstrating that the selection of the library plays a crucial role in determining the compound overview in thyme samples, despite both approaches used GC–MS-based libraries of standardized EI-MS spectrum with KI data (at 70 eV). Although most of the annotated features were exclusive between datasets, the compound class overview did not display disparate outcomes among them, since annotated metabolites in thyme samples were mainly represented by monoterpenoids, and sesquiterpenoids, among other miscellaneous compounds. The further statistical results showed that PLS-DA with the VIP analysis was a powerful tool for the reliable putative identification of 4 and 5 thyme markers by Compound Discoverer™ and MS-DIAL platforms, respectively. Similarly, most of the annotated markers were exclusive from each tested platform. The univariate statistical data analysis findings revealed the enrichment of most of the marker metabolites in Polish thyme, such as p -cymene, thymohydroquinone, and cis -calamenene. Interestingly, the MS-DIAL platform was able to extract metabolites distinctive of Spanish thyme (i.e., 5-methyl-1-nonyl-6,8-dioxabicyclo(3.2.1)octan-3-one).

Therefore, this study demonstrates that the MS-DIAL platform is a promising open science tool that can feasibly applied in food authenticity issues focused on the search for differential marker compounds, although it presents some limitations (e.g., univariate data analysis, including fold change analysis or basic statistics as t -tests, would be of great interest in comparative analysis of markers). Compound Discoverer™ remains a reliable data processing tool with highly customizable workflows, including univariate data analysis for marker interpretation and class comparison. Overall, this study has revealed that performing a fair evaluation of annotated features in untargeted analysis of complex matrices such as spices and herbs is especially challenging. Nevertheless, the findings encourage the use of both approaches for reliable identification of markers depending on the user’s availability. In this sense, this study provides a guidance of Compound Discoverer™/MS-DIAL workflows for users who worked in plant metabolomics based on GC-HRMS data.

Data availability

All datasets generated or analyzed during this study are included in this published article and its supplementary information files. Open access at the following Mendeley repository: https://doi.org/10.17632/969xws24rm.2 .

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Acknowledgements

The authors thank Sabater Spices (Murcia, Spain) for providing authentic Spanish and Polish thyme samples used in this case study.

Funding for open access publishing: Universidad de Almería/CBUA. ARP: “Plan Propio de Investigación y Transferencia de la Universidad de Almería, financiado por la Consejería de Universidad, Investigación e Innovación con fondos del Programa Operativo Fondos Europeos de Desarrollo Regional de Andalucía (FEDER) 2021–2027. Programa: Investigación Científica e Innovación 54.A”. Grant reference: CPUENTE2023/21.

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Araceli Rivera-Pérez: formal analysis; laboratory work; methodology; data curation; software analysis; writing—original draft preparation; project administration; funding acquisition. Antonia Garrido Frenich: supervision; project administration; funding acquisition; writing—review and editing.

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Rivera-Pérez, A., Garrido Frenich, A. Comparison of data processing strategies using commercial vs . open-source software in GC-Orbitrap-HRMS untargeted metabolomics analysis for food authentication: thyme geographical differentiation and marker identification as a case study. Anal Bioanal Chem (2024). https://doi.org/10.1007/s00216-024-05347-0

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Smartwatches and Atrial Fibrillation: What Works and What Needs Improvement?

May 29, 2024   |   Kevin Buda, DO

Expert Analysis

Quick Takes

• Clinicians will increasingly encounter heart rhythm data generated from consumer-grade devices.
• Consumer-grade smartphone-paired devices and smartwatches have very high sensitivity and specificity for atrial fibrillation.
• The specificity for arrhythmias with regular R-R intervals is very poor.

As the availability of smartphone-paired devices and smartwatches increases, clinicians will increasingly encounter data generated from consumer-grade devices. This expert analysis reviews several common consumer-grade devices, their specificity for detecting atrial fibrillation (AF), and the limitations of their use.

Most consumer-grade devices for detecting arrhythmias use photoplethysmography (PPG). This technology measures volumetric changes in blood flow on the basis of the intensity of reflected light measured from the skin's surface. This signal generates peaks proportional to pulsatile blood flow, with the peak-to-peak interval proportional to the R-R interval. Given this method's ease of detecting irregularity, PPG technology has primarily been validated for detecting AF.

The broadened availability of consumer-grade devices may increase the detection of AF in the subclinical phase. With stroke as the initial manifestation of AF in almost one-quarter of cases, early AF detection may help relieve its burden as a leading cause of disability in the United States. 1

The Accuracy

• Apple Watch. The Apple Watch (Apple Inc., Cupertino, California) monitors heart rate and rhythm using PPG. Newer models can also record an on-demand single-lead electrocardiogram (ECG). The Apple Heart Study investigators enrolled >400,000 participants without AF. People who received an irregular pulse notification had telemedicine visits with a clinician and received an ambulatory ECG monitor. Of the 2,064 patients with irregular pulse notifications, the positive predictive value (PPV) for AF was 84%. 2 Notably, Apple recently received a cease-and-desist order on some Apple Watches after the United States International Trade Commission (USITC) ruled that Apple Watch technology infringes on oxygen saturation patents held by Masimo Corporation (Irvine, California). The cease-and-desist order on relevant Apple Watches is scheduled to take effect on December 26. This order may significantly reduce the number of Apple Watches that are available for purchase until patent issues have been resolved.
• Kardiamobile. Kardiamobile (AliveCor, Mountain View, California) is a small handheld device that can provide a 30-sec single-lead ECG. One study included monitoring participants three times daily and whenever they felt palpitations, with findings of a higher rate of AF detection with the Kardiamobile device than with 24-hour ECGs (9.4% vs. 2%). 3 In another study, >1,000 patients without a history of AF were randomized to standard care or twice-weekly monitoring with Kardiamobile, with findings of a 3.8% detection rate for AF in the Kardiamobile arm compared with <1% in the standard-care arm. 4
• Fitbit. Fitbit (Google, Mountain View, California) is a wrist-worn device with PPG technology with 37 million monthly users as of 2022. 5 Similar to the Apple Watch, some newer models also incorporate the ability to perform a single-lead ECG. The Fitbit Heart Study had a similar design to the Apple Heart Study; >400,000 participants enrolled. Routine ambulatory ECG monitoring occurred in patients with irregular rhythm notifications. Among 1,057 participants with an irregular heart rate notification and an analyzable confirmatory ambulatory ECG, the PPV of irregular rhythms for AF when using consumer-grade screening with reflex to medical-grade confirmation was 98.2%. 6

Overall Efficacy

The findings of two meta-analyses included high specificity (94%) and sensitivity (96%) for AF detection with smartphones and noninferiority of smartwatches compared with medical-grade devices. 7,8

Benefits Compared With Medical-Grade Monitoring

• They are more widely available. 4
• They do not require a prescription.
• They can detect arrhythmias independently of ECG checks when the patient is free of symptoms.
• They have very high sensitivity and specificity for detecting AF. 7,8
• They can help monitor patients with established asymptomatic AF to assess AF rate and burden.

Limitations Compared With Medical-Grade Devices

• They are not worn continuously and need to be removed for charging. Therefore, their sensitivity for infrequent paroxysmal arrhythmias is lower.
• The specificity for arrhythmias with regular R-R intervals is very poor. 9
• Monitoring in patients with a low pretest probability of arrhythmias increases the false-positive rate.
• There are no guideline recommendations on what to do with information from consumer-grade devices.
• The large volume of data obtained from consumer-grade devices may further contribute to an already strained clinician workforce.

Future Directions

Given the higher false-positive rate in patients with a low pretest probability of AF, future studies need to determine the patients most likely to benefit from ambulatory monitoring. Further, it is unknown whether increased AF detection on consumer-grade heart rhythm monitoring increases appropriate anticoagulation prescription or lowers cardioembolic stroke risk.

The ongoing Heartline Study will assess the impact of AF detection with the Apple Watch on clinical outcomes. The primary endpoint is the time from randomization to the detection of AF. Secondary endpoints include health resource utilization, cost-effectiveness, and a composite including stroke, heart failure hospitalization, and all-cause death. 10

• Freedman B, Potpara TS, Lip GYH. Stroke prevention in atrial fibrillation. Lancet 2016;388:806-17.
• Perez MV, Mahaffey KW, Hedlin H, et al.; Apple Heart Study Investigators. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med 2019;381:1909-17.
• Koh KT, Law WC, Zaw WM, et al. Smartphone electrocardiogram for detecting atrial fibrillation after a cerebral ischaemic event: a multicentre randomized controlled trial. Europace 2021;23:1016-23.
• Halcox JPJ, Wareham K, Cardew A, et al. Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation 2017;136:1784-94.
• Statista. Number of active users of Fitbit from 2012 to 2022 (in millions) (Statista website). 2023. Available at: https://www.statista.com/statistics/472600/fitbit-active-users/ . Accessed 05/15/2024.
• Lubitz SA, Faranesh AZ, Selvaggi C, et al. Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study. Circulation 2022;146:1415-24.
• Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, et al. Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: a systematic review and meta-analysis. Arch Cardiovasc Dis 2021;114:4-16.
• Elbey MA, Young D, Kanuri SH, et al. Diagnostic utility of smartwatch technology for atrial fibrillation detection - a systematic analysis. J Atr Fibrillation 2021;13:[ePub ahead of print].
• Rajakariar K, Koshy AN, Sajeev JK, Nair S, Roberts L, Teh AW. Modified positioning of a smartphone based single-lead electrocardiogram device improves detection of atrial flutter. J Electrocardiol 2018;51:884-8.
• Gibson CM, Steinhubl S, Lakkireddy D, et al.; Heartline Steering Committee. Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study. Am Heart J 2023;259:30-41.

Clinical Topics: Arrhythmias and Clinical EP, Atrial Fibrillation/Supraventricular Arrhythmias

Keywords: Atrial Fibrillation, Wearable Electronic Devices

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GPT-4 is better than humans at financial forecasting, new study shows

• OpenAI's GPT-4 is better than humans at analyzing financial statements and making forecasts, according to a new study.
• "Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes," the study found.
• Trading strategies based on GPT-4 also delivered more profitable results than the stock market.

OpenAI's GPT-4 proved to be a better financial analyst than humans, according to a new study.

The findings could upend the financial services industry that, like other business sectors, is racing to adopt generative AI technologies.

According to the study conducted by the Booth School of Business at the University of Chicago, the large language model did a better job of analyzing financial statements and making predictions based on those statements.

"Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes," the study said. "The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle."

The study utilized "chain-of-thought" prompts that directed GPT-4 to identify trends in financial statements and calculate different financial ratios. From there, the large language model analyzed the information and predicted future earnings results.

"When we use the chain of thought prompt to emulate human reasoning, we find that GPT achieves an accuracy of 60%, which is remarkably higher than that achieved by the analysts," the study said. The human analysts were closer to the low 50% range with regard to prediction accuracy.

The large language models' ability to recognize financial patterns and business concepts with incomplete information suggests that the technology should play a key role in financial decision-making going forward, according to the study's authors.

Finally, the study found that applying GPT-4's financial acumen to trading strategies produced more profitable trading, with higher share ratios and alpha that ultimately beat the stock market.

"We find that the long-short strategy based on GPT forecasts outperforms the market and generates significant alphas and Sharpe ratios," the study said. 

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