case study analysis techniques

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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• Knowledge Base

Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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• Knowledge Base
• Methodology
• 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.

Prevent plagiarism, run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

Is this article helpful?

Shona McCombes

Other students also liked, correlational research | guide, design & examples, a quick guide to experimental design | 5 steps & examples, descriptive research design | definition, methods & examples.

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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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What is the Case Study Method?

Overview Dropdown up

Overview dropdown down, celebrating 100 years of the case method at hbs.

The 2021-2022 academic year marks the 100-year anniversary of the introduction of the case method at Harvard Business School. Today, the HBS case method is employed in the HBS MBA program, in Executive Education programs, and in dozens of other business schools around the world. As Dean Srikant Datar's says, the case method has withstood the test of time.

Case Discussion Preparation Details Expand All Collapse All

In self-reflection in self-reflection dropdown down, in a small group setting in a small group setting dropdown down, in the classroom in the classroom dropdown down, beyond the classroom beyond the classroom dropdown down, how the case method creates value dropdown up, how the case method creates value dropdown down, in self-reflection, in a small group setting, in the classroom, beyond the classroom.

How Cases Unfold In the Classroom

How cases unfold in the classroom dropdown up, how cases unfold in the classroom dropdown down, preparation guidelines expand all collapse all, read the professor's assignment or discussion questions read the professor's assignment or discussion questions dropdown down, read the first few paragraphs and then skim the case read the first few paragraphs and then skim the case dropdown down, reread the case, underline text, and make margin notes reread the case, underline text, and make margin notes dropdown down, note the key problems on a pad of paper and go through the case again note the key problems on a pad of paper and go through the case again dropdown down, how to prepare for case discussions dropdown up, how to prepare for case discussions dropdown down, read the professor's assignment or discussion questions, read the first few paragraphs and then skim the case, reread the case, underline text, and make margin notes, note the key problems on a pad of paper and go through the case again, case study best practices expand all collapse all, prepare prepare dropdown down, discuss discuss dropdown down, participate participate dropdown down, relate relate dropdown down, apply apply dropdown down, note note dropdown down, understand understand dropdown down, case study best practices dropdown up, case study best practices dropdown down, participate, what can i expect on the first day dropdown down.

Most programs begin with registration, followed by an opening session and a dinner. If your travel plans necessitate late arrival, please be sure to notify us so that alternate registration arrangements can be made for you. Please note the following about registration:

HBS campus programs – Registration takes place in the Chao Center.

India programs – Registration takes place outside the classroom.

Other off-campus programs – Registration takes place in the designated facility.

What happens in class if nobody talks? Dropdown down

Professors are here to push everyone to learn, but not to embarrass anyone. If the class is quiet, they'll often ask a participant with experience in the industry in which the case is set to speak first. This is done well in advance so that person can come to class prepared to share. Trust the process. The more open you are, the more willing you’ll be to engage, and the more alive the classroom will become.

Does everyone take part in "role-playing"? Dropdown down

Professors often encourage participants to take opposing sides and then debate the issues, often taking the perspective of the case protagonists or key decision makers in the case.

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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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• Case Reports

Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers

• January 2010
• The Qualitative Report 13(4)

• McMaster University

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Organizing Your Social Sciences Research Assignments

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• Types of Structured Group Activities
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• Acknowledgments

Definition and Introduction

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

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

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

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

How to Approach Writing a Case Analysis Paper

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

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

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

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

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

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

Structure and Writing Style

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

Introduction

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

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

Background Analysis

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

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

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

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

Identification of Problems

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

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

Alternative Solutions

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

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

Recommended Courses of Action

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

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

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

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

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

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

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Sales CRM Terms

What is Case Study Analysis? (Explained With Examples)

Oct 11, 2023

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

1°) What is Case Study Analysis?

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

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

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

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

1.1 - Definition of Case Study Analysis

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

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

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

1.2 - Advantages of Case Study Analysis

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

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

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

1.3 - Disadvantages of Case Study Analysis

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

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

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

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

2°) Examples of Case Study Analysis

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

2.1 - Example in a Startup Context

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

2.2 - Example in a Consulting Context

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

2.3 - Example in a Digital Marketing Agency Context

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

2.4 - Example with Analogies

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

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

About the author

Arnaud Belinga

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Mastering Case Study Analysis: Techniques & Examples

Unveiling the art of case study analysis: techniques, examples, and pitfalls to avoid.

In the realm of academia, mastering the art of case study analysis is akin to unlocking a treasure trove of critical thinking, analytical prowess, and scholarly excellence. Whether you’re a budding student or a seasoned academic, delving into the depths of case study analysis is an indispensable skill that transcends disciplinary boundaries. In this introductory segment, we’ll navigate through the labyrinth of case study analysis, unraveling its significance and priming ourselves for the intricate journey of effective academic paper writing .

Significance of Case Study Analysis

Case study analysis serves as a cornerstone in academic inquiry, offering a multifaceted lens through which complex phenomena can be dissected, scrutinized, and comprehensively understood. Unlike traditional theoretical frameworks, case studies provide a real-world context, allowing scholars to bridge the gap between theory and practice. Whether exploring intricate business dilemmas, unraveling historical conundrums, or dissecting sociopolitical dynamics, case study analysis empowers researchers to navigate the nuances of multifaceted scenarios, fostering a deeper understanding of the subject matter at hand.

Furthermore, case study analysis cultivates a myriad of invaluable skills essential for academic and professional success. From honing critical thinking and problem-solving abilities to refining communication and research prowess, the process of dissecting and interpreting case studies fosters intellectual agility and scholarly acumen. Moreover, the collaborative nature of case study analysis encourages vibrant discourse and interdisciplinary collaboration, enriching the academic landscape with diverse perspectives and insights.

At its core, effective academic paper writing transcends the mere dissemination of information; it embodies a meticulous process of inquiry, analysis, and synthesis. By setting the stage for effective academic paper writing, we embark on a transformative journey characterized by intellectual exploration, scholarly rigor, and eloquent expression.

To embark on this journey, it is imperative to lay a solid foundation rooted in meticulous preparation and strategic planning. From identifying the research question to conducting comprehensive literature reviews, the pre-writing phase sets the tone for the subsequent stages of academic paper writing. Moreover, cultivating a discerning eye for detail and adhering to scholarly conventions and ethical standards are indispensable prerequisites for crafting a compelling academic paper.

In essence, understanding the significance of case study analysis and setting the stage for effective academic paper writing are not mere academic exercises; they are transformative endeavors that empower scholars to transcend the confines of conventional thinking and embark on a journey of intellectual discovery and scholarly excellence. As we embark on this odyssey, let us embrace the challenges and opportunities that lie ahead, armed with the tools and techniques essential for navigating the intricate terrain of academia with finesse and fortitude.

Deciphering the Structure: Anatomy of a Case Study Analysis

In the intricate world of academia, mastering the art of case study analysis requires not only a keen intellect but also a structured approach. Just as a skilled surgeon navigates through the complexities of the human anatomy, a proficient scholar meticulously dissects the layers of a case study to uncover its underlying insights and implications. Let’s embark on this journey of exploration as we unravel the anatomy of a case study analysis, segment by segment.

• Introduction: Crafting a Compelling Opening Statement

The introduction serves as the proverbial gateway to your case study analysis, setting the stage for what lies ahead. Much like a captivating prologue in a novel, it entices the reader’s curiosity and provides a roadmap for the ensuing discussion. In crafting your opening statement, strive to succinctly outline the purpose of your analysis, establish the relevance of the case study, and highlight the key questions or issues at hand. By captivating the reader’s attention from the onset, you lay a solid foundation for engaging them in the subsequent exploration of the case study.

• Background: Providing Context and Setting the Scene

No case study exists in a vacuum; each scenario is intricately woven into the fabric of its broader context. The background section of your analysis serves to provide the necessary context and background information essential for understanding the case study. Here, you elucidate the historical, social, economic, or cultural factors that underpin the case, shedding light on the circumstances that gave rise to the issues under scrutiny. By painting a vivid picture of the contextual landscape, you enable readers to grasp the significance of the case study within its broader framework, thus facilitating a deeper understanding of the ensuing analysis.

• Analysis: Delving into the Core Issues and Presenting Arguments

At the heart of every case study analysis lies the critical examination of core issues and the presentation of cogent arguments. In this segment, you embark on a systematic exploration of the case study, dissecting its intricacies, and unraveling its underlying complexities. Here, analytical rigor reigns supreme as you apply theoretical frameworks, empirical evidence, and logical reasoning to deconstruct the case study and elucidate its underlying dynamics. Whether exploring organizational challenges, ethical dilemmas, or strategic decisions, your analysis serves to illuminate key insights and unearth actionable insights.

• Recommendations: Proposing Solutions and Actionable Insights

Armed with a comprehensive understanding of the case study, the recommendations segment represents the culmination of your analytical endeavors. Here, you leverage your insights and expertise to propose viable solutions and actionable recommendations aimed at addressing the issues identified in the case study. Whether advocating for organizational restructuring, policy reforms, or strategic initiatives, your recommendations should be grounded in sound analysis, feasibility, and alignment with the broader objectives of the case study. By offering practical guidance and strategic foresight, you empower stakeholders to navigate the challenges posed by the case study with confidence and efficacy.

• Conclusion: Summarizing Key Findings and Concluding Remarks

In the final act of your case study analysis, the conclusion serves as a reflective summary of the key findings, insights, and implications gleaned from your exploration. Here, you distill the essence of your analysis, reaffirming its significance and relevance within the broader context of academic inquiry. Whether summarizing the main arguments, reiterating the importance of your recommendations, or offering avenues for future research, the conclusion encapsulates the overarching narrative of your case study analysis, leaving readers with a lasting impression of your scholarly contribution.

In essence, the anatomy of a case study analysis embodies a systematic journey of exploration, analysis, and synthesis, guided by the principles of scholarly rigor and intellectual inquiry. By mastering each segment of the analysis, you unlock the transformative power of case studies, empowering yourself to unravel the complexities of real-world scenarios and generate actionable insights that drive positive change. As you embark on your own journey of case study analysis, remember to wield your analytical tools with precision, curiosity, and a relentless pursuit of scholarly excellence.

Mastering the Art: Writing Techniques for a Stellar Analysis

As we delve deeper into the realm of case study analysis, it becomes increasingly evident that the effectiveness of our analysis hinges not only on the depth of our insights but also on the clarity and precision with which we communicate them. In this segment, we will explore four essential writing techniques that are indispensable for crafting a stellar case study analysis.

1. Clarity over Complexity: The Importance of Clear and Concise Writing

In the pursuit of academic excellence, clarity reigns supreme. Clear and concise writing not only enhances the readability of your analysis but also fosters a deeper understanding of the subject matter. To achieve clarity, strive to articulate your ideas in simple, straightforward language, avoiding unnecessary jargon or convoluted terminology. Moreover, organize your thoughts in a logical manner, using headings, subheadings, and transitions to guide the reader through your analysis. By prioritizing clarity over complexity, you empower readers to engage with your analysis more effectively, thus maximizing the impact of your scholarly endeavors.

2. Utilizing Evidence: Incorporating Data and Facts to Support Your Analysis

In the realm of academia, assertions devoid of evidence hold little sway. To bolster the credibility of your analysis, it is imperative to incorporate relevant data, facts, and empirical evidence to substantiate your arguments. Whether citing statistical trends, referencing scholarly studies, or presenting case examples, evidence serves as the bedrock upon which your analysis rests. Moreover, critically evaluate the quality and reliability of your sources, prioritizing peer-reviewed research and authoritative sources. By grounding your analysis in tangible evidence, you not only fortify your arguments but also demonstrate your commitment to scholarly rigor and integrity.

3. Critical Thinking: Questioning Assumptions and Exploring Multiple Perspectives

At the heart of effective case study analysis lies the spirit of critical inquiry. Critical thinking entails questioning assumptions, challenging conventional wisdom, and exploring alternative perspectives. As you navigate through the nuances of the case study, adopt a skeptical mindset, probing beneath the surface to unearth underlying assumptions and biases. Moreover, consider the diverse array of perspectives and interpretations that may exist, recognizing that there are often multiple truths to be uncovered. By embracing critical thinking, you enrich your analysis with nuance and depth, elevating it beyond mere description to a nuanced exploration of underlying dynamics and implications.

4. Coherence and Cohesion: Ensuring Seamless Flow and Logical Progression

A well-crafted case study analysis is akin to a finely woven tapestry, characterized by coherence and cohesion. To achieve this, pay meticulous attention to the organization and structure of your analysis, ensuring that each section flows seamlessly into the next. Utilize transitional phrases and logical connectors to maintain the reader’s momentum and guide them through your analysis. Moreover, adopt a holistic perspective, considering the overarching narrative arc of your analysis and how each component contributes to the broader storyline. By prioritizing coherence and cohesion, you create a roadmap that facilitates the reader’s journey through your analysis, ensuring that they arrive at the destination with clarity and comprehension.

In essence, mastering the art of case study analysis requires more than just analytical prowess; it demands a mastery of the craft of scholarly writing. By prioritizing clarity, incorporating evidence, fostering critical thinking, and ensuring coherence and cohesion, you empower yourself to craft a stellar analysis that resonates with readers and makes a lasting scholarly impact. As you hone your writing techniques, remember that excellence is not a destination but a continuous journey of refinement and growth.

Pitfalls to Avoid: Common Mistakes in Case Study Analysis

In the pursuit of mastering the art of case study analysis, it is imperative to be mindful of the common pitfalls that can undermine the effectiveness and credibility of your analysis. By steering clear of these pitfalls, you can elevate your analysis to new heights of scholarly excellence and insight. Let’s explore three prevalent mistakes that researchers often encounter in case study analysis.

Superficial Analysis: Scratching the Surface Without Delving Deeper

One of the most glaring pitfalls in case study analysis is the temptation to skim the surface, providing a cursory overview of the case study without delving into its underlying complexities. Superficial analysis fails to unearth the rich insights and nuances embedded within the case study, resulting in a shallow understanding of the subject matter. To avoid this pitfall, commit yourself to thorough and comprehensive analysis, scrutinizing each facet of the case study with rigor and depth. Take the time to probe beneath the surface, interrogating assumptions, identifying patterns, and unraveling the intricacies that lie hidden beneath. By embracing the ethos of depth over breadth, you enrich your analysis with valuable insights and elevate it from a mere recounting of facts to a nuanced exploration of underlying dynamics and implications.

Overreliance on Opinions: Backing Arguments with Subjective Viewpoints Rather Than Empirical Evidence

In the realm of academic inquiry, opinions hold little weight without the backing of empirical evidence. Yet, a common pitfall in case study analysis is the tendency to rely too heavily on subjective viewpoints and personal opinions, rather than grounding arguments in tangible evidence. While it is essential to acknowledge and incorporate diverse perspectives, it is equally important to anchor your analysis in empirical data, facts, and scholarly research. Resist the temptation to extrapolate conclusions based solely on intuition or personal beliefs, and instead prioritize evidence-based reasoning and logical analysis. By substantiating your arguments with empirical evidence, you bolster the credibility and persuasiveness of your analysis, fostering a deeper appreciation for the insights you present.

Neglecting the Bigger Picture: Failing to Consider Broader Implications and External Factors

A myopic focus on the minutiae of the case study can obscure the broader context and implications that underpin the subject matter. Neglecting the bigger picture not only limits the scope of your analysis but also undermines its relevance and applicability in real-world contexts. To avoid this pitfall, adopt a holistic perspective, considering the broader societal, economic, political, and cultural factors that may influence the case study. Explore the ripple effects and downstream consequences of the issues under scrutiny, recognizing that every action has far-reaching implications beyond the immediate context of the case study. Moreover, remain attuned to external factors and emerging trends that may shape the trajectory of the case study, ensuring that your analysis remains dynamic and responsive to evolving realities. By embracing a panoramic view of the subject matter, you enrich your analysis with depth and foresight, positioning yourself as a thoughtful and insightful scholar capable of navigating the complexities of the world with clarity and acumen.

In conclusion, by avoiding the pitfalls of superficial analysis, overreliance on opinions, and neglecting the bigger picture, you can elevate your case study analysis to new heights of scholarly rigor and relevance. By committing yourself to thorough analysis, evidence-based reasoning, and holistic perspective-taking, you empower yourself to unravel the complexities of the case study with nuance and insight, leaving a lasting scholarly impact.

Case Study Analysis in Action: Real-Life Examples

Real-life case studies offer invaluable insights into the complexities of human behavior, organizational dynamics, and societal phenomena. By examining exemplary analyses from renowned scholars and experts, we can glean valuable lessons and insights that inform our own approach to case study analysis. Let’s explore some notable examples that showcase the transformative power of case study analysis in action.

1. Harvard Business School Case Studies

Harvard Business School (HBS) is renowned for its extensive collection of case studies spanning various industries, disciplines, and geographic regions. These case studies provide a rich tapestry of real-world scenarios, offering a glimpse into the challenges and opportunities faced by organizations across the globe. One notable example is the case study on “Netflix: Disrupting the Video Rental Industry,” authored by Professor Willy Shih and Stephen Kaufman. This case study delves into Netflix’s innovative business model and disruptive impact on the traditional video rental industry. Through rigorous analysis and strategic insights, the authors unravel the key success factors that propelled Netflix to prominence, while also exploring the challenges and dilemmas encountered along the way. By examining this case study, scholars and practitioners alike can extract valuable lessons on innovation, strategic management, and market disruption.

2. Medical Case Studies

Medical case studies offer a window into the intricate world of clinical diagnosis, treatment, and patient care. One notable example is the case study on “Patient X: A Case of Rare Neurological Disorder,” authored by Dr. Linda Smith and Dr. James Lee. This case study presents a challenging diagnostic dilemma encountered by medical practitioners, wherein a patient presents with perplexing symptoms that defy conventional classification. Through meticulous analysis and interdisciplinary collaboration, the medical team navigates through the diagnostic maze, ultimately unraveling the underlying neurological disorder and devising a tailored treatment plan. By dissecting this case study, medical professionals can glean valuable insights into diagnostic reasoning, clinical decision-making, and patient-centered care.

3. Social Science Case Studies

In the realm of social science research, case studies offer a powerful tool for exploring complex social phenomena and human behavior. One exemplary case study is the research on “Community Resilience in the Aftermath of Natural Disasters,” conducted by Dr. Sarah Johnson and Dr. Michael Chen. This case study examines the resilience of communities in the wake of natural disasters, drawing upon empirical evidence and qualitative interviews to elucidate the factors that contribute to community resilience and recovery. Through in-depth analysis and theoretical synthesis, the researchers uncover the interplay of social, economic, and environmental factors that shape community resilience, offering valuable insights for disaster preparedness and response efforts.

In essence, these real-life examples underscore the transformative potential of case study analysis in elucidating complex phenomena, informing strategic decision-making, and driving positive change. By studying exemplary analyses from renowned scholars and experts, we can extract valuable lessons and insights that enrich our own approach to case study analysis, empowering us to navigate the complexities of the world with clarity, insight, and scholarly rigor.

In the realm of academia and beyond, the art of case study analysis serves as a powerful vehicle for unraveling the intricacies of real-world phenomena, informing strategic decision-making, and driving positive change. Through our exploration of the anatomy of case study analysis, we have navigated through its various components, from crafting a compelling introduction to delving into the core issues, proposing actionable recommendations, and reflecting on key findings.

We have also examined the importance of writing techniques such as clarity, evidence utilization, critical thinking, coherence, and cohesion in crafting a stellar analysis. By prioritizing these techniques, scholars can effectively communicate their insights, engage readers, and make a lasting scholarly impact.

Moreover, we have identified common pitfalls to avoid in case study analysis, such as superficial analysis, overreliance on opinions, and neglecting the bigger picture. By steering clear of these pitfalls, scholars can elevate their analysis to new heights of scholarly rigor and relevance.

Finally, we have explored real-life examples of exemplary case study analyses from renowned scholars and experts, extracting valuable lessons and insights that inform our own approach to case study analysis.

In conclusion, mastering the art of case study analysis is not merely an academic exercise; it is a transformative journey characterized by intellectual curiosity, scholarly rigor, and a commitment to excellence. By embracing the principles of thorough analysis, evidence-based reasoning, critical inquiry, and holistic perspective-taking, scholars can unlock the transformative potential of case study analysis, empowering themselves to navigate the complexities of the world with insight, clarity, and scholarly acumen. As we embark on this journey of discovery, let us remain steadfast in our pursuit of knowledge, driven by a relentless curiosity to uncover the truths that lie hidden beneath the surface.

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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

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

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

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

Access your free e-book today.

What Is the Harvard Business School Case Study Method?

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

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

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

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

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

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

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

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

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

5 Benefits of Learning Through Case Studies

1. take new perspectives.

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

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

2. Hone Your Decision-Making Skills

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

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

3. Become More Open-Minded

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

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

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

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

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

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

4. Enhance Your Curiosity

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

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

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

5. Build Your Self-Confidence

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

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

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

How to Experience the Case Study Method

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

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

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

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

About the Author

Case Study Research in Software Engineering: Guidelines and Examples by Per Runeson, Martin Höst, Austen Rainer, Björn Regnell

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DATA ANALYSIS AND INTERPRETATION

5.1 introduction.

Once data has been collected the focus shifts to analysis of data. It can be said that in this phase, data is used to understand what actually has happened in the studied case, and where the researcher understands the details of the case and seeks patterns in the data. This means that there inevitably is some analysis going on also in the data collection phase where the data is studied, and for example when data from an interview is transcribed. The understandings in the earlier phases are of course also valid and important, but this chapter is more focusing on the separate phase that starts after the data has been collected.

Data analysis is conducted differently for quantitative and qualitative data. Sections 5.2 – 5.5 describe how to analyze qualitative data and how to assess the validity of this type of analysis. In Section 5.6 , a short introduction to quantitative analysis methods is given. Since quantitative analysis is covered extensively in textbooks on statistical analysis, and case study research to a large extent relies on qualitative data, this section is kept short.

5.2 ANALYSIS OF DATA IN FLEXIBLE RESEARCH

5.2.1 introduction.

As case study research is a flexible research method, qualitative data analysis methods are commonly used [176]. The basic objective of the analysis is, as in any other analysis, to derive conclusions from the data, keeping a clear chain of evidence. The chain of evidence means that a reader ...

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Five Analytic Techniques in Case Study Research

None of the analytic techniques should be considered easy to use, and all will need much practice to be used powerfully. Your objective should be to start modestly, work thoroughly and introspectively, and build your own analytic repertoire over time. The reward will eventually emerge in the form of com­pelling case study analyses and, ultimately, compelling case studies.

1. Pattern Matching

For case study analysis, one of the most desirable techniques is to use a pattern-matching logic. Such a logic (Trochim, 1989) compares an empiri­cally based pattern with a predicted one (or with several alternative predic­tions). If the patterns coincide, the results can help a case study to strengthen its internal validity.

If the case study is an explanatory one, the patterns may be related to the dependent or the independent variables of the study (or both). If the case study is a descriptive one, pattern matching is still relevant, as long as the predicted pattern of specific variables is defined prior to data collection.

Nonequivalent dependent variables as a pattern. The dependent-variables pattern may be derived from one of the more potent quasi-experimental research designs, labeled a “nonequivalent, dependent variables design” (Cook & Campbell, 1979, p. 118). According to this design, an experiment or quasi-experiment may have multiple dependent variables—that is, a vari­ety of relevant outcomes. For instance, in quantitative health studies, some outcomes may have been predicted to be affected by a treatment, whereas other outcomes may have been predicted not to be affected (Rosenbaum, 2002, pp. 210-211). For these studies as well as a case study, the pattern matching occurs in the following manner: If, for each outcome, the initially predicted values have been found, and at the same time alternative “pat­terns” of predicted values (including those deriving from methodological artifacts, or “threats” to validity) have not been found, strong causal infer­ences can be made.

For example, consider a single case in which you are studying the effects of a newly decentralized office computer system. Your major proposition is that—because each peripheral piece of equipment can work independently of any server—a certain pattern of organizational changes and stresses will be produced. Among these changes and stresses, you specify the following, based on propositions derived from previous decentralization theory:

• employees will create new applications for the office system, and these applica­tions will be idiosyncratic to each employee;
• traditional supervisory links will be threatened, as management control over work tasks and the use of central sources of information will be diminished;
• organizational conflicts will increase, due to the need to coordinate resources and services across the decentralized units; but nevertheless,
• productivity will increase over the levels prior to the installation of the new system.

In this example, these four outcomes each represent different dependent vari­ables, and you would assess each with different measures. To this extent, you have a study that has specified nonequivalent dependent variables. You also have predicted an overall pattern of outcomes covering each of these variables. If the results are as predicted, you can draw a solid conclusion about the effects of decentralization. However, if the results fail to show the entire pattern as pre­dicted—that is, even if one variable does not behave as predicted—your initial proposition would have to be questioned (see BOX 26 for another example).

Pattern Matching on Each of Multiple Outcomes

Researchers and politicians alike recognize that U.S. military bases, located across the country, contribute significantly to a local economy’s housing, employment, and other markets. When such bases close, a corresponding belief is that the community will suffer in some catastrophic (both economic and social) manner.

To test the latter proposition, Bradshaw (1999) conducted a case study of a do- sure that had occurred in a modestly sized California community. He first identified a series of sectors (e.g., housing sales, civilian employment, unemployment, popula­tion turnover and stability, and retail markets) where catastrophic outcomes might have been feared, and he then collected data about each sector before and after the base closure. A pattern-matching procedure, examining the pre-post patterns of outcomes in every sector and also in comparison to other communities and statewide trends, showed that the outcomes were much less severe than antici­pated. Some sectors did not even show any decline. Bradshaw also presented evi­dence to explain the pattern of outcomes, thereby producing a compelling argument for his conclusions.

This first case could then be augmented by a second one, in which another new office system had been installed, but of a centralized nature—that is, the equipment at all of the individual workstations had been networked. Now you would predict a different pattern of outcomes, using the same four dependent variables enumerated above. And now, if the results show that the decentral­ized system (Case A) had actually produced the predicted pattern and that this first pattern was different from that predicted and produced by the centralized system (Case B), you would be able to draw an even stronger conclusion about the effects of decentralization. In this situation, you have made a theo­retical replication across cases. (In other situations, you might have sought a literal replication by identifying and studying two or more cases of decen­tralized systems.)

Finally, you might be aware of the existence of certain threats to the valid­ity of this logic (see Cook & Campbell, 1979, for a full list of these threats). For example, a new corporate executive might have assumed office in Case A, leaving room for a counterargument: that the apparent effects of decentraliza­tion were actually attributable to this executive’s appointment and not to the newly installed office system. To deal with this threat, you would have to iden­tify some subset of the initial dependent variables and show that the pattern would have been different (in Case A) if the corporate executive had been the actual reason for the effects. If you only had a single-case study, this type of procedure would be essential; you would be using the same data to rule out arguments based on a potential threat to validity. Given the existence of a sec­ond case, as in our hypothetical example, you also could show that the argu­ment about the corporate executive would not explain certain parts of the pattern found in Case B (in which the absence of the corporate executive should have been associated with certain opposing outcomes). In essence, your goal is to identify all reasonable threats to validity and to conduct repeated comparisons, showing how such threats cannot account for the dual patterns in both of the hypothetical cases.

Rival explanations as patterns. The use of rival explanations, besides being a good general analytic strategy, also provides a good example of pattern match­ing for independent variables. In such a situation (for an example, see BOX 27), several cases may be known to have had a certain type of outcome, and your investigation has focused on how and why this outcome occurred in each case.

Pattern Matching for Rival Explanations and Replicating across Multiple Cases

A common policy problem is to understand the conditions under which new research findings can be made useful to society. This topic was the subject of a mul­tiple-case study (Yin, 2003, chap. 1, pp. 20-22). For nine different cases, the investi­gators first provided definitive evidence that important research findings had indeed been put into practical use in every case.

The main research inquiry then dealt with “how” and “why” such outcomes had occurred. The investigators compared three theories (“rivals”) from the prevailing literature, that (a) researchers select their own topics to study and then successfully disseminate their findings to the practical world (technology “push”), (b) the prac­tical world identifies problems that attract researchers’ attention and that then leads to successful problem solving (demand “pull”), and (c) researchers and practi­tioners work together, customizing an elongated process of problem identification and solution testing (“social interaction”). Each theory predicts a different pattern of rival events that should precede the preestablished outcome. For instance, the demand “pull” theory requires the prior existence of a problem as a prelude to the initiation of a research project, but the same condition is not present in the other two theories.

For the nine cases, the events turned out to match best a combination of the sec­ond and third theories. The multiple-case study had therefore pattern-matched the events in each case with different theoretical predictions and also used a replication logic across the cases.

This analysis requires the development of rival theoretical propositions, articulated in operational terms. The desired characteristic of these rival expla­nations is that each involves a pattern of independent variables that is mutually exclusive: If one explanation is to be valid, the others cannot be. This means that the presence of certain independent variables (predicted by one explana­tion) precludes the presence of other independent variables (predicted by a rival explanation). The independent variables may involve several or many dif­ferent types of characteristics or events, each assessed with different measures and instruments. The concern of the case study analysis, however, is with the overall pattern of results and the degree to which the observed pattern matches the predicted one.

This type of pattern matching of independent variables also can be done either with a single case or with multiple cases. With a single case, the suc­cessful matching of the pattern to one of the rival explanations would be evi­dence for concluding that this explanation was the correct one (and that the other explanations were incorrect). Again, even with a single case, threats to validity—basically constituting another group of rival explanations—should be identified and ruled out. Moreover, if this identical result were addition­ally obtained over multiple cases, literal replication of the single cases would have been accomplished, and the cross-case results might be stated even more assertively. Then, if this same result also failed to occur in a sec­ond group of cases, due to predictably different circumstances, theoretical replication would have been accomplished, and the initial result would stand yet more robustly.

Simpler patterns. This same logic can be applied to simpler patterns, having a minimal variety of either dependent or independent variables. In the simplest case, where there may be only two different dependent (or independent) vari­ables, pattern matching is possible as long as a different pattern has been stip­ulated for these two variables.

The fewer the variables, of course, the more dramatic the different patterns will have to be to allow any comparisons of their differences. Nevertheless, there are some situations in which the simpler patterns are both relevant and compelling. The role of the general analytic strategy would be to determine the best ways of contrasting any differences as sharply as possible and to develop theoretically significant explanations for the different outcomes.

Precision of pattern matching. At this point in the state of the art, the actual pattern-matching procedure involves no precise comparisons. Whether one is predicting a pattern of nonequivalent dependent variables, a pattern based on rival explanations, or a simple pattern, the fundamental comparison between the predicted and the actual pattern may involve no quantitative or statistical criteria. (Available statistical techniques are likely to be irrelevant because each of the variables in the pattern will probably represent a single data point, and none will therefore have a “variance.”) The most quantitative result will likely occur if the study had set preestablished benchmarks (e.g., productivity will increase by 10%) and the value of the actual outcome was then compared to this benchmark.

Low levels of precision can allow for some interpretive discretion on the part of the investigator, who may be overly restrictive in claiming a pattern to have been violated or overly lenient in deciding that a pattern has been matched. You can make your case study stronger by developing more pre­cise measures. In the absence of such precision, an important suggestion is to avoid postulating very subtle patterns, so that your pattern matching deals with gross matches or mismatches whose interpretation is less likely to be challenged.

2. Explanation Building

A second analytic technique is in fact a special type of pattern matching, but the procedure is more difficult and therefore deserves separate attention. Here, the goal is to analyze the case study data by building an explanation about the case.

As used in this chapter, the procedure is mainly relevant to explanatory case studies. A parallel procedure, for exploratory case studies, has been commonly cited as part of a hypothesis-generating process (see Glaser & Strauss, 1967), but its goal is not to conclude a study but to develop ideas for further study.

Elements of explanations. To “explain” a phenomenon is to stipulate a pre­sumed set of causal links about it, or “how” or “why” something happened. The causal links may be complex and difficult to measure in any precise man­ner (see BOX 28).

In most existing case studies, explanation building has occurred in narra­tive form. Because such narratives cannot be precise, the better case studies are the ones in which the explanations have reflected some theoretically sig­nificant propositions. For example, the causal links may reflect critical insights into public policy process or into social science theory. The public policy propositions, if correct, can lead to recommendations for future pol­icy actions (see BOX 29A for an example); the social science propositions, if correct, can lead to major contributions to theory building, such as the transition of countries from agrarian to industrial societies (see BOX 29B for an example).

Explanation Building in a Single-Case Study

Why businesses succeed or fail continues to be a topic of popular as well as research interest. Explanations are definitely needed when failure occurs with a firm that, having successfully grown for 30 years, had risen to become the number two com­puter maker in the entire country and, across all industries, among the top 50 cor­porations in size. Edgar Schein’s (2003) single-case study assumed exactly that challenge and contains much documentation and interview data (also see BOX 46, Chapter 6, p. 188).

Schein, a professor at MIT, had served as a consultant to the firm’s senior manage­ment during nearly all of its history. His case study tries to explain how and why the company had a “missing gene”—one that appeared critical to the business’s survival. The author argues that the gene was needed to overcome the firm’s other tendencies, which emphasized the excellent and creative quality of its technical operations. Instead, the firm should have given more attention to its business and marketing oper­ations. The firm might then have overcome its inability to address layoffs that might have pruned deadwood in a more timely manner and set priorities among competing development projects (the firm developed three different PCs, not just one).

Explanation Building in Multiple-Case Studies

29A. A Study of Multiple Communities

In a multiple-case study, one goal is to build a general explanation that fits each indi­vidual case, even though the cases will vary in their details. The objective is analo­gous to creating an overall explanation, in science, for the findings from multiple experiments.

Martha Derthick’s (1972) New Towns In-Town: Why a Federal Program Failed is a book about a housing program under President Lyndon Johnson’s administration. The federal government was to give its surplus land—located in choice inner-city areas—to local governments for housing developments. But after 4 years, little progress had been made at the seven sites—San Antonio, Texas; New Bedford, Massachusetts; San Francisco, California; Washington, D.C.; Atlanta, Georgia; Louisville, Kentucky; and Clinton Township, Michigan—and the program was con­sidered a failure.

Derthick’s (1972) account first analyzes the events at each of the seven sites. Then, a general explanation—that the projects failed to generate sufficient local support— is found unsatisfactory because the condition was not dominant at ail of the sites.

According to Derthick, local support did exist, but “federal officials had nevertheless^ stated such ambitious objectives that some degree of failure was certain” (p. 91). As a result, Derthick builds a modified explanation and concludes that “the surplus lands program failed both because the federal government had limited influence at the local level and because it set impossibly high objectives” (p. 93).

29B. A Study of Multiple Societies

Moore’s (1966) book covers the transformation from agrarian to industrial soci­eties in six different countries—England, France, the United States, China, Japan, and India—and the general explanation of the role of the upper classes and the peas­antry is a basic theme that emerges and that became a significant contribution to the field of history.

Iterative nature of explanation building. The explanation-building process, for explanatory case studies, has not been well documented in operational terms. However, the eventual explanation is likely to be a result of a series of iterations:

• Making an initial theoretical statement or an initial proposition about policy or social behavior
• Comparing the findings of an initial case against such a statement or proposition
• Revising the statement or proposition
• Comparing other details of the case against the revision
• Comparing the revision to the facts of a second, third, or more cases
• Repeating this process as many times as is needed

In this sense, the final explanation may not have been fully stipulated at the beginning of a study and therefore differs from the pattern-matching approaches previously described. Rather, the case study evidence is examined, theoretical positions are revised, and the evidence is examined once again from a new perspective in this iterative mode.

The gradual building of an explanation is similar to the process of refining a set of ideas, in which an important aspect is again to entertain other plausible or rival explanations. As before, the objective is to show how these rival explana­tions cannot be supported, given the actual set of case study events.

Potential problems in explanation building . You should be forewarned that this approach to case study analysis is fraught with dangers. Much analytic insight is demanded of the explanation builder. As the iterative process progresses, for instance, an investigator may slowly begin to drift away from the original topic of interest. Constant reference to the original purpose of the inquiry and the possible alternative explanations may help to reduce this potential problem. Other safeguards already have been covered by Chapters 3 and 4—that is, the use of a case study protocol (indicating what data were to be collected), the establishment of a case study database for each case (formally storing the entire array of data that were collected, available for inspection by a third party), and the following of a chain of evidence.

EXERCISE 5.3 Constructing an Explanation

Identify some observable changes that have been occurring in your neigh­borhood (or the neighborhood around your campus). Develop an explana­tion for these changes and indicate the critical set of evidence you would collect to support or challenge this explanation. If such evidence were avail­able, would your explanation be complete? Compelling? Useful for investi­gating similar changes in another neighborhood?

3. Time-Series Analysis

A third analytic technique is to conduct a time-series analysis, directly analo­gous to the time-series analysis conducted in experiments and quasi-experiments. Such analysis can follow many intricate patterns, which have been the subject of several major textbooks in experimental and clinical psychology with sin­gle subjects (e.g., see Kratochwill, 1978); the interested reader is referred to such works for further detailed guidance. The more intricate and precise the pattern, the more that the time-series analysis also will lay a firm foundation for the conclusions of the case study.

Simple time series. Compared to the more general pattern-matching analysis, a time-series design can be much simpler in one sense: In time series, there may only be a single dependent or independent variable. In these circum­stances, when a large number of data points are relevant and available, statis­tical tests can even be used to analyze the data (see Kratochwill, 1978).

However, the pattern can be more complicated in another sense because the appropriate starting or ending points for this single variable may not be clear. Despite this problem, the ability to trace changes over time is a major strength of case studies—which are not limited to cross-sectional or static assessments of a particular situation. If the events over time have been traced in detail and with precision, some type of time-series analysis always may be possible, even if the case study analysis involves some other techniques as well (see BOX 30).

Using Time-Series Analysis in a Single-Case Study

In New York City, and following a parallel campaign to make the city’s subways safer, the city’s police department took many actions to reduce crime in the city more broadly. The actions included enforcing minor violations (“order restoration and maintenance”), installing computer-based crime-control techniques, and reorganiz­ing the department to hold police officers accountable for controlling crime.

Kelling and Coles (1997) first describe all of these actions in sufficient detail to make their potential effect on crime reduction understandable and plausible. The case study then presents time series of the annual rates of specific types of crime over a 7-year period During this period, crime initially rose for a couple of years and then declined for the remainder of the period. The case study explains how the tim­ing of the relevant actions by the police department matched the changes in the crime trends. The authors cite the plausibility of the actions’ effects, combined with the timing of the actions in relation to the changes in crime trends, to support their explanation for the reduction in crime rates in the New York City of that era.

The essential logic underlying a time-series design is the match between the observed (empirical) trend and either of the following: (a) a theoretically sig­nificant trend specified before the onset of the investigation or (b) some rival trend, also specified earlier. Within the same single-case study, for instance, two different patterns of events may have been hypothesized over time. This is what D. T. Campbell (1969) did in his now-famous study of the change in Connecticut’s speed limit law, reducing the limit to 55 miles per hour in 1955, The predicted time-series pattern was based on the proposition that the new law (an interruption” in the time series) had substantially reduced the number of fatalities, whereas the other time-series pattern was based on the proposi­tion that no such effect had occurred. Examination of the actual data points— that is, the annual number of fatalities over a period of years before and after the law was passed—then determined which of the alternative time series best matched the empirical evidence. Such comparison of “interrupted time series” within the same case can be used in many different situations.

The same logic also can be used in doing a multiple-case study, with con­trasting time-series patterns postulated for different cases. For instance, a case study about economic development in cities may have examined the reasons that a manufacturing-based city had more negative employment trends than those of a service-based city. The pertinent outcome data might have consisted of annual employment data over a prespecified period of time, such as 10 years. In the manufacturing-based city, the predicted employment trend might have been a declining one, whereas in the service-based city, the predicted trend might have been a rising one. Similar analyses can be imagined with regard to the examination of youth gangs over time within individual cities, changes in health status (e.g., infant mortality), trends in college rankings, and many other indicators. Again, with appropriate data, the analysis of the trends can be sub­jected to statistical analysis. For instance, you can compute “slopes” to cover time trends under different conditions (e.g., comparing student achievement trends in schools with different kinds of curricula) and then compare the slopes to determine whether their differences are statistically significant (see Yin, Schmidt, & Besag, 2006). As another approach, you can use regression discon­tinuity analysis to test the difference in trends before and after a critical event, such as the passing of a new speed limit law (see D. T. Campbell, 1969).

Complex time series. The time-series designs can be more complex when the trends within a given case are postulated to be more complex. One can postu­late, for instance, not merely rising or declining (or flat) trends but some rise followed by some decline within the same case. This type of mixed pattern, across time, would be the beginning of a more complex time series. The rele­vant statistical techniques would then call for stipulating nonlinear models. As always, the strength of the case study strategy would not merely be in assess­ing this type of time series but also in having developed a rich explanation for the complex pattern of outcomes and in comparing the explanation with the outcomes.

Greater complexities also arise when a multiple set of variables—not just a single one—are relevant to a case study and when each variable may be pre­dicted to have a different pattern over time. Such conditions can especially be present in embedded case studies: The case study may be about a single case, but extensive data also cover an embedded unit of analysis (see Chapter 2, Figure 2.3). BOX 31 contains two examples. The first (see BOX 31 A) was a single-case study about one school system, but hierarchical linear models were used to analyze a detailed set of student achievement data. The second (see BOX 3IB) was about a single neighborhood revitalization strategy taking place in several neighborhoods; the authors used statistical regression models to analyze time trends for the sales prices of single-family houses in the tar­geted and comparison neighborhoods and thereby to assess the outcomes of the single strategy.

More Complex Time’Series Analyses: Using Quantitative Methods When Single’Case Studies Have an Embedded Unit of Analysis

31 A. Evaluating the Impact of Systemwide Reform in Education

Supovitz and Taylor (2005) conducted a case study of Duval County School District in Florida, with the district’s students serving as an embedded unit of analysis. A quantitative analysis of the students’ achievement scores over a 4-year period, using hierarchical linear models adjusted for confounding factors, showed “little evidence of sustained systemwide impacts on student learning, in comparison to other districts.”

The case study includes a rich array of field observations and surveys of principals, tracing the difficulties in implementing new systemwide changes prior to and during the 4-year period. The authors also discuss in great detail their own insights about systemwide reform and the implications for evaluators—that such an “intervention” is hardly self-contained and that its evaluation may need to embrace more broadly the institutional environment beyond the workings of the school system itself.

31B. Evaluating a Neighborhood Revitalization Strategy

Galster, Tatian, and Accordino (2006) do not present their work as a case study. The aim of their study was nevertheless to evaluate a single neighborhood revitalization strategy (as in a single-case study) begun in 1998 in Richmond, Virginia. The article presents the strategy’s rationale and some of its implementation history, and the main conclusions are about the revitalization strategy. However, the distinctive ana­lytic focus is on what might be considered an “embedded” unit of analysis: the sales prices of single-family homes. The overall evaluation design is highly applicable to a wide variety of embedded case studies.

To test the effectiveness of the revitalization strategy, the authors used regression models to compare pre- and postintervention (time series) trends between housing prices in targeted and comparison neighborhoods. The findings showed that the revitalization strategy had “produced substantially greater appreciation in the mar­ket values of single-family homes in the targeted areas than in comparable homes in X^similarly distressed neighborhoods.”

In general, although a more complex time series creates greater problems for data collection, it also leads to a more elaborate trend (or set of trends) that can strengthen an analysis. Any match of a predicted with an actual time series, when both are complex, will produce strong evidence for an initial the­oretical proposition.

Chronologies. The compiling of chronological events is a frequent technique in case studies and may be considered a special form of time-series analysis. The chronological sequence again focuses directly on the major strength of case studies cited earlier—that case studies allow you to trace events over time.

You should not think of the arraying of events into a chronology as a descriptive device only. The procedure can have an important analytic pur­pose—to investigate presumed causal events—because the basic sequence of a cause and its effect cannot be temporally inverted. Moreover, the chronol­ogy is likely to cover many different types of variables and not be limited to a single independent or dependent variable. In this sense, the chronology can be richer and more insightful than general time-series approaches. The ana­lytic goal is to compare the chronology with that predicted by some explana­tory theory—in which the theory has specified one or more of the following kinds of conditions:

• Some events must always occur before other events, with the reverse sequence being impossible.
• Some events must always be followed by other events, on a contingency
• Some events can only follow other events after a prespecified interval of time.
• Certain time periods in a case study may be marked by classes of events that differ substantially from those of other time periods.

If the actual events of a case study, as carefully documented and deter­mined by an investigator, have followed one predicted sequence of events and not those of a compelling, rival sequence, the single-case study can again become the initial basis for causal inferences. Comparison to other cases, as well as the explicit consideration of threats to internal validity, will further strengthen this inference.

Summary conditions for time-series analysis. Whatever the stipulated nature of the time series, the important case study objective is to examine some relevant “how” and “why” questions about the relationship of events over time, not merely to observe the time trends alone. An interruption in a time series will be the occasion for postulating potential causal relationships; similarly, a chronological sequence should contain causal postulates.

On those occasions when the use of time-series analysis is relevant to a case study, an essential feature is to identify the specific indicator(s) to be traced over time, as well as the specific time intervals to be covered and the presumed temporal relationships among events, prior to collecting the actual data. Only as a result of such prior specification are the relevant data likely to be collected in the first place, much less analyzed properly and with minimal bias.

In contrast, if a study is limited to the analysis of time trends alone, as in a descriptive mode in which causal inferences are unimportant, a non-case study strategy is probably more relevant—for example, the economic analysis of consumer price trends over time.

Note, too, that without any hypotheses or causal propositions, chronologies become chronicles—valuable descriptive renditions of events but having no focus on causal inferences.

EXERCISE 5.4 Analyzing Time-Series Trends

Identify a simple time series—for example, the number of students enrolled at your university for each of the past 20 years. How would you compare one period of time with another within the 20-year period? If the university admissions policies had changed during this time, how would you compare the effects of such policies? How might this analysis be considered part of a broader case study of your university?

4. Logic Models

This fourth technique has become increasingly useful in recent years, especially in doing case study evaluations (e.g., Mulroy & Lauber, 2004). The logic model deliberately stipulates a complex chain of events over an extended period of time. The events are staged in repeated cause-effect-cause- effect patterns, whereby a dependent variable (event) at an earlier stage becomes the independent variable (causal event) for the next stage (Peterson & Bickman, 1992; Rog & Huebner, 1992). Evaluators also have demonstrated the benefits when logic models are developed collaboratively—that is, when evaluators and the officials implementing a program being evaluated work together to define a program’s logic model (see Nesman, Batsche, & Hernandez, 2007). The process can help a group define more clearly its vision and goals, as well as how the sequence of programmatic actions will (in theory) accomplish the goals.

As an analytic technique, the use of logic models consists of matching empir­ically observed events to theoretically predicted events. Conceptually, you therefore may consider the logic model technique to be another form of pattern matching. However, because of their sequential stages, logic models deserve to be distinguished as a separate analytic technique from pattern matching.

Joseph Wholey (1979) was at the forefront in developing logic models as an analytic technique. He first promoted the idea of a “program” logic model, tracing events when a public program intervention was intended to produce a certain outcome or sequence of outcomes. The intervention could initially pro­duce activities with their own immediate outcomes; these immediate outcomes could in turn produce some intermediate outcomes; and in turn, the interme­diate outcomes were supposed to produce final or ultimate outcomes.

To illustrate Wholey’s (1979) framework with a hypothetical example, consider a school intervention aimed at improving students’ academic perfor­mance. The hypothetical intervention involves a new set of classroom activi­ties during an extra hour in the school day (intervention). These activities provide time for students to work with their peers on joint exercises (immedi­ate outcome). The result of this immediate outcome is evidence of increased understanding and satisfaction with the educational process, on the part of the participating students, peers, and teachers (intermediate outcome). Eventually, the exercises and the satisfaction lead to the increased learning of certain key concepts by the students, and they demonstrate their knowledge with higher test scores (ultimate outcome).

Going beyond Wholey’s (1979) approach and using the strategy of rival explanations espoused throughout this book, an analysis also could entertain rival chains of events, as well as the potential importance of spurious external events. If the data were supportive of the preceding sequence involving the extra hour of schooling, and no rivals could be substantiated, the analysis could claim a causal effect between the initial school intervention and the later increased learning. Alternatively, the conclusion might be reached that the specified series of events was illogical—for instance, that the school interven­tion had involved students at a different grade level than whose learning had been assessed. In this situation, the logic model would have helped to explain a spurious finding.

The program logic model strategy can be used in a variety of circumstances, not just those where a public policy intervention has occurred. A key ingredi­ent is the claimed existence of repeated cause-and-effect sequences of events, all linked together. The links may be qualitative or, with appropriate data involving an embedded unit of analysis, even can be tested with structural equation models (see BOX 32). The more complex the link, the more defini­tively the case study data can be analyzed to determine whether a pattern match has been made with these events over time. Four types of logic models are discussed next. They mainly vary according to the unit of analysis that might be relevant to your case study.

Testing a Logic Model of Reform in a Single School System

An attempted transformation of a major urban school system took place in the 1980s, based on the passage of a new law that decentralized the system by installing powerful local school councils for each of the system’s schools.

Bryk, Bebring, Kerbow, Rollow, and Easton (1998) evaluated the transformation, including qualitative data about the system as a whole and about individual schools (embedded units of analysis) in the system. At the same time, the study also includes a major quantitative analysis, taking the form of structural equation mod­eling with data from 269 of the elementary schools in the system. The path analysis is made possible because the single case (the school system) contains an embedded unit of analysis (individual schools).

The analysis tests a complex logic model whereby the investigators claim that pre­reform restructuring will produce strong democracy for a school, in turn producing the systemic restructuring of the school, and finally producing innovative instruc­tion. The results, being aggregated across schools, pertain to the collective experi­ence across all of the schools and not to any single school—in other words, the overall transformation of the system (single case) as a whole.

Individual-level logic model. The first type assumes that your case study is about an individual person, with Figure 5.2 depicting the behavioral course of events for a hypothetical youth. The events flow across a series of boxes and arrows reading from left to right in the figure. It suggests that the youth may be at risk for becoming a member of a gang, may eventually join a gang and become involved in gang violence and drugs, and even later may participate in a gang-related criminal offense. Distinctive about this logic model is the series of 11 numbers associated with the various arrows in the figure. Each of the 11 represents an opportunity, through some type of planned intervention (e.g., community or public program), to prevent an individual youth from continu­ing on the course of events. For instance, community development programs (number 1) might bring jobs and better housing to a neighborhood and reduce the youth’s chances of becoming at risk in the first place. How a particular youth might have encountered and dealt with any or all of the 11 possible interventions might be the subject of a case study, with Figure 5.2 helping you to define the relevant data and their analysis.

Firm or organizational-level logic model . A second type of logic model traces events taking place in an individual organization, such as a manufacturing firm. Figure 5.3 shows how changes in a firm (Boxes 5 and 6 in Figure 5.3) are claimed to lead to improved manufacturing (Box 8) and eventually to improved business performance (Boxes 10 and 11). The flow of boxes also reflects a hypothesis—that the initial changes were the result of external bro­kerage and technical assistance services. Given this hypothesis, the logic model therefore also contains rival or competing explanations (Boxes 12 and 13). The data analysis for this case study would then consist of tracing the actual events over time, at a minimum giving close attention to their chrono­logical sequence. The data collection also should have tried to identify ways in which the boxes were actually linked in real life, thereby corroborating the layout of the arrows connecting the boxes.

An alternative configuration for an organizational-level logic model. Graphically, nearly all logic models follow a linear sequence (e.g., reading from left to right or from top to bottom). In real life, however, events can be more dynamic, not necessarily progressing linearly. One such set of events might occur in relation to the “reforming” or “transformation” of an organization. For instance, business firms may undergo many significant operational changes, and the business’s mission and culture (and even name) also may change. The sig­nificance of these changes warrants the notion that the entire business has been transformed (see Yin, 2003, chaps. 6 and 10, for a case study of a single firm and then the cross-case analysis of a group of transformed firms). Similarly, schools or school systems can sufficiently alter their way of doing business that “sys­temic reform” is said to be occurring. In fact, major public initiatives deliber­ately aim at improving schools by encouraging the reform of entire school systems (i.e., school districts). However, neither the business transformation nor school reform processes are linear, in at least two ways. First, changes may reverse course and not just progress in one direction. Second, the completed transformation or systemic reform is not necessarily an end point implied by the linear logic model (i.e., the final box in the model); continued transforming and reforming may be ongoing processes even over the long haul.

Figure 5.4 presents an alternatively configured, third type of logic model, reflecting these conditions. This logic model tracks all of the main activities in a school system (the initials are decoded in the key to the figure)—over four periods of time (each time interval might represent a 2- or 3-year period of time). Systemic reform occurs when all of the activities are aligned and work together, and this occurs at t 3 in Figure 5.4. At later stages, however, the reform may regress, represented by t 4 , and the logic model does not assume that the vacillations will even end at t 4 . As a further feature of the logic model, the entire circle at each stage can be positioned higher or lower, representing the level of student performance—the hypothesis being that systemic reform will be associated with the highest performance. The pennants in the middle of the circle indicate the number of schools or classrooms implementing the desired reform practices, and this number also can vacillate. Finally, the logic model contains a “metric,” whereby the positioning of the activities or the height of the circle can be defined as a result of analyzing actual data.

Program-level logic model. Returning to the more conventional linear model, Figure 5.5 contains a fourth and final type of logic model. Here, the model depicts the rationale underlying a major federal program, aimed at reducing the incidence of HIV/AIDS by supporting community planning and prevention initiatives. The program provides funds as well as technical assistance to 65 state and local health departments across the country. The model was used to organize and analyze data from eight case studies, including the collection of data on rival explanations, whose potential role also is shown in the model (see Yin, 2003 chap. 8, for the entire multiple-case study).

Summary. Using logic models represents a fourth technique for analyzing case study data. Four types of logic models, applicable to different units of analysis and situations, have been presented. You should define your logic model prior to collecting data and then “test” the model by seeing how well the data sup­port it (see Yin, 2003, for several examples of case studies using logic models).

5. Cross-Case Synthesis

A fifth technique applies specifically to the analysis of multiple cases (the previous four techniques can be used with either single- or multiple-case stud­ies). The technique is especially relevant if, as advised in Chapter 2, a case study consists of at least two cases (for a synthesis of six cases, see Ericksen & Dyer, 2004). The analysis is likely to be easier and the findings likely to be more robust than having only a single case. BOX 33 presents an excellent example of the important research and research topics that can be addressed by having a “two-case” case study. Again, having more than two cases could strengthen the findings even further.

Cross-case syntheses can be performed whether the individual case studies have previously been conducted as independent research studies (authored by different persons) or as a predesigned part of the same study. In either situa­tion, the technique treats each individual case study as a separate study. In this way, the technique does not differ from other research syntheses—aggregating findings across a series of individual studies (see BOX 34). If there are large numbers of individual case studies available, the synthesis can incorporate quantitative techniques common to other research syntheses (e.g., Cooper & Hedges, 1994) or meta-analyses (e.g., Lipsey, 1992). However, if only a mod­est number of case studies are available, alternative tactics are needed.

One possibility starts with the creation of word tables that display the data from the individual cases according to some uniform framework. Figure 5.6 has an example of such a word table, capturing the findings from 14 case studies of orga­nizational centers, with each center having an organizational partner (COSMOS Corporation, 1998). Of the 14 centers, 7 had received programmatic support and were considered intervention centers; the other 7 were selected as comparison centers. For both types of centers, data were collected about the center’s ability to co-locate (e.g., share facilities) with its partnering organization—this being only one of several outcomes of interest in the original study.

Using a “Two-Case” Case Study to Test a Policy-Oriented Theory

The international marketplace of the 1970s and 1980s was marked by Japan’s promi­nence. Much of its strength was attributable to the role of centralized planning and support by a special governmental ministry—considered by many to be an unfair competitive edge, compared to the policies in other countries. For instance, the United States was considered to have no counterpart support structures. Gregory Hooks’s (1990) excellent case study points to a counterexample frequently ignored by advocates: the role of the U.S. defense department in implementing an industrial planning policy within defense-related industries.

Hooks (1990) provides quantitative data on two cases—the aeronautics industry and the microelectronics industry (the forerunner to the entire computer chip mar­ket and its technologies, such as the personal computer). One industry (aeronautics) has traditionally been known to be dependent upon support from the federal gov­ernment, but the other has not. In both cases, Hooks’s evidence shows how the defense department supported the critical early development of these industries through financial support, the support of R&D, and the creation of an initial cus­tomer base for the industry’s products. The existence of both cases, and not the aero­nautics industry alone, makes the author’s entire argument powerful and persuasive.

Eleven Program Evaluations and a Cross-“Case” Analysis

Dennis Rosenbaum (1986) collected 11 program evaluations as separate chapters in an edited book. The 11 evaluations had been conducted by different investigators, had used a variety of methods, and were not case studies. Each evaluation was about a different community crime prevention intervention, and some presented ample quantitative evidence and employed statistical analyses. The evaluations weie delib­erately selected because nearly all had shown positive results. A cross-case analysis was conducted by the present author (Yin, 1986), treating each evaluation as if it were a separate “case.” The analysis dissected and arrayed the evidence from the 11 evaluations in the form of word tables. Generalizations about successful community crime prevention, independent of any specific intervention, were then derived by using a replication logic, given that all of the evaluations had shown positive results.

The overall pattern in the word table led to the conclusion that the interven­tion and comparison centers did not differ with regard to this particular out­come. Additional word tables, reflecting other processes and outcomes of interest, were examined in the same way. The analysis of the entire collection of word tables enabled the study to draw cross-case conclusions about the intervention centers and their outcomes.

Complementary word tables can go beyond the single features of a case and array a whole set of features on a case-by-case basis. Now, the analysis can start to probe whether different groups of cases appear to share some similar­ity and deserve to be considered instances of the same “type of general case. Such an observation can further lead to analyzing whether the arrayed case studies reflect subgroups or categories of general cases—raising the possibil­ity of a typology of individual cases that can be highly insightful.

An important caveat in conducting this kind of cross-case synthesis is that the examination of word tables for cross-case patterns will rely strongly on argumentative interpretation, not numeric tallies. Chapter 2 has previously pointed out, however, that this method is directly analogous to cross-experiment interpretations, which also have no numeric properties when only a small number of experiments are available for synthesis. A challenge you must be prepared to meet as a case study investigator is therefore to know how to develop strong, plausible, and fair arguments that are supported by the data.

Source: Yin K Robert (2008), Case Study Research Designs and Methods , SAGE Publications, Inc; 4th edition.

13 Aug 2021

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Home » Management Concepts » Case Study Analysis Techniques

Case Study Analysis Techniques

A case study is a concise description of a situation which exists or a series of events which have taken place in an organization. This description may be drawn from actual events in a particular organization or it may be a fabricated description which draws its inspiration from several parts of the author’s experience. Whatever its source, this description (perhaps with organization charts and tables of data included) is the scenario which you will be asked to analyze. Often these scenarios describe a number of things which have gone wrong or indicate things left undone which should have been done and sometimes illustrate effective and sometimes ineffective practice and management.

Usually you will be given questions to answer or a course of action to comment on or you will be invited to make recommendations which have to be supported by argument and analysis.

This method of learning from case studies has long been the core of most business schools teaching. Students learn by ‘participating’ actively in the business case , rather than ‘passively’ studying the theory behind it.

In the conventional business school the treatment of case studies usually falls in several parts:

• The students individually analyze the case and prepare their own comments on the situations they discover, together with some possible solutions.
• They may then discuss the case, formally or informally, as part of a team of students.
• They will then attend a classroom session during which the various ideas developed by the individuals and groups will be tested against each other. The professor’s role in this session will be to ‘chair’ the discussion ensuring that the students fully develop their own ideas.
• At the end of this classroom session the professor will summarize the principal learning points that emerged from the case.

The most important part of this treatment is [1], the individual analysis. You will gain a lot from this element. Your task will be to identify the relevant principles and concepts from the course and shoe how they are indeed useful in understanding the situation and generating recommendations. To do this, you will not need to know the technicalities of the selected industry or organization. Indeed, if you do have some expert or inside knowledge it will be a disadvantage, unless you can resist the temptation to dwell on technicalities rather than on the central issue(s) involved.

Elements [2] and [3] must inevitable be less immediately available since usually ‘participation’ occurs less often than one might imagine. With a typical class size of 50 or so, a few individuals will almost certainly dominate the discussions. Despite the best intentions of the professor, many, if not the majority of the students will be spectators, not active participants.

You will be provided with the full range of opportunities in working on some of the case studies in this course but for some of the case studies that may not be the case. For these, we may provide the professor’s view in the given case study but it is important to note that, like real life, there is no correct solution; there is one opinion among many. The value in each case lies in your developing your own opinions which might, quite justifiably, be totally different.

There are case study analysis techniques that can make reading and analyzing a case study somewhat easier, and certainly faster. They are, incidentally, case study analysis techniques that can be applied almost as productively to the textual material that you are required to study in this course but they are particularly applicable to the case studies.

The first technique is to annotate the case study material . The best way is to use a highlighter, or fluorescent marker, to emphasize the words and passages you think are critical, or at least are relevant to the questions being asked. In addition you can use an ordinary ball-point pen, preferable red, to add your comments in the margins. By these means you can most easily, and immediately, see which elements of the case study to concentrate on. The text may contain a few ideas, albeit often unintentional, that can offer insights into how the company really works; in any case it will still be meaningful in providing the overall context for the material critical for the analysis.

This leads to the second technique, which is to top read the case study material several times , with different priorities each time. The first read should be quick ‘skim’, so that you can put the second more detailed reading into perspective. It is often fatal to get too quickly immersed in the details at the beginning of the case study, without knowing what comes later. The later material may give you a totally different perspective. The first ‘skim’ should also allow you to rule out the most obviously irrelevant material, and may already allow you to highlight certain of the key elements. The second read should be more studies, though it will not now cover the irrelevant material, and should attempt to abstract all the key points.

The third, and subsequent, reads can then home-in on those key points to begin the case study analysis proper.

These techniques can be applied to the statistical material just as much as to the textual matter, and it is sometimes worth reworking the key elements of tables into ratios that you find more meaningful. But, once more, beware that this can easily absorb a great deal of time and is not usually necessary.

Finally, conduct your reading and subsequent analysis with regard to the questions you are being asked.

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• Open access
• Published: 11 June 2024

Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study

• Rachel Macdiarmid   ORCID: orcid.org/0000-0003-4791-7417 1 ,
• Eamon Merrick   ORCID: orcid.org/0000-0003-4269-6360 2 , 3 &
• Rhona Winnington   ORCID: orcid.org/0000-0002-6504-2856 1  

BMC Nursing volume  23 , Article number:  399 ( 2024 ) Cite this article

217 Accesses

Metrics details

Graduate Entry Nursing (GEN) programmes have been introduced as another entry point to nurse registration. In the development of a new GEN programme, a problem-based approach to learning was used to develop critical thinking and clinical reasoning skills of motivated and academically capable students.

To explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

An educational design research approach was used to explore the learning experiences of GEN students using an unfolding case study approach situated in experiential pedagogy and the teaching experiences of the academics who designed it. Data were collected through semi-structured interviews with students once they had finished the course and weekly reflective diary recordings by academic staff throughout implementation. Thematic analysis was used to analyse the data.

Student reflections highlighted that this cohort had insight into how they learned and were comfortable voicing their needs to academic staff. While the unfolding case studies were not liked by all participants, for some it offered a unique learning opportunity; particularly when scaffolded with podcasts, simulation labs, tutorials and clinical placements. Staff reflections primarily aligned with student experiences.

The gaps highlighted in the delivery of the course suggest that a blended pedagogical approach to graduate entry nurse education is required. Specifically, GEN students are aware of the learning needs and are happy to express these to academic staff, thus suggesting that engaging with a co-design curriculum approach will benefit future cohorts.

Peer Review reports

Graduate entry nursing students begin their degrees as experienced learners and must develop critical thinking skills within the shortened degree time frame.

What is already known

Graduate entry students are experienced and academically capable learners who begin with a diverse range of life and career experiences.

What this paper adds

Graduate entry students would benefit by being involved in curriculum design to acknowledge the unique skill set that they bring.

Introduction

Graduate Entry Nursing (GEN) degrees, or second degrees leading to eligibility for nursing registration, have recently been introduced to New Zealand. GEN students are known to be academically capable, motivated, and driven, bringing with them a range of life experiences, and have often had significant careers before enrolment [ 1 , 2 ]. Previous research has identified that teaching and learning methods must be carefully planned and innovative [ 1 ].

Pre-registration nursing education programmes prepare nursing students to provide safe nursing care with crucial skills expected of nursing graduates, including critical thinking and clinical reasoning. Clinical reasoning enables students to approach clinical issues with a problem-solving lens that relies on gathering assessment data and intervening and evaluating the patient’s response to the intervention [ 3 ].

Problem-Based Learning (PBL) aligns with the fundamental elements of authentic learning approaches [ 4 ], where learning is situated in real-world contexts [ 5 ]. Problem-based learning is considered to be an experiential teaching and learning approach that helps students develop a critical lens and clinical reasoning skills [ 6 , 7 ]. The use of PBL in nursing education is well established with previous research focused on students’ experiences and satisfaction [ 8 ]; factors that facilitate or hinder students' learning [ 9 ]; and the development of critical thinking skills [ 10 ].

Graduate entry nursing students report enjoyment of the active learning sets that enabled discussion surrounding case studies, scenarios, and practice issues [ 11 ]. Cangelosi’s [ 12 ] phenomenological study found that although time-poor, GEN students welcomed learning opportunities that were not traditional and facilitated their development and growth.

However, there is conflicting evidence regarding the effectiveness of PBL in nursing. For example, McCormick et al. [ 13 ] compared undergraduate student performance using differing teaching approaches, such as unfolding simulation scenarios versus recorded lectures and found these to be of benefit to students. Carter and Welch [ 14 ] compared the results of associate degree nursing students who attended lectures to those whose learning was informed by an unfolding case study. In contrast to McCormick’s et al.’s [ 13 ] earlier positive results, these authors found both groups of students performed worse in the post-test.

As previous research has identified that new graduate nurses do not always have critical thinking skills, using an unfolding case study approach can reflect the reality of clinical practice where not all the relevant information is known at the first encounter with the patient [ 14 , 15 , 16 ].

Nonetheless, while several studies have investigated the use of unfolding case studies in undergraduate preregistration programmes there is little evidence that supports the use of these with more academically capable GEN students. This article reports on a qualitative interpretivist study that used an educational design methodology to explore the experiences of GEN students who participated in the programme of learning and the experiences of the academics who designed it.

Educational Design Research (EDR) is an iterative, pragmatic, and reflective methodology well suited to small projects [ 17 ]. It has arisen from design-based research and can include both quantitative and qualitative data collection methods. EDR was selected as it fitted with our desire to develop new ways of teaching alongside gaining feedback from both academic staff and students. In the first phase of this research, we redesigned the teaching and learning strategies for a component of the GEN programme [ 18 ].

EDR has four phases (Table  1 ) [ 17 ]:

Aims and objectives

The study aimed to explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

Theoretical framework

To enable the development of clinical reasoning skills a scaffolded learning approach was implemented that involved unfolding case studies designed to represent the health needs of the New Zealand population, thus, encouraging critical thinking. Unfolding case studies reflective of situations that students might face in the future were used to encourage students to consider and analyse information, provoke further questioning and identify the information required to narrow their inquiries [ 14 , 15 ]. Supported by this evidence the academic staff built a learning environment where a regular teaching schedule (two days of lectures and one day of clinical labs per week), was complemented with online resources. Initial questions about the case study were provided on the learning management system. Students attended simulations where they responded to the case and answered questions critical to unpacking the ‘patients’ reality. Alongside the unfolding case studies were podcasts where experts were interviewed on topics related to the case. Tutorials enabled students to collaboratively construct answers and share their perspectives; at the end of each week students shared their answers in an online discussion forum.

Methods and setting

This study was conducted at an education facility in New Zealand offering undergraduate and GEN programmes. The participants are academics involved in the design and delivery of the course and one cohort of students of the GEN programme. This article reports on Phase 2 and 3 of the EDR approach, the academic staff’s reflective diary during course delivery, and students' feedback after the course was completed the first time. The methods were reported using the Consolidated Criteria for Reporting Qualitative Studies (COREQ) [ 19 ].

Participants

Purposeful sampling was used as the researchers were keen to explore the experiences of a specific GEN cohort [ 20 ]. Academic staff involved in the weekly reflective diaries are also the research team ( n  = 3). All students in the identified cohort ( n  = 7) were invited to participate, totalling ten possible participants. Student participants were approached via an advertisement on the university’s learning management system. Students were asked to contact the research assistant, who was separate from the academic staff and was not involved in the delivery of the GEN programme; five students agreed to participate. A $20 petrol voucher was offered to those who participated.

Data collection and analysis

In keeping with education design methodology, the authors met weekly to reflect on their experiences of delivering the content and guiding students. The weekly reflective conversations, between 60–90 min in length, followed a simple format of ‘what worked, what didn’t work, and what would we (as academic staff) change?’ Face to face student interviews were conducted by the research assistant at a time and place convenient to the students using semi-structured questions that were developed by the research team (see Additional file 1 ).

The semi-structured interviews ( n  = 5) and reflective meetings ( n  = 9) were recorded and transcribed verbatim by a research assistant who had signed a confidentiality agreement. All identifying information was deleted from the transcripts by the research assistant before the research team reviewed the data; each recording and transcript was allocated a unique identifier, for example ‘participant one’.

Thematic analysis [ 21 , 22 ] was used to analyse the data. First, the research team independently read the transcribed interviews to familiarise themselves with the data and identified initial codes. Second, the researchers met and reviewed all transcripts to identify themes and reached consensus on the themes emerging from the data. Themes were established once more than 50% of the participants stated the same issue/thought/perception. A matrix was developed whereby common themes were identified, with quotes demonstrating the themes collated to establish an audit trail.

Reflexivity

Central to this study given the proximity of staff to this student cohort, a reflexive stance was essential. Reflexivity is an engendered practice and was used in this instance not to influence the direction and outcome of the research but to allow the researchers to engage in the data to produce viable and valuable outcomes for future staff and students. Specifically, this reflexive practice provided a means for the research to be rigorous through the consideration of the vulnerability of the participating student cohort, thus inciting reflection-before-action [ 23 ].

Ethical considerations

Ethical approval for this study was obtained from the Auckland University of Technology Ethics Committee (AUTEC) (19/233). Given the potential power differential in the student/staff relationship present, participants were approached via an online advertisement and followed up by an independent research assistant. This is key to the success of the project, as such research undertakings have the potential for conflict of interest to exist [ 24 ]. The academic staff recordings were also undertaken with the knowledge that these would remain confidential to the participants and transcriber only, with a memorandum of understanding completed to this effect. Participant information sheets were given to students interested in joining the study to ensure they knew what it entailed and how their safety and identity would be managed. Written consent was obtained before the interviews were undertaken, with oral consent obtained at the beginning of each interview.

Three dominant themes emerged, which focused on the experiences of both GEN students and teaching staff. These were:

Reflective learning: Students and staff ability to clarify what worked and what did not work

Evaluation of learning: Students and staff being insightful about their ways of learning and needs

Challenges: Planning and delivering appropriate content for GEN students is challenging for teaching staff.

Within these overarching themes, subthemes were developed and will be presented in the following data results (Table 2 ).

Reflective learning

The exploration of student and staff experiences and responses to the unfolding case studies unearths what worked and what was problematic for both parties.

Unfolding case study as problem-based approach

The student experiences of using an unfolding case study approach were divided. Some students enjoyed the case scenarios but did not necessarily find them beneficial in terms of knowledge advancement as.

“ I personally, like the case studies but personally I didn’t really find that they enhanced my learning in like the clinical setting ” (P1)

or that they were relevant to clinical practice in that.

“… some of it was definitely relatable but I just found it was very different in the clinical setting compared with doing this theoretical case setting ” (P1).

A second student supported this idea that the case studies did not add practical clinical knowledge value as.

“ I mean for me the case studies weren’t challenging…I didn’t think the case studies added anything extra into my practice, they didn’t challenge my clinical reasoning or anything like that ” (P2).

Of note was that those students with previous professional healthcare backgrounds found the use of an unfolding case study approach problematic in that.

“ I found that quite a challenge. I think because with my clinical background I was sort of going straight into, yeah like I wanted more information so you know I probably would have preferred…to have a different case study every week or have all the information…and I’d be like well what about this, what about that? ” (P5).

Participant One, however, noted that while the case studies may not have added knowledge value, they were helpful at times as.

“ …one example is we learnt about arterial blood gases and then I was on placement I came across that literally [on] day one, so was really nice to be able to put something that I’d learnt in class into practice ” (P1).

While some students were less keen on the case study approach and found them hard work, others thought they provided opportunities to encourage discussion, clinical reasoning, and autonomous thinking as.

“ there was no right or wrong answer, you just had to prove your point to say I think it is this because of this, and someone else can say something else and just kind of still prove it because it was a quite grey [area] but I actually found that it really got us thinking ” (P3).

Moreover, the same participant acknowledged that.

“…I think that’s the whole idea of the course [GEN Programme] because at this level they shouldn’t be spoon-feeding you…you should be able to think for yourself and reason things out ” (P3).

Although some discord was present with regard to the case study approach, one participant did acknowledge the value of being able to break down a huge scenario into manageable sections to enhance understanding and clinical decision-making, as.

“ when you break it down it makes it easier to kind of work out what you’re going to do and what steps you’re going to do ” (P4), and that “ because you start looking at the smaller things that you need to do rather than just the big bits ” (P4).

It appears, however, that staff involved in the programme of learning were pleased with the overall notion that problem-based learning approach offered a ‘practical’ means through which to discuss what is the hands-on job of nursing. Specifically,

“ the second session around child abuse and recognising child abuse…took me a bit by surprise as I wasn’t expecting that to go very well and it went extraordinarily well, mostly because it was case based again and story based ” (L1).

Moreover, with regard to encouraging discussion and clinical reasoning at a postgraduate level,

“ I think we’ve really pulled out the difference [of] what we’re expecting of them [GEN students] as opposed to what they may have been used to” (L1).

Use of podcasts

While the use of technology is not necessarily a completely new strategy in tertiary education, here we have linked podcasts recorded with experts in their fields which related to the unfolding case studies, Again, however, there was division in the value of podcast recordings, with some students really enjoying them, saying.

“ I liked the podcasts yeah, I found the podcasts really good especially when there was [sic] different people talking about it, yeah...podcasts are good, like to just chuck on in the car or at the gym ” (P2).

Moreover, some found them easy to listen to because.

“… it’s a different way to learn because like you’ve got YouTube videos and you’ve got books and stuff but podcasts are kind of like easy ” (P2).

Some students found the podcasts particularly engaging saying.

…I just remember listening to it and I think I was in the car and I had stopped because I was on my way home…and I was still listening to it in the garage like when I was home and I was like oh this is a really interesting podcast ” (P2).

Participant three also thought podcasts a positive addition to the resources saying.

“ yeah they were helpful…there was one I listened to…they were talking about dying…I know that [one of the lecturers’] kind of research is kind of talking about death, euthanasia and all this kind of thing, and for some reasons, I don’t know why, maybe that’s why I still remember, I can say it’s the only podcast I really listened to and it was really good because it gave me a good insight as to what is happening… ” (P3)

This positive response was also noted in face-to-face class time as one staff member reported that.

“ they [the students] loved the person who was interviewed, and the feedback was it was really nice to hear a conversation about different perspectives ” (L1).

Yet, not all students were of this opinion, with some advising the podcasts were too long (approximately 60 min each), that they can be distracting, that they preferred videos and images or an in-person discussion, saying.

“ I find podcasts…I tend to switch off a bit, a bit quicker than if I was watching something, I would probably prefer, rather than watching a podcast [sic] I’d rather have an in-class discussion with the person” (P4).

Participant one said that they too struggled with podcasts because.

“ I’m more visual so I like to look at things and see like a slide I guess or what they’re talking about or, so I sort of zone out when it’s just talking and nothing to look at, so that’s what I personally struggle with, they [podcasts] are helpful it’s just I’m more a visual learner ” (P1).

While there were some negative responses to the podcasts, another participant acknowledged their value but offered their own solutions to learning, saying that.

“ I listened to a few podcasts that were put up, because they’re just easy to listen to ” (P2).

but felt that overall there were insufficient resources made available to students and therefore.

“ just went to YouTube and just, any concepts that I was unfamiliar with or stuff in class that we went over and when I went home I was like [I have] no idea what they talked about, I just found my own videos on YouTube… ” (P2).

Evaluation of learning

Learning experiences are unique to each GEN student, as are those experienced by the teaching staff. The data collected highlighted this clearly from both perspectives, offering a particularly strong insight into how this cohort of students’ function.

Approaches to learning

It was evident that these GEN students were aware of their approach to learning and that perhaps the structure of the teaching module did not align with their needs as.

“ I’m not really the best at utilising online things I’m a really hands on learner and things like a lecture…but you know if it’s yeah, more like class time, it’s sort of more my, my learning style [I] guess ” (P5).

A number of students were able to identify that they were visual learners as.

“ I use videos more because I guess I’m more of a visual learner as well and I learn better by seeing things instead of reading a huge article, I think that [videos] it helps me a bit more” (P4).

Another student, however, preferred a discussion based approach as opposed to either videos or podcasts saying that.

“ if it’s interesting, if it’s a topic that you can like relate to [through a podcast] or something it’s fine, but for me I just switch off not really taking a lot of the information [in] whereas in a discussion setting you can ask questions and you can interact with the person, yeah I find that would be a bit more helpful ” (P4).

This approach to learning through discussion was also noted when the teaching staff reflected on their experiences in that in one teaching session the GEN students.

“ were engaged, they were round a table with the second speaker talking and what I think enabled the discussion was that she [the speaker] was using her data as stories and so she was reading them, actually she got them [the students] to read them out” (L3).

The notion of learning styles, however, was not as linear as being visual or auditory or practical, as one student noted that a combination of styles was preferable to enhance learning, saying that.

“ if we weren’t able to have lectures like a recorded lecture so that there was a PowerPoint and just someone actually talking you through it, like I know there’s the YouTube videos…some of them were a little bit helpful, but like I just felt that sometimes we missed the teaching aspect of it. There’s a lot of self-directed stuff but definitely like a recorded lecture every week to go along with the readings and extra videos to watch ” (P5).

Students as insightful and engaged

While GEN students are known for their tenacity and ability to cope with the pressure and fast paced delivery, some students discovered that this did not necessarily equate with their preferred approach to learning. This cohort of GEN students were insightful in terms of their strengths and weaknesses in relation to knowledge acquisition. The use of the unfolding case studies, however, caused some frustrations as.

“ for me it was challenging in the fact that I felt I actually got frustrated because I’m thinking well I want to know this, I want to know that and yeah not getting all the information that I wanted at the time ” (P5).

This participant went further, saying that.

“ I definitely found that difficult [lack of information] I felt like [I] wasn’t getting as much information as I wanted to be able to make my clinical decisions ” (P5),

however this may have been due to the student’s background as their.

“my background is in paramedicine ” where “ we get a lot of information in a very short amount of time ” (P5).

Some fundamental issues were raised by the participants in terms of how much study is required for them to acquire the new knowledge. As one student highlighted,

“ I have a really terrible memory, so I kind of need to listen to things a few times or write it down and then watch a video and do some more reading and then like it’s good having another element to get into your brain you know ” (P2).

For one student, a solution to this was to ensure they did their preparation before attending class as.

“ you’re supposed to have read these things before coming to class, some people don’t but my kind of person, I’d read before coming to class and I tended to answer those questions so the critical, analytical part of me would be trying to find out and come up with a reasonable answer…” (P3).

For another participant, they took an alternative pathway to learning as they.

“ I just watch it and I don’t take [it in], it just sits in the back of my head because sometimes it’s building on top of previous knowledge so just, I just watch it to see if I can gain anything from that, I don’t necessarily take down notes or anything, but I just watch it so that it’s there you know ” (P4).

The pace of content delivery appeared problematic for some students, especially in relation to the practical sessions, with one student highlighting that.

“ personally I didn’t’ really like it and most of the time they were rushing, I was always like can I write this down to go back home to like really make sense of it and then sometimes obviously, sometimes I would have to say can I stay back and practice this thing again [as] I didn’t grab it as quickly as others did and the essence of the labs is that it’s grab all of these things ” (P3).

Challenges: Teaching staff experiences of GEN student learning

While on the whole the teaching staff were able to gauge the learning needs of this GEN cohort, the expectations of both parties did not always align, with one staff member reporting that.

“ the two biggest challenges was [sic] getting them [the students] to unpack already learned behaviour and [to] acknowledge their own limitations or bias ” (L1),

however by the end of the semester the same staff member reported that.

“ I think we made a lot of progress in getting them to acknowledge how they learn ” (L1).

Moreover, the challenges anticipated in teaching GEN students were not those that transpired in that.

“ I actually thought going into the first paper I was pretty excited as to how it was going to roll out, the problems I encountered were not the problems I anticipated ” (L3).

The vocality of this cohort was tangible, however, when content did not meet their needs, interest or expectations with the students saying,

“ that they didn’t do the materials because it wasn’t of interest to them and requested other teaching very much related to the assignment as opposed to anything else …” (L1).

It was expected that the GEN students would be participatory both in class and online irrespective of their ways of learning, but there was a difference in both responses and comfort with this form of engagement. One student that talked about the unfolding case study and the online component of assessment as being problematic said that.

“.. we had to put up about 250 words of something related to the case study every week and then we spoke to someone else, [I] didn’t really like the responses…I didn’t really like having to respond to someone else ” (P3).

Yet in contrast to this statement, the teaching staff were delighted that.

“…actually I got some fantastic questions from one of the students…emailed to me on Monday night about the case that was online for them, questions that I didn’t talk about in [the] lecture, I didn’t introduce the concept…they’re talking about concepts that are currently undergoing international clinical trials” (L1).

This study explored the experiences of both GEN students and academics using unfolding case studies situated in experiential learning pedagogy. The use of unfolding case studies supported with podcasts embraced our idea of developing content situated in real-life contexts. Learning was scaffolded using different teaching approaches such as podcasts, and experiential simulated learning, to offer learners multiple ways of engaging with content. Scaffolding is recognised as learning material being broken into smaller chunks of learning and in this way aligns with case-based learning [ 25 ]. In this way, we hoped that not only would students engage in problem-solving, and develop clinical decision-making skills [ 26 , 27 ], but that they would also achieve deep and lifelong learning and ultimately have an ‘aha’ moment when it all made sense.

Reflections on using an unfolding case study approach

Findings were divided, with some students enjoying the unfolding case studies and others describing them as not sufficiently challenging. The scaffolded learning approach that we developed incorporated a range of teaching approaches that enabled them to engage with the content in a way that fitted in with their lifestyle, even if the teaching method did not align with their individual learning preferences. Students reported differing views about the case studies; some enjoyed the unfolding nature while others wanted more context and direction to feel that they could make an informed clinical decision. Nonetheless, even though they did not like information being presented in smaller chunks one student recognised it meant they analysed the information they received more deeply.

Other learning tools such as podcasts were not always valued by participants and yet, the fact that students were able to provide feedback on their use does indicate that they at least attempted to engage with them.

Student reflections indicate that perhaps the use of unfolding case studies as a learning approach is not the solution to engagement, and that often more traditional teaching methods were preferred Indeed, Hobbs and Robinson’s [ 28 ] study of undergraduate nursing students in the US supported Carter and Welch’s [ 14 ] findings that the use of unfolding case studies were of no direct benefit, whilst Ellis et al.’s., [ 29 ] study confirmed that for final year nurse practitioner students unfolding case studies were beneficial in developing critical thinking and stimulating clinical reasoning. Considering these two conflicting findings, further consideration is needed of how to engage highly motivated GEN students.

As such, our results suggest it can be difficult to predict the needs of the GEN students given the diversity of their previous academic qualifications, career, and often significant life experience they bring to the programme [ 30 , 31 ]. Interestingly students in this study simultaneously demonstrated insight into their needs supporting their previous academic study experience and felt sufficiently secure to voice them, which supports evidence found in D’Antonio et al.’s [ 32 ] study. This suggests that GEN students’ capabilities need to be embraced and incorporated when planning curriculum and scaffolding learning. Anecdotally, we have found that students embrace experiential learning such as that offered in simulation labs whether this involves the use of simulated manikins or not, it seems the hands-on learning offers not only the opportunity to experience simulated reality but also fosters collaboration and problem solving with peers that enables them to dwell in learning of what it is to be a nurse.

Graduate entry students recognised as experienced learners

Our students were not overwhelmingly supportive of the pedagogical approach of unfolding case studies we adopted. As previously recognised GEN students are experienced learners and whilst having differing educational backgrounds bring individual experience and knowledge of their own approach to their learning. Nonetheless, the value of their previous learning experience appears problematic in that those learned behaviours and attitudes need to be refocused to engage with learning how to become a nurse, as demonstrated in the academic staff reflections. Despite this background experience and perceived confidence, some students reflected that online engagement that involved exploring the case studies in discussion forums with colleagues was uncomfortable. This was surprising to the academic staff and contrasted sharply with their reflections on the activity but has been previously noted by Boling et al., [ 33 ].

Implications

Given the disparity that exists between student and academic staff experiences, as demonstrated in our study, co-designing content delivery may offer a progressive solution. By engaging ‘students as partners’ it offers them a much deeper level of involvement in future teaching delivery through collaboration and reciprocation of ideas, thus culminating in appropriate curriculum design [ 34 ]. Collaborating with students in course design might facilitate students learning as they become cognisant of the active engagement of academic staff [ 9 , 10 , 35 ]. In the future, we aim to involve students in any curriculum review and course development to ensure their perspectives influence curriculum design and content delivery.

Even so, our initial intention of scaffolding learning by offering different ways for students to engage with content is supported by recent research by Dong et al. [ 36 ] who found that students performed better academically in a flipped classroom. This point, in association with our findings, suggests that the best approach to content delivery for graduate entry nursing students is to ensure students are involved in curriculum and course design alongside the delivery of learning experiences that are well facilitated and supported by faculty so that students are aware of the expectations, required of them, and importantly how they will be assessed.

Limitations

We acknowledge that the sample size in this study is small in terms of generalisability. However, our findings offer interesting, detailed and in-depth insights into the experiences and needs of both GEN students and the academic staff involved in the development and delivery of educational material. Further work needs to be undertaken to evaluate the experiences of GEN students from a range of educational providers. A longitudinal study has been undertaken to explore the motivations and experiences of GEN students in Australasia [ 37 ], which will also support these findings regarding the learning needs of GEN students.

This study has provided a platform through which academics and GEN students can share their insights of teaching and learning experiences. The results offer a clear insight into what these students expect and need to expedite their learning and how teaching staff must respond. While participants' views were somewhat mixed in relation to the use of unfolding case studies and scaffolded learning these results demonstrate how GEN students are aware of their personal ways of learning and how this translates in terms of education needs. The sharing of these experiences provides an insightful lens through which to re-evaluate pedagogical approaches for GEN students. As such, we suggest that to meet the needs of GEN student’s not only is a blended pedagogical approach appropriate but expanding education design boundaries further through a co-design focused approach to GEN programme design.

Availability for data and materials

The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions of the participants, but are available from the corresponding author on reasonable request.

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Analysis of Raise Boring with Grouting as an Optimal Method for Ore Pass Construction in Incompetent Rock Mass—A Case Study

• Published: 21 June 2024

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• Cluber Rojas 1 ,
• Angelina Anani   ORCID: orcid.org/0000-0001-9125-6877 2 ,
• Eduardo Cordova 1 ,
• Wedam Nyaaba 3 ,
• Edward Wellman 2 &
• Sefiu O. Adewuyi 2  

The construction of ore pass systems in underground mines is a high-risk activity, especially in an environment with incompetent rock mass. This study aims to investigate the optimal method for ore pass construction in incompetent rock masses. We evaluated the conventional and raise boring (RB) methods based on safety, efficiency, excavation control, and ground support for ore pass construction. We also performed a stability analysis using analytical Q-raise ( Q R method) and kinematic analysis methods for ore pass construction with a Raise Borer before and after grout injection of the rock mass. As a case study, an ore pass (diameter, 3 m; depth, 100 m) within an incompetent rock mass was considered to gain further insight. The rock mass was characterized according to the classification methods Q Barton, rock quality designation (RQD), rock mass rating (RMR), and geological strength index (GSI). The grout intensity number (GIN) method of grout injection is used. The safety factor (<1.075) obtained before injection was lower than the acceptance criteria in all sections of the rock mass. However, grout injection before Raise Borer excavation resulted in a rock mass safety factor greater than 1.5. Using RB without pre-grouting in the case study indicated that the maximum unsupported diameter (MUSD) of the ore pass was less than the required 3 m. On the contrary, an MUSD of the rock mass post-grouting was equal to or larger than 3 m at all depths.

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Angelina Anani, Edward Wellman & Sefiu O. Adewuyi

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Rojas, C., Anani, A., Cordova, E. et al. Analysis of Raise Boring with Grouting as an Optimal Method for Ore Pass Construction in Incompetent Rock Mass—A Case Study. Mining, Metallurgy & Exploration (2024). https://doi.org/10.1007/s42461-024-01023-0

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Coupled CFD-FEM analysis of the damage causes of the retention bunker: a case study at hard coal mine

• Tomasz Janoszek 1 &
• Marek Rotkegel 1  

Scientific Reports volume  14 , Article number:  14189 ( 2024 ) Cite this article

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Underground coal storage bunkers serve as crucial infrastructural components in the coal mining industry, providing secure and accessible locations for the storage of mined coal. The interaction between stored coal and underground water in coal storage bunkers indeed poses significant challenges due to the unpredictable nature of the resulting coal-water mixture. This phenomenon is particularly prevalent in coal mines operating under water hazards, where groundwater infiltration into storage areas can lead to the formation of coal-water mixtures, altering the physical properties of the stored coal. The interaction between coal and water can result in the formation of coal-water mixtures (hydromixture), which exhibit complex rheological properties. These mixtures may vary in viscosity, density, and particle size distribution, making their behavior difficult to predict. Underground water may exert hydrostatic pressure on the stored coal, influencing its mechanical behavior and compaction properties. Changes in pressure can result in coal compaction or expansion, affecting bunker stability and the integrity of surrounding rock strata. The main goal of the paper was to determine the values of pressure field variations exerted by the flowing hydromixture within underground coal storage bunkers. This objective reflects a critical aspect of understanding the dynamic behavior of coal-water mixtures (hydromixture) under varying conditions, particularly in environments where water hazards pose significant challenges to storage and operational stability. The paper utilized computational fluid dynamics (CFD) methods to examine the changes in pressure within underground coal storage bunkers induced by the flow of coal-water mixtures. The examination of damage to an underground coal storage bunker due to stress distribution was conducted using the finite element method (FEM). This computational technique is widely utilized in engineering and structural analysis to model complex systems and predict the behavior of materials under various loading conditions The results of the CFD numerical simulation were compared with the mathematical models.

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Introduction.

Retention bunkers serve as crucial components in the mining sector, primarily used for storing and controlling the flow of mined coal to the surface. Underground coal storage bunkers serve as large containers capable of holding significant quantities of coal, providing flexibility to accommodate fluctuations in production and demand. These structures play a crucial role in ensuring a continuous and reliable supply of coal to surface operations while allowing for efficient handling and transportation 1 . Underground coal storage bunkers are designed to hold massive amounts of coal, providing a buffer to accommodate increases in production or disruptions in transportation. Internal loads from stored coal within underground storage bunkers can be estimated based on industry standards or guidelines specific to coal storage facilities described in 2 , 3 . Standards for shaft enclosures can serve as valuable references. To assess the operation of the retention bunker construction and the state of its stress, it is critical to know the size of the loads that the bunker is subjected to. During filling and emptying operations, the movement of coal within the bunker induces flow dynamics, including bulk flow, compaction, segregation, and bridging. These flow phenomena can lead to non-uniform loading patterns, localized stress concentrations, and potential instabilities within the bunker structure. Understanding and managing these dynamic flow processes are essential for ensuring the structural integrity and operational safety of the bunker. The issue of internal loads exerted by stored coal on the construction of retention bunkers remains a complex and unresolved challenge. The authors in the study 4 introduce a stochastic model designed to determine optimal bunker placements and capacities within a system featuring a single production section and a series of interconnected conveyors The paper 5 calculates the grain size distribution of the carried copper ore based on photos taken over the main haulage conveyor, which feeds mined material to the underground bunker located at the skip-filling station of the shaft. The authors of paper 2 compiled illustrations depicting typical bunker damage and outlined research techniques for evaluating their technical condition. An important and practical challenge in coal mine production is selecting the optimal location for the coal bunker. Due to the intricate interplay between influencing variables and optimization objectives, linear programming often results in a local rather than a global optimization solution. To address this issue and determine the optimal position for the coal bunker within the coal mine transportation system, the authors of research 6 propose a combination of a nonlinear programming model and the particle swarm optimization (PSO) technique.Equipment used in retention bunkers can experience abrasive wear due to the handling of granular materials such as rocks, pebbles, and sand, leading to potential local failure. Understanding how granular material flows during transport in a granular material handling system is a critical characteristic to consider in designing and operating retention bunkers effectively. In the study 7 , the structure of the tipper body is modeled using the finite element approach, while the granular material is modeled using the discrete element method. This combination of modeling techniques allows for a comprehensive analysis of both the structural behavior of the tipper body and the flow dynamics of the granular material it handles.One significant issue in underground coal mines is the severe damage caused by floor heave in the coal-filled chamber of a vertical coal bunker. In conventional vertical coal bunker systems, the entire weight of the bunker is typically supported by a coal-feeder chamber (CFC). However, maintaining CFCs within soft, expanding floor rock poses a significant challenge for underground coal mines. Additionally, the primary zone of deformation for the entire rock mass surrounding the bunker occurs within the coal seam section that extends into the interior of the bunker. For the construction of vertical bunkers without CFCs in coal mines with soft, expanding floor rocks, the authors of the paper 8 , 9 , 10 , 11 provide valuable references. In the paper 12 , a mechanism for preventing coal bunker blocking was described. This mechanism aims to prevent coal bridging and ensure effective feeding of coal into the bunker. The paper 13 , 14 investigated the coal storage behavior of bunker construction. The authors presented a case study involving a stacker chute system that transfers coal from one receiving conveyor to one outgoing (boom) conveyor to demonstrate the application of two techniques in a real-life high-throughput installation. The study utilized site observations and discrete element method (DEM) simulations to evaluate the effectiveness of traditional and modified continuum method approaches. Furthermore, the paper includes a sensitivity analysis of the modeling parameters. The stability of the retention bunker under excavation slope conditions, considering deformation with and without reinforcement were tested in 15 . The modeling results indicate that the stress field redistributes during excavation and reinforcing, effectively preventing horizontal slope displacement. However, it is noted that the soil at the base of the bunker needs to be strongly compacted and supported by piles. To predict deformation trends, design reinforcement schemes, reduce excessive heave at the base of excavation, minimize horizontal displacement of the excavation slope, and ensure project safety, the stability of the bunker was simulated using the finite element method. The main finding of the research was that abrasive processes lead to corrosion and wear of steel components. In study 16 , tests were conducted to identify areas of the hopper surface that were more damaged and to determine how grain shape affected the bunker’s performance. The relative wear on hoppers caused by friction during operation was assessed using a discrete element technique. Additionally, the wear and tear on steel elements of bunkers operated in underground copper ore mines due to abrasive processes were analyzed in paper 17 . The bunker elements’ linings were found to undergo severe abrasive wear. Among various parameters influencing the abrasive wear of the examined bin elements, the varied physical and mechanical properties of the copper ore were identified as the most significant. Additionally, the safety construction of the coal bunker, focusing on the thickness of the safety aquifer in the bunker roof and the water drainage ability of the aquifer in the sandstone of the roof, was studied in 18 .

The analysis of available literature highlights two main areas of concern regarding underground coal storage bunkers: abrasion processes associated with material transport and the influence of geological and hydrogeological conditions on bunker deformation and stability. These issues have been addressed using stochastic methods and the discrete element method (DEM) numerical method.

The strength and functionality of the retention bunker outlet closure devices are crucial for the correct and trouble-free operation of transporting excavated material to the surface The safety of crews operating the bunker during the dispensing of excavated material, especially underground coal, is paramount. Control measures for the dispensing process from the bunker to the conveyor belt are crucial for maintaining a safe working environment. Engineering knowledge combined with regulations forms the foundation for designing appropriate retention bunker enclosures, surface linings, and strutting elements to mitigate abrasion and ensure structural integrity. The design of the retention bunker outlet also requires a proper approach. Designing a functional and reliable mechanism for metering the spoil and closing the outlet from the bunker is crucial for efficient material handling and operational safety. Additionally, using appropriately strong components, such as plating sheets, stiffeners, screw connections, and other structural elements, ensures the durability and integrity of the outlet structure. At the design stage, engineers must carefully consider the anticipated operating conditions of the device to ensure its effectiveness, reliability, and safety. In this design issue, they are strongly related to the properties of the material stored in the bunker—the underground coal. In the case of the storage of bulk materials, including the underground rock and coal, an important parameter is the moisture content of the material and its accumulation of water, which significantly affects the behaviour of the excavated material. The increased water content of the stored material creates a hydromixture, which has a completely different effect on the bunker outlet closure devices. The load in hydrostatic or hydrodynamic forms affects all the walls of the outlet, and its value is greater than the dry and loose-grained excavated materials. This can lead to exceeding the strength of the various components of the bunker structure and damaging them. Consequently, this leads to the uncontrolled discharge of unreliable excavated material in the form of hydromixture and filling the chamber space under the retention bunker. Examples of such incidents include the recent accidents that occurred at the Ziemowit 19 and Bogdanka 20 hard coal mines.

The incident at the Piast-Ziemowit hard coal mine on February 19, 2021, involved the activation of a fire extinguishing device in the Z-3 retention bunker. The water flowing into the excavated material stored in the reservoir bunker, causing coal and rock suspended in water to enter the roadway. Seven workers were tasked with addressing the failure, but during their work, another hydromixture of coal and rock entered the coal mine roadway from the underground bunker. The hydromixture engulfed the working employees, resulting in six of them escaping from the roadway, while one worker was buried and tragically died 19 . The hydromixture filled the coal mine roadway nearly halfway to its height.At LW Bogdanka S.A., on June 8, 2022, there was an outflow of coal and rock suspended in water from the 5fB retention bunker to the coal mine roadway at a depth of 920 m. The retention bunker collected excavated materials from one longwall and three coal mine roadways. At the time of the accident, there were four miners in the roadway. Due to the uncontrolled outflow of coal and rock suspended in water, one miner was partially buried, and tragically, a second miner was completely buried and died as a result 20 . The outflowing mixture, approximately 280 m 3 in volume, filled the mine roadway to more than half its height, reaching a maximum of 2.8 m.

The reliability of retention bunker devices is paramount as it directly impacts the safety of personnel working with and around them. Incidents involving retention bunkers can lead to serious consequences, including injuries and fatalities. Therefore, ensuring the reliability of these devices through robust design, regular maintenance, and adherence to safety protocols is crucial for protecting the well-being of workers and maintaining safe operations in mining environments.

The article analysis the causes of damage to retention bunker construction elements under typical operating conditions. It employs numerical methods to replicate conditions and investigate the factors contributing to bunker damage. Computational Fluid Dynamics (CFD) solver, specifically SolidWorks Flow Simulation, was utilized to analyze pressure distribution within the bunker. The results from CFD simulations were used to assess mechanical parameters of the retention bunker model using the Finite Element Method (FEM) solver in Solidworks 2018 Simulation. To achieve this, a 3D model of the underground bunker was reconstructed, initial and boundary conditions were defined, and simulation results were meticulously analyzed.

Materials and methods

Retention bunker in-situ analysis.

Figure  1 depicts the underground coal bunker in its in-situ conditions with the observed damage. Specifically, the screw connection (identified as number 1 in Fig.  1 ) has become disconnected, leading to a leak in one of the bunker’s shields (identified as number 4 in Fig.  1 ). Consequently, all stored material within the retention bunker has spilled into the underground excavation, disturbing the proper transport of coal via the conveyor belt.

Retention bunker during the in-situ inspection of damages: 1—screw connection, 2—receiving bin, 3—vertical tube, 4—damaged upper shield, 5—conveyor belt.

During the in-situ inspection, moisture stains were observed on the outer surface of the bunker. These moisture stains are depicted in Fig.  1 as a green line. The presence of these stains indicates that natural or process water has infiltrated the coal stored within the retention bunker. This water-coal interaction has resulted in the formation of a hydromixture, altering the physical properties of the coal from a static solid to a dynamic fluid. Consequently, this change has exacerbated the stability issues of the underground bunker structure.

Retention bunker under the load

Due to its purpose, a retention bunker generates mass forces as a result of the stored solid material. These mass forces within the gravitational field produce static pressure, which increases with the depth of the bunker. The magnitude of the mass forces is influenced by the density of the material. Figure  2 illustrates an example of the static pressure distribution along the underground bunker during the storage of solid material. The geometry of the bunker is divided into two zones based on the pressure distribution. Zone I represents the area where the hydraulic pressure (pn) is directed normally towards the vertical wall of the bunker. Zone II depicts the distribution of hydraulic pressure (pv) oriented vertically towards the wall of the receiving bin (Fig.  2 -1, 2).

Dimensions and determination of static pressures in retention bunker: 1—lower shield, 2—upper shield, 3—receiving bin (hopper).

The bunker is loaded with a solid material along the vertical wall (tube) and along the receiving bin shield, as can be observed from the arrangement of forces illustrated in Fig.  2 . However, because the upper shield of the bunker (Fig. 2 -2, 1 -4) does not have contact with the accumulated material, the additional reinforcement of the upper shield to protect against potential additional loads was not considered. Therefore, it is possible that the change from a solid material (coal) to a two-phase hydromixture (coal and rock suspended in water) may have caused damage to the upper shield (Fig. 2 -2).

The value of the hydraulic pressure along zone I due to the stored solid material can be estimated based on the equation:

The value of the hydraulic pressure (p v ) along zone II can be estimated based on the equation:

where: \({p}_{n}\) —pressure along the vertical wall of the bunker, N m −2 . \({p}_{v}\) —pressure along the receiving bin of the bunker, N m −2 . \(\rho\) —density, kg m −3 . \(g\) —gravity, m s −2 . \({\Delta h}_{cI}\) —depth increment of the vertical wall (tube) of the bunker, m. \({\Delta h}_{cII}\) —depth increment of the receiving bin of the bunker, m. \(D\) —diameter, m. \(\alpha\) —inclination angle of the receiving bin (hopper). The force distribution (F) along the retention bunker can be estimated based on the equation:

where: \(F\) —force, N. \(\rho\) —density, kg m −3 . \(g\) —gravity, m s −2 . \(\Delta H\) —depth increment, m

An essential characteristic of coal is density. The investigations in 21 demonstrated that increased density can be achieved by an agglomeration process when there is an excess of moisture. Small particles of coal combine into larger ones and take on a new form of structure, characterised by individual properties different from those of the original particles. The agglomeration process uses intermediate media that directly affect the rate of agglomerate formation. These include, for example, water 22 . Depending on the particle size, impurities, and moisture, the bulk density of the hard coal can change from 700 to 1100 kgm −3 23 . For the model study, it was assumed that the density of the hydromixture varied within the range of 1450–2000 kgm −3 . This range was chosen to encompass a realistic spectrum of densities that the hydromixture could potentially exhibit under different operating conditions. By considering this range, the study aimed to capture the potential variations in material properties and their effects on bunker performance and stability. Studies referenced in the paper 24 , 25 , 26 This coefficient plays a significant role in determining the resistance to sliding or movement between solid surfaces in contact with each other. In the case of water inflow, slippage occurs at the boundary between grain surfaces, initiating the process of movement between contacting surfaces. Assuming a coal density of 1100 kgm −3 , the hydromixture density equal to 1450 kgm −3 , the volume fraction of coal is 0.75 and the water is 0.25; for 1600 kgm −3 , the volume fraction of coal is 0.69 and 0.31; for 1800 kgm −3 , the volume fraction of coal is 0.61 and the water is 0.39; and for density 2000 kgm −3 , the volume fraction of coal is 0.55 and the water is 0.45. Relationships (1) and (2) were interpreted in Fig.  3 in function of the hydromixture density and the bunker depth. The real geometrical parameters of the retention bunker were adopted in the calculations as shown in Fig.  2 : the bunker diameter (D) is 1.91 m, the inclination angle of the receiving bin is 40°, the total depth (H) of the bunker is 10.7 m, where the depth of zone I is 8.1 m.

Static pressure distribution depending on the depth and the density: ( a )—ρ = 1450 kgm −3 , ( b )—ρ = 1600 kgm −3 , ( c )—ρ = 1800 kgm −3 , ( d )—ρ = 2000 kgm −3 .

In Fig.  3 , the following observations can be made regarding the relationship between material density and maximum pressure within the retention bunker:

For a density of 1450 kg/m 3 , the maximum pressure is approximately 240 kPa.

For a density of 1600 kg/m 3 , the maximum pressure is approximately 270 kPa.

When the material density reaches 1800 kg/m 3 , the maximum pressure recorded is approximately 300 kPa.

For a density of 2000 kg/m 3 , the maximum pressure is approximately 320 kPa

In the case of a change in bunker depth, it is possible to observe a linear increase in static pressure up to a certain depth. This linear increase occurs as a result of the gravitational forces acting on the material stored within the bunker, causing the pressure to incrementally rise with depth. Below the 8-m level, a significant increase in static pressure can indeed be observed, amounting to approximately 60%. This increase is primarily attributed to the change in the inclination angle (α) of the receiving bin. The results of the calculations indicate that depth and density are significant variables that influence the load on a retention bunker. In Fig.  4 , the distribution of forces along the retention bunker illustrates that the force value is indeed dependent on both the density and height of the material stored within the bunker.

Force distribution depending on the depth and the density: ( a )—ρ = 1450 kgm −3 , ( b )—ρ = 1600 kgm −3 , ( c )—ρ = 1800 kgm −3 , ( d )—ρ = 2000 kgm −3 .

The maximum value of the force distribution in Fig.  4 varies as follows:

For a density of 1450 kg/m 3 , the maximum force is 432 kN (Fig.  4 -a).

For a density of 1600 kg/m 3 , the maximum force increases to 472 kN (Fig.  4 -b).

When the density reaches 1800 kg/m 3 , the maximum force further increases to 526 kN (Fig.  4 -c).

Finally, for a density of 2000 kg/m 3 , the maximum force reaches 590 kN (Fig.  4 -d).

Figures  3 and 4 indicate that the shield (Fig.  2 -1) of the receiving bin is subjected to significant loads. The damage occurred as a result of dynamic mass forces associated with the formation of a hydromixture consisting of water and coal. Equations ( 1 ), ( 2 ), and ( 3 ) represent a static model that describes the behavior of the retention bunker under static conditions. These equations do not account for dynamic phenomena such as the formation of hydromixtures, fluid flow, or other transient effects.The hydromixture, characterized by its fluidity and instability, poses a potential threat due to the changing load it imposes on the retention bunker.To estimate the dynamic influence on the strength of the underground bunker construction, Computational Fluid Dynamics (CFD) methods were utilized 22 . The results obtained from the Computational Fluid Dynamics (CFD) simulations provided valuable insights into the dynamic behavior of the hydromixture within the bunker. These insights were then used to predict the stress distribution in the bunker’s elements using the Finite Element Method (FEM).

Numerical analysis

In order to calculate the values of pressure field variations in the volume limited by the retention bunker construction, model tests were carried out.

Numerical grid

The Navier–Stokes equations, which are formulations of the laws of conservation of mass, momentum, and energy, are solved in fluid regions of the retention bunker model, namely 22 , 27 :

Conservation of mass:

Conservation of momentum:

Conservation of energy:

where: \(\rho\) —density, [kg m 3 ]. t —time, [s]. u i , u j —axial velocity, [m s −1 ]. x i , x j —axial coordinate, [m]. \({\tau }_{ij}\) —stress tension, \({\tau }_{ij}=\mu {s}_{ij}\) ; \({\tau }_{ij}^{R}={\mu }_{t}{s}_{ij}-\frac{2}{3}\rho k{\delta }_{ij}\) . p —static pressure, [Pa]. S i —source of momentum, H —total enthalpy, [J·(kg K) −1 ]. h —enthalpy, [J·(kg K) −1 ]. Q H —heat flux, [W m −2 ].

These equations are completed with equations describing the fluid’s state as well as empirical relationships between temperature and the fluid’s density, viscosity, and thermal conductivity. During the numerical simulations, it was assumed that the inflow of fluid (water) into the space of an underground bunker filled with excavated material (coal) resulted in a loss of stability. This loss of stability led to a dynamic outflow of coal and water in the form of a hydromixture towards the outlet of the retention bunker. This scenario reflects the potential conditions where the inflow of water into the bunker can destabilize the stored material, leading to fluidization and the formation of a hydromixture. In Fig.  5 , the variation of the physical parameters of the hydromixture, including dynamic viscosity (Fig.  5 a), specific heat (Fig.  5 b), and heat transfer coefficient (Fig.  5 c), is characterized by the corresponding graphs as a function of temperature.

Adopted parameters of the hydromixture depending on the temperature: ( a )—dynamic viscosity, ( b )—specific heat, ( c )—thermal conductivity.

The transport equations for the turbulent kinetic energy and its dissipation rate were employed using the k-ε model. The laminar, turbulent and transitional flows of homogeneous fluids are described by the following turbulence conservation laws in the modified k-turbulence model with damping functions proposed by 28 :

For turbulence kinetic energy:

For dissipation energy:

where: C ε1 —empirical constant, C ε1  = 1.44, C ε2 —empirical constant, C ε2  = 1.92, C µ —empirical constant, C µ  = 0.09, k —velocity fluctuation (turbulence) kinetic energy [m 2  s −2 ], P B —local vorticity fluctuation production, ε— turbulence kinetic energy dissipation rate [m 2  s −3 ], μ t —turbulent viscosity [Pa s], σ k —turbulent Prandtl number σ k  = 1.0, σ ε —turbulent Prandtl number σ ε  = 1.3, σ B —turbulent Prandtl number σ B  = 0.9, µ —fluid dynamic viscosity [Pa s], f 1 , f 2 —empirical constant.

The Finite Elements Method (FEM) approach was used to do calculations in a SolidWorks simulation module. Computer programmers utilize internal forces and displacements, along with computational methods based on the finite element method (FEM), to automatically calculate the Huber-Mises-Hencky reduced stress. The von Mises-Hencky theory, also known as the Maximum distortion energy theory or the Shear-energy theory, forms the basis for the maximum von Mises stress criterion. The von Mises stress can be expressed in relation to the major stresses σ 1 , σ 2 , and σ 3 as follows 29 :

According to the theory, a ductile material begins to yield or deform plastically when the stress limit is reached by the von Mises stress. The stress limit is typically determined using the yield strength.

The following boundary conditions were considered:

In the case of the CFD solver:

Dynamic viscosity of hydromixture depending on the temperature: µ(T) = 4e −8 T 2 −4e−5 T + 0.0082 [Pa s] (Fig.  5 a),

Specific heat of hydromixture depending on the temperature: Cp(T) = 0.0172T 2 –11.4 T + 6062.2 [J kg −1  K −1 ] (Fig.  5 b),

Thermal conductive of hydromixture depending on the temperature: λ(T) = −6e −6 T 2  + 0.005 T−0.332 [W m −1  K −1 ] (Fig.  5 c),

Temperature: T = 298.15 [K] (25[°C]),

Density: from 1450 to 2000 kg m −3 ,

Turbulent model: k-ε,

Time of calculations: 3 s,

Gravity: 9.81 [m s −2 ].

In the case of the FEM solver:

Type of FEM analysis: linear static,

Coefficient of elasticity: 210,000 [N mm −2 ],

Poisson’s ratio: 0.28,

Shear stress coefficient: 79,000 [N mm −2 ],

Density: 7800 [kg m −3 ],

Tensile strength: 360 [N mm −2 ],

Yield strength: 235 [N mm −2 ],

Thermal conductivity coefficient: 14 [W (m K) −1 ],

Specific heat: 440 [J (kg K) −1 ].

In order to simplify the issue, it was assumed that the cross-section of the retention bunker is constant throughout the analysed vertical section. In reality, it is variable. In the upper part, the bunker has a cylindrical cross-section with a diameter of about 1.91 m, which changes to a square cross-section in the lower part of the bunker (receiving bin). This approach makes it possible to disregard possible turbulence generated at the points where the cross-section changes. Despite the simplifications adopted, the model allows us to learn about the phenomena occurring in a reservoir filled with unreliable spoil.

Figure  6 depicts a 3D model of the retention bunker. The underground coal storage bunker has a vertical cylindrical shape with a height of approximately 8.1 m and a diameter of about 1.91 m. The hopper bottom, as shown in Fig.  2 -1 and 2, features a square cross-section with dimensions of approximately 3.0 m in height and approximately 1.45 m by 1.6 m in width. The total height of the retention bunker is approximately 10.70 m.

3D model of the Retention bunker: ( a )—front view, ( b )—top view, ( c )—isometric view.

Figure  7 illustrates the location of the damaged element on the retention bunker model, highlighted in blue. This marked element was subjected to monitoring for changes in pressure and force distribution values resulting from static pressure caused by a flowing hydromixture with varying density. The Computational Fluid Dynamics (CFD) model underwent testing for a duration of 3 s.

Monitored goals (blue colour) of the underground retention bunker: 1—damaged upper shield, 2—lower shield, 3—tube.

Figure  8 displays the cross-section of the retention bunker model, highlighting the volume of the hydromixture at time t = 0 s, along with its corresponding dimensions. This visual representation presents the initial distribution and extent of the hydromixture within the bunker.

Height—10.70 m,

Diameter—1.91 m,

Volume—26.63 m 3 .

Computational model of the retention bunker (1) and the hydromixture (2) during examination at time t = 0 s.

The choice of mesh type is crucial for accurately simulating fluid flow using the Navier–Stokes equations. In this context, Cartesian meshes were preferred, especially when combined with the Immersed Boundary (IB) method.The Immersed Boundary (IB) technique is particularly advantageous because eliminates the need for a mesh that conforms to boundaries.The process of creating a Cartesian mesh has been started by defining a set of rectangular cells, also known as cuboids or voxels. The cuboids were generated by intersecting planes that are parallel to the axes of the coordinate system.The mesh was improved using a variety of adaptation criteria that can be specified for each solid body (small features, narrow channels, curvature, etc.), as well as automatically in response to gradients in the solution (by splitting each cuboid into 8 geometrically identical cuboids).

The geometry of the retention bunker provides the basis for creating an internal fluid volume suitable for numerical simulations. To accurately represent the flow behavior within the bunker, a mesh with varying refinement levels is generated, as depicted in Fig.  9 .The geometry of the retention bunker has been provided as the basis for creating an internal fluid volume. To accurately represent the flow behavior within the bunker, a mesh with varying refinement levels was generated, as depicted in Fig.  9 .

Numerical grid of the retention bunker generated for the purpose of the CFD simulation.

It is important to use an appropriately numerical grid for the purpose to ensure that the results of the CFD numerical calculations are appropriate. Figure  10 depicts a grid refinement study that has been conducted to assess the effect of grid resolution on the results of the inner pressure change due to dynamic interactions with the coal-water mixture and the retention bunker model.The mesh study has been focused on evaluating the numerical grid’s influence on the simulation results, particularly considering a density of 2000 kg/m 3 for the coal-water mixture.

Mesh study results in the numerical grid refinement.

The results of the mesh study were compared in Table 1 .

Table 1 shows that the fine and very fine meshes predicted similar results, while the coarse and normal meshes forecast pressure with less accuracy. Based on the results of the convergence investigation, it was decided that the numerical grid in the model would have more than 155,799 computational cells.

A very important stage of the Finite Element Method (FEM) analysis is meshing. The information gathered from each element that makes up the 3D model is combined by FEM to predict the behaviour of the model shown in Fig.  8 . The software calculates the model’s global element size while taking its volume, surface area, and other geometric characteristics into account. The geometry and dimensions of the model, the size of the element, the mesh tolerance, the mesh control, and the contact criteria all affect how big the created mesh (number of nodes and elements) will be. Figure  11 shows the numerical grid in form of the 3D tetrahedral solid elements.

Numerical grid generated for the purpose of the FEM simulation.

The numerical grid shown in Fig.  11 was formed by 501,906 elements, connected with 1,013,521 nodes.

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

The results obtained from CFD simulation are shown in Figs.  12 – 15 and in Table 2 . The results obtained from the FEM analysis are shown in Table 3 . Figures 12 – 15 present the force changes at the monitored element of shields in the underground bunker 3D model. Figures 12 a– 15 a present the force distribution in the lower shield of the bunker are shown in Figs. 2 -2 and 7 -1 Table 2 shows the static pressure changes along the underground bunker for the developed 3D model shown in Fig.  8 . The values obtained from the CFD simulation in Table 2 were compared with the results obtained from the mathematical model.

The force distribution depending on the time for the 1450 kg m −3 : ( a )—force distribution along the lower shield (Fig.  8 -2); ( b )—force along the upper shield (Fig.  8 -1).

The force distribution depending on the time for the 1600 kg m −3 : ( a )—force distribution along the lower shield (Fig.  8 -2); ( b )—force along the upper shield (Fig.  8 -1).

The force distribution depending on the time for the 1800 kg m −3 : ( a )—force along the lower shield (Fig.  8 -2); ( b )—force along the upper (damaged) shield (Fig.  8 -1).

The force distribution depending on the time for the 2000 kg m −3 : ( a )—force along the lower shield (Fig.  8 -2); ( b )—force distribution along the upper (damaged) shield (Fig.  8 -1).

Figure  12 shows that for the hydromixture density of 1450 kg m −3 , force distribution along the bottom shield (Figs.  2 -1 and 7 -1) is 501.884 kN for the time interval up to 1.0 s (Fig.  12 a), while the force monitored at the upper (damaged) shield (shown in Fig. 2 -2 and 7 -1) is increasing to a value of about 231.312 kN, reaching its maximum in 1.08 s.

Figure  13 shows that for the hydromixture density of 1600 kg m −3 , force distribution along the bottom shield (Figs.  2 -1 and 7 -2) is 553.825 kN for the time interval up to 1.03 s (Fig.  13 a), while the force monitored at the upper (damaged) shield (shown in Figs. 2 -2, 7 -2) is increasing to a value of about 254.846 kN, reaching its maximum in 1.08 s.

Figure  14 shows that for the hydromixture density of 1800 kg m −3 , force distribution along the bottom shield (Figs.  2 -1, 7 -2) is 625.487 kN for the time interval up to 1.02 s (Fig.  14 a), while the force monitored at the upper (damaged) shield (shown in Fig. 2 -2, 7 -2) is increasing to a value of about 288.651 kN, reaching its maximum in 1.08 s.

Figure  15 shows that for the hydromixture density of 2000 kg m −3 , force distribution along the bottom shield (Figs.  2 -1, 7 -2) is 694.491 kN for the time interval up to 1.075 s (Fig.  15 a), while the force monitored at the upper (damaged) shield (shown in Figs. 2 -2, 7 -2) is increasing to a value of about 314.690 kN, reaching its maximum in 1.07 s.

In Figs.  12 – 15 , between 1.0 and 1.5 s, a maximal force peak of about 231 kN, 254 kN, 288 kN, and 314 kN was observed, which caused damage to the bunker shield (Figs  7 -1, 1 -4). This phenomenon is well illustrated in Fig.  16 .

Map of the volume fraction behaviour of the hydromixture during time: ( a )—for t = 0 s, ( b )—for t = 1.09 s, ( c )—for t = 1.5 s and ( d )—for t = 2.0 s.

The maps in Fig.  16 show that the hydromixture in a time of 1.09 s moves towards the analysed shield of the bunker (presented in Fig.  7 -1), reaching full contact after a time of 1.25 s.

The comparison of the pressure change along the underground bunker obtained from the CFD solver and the mathematical models (1) and (2) are presented in Table 2 . However, Table 3 presents the results of the displacement and the von Mises stress distribution obtained from the FEM numerical calculations.

Table 2 provides a comparison of the pressure values obtained from the mathematical model and the Computational Fluid Dynamics (CFD) numerical simulation for a hydromixture density of 2000 kg m −3 . The comparison reveals that there is a discrepancy between the pressure values obtained from the mathematical model and the CFD numerical simulation. The pressure value from the mathematical model is higher (320.494 kPa) compared to that from the CFD numerical simulation (299.897 kPa). Similar to the comparison for the hydromixture density of 2000 kg m −3 , there is a difference between the pressure values obtained from the mathematical model and the CFD numerical simulation for a hydromixture density of 1800 kg m −3 . In this case, the pressure value from the mathematical model is higher (291.192 kPa) compared to that from the CFD numerical simulation (280.924 kPa). For the hydromixture density of 1600 kg m −3 , the value of the pressure is 258.837 kPa obtained from the mathematical model, while from the CFD numerical simulation is 260.093 kPa. In the case of the hydromixture density of 1450 kg m −3 , the value of the pressure is 234.571 kPa obtained from the mathematical model, while from the CFD numerical simulation is 245.307 kPa. The values collected in Table 2 are illustrated in Fig.  17 .

Comparison of the static pressure results obtained from the mathematical model and the CFD numerical simulations.

The stress values for the analyzed upper shield (Fig.  7 -1) in Table 3 exceed the allowable value of 235 MPa. It means that the upper shield has experienced excessive loading, which has led to stress concentrations resulting in the failure of the screw connections.

Results analysis

To estimate the differences in indication error between the pressure values shown in Tables 2 and 3 , the following equation can be used 12 :

where: \({x}_{numer}\) is the results obtained from the CFD numerical simulations, \({x}_{math}\) is the results obtained from the mathematical model,

Figure  18 shows the difference in indication error for different hydromixture densities:

For the hydromixture density of 1450 kg m −3 , the indication error is approximately 14%.

For the hydromixture density of 1600 kg m −3 , the indication error is approximately 16%.

For the hydromixture density of 1800 kg m −3 , the indication error is approximately 16%.

For the hydromixture density of 2000 kg m −3 , the indication error is approximately 15%.

Comparison of the force distribution results obtained from the mathematical model and the CFD numerical simulations.

From the observation of the results shown in Table 3 , the displacement of the monitored upper shield, as shown in Fig.  7 -1, changes from:

0.0370 m for the hydromixture with a density of 1450 kg m −3 ,

0.0408 m for the hydromixture with a density of 1600 kg m −3 ,

0.0458 m for the hydromixture with a density of 1800 kg m −3 , to

0.0510 m for the hydromixture with a density of 2000 kg m −3 .

In the case of the stress distribution, it can be observed that the stress value exceeded the allowable stress values (Table 3 a). The allowable stress value for the monitored shield is 235 MPa. In addition, it was observed that exceedances of the permissible stresses of the screw connection (Fig. 1 -1) were exceeded, as shown in Table 3 b. The allowable stress value for the screw connection is 640 MPa.

The novelty and significance of the study lie in its exploration of the dynamic interaction between a retention bunker and a coal-water hydromixture. This aspect represents a crucial yet relatively unexplored domain within underground coal mining operations. By investigating how the presence of water alters the behaviour of stored coal within the bunker, the study sheds light on a complex and often overlooked aspect of mining infrastructure.

Traditional analyses of retention bunkers have primarily focused on static conditions, neglecting the dynamic effects induced by the presence of water. However, in real-world mining scenarios, the infiltration of water into underground storage facilities is a common occurrence, leading to the formation of coal-water hydromixtures. Understanding how these mixtures behave and interact with the bunker structure is paramount for ensuring the safety and efficiency of mining operations.

The study utilizes a combined approach of Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) to simulate and analyze the behavior of the retention bunker under varying conditions. This integration of CFD and FEM allows for a comprehensive investigation of both fluid flow dynamics and structural mechanics within the bunker system, providing a holistic understanding of its performance.

The applied research methodology made it possible to determine that the probable causes of damage to the structural elements of the retention bunker. The inflow of process or natural water led to changes in the physical and mechanical parameters of stored material, such as the angle of internal friction and the angle of natural repose. Internal friction characterises the resistance that rock particles of the same body put under shear stress. The resistance of internal friction is due to the existence of cohesion forces and depends on the freedom of movement of the particles in a given body. The internal friction angle for excavated material typically changes within the range of 16–35° 30 . The constant value of the coefficient of internal friction is necessary for the design of transport and auxiliary equipment and the calculation of their energy effectiveness. The coefficient of friction of the bulk material against the walls of the shielding equipment and components of the conveying machinery depends on whether the excavated material is static or in relative motion. The quantity that characterises frictional forces is the coefficient of friction, whether dynamic during motion or static, when the process of sliding the contacting surfaces of different bodies is initiated. The difference between the value of the coefficient of static and dynamic friction depending on the roughness of the contacting surfaces 24 . The inflow of water increasing the sliding between the surfaces of the excavated material grains. Under conditions of high humidity, due to the inflow of water, the angle of natural repose of the excavated material is disturbed. As a result of this phenomenon, the excavated material moved towards the shield shown in Fig.  8 -1, which, according to the retention bunker designer, was not considered a potentially loaded element. A sudden increase in the value of the force observed in Fig.  12 b, 13 b, 14 b and 15 b occurred and caused to damage to the retention bunker.

The disturbance in the angle of natural repose of the excavated material resulted in the formation of the fluid hammer, as observed in Fig.  16 , and an increase in the allowable force, as observed in Figs.  12 a– 15 a. + The retention bunker was damaged due to hydraulic shock generated by a fluid consisting of coal and rocks suspended in water, which led to shear stresses in the screw connection and damaged the flat steel sheathing shown in Fig.  7 -1 as a result.

Conclusions

The article presents the results of modelling studies to determine the causes of damage to a retention bunker designed to store coal. Model studies were conducted by coupling CFD and FEM methods. CFD numerical simulations made it possible to determine the predicted values of pressure and forces in the retention bunker under the hydromixture flow conditions. The FEM numerical simulations made it possible to determine the predicted changes in stress values and displacement based on the CFD solver results. It was assumed that process water may have flowed into the stored material in the retention bunker, causing a change in the operation condition from static to dynamic. The effect of changes in the density of the hydromixture on the value of pressure changes and stresses in the structural elements of the retention bunker was investigated.

The results of the model tests enabled the formulation of the following conclusions:

The retention bunker damage was caused by the increase in the values of the force shown in Figs.  12 – 15 , monitored at the retention bunker steel sheathing element presented in Fig.  7 -1, due to hydraulic shock caused by the fluid motion consisting of coal and rock suspended in water,

The loss of stability of the material stored in the retention bunker was caused by the disturbance of the geotechnical parameters of the excavated material due to the inflow of natural or process water,

The change in density of the hydromixture directly influences the value of shear stress (pressure), displacement, and stresses in the structural elements of the retention bunker,

The results of the CFD numerical simulation are comparable with the results obtained from the mathematical models, which prove the correctness of the CFD numerical model assumptions adopted,

The discrepancies between numerical and mathematical results could arise due to various factors, including simplifications or assumptions made in the mathematical model, numerical discretization errors in the CFD simulation, convergence criteria, turbulence modeling, boundary conditions, or other modeling considerations,

The novelty and significance of the study lie in its exploration of the dynamic interaction between a retention bunker and a coal-water hydromixture. This aspect represents a crucial yet relatively unexplored domain within underground coal mining operations. By investigating how the presence of water alters the behavior of stored coal within the bunker, the study sheds light on a complex and often overlooked aspect of mining infrastructure,

The use of computational fluid dynamics (CFD) methods in combination with finite element methods (FEM) enables the identification of the pressure (shear stresses) values of hydromixture and the von Mises stress in the shield of the retention bunker, which has contributed to damage as a result of dynamic loads originating from the hydromixture motion,

The coupling of CFD with FEM provides a synergistic approach, where the fluid flow results from CFD simulations serve as input conditions for the structural analysis conducted with FEM. This integrated simulation framework enables a comprehensive evaluation of the bunker’s performance, considering both fluid–structure interaction effects and their implications on safety, stability, and operational efficiency,

The utilization of CFD coupled with FEM offers a powerful methodology for simulating and analyzing the behavior of retention bunkers under varying conditions. It enables engineers to gain valuable insights into the complex interplay between fluid dynamics and structural mechanics, guiding the design, optimization, and maintenance of underground coal storage facilities,

The comparison between the stress values obtained from the numerical analysis and the shield’s designed strength provides valuable insight into the structural integrity and safety of the retention bunker under the analyzed conditions,

The study showcases the effectiveness of CFD-FEM coupling in predicting the structural response of the retention bunker to dynamic loading from the hydromixture. By integrating CFD simulations with FEM analysis, the study provides insights into the mechanical integrity and stability of the bunker under transient flow conditions.

Data availability

All data generated or analysed during this study are included in this published article.

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Janoszek, T., Rotkegel, M. Coupled CFD-FEM analysis of the damage causes of the retention bunker: a case study at hard coal mine. Sci Rep 14 , 14189 (2024). https://doi.org/10.1038/s41598-024-65034-z

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Associations between deep venous thrombosis and thyroid diseases: a two-sample bidirectional Mendelian randomization study

• Lifeng Zhang 1   na1 ,
• Kaibei Li 2   na1 ,
• Qifan Yang 1 ,
• Yao Lin 1 ,
• Caijuan Geng 1 ,
• Wei Huang 1 &
• Wei Zeng 1  

European Journal of Medical Research volume  29 , Article number:  327 ( 2024 ) Cite this article

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Some previous observational studies have linked deep venous thrombosis (DVT) to thyroid diseases; however, the findings were contradictory. This study aimed to investigate whether some common thyroid diseases can cause DVT using a two-sample Mendelian randomization (MR) approach.

This two-sample MR study used single nucleotide polymorphisms (SNPs) identified by the FinnGen genome-wide association studies (GWAS) to be highly associated with some common thyroid diseases, including autoimmune hyperthyroidism (962 cases and 172,976 controls), subacute thyroiditis (418 cases and 187,684 controls), hypothyroidism (26,342 cases and 59,827 controls), and malignant neoplasm of the thyroid gland (989 cases and 217,803 controls. These SNPs were used as instruments. Outcome datasets for the GWAS on DVT (6,767 cases and 330,392 controls) were selected from the UK Biobank data, which was obtained from the Integrative Epidemiology Unit (IEU) open GWAS project. The inverse variance weighted (IVW), MR-Egger and weighted median methods were used to estimate the causal association between DVT and thyroid diseases. The Cochran’s Q test was used to quantify the heterogeneity of the instrumental variables (IVs). MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO) was used to detect horizontal pleiotropy. When the causal relationship was significant, bidirectional MR analysis was performed to determine any reverse causal relationships between exposures and outcomes.

This MR study illustrated that autoimmune hyperthyroidism slightly increased the risk of DVT according to the IVW [odds ratio (OR) = 1.0009; p  = 0.024] and weighted median methods [OR = 1.001; p  = 0.028]. According to Cochran’s Q test, there was no evidence of heterogeneity in IVs. Additionally, MR-PRESSO did not detect horizontal pleiotropy ( p  = 0.972). However, no association was observed between other thyroid diseases and DVT using the IVW, weighted median, and MR-Egger regression methods.

Conclusions

This study revealed that autoimmune hyperthyroidism may cause DVT; however, more evidence and larger sample sizes are required to draw more precise conclusions.

Introduction

Deep venous thrombosis (DVT) is a common type of disease that occurs in 1–2 individuals per 1000 each year [ 1 ]. In the post-COVID-19 era, DVT showed a higher incidence rate [ 2 ]. Among hospitalized patients, the incidence rate of this disease was as high as 2.7% [ 3 ], increasing the risk of adverse events during hospitalization. According to the Registro Informatizado Enfermedad Tromboembolica (RIETE) registry, which included data from ~ 100,000 patients from 26 countries, the 30-day mortality rate was 2.6% for distal DVT and 3.3% for proximal DVT [ 4 ]. Other studies have shown that the one-year mortality rate of DVT is 19.6% [ 5 ]. DVT and pulmonary embolism (PE), collectively referred to as venous thromboembolism (VTE), constitute a major global burden of disease [ 6 ].

Thyroid diseases are common in the real world. Previous studies have focused on the relationship between DVT and thyroid diseases, including thyroid dysfunction and thyroid cancer. Some case reports [ 7 , 8 , 9 ] have demonstrated that hyperthyroidism is often associated with DVT and indicates a worse prognosis [ 10 ]. The relationship between thyroid tumors and venous thrombosis has troubled researchers for many years. In 1989, the first case of papillary thyroid carcinoma presenting with axillary vein thrombosis as the initial symptom was reported [ 11 ]. In 1995, researchers began to notice the relationship between thyroid tumors and hypercoagulability [ 12 ], laying the foundation for subsequent extensive research. However, the aforementioned observational studies had limitations, such as small sample sizes, selection bias, reverse causality, and confounding factors, which may have led to unreliable conclusions [ 13 ].

Previous studies have explored the relationship of thyroid disease and DVT and revealed that high levels of thyroid hormones may increase the risk of DVT. Hyperthyroidism promotes a procoagulant and hypofibrinolytic state by affecting the von Willebrand factor, factors VIII, IV, and X, fibrinogen, and plasminogen activator inhibitor-1 [ 14 , 15 ]. At the molecular level, researchers believe that thyroid hormones affect coagulation levels through an important nuclear thyroid hormone receptor (TR), TRβ [ 16 ], and participate in pathological coagulation through endothelial dysfunction. Thyroid hormones may have non-genetic effects on the behavior of endothelial cells [ 17 , 18 ]. In a study regarding tumor thrombosis, Lou [ 19 ] found that 303 circular RNAs were differentially expressed in DVT using microarray. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the most significantly enriched pathways included thyroid hormone-signaling pathway and endocytosis, and also increased level of proteoglycans in cancer. This indicated that tumor cells and thyroid hormones might interact to promote thrombosis. Based on these studies, we speculated that thyroid diseases, including thyroid dysfunction and thyroid tumors, may cause DVT.

Mendelian randomization (MR) research is a causal inference technique that can be used to assess the causal relationship and reverse causation between specific exposure and outcome factors. If certain assumptions [ 20 ] are fulfilled, genetic variants can be employed as instrumental variables (IVs) to establish causal relationships. Bidirectional MR analysis can clarify the presence of reverse causal relationships [ 21 ], making the conclusions more comprehensive. Accordingly, we aimed to apply a two-sample MR strategy to investigate whether DVT is related to four thyroid diseases, including autoimmune hyperthyroidism, subacute thyroiditis, hypothyroidism, and thyroid cancer.

Study design

MR relies on single nucleotide polymorphisms (SNPs) as IVs. The IVs should fulfill the following three criteria [ 22 ]: (1) IVs should be strongly associated with exposure. (2) Genetic variants must be independent of unmeasured confounding factors that may affect the exposure–outcome association. (3) IVs are presumed to affect the outcome only through their associations with exposure (Fig.  1 ). IVs that met the above requirements were used to estimate the relationship between exposure and outcome. Our study protocol conformed to the STROBE-MR Statement [ 23 ], and all methods were performed in accordance with the relevant guidelines and regulations.

The relationship between instrumental variables, exposure, outcome, and confounding factors

Data sources and instruments

Datasets (Table  1 ) in this study were obtained from a publicly available database (the IEU open genome-wide association studies (GWAS) project [ 24 ] ( https://gwas.mrcieu.ac.uk )). There was no overlap in samples between the data sources of outcome and exposures. Using de-identified summary-level data, privacy information such as overall age and gender were hidden. Ethical approval was obtained for all original work. This study complied with the terms of use of the database.

MR analysis was performed using the R package “TwoSampleMR”. SNPs associated with each thyroid disease at the genome-wide significance threshold of p  < 5.0 × 10 –8 were selected as potential IVs. To ensure independence between the genetic variants used as IVs, the linkage disequilibrium (LD) threshold for grouping was set to r 2  < 0.001 with a window size of 10,000 kb. The SNP with the lowest p -value at each locus was retained for analyses.

Statistical analysis

Multiple MR methods were used to infer causal relationships between thyroid diseases and DVT, including the inverse variance weighted (IVW), weighted median, and MR-Egger tests, after harmonizing the SNPs across the GWASs of exposures and outcomes. The main analysis was conducted using the IVW method. Heterogeneity and pleiotropy were also performed in each MR analysis. Meanwhile, the MR-PRESSO Global test [ 25 ] was utilized to detect horizontal pleiotropy. The effect trend of SNP was observed through a scatter plot, and the forest plot was used to observe the overall effects. When a significant causal relationship was confirmed by two-sample MR analysis, bidirectional MR analysis was performed to assess reverse causal relationships by swapping exposure and outcome factors. Parameters were set the same as before. All abovementioned statistical analyses were performed using the package TwoSampleMR (version 0.5.7) in the R program (version 4.2.1).

After harmonizing the SNPs across the GWASs for exposures and outcomes, the IVW (OR = 1.0009, p  = 0.024, Table  2 ) and weighted median analyses (OR = 1.001, p  = 0.028) revealed significant causal effects between autoimmune hyperthyroidism and DVT risk. Similar results were observed using the weighted median approach Cochran’s Q test, MR-Egger intercept, and MR-PRESSO tests suggested that the results were not influenced by pleiotropy and heterogeneity (Table  2 ). However, the leave-one-out analysis revealed a significant difference after removing some SNPs (rs179247, rs6679677, rs72891915, and rs942495, p  < 0.05, Figure S2a), indicating that MR results were dependent on these SNPs (Figure S2, Table S1). No significant effects were observed in other thyroid diseases (Table  2 ). The estimated scatter plot of the association between thyroid diseases and DVT is presented in Fig.  2 , indicating a positive causal relationship between autoimmune hyperthyroidism and DVT (Fig.  2 a). The forest plots of single SNPs affecting the risk of DVT are displayed in Figure S1.

The estimated scatter plot of the association between thyroid diseases and DVT. MR-analyses are derived using IVW, MR-Egger, weighted median and mode. By fitting different models, the scatter plot showed the relationship between SNP and exposure factors, predicting the association between SNP and outcomes

Bidirectional MR analysis was performed to further determine the relationship between autoimmune hyperthyroidism and DVT. The reverse causal relationship was not observed (Table S2), which indicated that autoimmune hyperthyroidism can cause DVT from a mechanism perspective.

This study used MR to assess whether thyroid diseases affect the incidence of DVT. The results showed that autoimmune hyperthyroidism can increase the risk of DVT occurrence, but a reverse causal relationship was not observed between them using bidirectional MR analysis. However, other thyroid diseases, such as subacute thyroiditis, hypothyroidism, and thyroid cancer, did not show a similar effect.

Recently, several studies have suggested that thyroid-related diseases may be associated with the occurrence of DVT in the lower extremities, which provided etiological clues leading to the occurrence of DVT in our subsequent research. In 2006, a review mentioned the association between thyroid dysfunction and coagulation disorders [ 26 ], indicating a hypercoagulable state in patients with hyperthyroidism. In 2011, a review further suggested a clear association between hypothyroidism and bleeding tendency, while hyperthyroidism appeared to increase the risk of thrombotic events, particularly cerebral venous thrombosis [ 27 ]. A retrospective cohort study [ 28 ] supported this conclusion, but this study only observed a higher proportion of concurrent thyroid dysfunction in patients with cerebral venous thrombosis. The relationship between thyroid function and venous thromboembolism remains controversial. Krieg VJ et al. [ 29 ] found that hypothyroidism has a higher incidence rate in patients with chronic thromboembolic pulmonary hypertension and may be associated with more severe disease, which seemed to be different from previous views that hyperthyroidism may be associated with venous thrombosis. Alsaidan [ 30 ] also revealed that the risk of developing venous thrombosis was almost increased onefold for cases with a mild-to-moderate elevation of thyroid stimulating hormone and Free thyroxine 4(FT4). In contrast, it increased twofold for cases with a severe elevation of thyroid stimulating hormone and FT4. Raised thyroid hormones may increase the synthesis or secretion of coagulation factors or may decrease fibrinolysis, which may lead to the occurrence of coagulation abnormality.

Other thyroid diseases are also reported to be associated with DVT. In a large prospective cohort study [ 31 ], the incidence of venous thromboembolism was observed to increase in patients with thyroid cancer over the age of 60. However, other retrospective studies did not find any difference compared with the general population [ 32 ]. In the post-COVID-19 era, subacute thyroiditis has received considerable attention from researchers. New evidence suggests that COVID-19 may be associated with subacute thyroiditis [ 33 , 34 ]. Mondal et al. [ 35 ] found that out of 670 COVID-19 patients, 11 presented with post-COVID-19 subacute thyroiditis. Among them, painless subacute thyroiditis appeared earlier and exhibited symptoms of hyperthyroidism. Another case report also indicated the same result, that is, subacute thyroiditis occurred after COVID-19 infection, accompanied by thyroid function changes [ 36 ]. This led us to hypothesize that subacute thyroiditis may cause DVT through alterations in thyroid function.

This study confirmed a significant causal relationship between autoimmune hyperthyroidism and DVT ( p  = 0.02). The data were tested for heterogeneity and gene pleiotropy using MR-Egger, Cochran’s Q, and MR-PRESSO tests. There was no evidence that the results were influenced by pleiotropy or heterogeneity. In the leave-one-out analysis, four of the five selected SNPs showed significant effects of autoimmune hyperthyroidism on DVT, suggesting an impact of these SNPs on DVT outcome. Previous studies have focused on the relationship between hyperthyroidism and its secondary arrhythmias and arterial thromboembolism [ 37 , 38 ]. This study emphasized the risk of DVT in patients with hyperthyroidism, which has certain clinical implications. Prophylactic anticoagulant therapy was observed to help prevent DVT in patients with hyperthyroidism. Unfortunately, the results of this study did not reveal any evidence that suggests a relationship between other thyroid diseases and DVT occurrence. This may be due to the limited database, as this study only included the GWAS data from a subset of European populations. Large-scale multiracial studies are needed in the future.

There are some limitations to this study. First, it was limited to participants of European descent. Consequently, further investigation is required to confirm these findings in other ethnicities. Second, this study did not reveal the relationship between complications of hyperthyroidism and DVT. Additionally, this study selected IVs from the database using statistical methods rather than selecting them from the real population. This may result in weaker effects of the screened IVs and reduce the clinical significance of MR analysis. Moreover, the definitions of some diseases in this study were not clear in the original database, and some of the diseases were self-reported, which may reduce the accuracy of diagnosis. Further research is still needed to clarify the causal relationship between DVT and thyroid diseases based on prospective cohort and randomized controlled trials (RCTs).

This study analyzed large-scale genetic data and provided evidence of a causal relationship between autoimmune hyperthyroidism and the risk of DVT, Compared with the other thyroid diseases investigated. Prospective RCTs or MR studies with larger sample sizes are still needed to draw more precise conclusions.

Availability of data and materials

The IEU open gwas project, https://gwas.mrcieu.ac.uk/

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Lifeng Zhang and Kaibei Li have contributed equally to this work and share the first authorship.

Authors and Affiliations

Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, No. 39, Shierqiao Road, Jinniu District, Chengdu, 610072, Sichuan, People’s Republic of China

Lifeng Zhang, Qifan Yang, Yao Lin, Caijuan Geng, Wei Huang & Wei Zeng

Disinfection Supply Center, Hospital of Chengdu University of Traditional Chinese Medicine, No. 39, Shierqiao Road, Jin Niu District, Chengdu, 610072, Sichuan, People’s Republic of China

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Contributions

Conception and design: LFZ and WZ. Analysis and interpretation: LFZ, KBL and WZ. Data collection: LFZ, QFY, YL, CJG and WH. Writing the article: LFZ, KBL. Critical revision of the article: LFZ, GFY and WZ. Final approval of the article: LFZ, KBL, YL, CJG, WH, QFY and WZ. Statistical analysis: YL, QFY.

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Correspondence to Wei Zeng .

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Zhang, L., Li, K., Yang, Q. et al. Associations between deep venous thrombosis and thyroid diseases: a two-sample bidirectional Mendelian randomization study. Eur J Med Res 29 , 327 (2024). https://doi.org/10.1186/s40001-024-01933-1

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Examining the response to covid-19 in logistics and supply chain processes: insights from a state-of-the-art literature review and case study analysis.

1. Introduction

• RQ1 (scientific): How have researchers studied the impact of COVID-19 on logistics and supply chain processes? Which industrial sectors were mostly studied and why? Which additional topics can be related to COVID-19 and logistics/supply chain?
• RQ2 (practical): What effects of COVID-19 on logistics and supply chain processes were experienced by companies?

2. Materials and Methods

2.1. systematic literature review, 2.1.1. sample creation, 2.1.2. descriptive analyses, 2.1.3. paper classification.

• Macro theme: sustainability, resilience, risk, information technology, economics, performance, planning and food security. This classification represents paper’s core topic.
• Industrial sector: aerospace, agri-food, apparel, automotive, construction, e-commerce, electronic, energy, fast-moving consumer goods, food, healthcare, logistics, manufacturing and service.
• Data collection method: questionnaire/interview, third-party sources or case study. This classification represents the method used by the authors to collect the data useful to their study.
• Research method: statistical, decision-making, simulation, empirical, literature review or economic. This category describes the tool used by the authors to conduct the study and reach the related goals.
• Specific method, e.g., descriptive statistics, structural equation modeling (SEM), multi-criteria decision making (MCDM), etc.; this feature describes more accurately the type of work carried out by the authors and the tools used.
• Country: it reflects the geographical area in which the study was carried out, in terms, for instance, of the country in which a sample of people has been interviewed or where empirical data were collected, or where the simulation was set. This method of classification, although more elaborated, was preferred over traditional approaches, in which the country of the study is defined based merely on the affiliation of the first author of the paper, because the exact knowledge of the country in which the study was carried out is, for sure, a more representative source of information about the research. This is true in general, but it is even more important for this subject matter, as the management of the COVID-19 pandemic was made on a country or regional basis, with significant differences from country to country; knowing the exact location of the study helps in better interpreting the research outcomes. Possible entries in this field also include “multiple countries” and “not specified”, with the obvious meanings of the terms.

2.1.4. Cross-Analyses

2.1.5. interrelated aspects, 2.2. case study, 2.2.1. data collection.

• Economic data: some key economic data were retrieved from the company’s balance sheet, from 2019 up to the latest available document, which refers to 2022.
• Organizational data: these data describe changes in the operational, decision-making and business structure of the company in terms, e.g., of number of employees hired, number of drivers, etc.
• The related data were collected and elaborated between July and September 2023.

2.2.2. Survey Phase

2.2.3. analysis and summary, 3. results—systematic literature review, 3.1. descriptive statistics, 3.2. common classification fields, 3.2.1. macro theme, 3.2.2. industrial sector, 3.2.3. data collection method, 3.2.4. research method, 3.2.5. country, 3.3. cross-analyses, 3.3.1. macro theme vs. industrial sector, 3.3.2. research method vs. macro theme, 3.4. interrelated aspects, 4. results—case study, 4.1. company overview, 4.2. pre-covid-19 period, 4.3. covid-19 period, 4.4. post-covid-19 period, 4.5. analysis and summary.

• Strengths : at present, Company A benefits from a robust network of relationships with customers and suppliers (e.g., drivers), which was leveraged during the pandemic period to provide a rapid response to the increased request by the consumers. The company has also leveraged the usage of digital technologies, which made logistics activities more efficient and, again, allowed the company to respond to consumer demand in the pandemic period.
• Weaknesses : Company A has suffered from low economic results, in particular in the post-COVID-19 period, mainly due to the high production costs. Efforts must be made by the company to reduce expenses. At the same time, however, the service level, in terms of delivery lead time or on-time delivery, should be safeguarded.
• Opportunities : the growth of e-commerce, experienced in the COVID-19 period but expected to last over time, creates opportunities for increasing the volume of items handled by Company A. Indeed, the survey phase demonstrated that the company’s consumers have shifted towards the usage of online sales; hence, the company could consider investing in this area to increase its market share. By leveraging the e-commerce logistics and diversifying service, expansions could also be possible at an international level. Even if the company has already embraced the implementation of digital technologies, some emerging technologies (e.g., drones or advanced traceability systems) could also be introduced for further improving the logistics efficiency. Finally, sustainability is another opportunity to be leveraged, because of the current push towards the adoption of environmental-friendly logistics solutions. Examples of those solutions include a reduction in CO 2 emissions, and the usage of electric vehicles or zero-impact materials.
• Threats : the growth of e-commerce can be seen as an opportunity, but because many logistics companies have already entered this field, the sector is characterized by very high competition, which could limit the market share of Company A; this could instead be seen as a threat needing to be properly managed. Another threat comes from the increased cost of fuel, which, for sure, for a logistics company plays an important role in determining the cost of the transport activities (also, having previously observed that the company suffered from a limited revenue in recent years). This factor could further push towards the adoption of environmentally friendly transport modes (e.g., electric vehicles), which have been previously mentioned as an opportunity for leveraging in the logistics sector.

5. Conclusions

5.1. answer to the research questions, 5.2. scientific and practical implications, 5.3. suggestions for future research directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Monferdini, L.; Bottani, E. Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis. Appl. Sci. 2024 , 14 , 5317. https://doi.org/10.3390/app14125317

Monferdini L, Bottani E. Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis. Applied Sciences . 2024; 14(12):5317. https://doi.org/10.3390/app14125317

Monferdini, Laura, and Eleonora Bottani. 2024. "Examining the Response to COVID-19 in Logistics and Supply Chain Processes: Insights from a State-of-the-Art Literature Review and Case Study Analysis" Applied Sciences 14, no. 12: 5317. https://doi.org/10.3390/app14125317

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