methodology case study methods

Case Study Methods and Examples

By Janet Salmons, PhD Manager, Sage Research Methods Community

What is Case Study Methodology ?

Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it. Or, case study can refer to a type of academic writing that typically delves into a problem, process, or situation.

Case study methodology can entail the study of one or more "cases," that could be described as instances, examples, or settings where the problem or phenomenon can be examined. The researcher is tasked with defining the parameters of the case, that is, what is included and excluded. This process is called bounding the case , or setting boundaries.

Case study can be combined with other methodologies, such as ethnography, grounded theory, or phenomenology. In such studies the research on the case uses another framework to further define the study and refine the approach.

Case study is also described as a method, given particular approaches used to collect and analyze data. Case study research is conducted by almost every social science discipline: business, education, sociology, psychology. Case study research, with its reliance on multiple sources, is also a natural choice for researchers interested in trans-, inter-, or cross-disciplinary studies.

The Encyclopedia of case study research provides an overview:

The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case.

It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed methods because this they use either more than one form of data within a research paradigm, or more than one form of data from different paradigms.

A case study inquiry could include multiple types of data:

multiple forms of quantitative data sources, such as Big Data + a survey

multiple forms of qualitative data sources, such as interviews + observations + documents

multiple forms of quantitative and qualitative data sources, such as surveys + interviews

Case study methodology can be used to achieve different research purposes.

Robert Yin , methodologist most associated with case study research, differentiates between descriptive , exploratory and explanatory case studies:

Descriptive : A case study whose purpose is to describe a phenomenon. Explanatory : A case study whose purpose is to explain how or why some condition came to be, or why some sequence of events occurred or did not occur. Exploratory: A case study whose purpose is to identify the research questions or procedures to be used in a subsequent study.

Robert Yin’s book is a comprehensive guide for case study researchers!

You can read the preface and Chapter 1 of Yin's book here . See the open-access articles below for some published examples of qualitative, quantitative, and mixed methods case study research.

Mills, A. J., Durepos, G., & Wiebe, E. (2010). Encyclopedia of case study research (Vols. 1-0). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412957397

Yin, R. K. (2018). Case study research and applications (6th ed.). Thousand Oaks: SAGE Publications.

Open-Access Articles Using Case Study Methodology

As you can see from this collection, case study methods are used in qualitative, quantitative and mixed methods research.

Ang, C.-S., Lee, K.-F., & Dipolog-Ubanan, G. F. (2019). Determinants of First-Year Student Identity and Satisfaction in Higher Education: A Quantitative Case Study. SAGE Open. https://doi.org/10.1177/2158244019846689

Abstract. First-year undergraduates’ expectations and experience of university and student engagement variables were investigated to determine how these perceptions influence their student identity and overall course satisfaction. Data collected from 554 first-year undergraduates at a large private university were analyzed. Participants were given the adapted version of the Melbourne Centre for the Study of Higher Education Survey to self-report their learning experience and engagement in the university community. The results showed that, in general, the students’ reasons of pursuing tertiary education were to open the door to career opportunities and skill development. Moreover, students’ views on their learning and university engagement were at the moderate level. In relation to student identity and overall student satisfaction, it is encouraging to state that their perceptions of studentship and course satisfaction were rather positive. After controlling for demographics, student engagement appeared to explain more variance in student identity, whereas students’ expectations and experience explained greater variance in students’ overall course satisfaction. Implications for practice, limitations, and recommendation of this study are addressed.

Baker, A. J. (2017). Algorithms to Assess Music Cities: Case Study—Melbourne as a Music Capital. SAGE Open. https://doi.org/10.1177/2158244017691801

Abstract. The global Mastering of a Music City report in 2015 notes that the concept of music cities has penetrated the global political vernacular because it delivers “significant economic, employment, cultural and social benefits.” This article highlights that no empirical study has combined all these values and offers a relevant and comprehensive definition of a music city. Drawing on industry research,1 the article assesses how mathematical flowcharts, such as Algorithm A (Economics), Algorithm B (Four T’s creative index), and Algorithm C (Heritage), have contributed to the definition of a music city. Taking Melbourne as a case study, it illustrates how Algorithms A and B are used as disputed evidence about whether the city is touted as Australia’s music capital. The article connects the three algorithms to an academic framework from musicology, urban studies, cultural economics, and sociology, and proposes a benchmark Algorithm D (Music Cities definition), which offers a more holistic assessment of music activity in any urban context. The article concludes by arguing that Algorithm D offers a much-needed definition of what comprises a music city because it builds on the popular political economy focus and includes the social importance of space and cultural practices.

Brown, K., & Mondon, A. (2020). Populism, the media, and the mainstreaming of the far right: The Guardian’s coverage of populism as a case study. Politics. https://doi.org/10.1177/0263395720955036

Abstract. Populism seems to define our current political age. The term is splashed across the headlines, brandished in political speeches and commentaries, and applied extensively in numerous academic publications and conferences. This pervasive usage, or populist hype, has serious implications for our understanding of the meaning of populism itself and for our interpretation of the phenomena to which it is applied. In particular, we argue that its common conflation with far-right politics, as well as its breadth of application to other phenomena, has contributed to the mainstreaming of the far right in three main ways: (1) agenda-setting power and deflection, (2) euphemisation and trivialisation, and (3) amplification. Through a mixed-methods approach to discourse analysis, this article uses The Guardian newspaper as a case study to explore the development of the populist hype and the detrimental effects of the logics that it has pushed in public discourse.

Droy, L. T., Goodwin, J., & O’Connor, H. (2020). Methodological Uncertainty and Multi-Strategy Analysis: Case Study of the Long-Term Effects of Government Sponsored Youth Training on Occupational Mobility. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 147–148(1–2), 200–230. https://doi.org/10.1177/0759106320939893

Abstract. Sociological practitioners often face considerable methodological uncertainty when undertaking a quantitative analysis. This methodological uncertainty encompasses both data construction (e.g. defining variables) and analysis (e.g. selecting and specifying a modelling procedure). Methodological uncertainty can lead to results that are fragile and arbitrary. Yet, many practitioners may be unaware of the potential scale of methodological uncertainty in quantitative analysis, and the recent emergence of techniques for addressing it. Recent proposals for ‘multi-strategy’ approaches seek to identify and manage methodological uncertainty in quantitative analysis. We present a case-study of a multi-strategy analysis, applied to the problem of estimating the long-term impact of 1980s UK government-sponsored youth training. We use this case study to further highlight the problem of cumulative methodological fragilities in applied quantitative sociology and to discuss and help develop multi-strategy analysis as a tool to address them.

Ebneyamini, S., & Sadeghi Moghadam, M. R. (2018). Toward Developing a Framework for Conducting Case Study Research . International Journal of Qualitative Methods . https://doi.org/10.1177/1609406918817954

Abstract. This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many researchers commented on the methodological issues of the case study research from their point of view thus, presenting a comprehensive framework was missing. We try representing a general framework with methodological and analytical perspective to design, develop, and conduct case study research. To test the coverage of our framework, we have analyzed articles in three major journals related to the management of technology and innovation to approve our framework. This study represents a general structure to guide, design, and fulfill a case study research with levels and steps necessary for researchers to use in their research.

Lai, D., & Roccu, R. (2019). Case study research and critical IR: the case for the extended case methodology. International Relations , 33 (1), 67-87. https://doi.org/10.1177/0047117818818243

Abstract. Discussions on case study methodology in International Relations (IR) have historically been dominated by positivist and neopositivist approaches. However, these are problematic for critical IR research, pointing to the need for a non-positivist case study methodology. To address this issue, this article introduces and adapts the extended case methodology as a critical, reflexivist approach to case study research, whereby the case is constructed through a dynamic interaction with theory, rather than selected, and knowledge is produced through extensions rather than generalisation. Insofar as it seeks to study the world in complex and non-linear terms, take context and positionality seriously, and generate explicitly political and emancipatory knowledge, the extended case methodology is consistent with the ontological and epistemological commitments of several critical IR approaches. Its potential is illustrated in the final part of the article with reference to researching the socioeconomic dimension of transitional justice in Bosnia and Herzegovina.

Lynch, R., Young, J. C., Boakye-Achampong, S., Jowaisas, C., Sam, J., & Norlander, B. (2020). Benefits of crowdsourcing for libraries: A case study from Africa . IFLA Journal. https://doi.org/10.1177/0340035220944940

Abstract. Many libraries in the Global South do not collect comprehensive data about themselves, which creates challenges in terms of local and international visibility. Crowdsourcing is an effective tool that engages the public to collect missing data, and it has proven to be particularly valuable in countries where governments collect little public data. Whereas crowdsourcing is often used within fields that have high levels of development funding, such as health, the authors believe that this approach would have many benefits for the library field as well. They present qualitative and quantitative evidence from 23 African countries involved in a crowdsourcing project to map libraries. The authors find benefits in terms of increased connections between stakeholders, capacity-building, and increased local visibility. These findings demonstrate the potential of crowdsourced approaches for tasks such as mapping to benefit libraries and similarly positioned institutions in the Global South in multifaceted ways.

Mason, W., Morris, K., Webb, C., Daniels, B., Featherstone, B., Bywaters, P., Mirza, N., Hooper, J., Brady, G., Bunting, L., & Scourfield, J. (2020). Toward Full Integration of Quantitative and Qualitative Methods in Case Study Research: Insights From Investigating Child Welfare Inequalities. Journal of Mixed Methods Research, 14 (2), 164-183. https://doi.org/10.1177/1558689819857972

Abstract. Delineation of the full integration of quantitative and qualitative methods throughout all stages of multisite mixed methods case study projects remains a gap in the methodological literature. This article offers advances to the field of mixed methods by detailing the application and integration of mixed methods throughout all stages of one such project; a study of child welfare inequalities. By offering a critical discussion of site selection and the management of confirmatory, expansionary and discordant data, this article contributes to the limited body of mixed methods exemplars specific to this field. We propose that our mixed methods approach provided distinctive insights into a complex social problem, offering expanded understandings of the relationship between poverty, child abuse, and neglect.

Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S., & Waseem, A. (2019). Case Study Method: A Step-by-Step Guide for Business Researchers . International Journal of Qualitative Methods . https://doi.org/10.1177/1609406919862424

Abstract. Qualitative case study methodology enables researchers to conduct an in-depth exploration of intricate phenomena within some specific context. By keeping in mind research students, this article presents a systematic step-by-step guide to conduct a case study in the business discipline. Research students belonging to said discipline face issues in terms of clarity, selection, and operationalization of qualitative case study while doing their final dissertation. These issues often lead to confusion, wastage of valuable time, and wrong decisions that affect the overall outcome of the research. This article presents a checklist comprised of four phases, that is, foundation phase, prefield phase, field phase, and reporting phase. The objective of this article is to provide novice researchers with practical application of this checklist by linking all its four phases with the authors’ experiences and learning from recently conducted in-depth multiple case studies in the organizations of New Zealand. Rather than discussing case study in general, a targeted step-by-step plan with real-time research examples to conduct a case study is given.

VanWynsberghe, R., & Khan, S. (2007). Redefining Case Study. International Journal of Qualitative Methods, 80–94. https://doi.org/10.1177/160940690700600208

Abstract. In this paper the authors propose a more precise and encompassing definition of case study than is usually found. They support their definition by clarifying that case study is neither a method nor a methodology nor a research design as suggested by others. They use a case study prototype of their own design to propose common properties of case study and demonstrate how these properties support their definition. Next, they present several living myths about case study and refute them in relation to their definition. Finally, they discuss the interplay between the terms case study and unit of analysis to further delineate their definition of case study. The target audiences for this paper include case study researchers, research design and methods instructors, and graduate students interested in case study research.

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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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 .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Published: 27 June 2011

The case study approach

Sarah Crowe 1 ,
Kathrin Cresswell 2 ,
Ann Robertson 2 ,
Guro Huby 3 ,
Anthony Avery 1 &
Aziz Sheikh 2

BMC Medical Research Methodology volume 11 , Article number: 100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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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 Professor of Business Administration, Distinguished University Service Professor, and former dean of Harvard Business School.

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Case Study Research Method in Psychology

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Editor-in-Chief for Simply Psychology

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

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

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

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

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

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

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

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

Famous Case Studies

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

Clinical Case Studies

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

Child Psychology Case Studies

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

Types of Case Studies

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

Where Do You Find Data for a Case Study?

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

1. Primary sources

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

2. Secondary sources

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

3. Archival records

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

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

4. Organizational records

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

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

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

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

How do I Write a Case Study in Psychology?

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

1. Introduction

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

2. Case Presentation

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

3. Management and Outcome

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

4. Discussion

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

5. Additional Items

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

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further Information

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

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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Open access
Published: 02 May 2024

Research on the method of determining the block size for an open-pit mine integrating mining parameters and shovel-truck’s operation efficiency

Weiqiang Guo ORCID: orcid.org/0000-0003-0095-2637 1 ,
Guangwei Liu ORCID: orcid.org/0000-0002-1678-7472 1 ,
Jiaming Li 1 ,
Senlin Chai ORCID: orcid.org/0000-0002-8464-9522 2 &
Shupeng Guo 3

Scientific Reports volume 14 , Article number: 10119 ( 2024 ) Cite this article

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Energy science and technology
Engineering

The production plan of an open-pit mine depends on the block model, so it's crucial to determine the appropriate method and size for partitioning it. This study proposes a new method based on a closed shell three-dimensional geological model for determining block model size in open-pit mines. Instead of using regular block models, the shell model is directly cut, and the discrete geological body is referred to as the "mining model." Mining parameters and the shovel-truck's performance are integrated into the method. Bench height determines the Z-axis size, bench slope angle determines the inclination angle, and shovel width determines the X-axis size of the block model. The operation efficiency of the shovel-truck considers the probability distribution of simultaneous operations, allowing the determination of the Y-axis size of block models for different types of shovels. The developed "Mining Model" module in the software "Life Cycle Mining System" is used for practical implementation. By comparing the results with traditional block models, the superiority of the proposed method is demonstrated. This study provides a more accurate model for optimizing the production plan of open-pit mines throughout their life cycle.

Introduction

The block model serves as the foundation for various crucial tasks in open-pit mining, including production planning, grade and geological resource reserve estimation, ultimate pit design, and ore quality control. It provides a digital representation of the spatial distribution of deposit attributes. The block model's accuracy and reliability directly impact the investment, production, operational costs, and even the longevity of the mining operation 1 , 2 , 3 , 4 . Moreover, the open-pit mine's three-dimensional deposit model is discretized into a block model, allowing for the simulation of the dynamic evolution process throughout the mining life cycle 5 , 6 .

The Selective Mining Unit (SMU) represents the smallest unit in mining, designated as an ore block if the average grade of the SMU is equal to or higher than the cutoff grade; otherwise, it is considered an unminable waste block 7 . During the production phase of mining, it is theoretically ideal for the block size to align with the SMU, ensuring that the extracted tonnage closely approximates the results of reserve estimation. Therefore, the rational selection of block size in resource reserve estimation is essentially a determination of the SMU. In the case of open-pit mining, the determination of the SMU is influenced by various factors, categorized into geological, technical, and economic factors. Technical factors include mining parameters and equipment operational capabilities specific to open-pit mining.

The choice of block size and shape holds significant importance in resource quality assessment, mine optimization, and mine planning processes, directly influencing the economic viability of mining projects. Intuitively, a smaller block size should result in a more accurate block model. However, during practical implementation, the statistical problems of geological spatial information, and computer computing power constraints pose challenges in determining the optimal block size. Nevertheless, establishing the appropriate block size remains a crucial factor for effective open-pit mine production scheduling and the optimization of mining operations 8 . Currently, the prevailing approach in open-pit mining involves the utilization of the conventional block model construction method. This method entails filling the three-dimensional geological solid model with standardized blocks of a set size. Through this process, the construction of the block model is accomplished, and the three-dimensional geological model is discretized accordingly 9 . Subsequently, the spatial estimation interpolation method is employed to assign the geological attributes to each individual block, thereby facilitating the simulation of open-pit mining operations based on the block's designated mining sequence 10 , 11 .

The determination of block size division parameters is a complex engineering decision that has been extensively studied by numerous scholars. Yarahmadi 12 developed a computer program to determine geometry and size of rock blocks in two dimensional spaces. Hartman 13 emphasized that the size of the block model can be determined by considering the mining method and the selectivity of the ore, aiming to minimize ore dilution. David 14 conducted a comprehensive study on the block size problem utilizing advanced geostatistical methods. The research highlighted the crucial significance of block size selection in determining recoverable resource reserves, evaluating resource quality, optimizing mine designs, and formulating mining plans. The findings emphasized that the choice of block size directly impacts the economic benefits of mining operations. Jara et al. 15 also conducted a study investigating the impact of block model size on open-pit mine design and production planning. They quantified the influence of block model size on mining selectivity, such as grade and dilution, and assessed its effects on the economic outcomes of mining, including income, costs, and discounted cash flow. The findings revealed that as the block size increases, the overall quality of the ore body improves, but the average grade decreases. Additionally, the study observed a decrease in discounted cash flow with larger block model sizes. Similarly, Birch 16 examined the influence of block size on the final grade-tonnage curve and observed its impact on the average grade of the ore body. Furthermore, variations in block size were found to affect the overall income and net present value of the mine. Ruiseco 17 proposed guidelines for determining the block size in relation to the horizontal direction of the ore body. The recommendation suggests that the block size should be greater than 1/4 of the average distance between exploration boreholes and not less than 1/3 of the distance between two boreholes. Furthermore, it is advised that the block height should not exceed the height of the working bench. Furthermore, several researchers have taken into account various uncertainties, including average grade, recovery rate, production capacity, and dilution degree. They have employed Multi-Criteria Decision-Making (MCDM) methods to effectively select the appropriate size of the block model. Leuangthong et al. 18 , 19 highlighted that the determination of block size relies on various factors, including mining equipment, mining method, mining direction, and the sedimentary environment of the ore body. Recognizing the interconnected nature of these factors, they proposed the utilization of the Analytic Hierarchy Process (AHP) to select the optimal block size for mine production planning. Hayati et al. 8 employed the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to ascertain the optimal block size for the Angouran open-pit mine in Iran, resulting in the determination of a 10 m-sized block model. Subsequently, Abdollahei 20 proposed the utilization of the Fuzzy Delphi Analytic Hierarchy Process (FDAHP) and Fuzzy Multi-Objective Ratio Analysis (FMOORA) to optimize the size of the block model. These methods provide a framework for assessing and determining the most favorable block model size. Stevanovic et al. 21 adopted a range of 1/3 to 1/2 of the borehole spacing in the X and Y directions, based on the arrangement of the exploration work, as the division size range for blocks. The Z direction was constrained by the bench height, determining multiple sets of block size division schemes. Ultimately, the (AHP) was utilized to identify the optimal block size for mine production planning, considering the defined range of division sizes.

In summary, researchers have dedicated their efforts to studying the impact of block size on ore dilution and boundary grade, as well as determining the most suitable block size to minimize ore dilution and error in boundary grade selection. In theory, reducing the block size leads to improved accuracy and minimizes the effects on ore dilution and boundary grade. However, practical limitations in computer computing power prevent achieving the ideal block size. It is essential to acknowledge that no matter how small the block size becomes, it can only approximate the original three-dimensional geological body and cannot fully represent the volume and grade of the actual entity. Simultaneously, researchers commonly employ techniques such as integer programming, linear programming, mixed integer programming, and dynamic programming to formulate the mathematical model for open-pit mine production planning 22 , 23 , 24 , 25 . The mathematical models developed using the methods rely on regular cube or cuboid blocks as the foundational representation. However, during the simulation of mine production, the shape of the working bench side often deviates from the regular shape observed in practical mining operations. Meanwhile, the formulation of the open-pit mine production plan does not accurately correspond to the real output of each excavator's working area as depicted in the block model. Therefore, this research plan has introduced a novel approach by altering the foundation of the block model. It proposes a method to divide the size of the open-pit mine block model, taking into account the stripping process parameters and the efficiency of vehicle-shovel equipment.

Open-pit coal mining employs benches as the fundamental mining operational unit, extracting in a strip-sequence manner. The mining parameters and the operational capabilities of the truck-shovel system are inherently related to the block model. Thus, this study departs from the conventional approach of approximating the three-dimensional geological model using regular block models to represent the mining process, instead mapping the stripping parameters and equipment operational capabilities onto the block model. This serves as a dimensional parameter for block model segmentation. This approach can reduce constraints in subsequent studies on optimizing open-pit coal mining plans, enhance the efficiency of solving optimization models, and align more closely with on-site construction and operational realities.

Based on this research approach and considering temporary equipment failures, maintenance, and unbalanced truck-shovel combinations in actual production operations that affect the overall production system output, thereby influencing the practical operational capabilities of the truck-shovel system, the study incorporates a probabilistic analysis method as presented in reference 26 . The literature suggests that the actual number of operational trucks and shovels fluctuates around a certain mathematical expectation, and the study employs probability analysis to investigate the actual operational capabilities of the equipment. Each mining equipment's daily production is considered the volume of an individual mining block in the field, integrating mining parameters. The study proposes a method that integrates mining parameters and truck-shovel equipment operational capabilities into the discretization of a three-dimensional geological model for open-pit coal mining. This approach provides a crucial model foundation for optimizing production plans in open-pit coal mining.

Mining parameters constraint

The conventional approach involves utilizing the standard block model to populate the three-dimensional geological body. Subsequently, the dynamic deduction of the open-pit mine production plan is carried out from this foundation, as depicted in Fig. 1 . When the block model is filled to the irregular final pit boundary, if the centroid position of the block is within the boundary, the block is filled, but there will be a multi-volume calculation part. If the mass center point of the block is outside the boundary, it will not be filled, but the volume will be undercounted, as shown in Fig. 2 a. In cases where the geological body is extensive, and there exist thousands of block models, the cumulative effect of errors arising from block filling at the final pit boundary becomes significant. This, in turn, adversely impacts the accuracy of determining the quantity of ore and rock during the formulation of the production plan. The block model proposed in this study is generated by cutting directly on the closed shell 3D geological body, as shown in Fig. 3 , and the discrete geological body is defined as ‘Mining model’.

Dynamic evolution of the open-pit mine production scheduling with regular block models.

Comparison of ore deposit block models.

Mining model.

The mining parameters depend on the open-pit mining technology. The basic parameters include bench height h , bench slope angle α , mining belt width A , and bench width B 27 . Taking into consideration the on-site operations, the constrained block model incorporates the actual dimensions. The Z -axis size of the block corresponds to the bench height h . The Y -axis size represents the mining belt width A . The block dip angle is equivalent to the bench slope angle α . Lastly, the X -axis size is determined based on the daily operation efficiency of various excavator types. The division method proposed in this paper has no deviation from the three-dimensional geological body in accuracy and ensures the integrity of the step shape. The specific morphology is shown in Fig. 2 b.

Bench height constraint

The ore and rock in open-pit mines is generally divided into multiple benches for extraction. The division of benches should be conducive to maximizing equipment efficiency, improving ore quality, and ensuring operational safety. The bench height is one of the most important geometric parameters in open-pit mining 28 . Factors influencing the bench height include the burial conditions and properties of rock formations, operational conditions of mining equipment, drilling and blasting operations, production scale, technical specifications of mining and loading equipment, layout of transportation routes, and mining advancing speed, among others. For excavators with a bucket capacity of 3 m 3 to 4 m 3 , the bench height ranges from 10 to 15 m. Larger excavators can have bench heights of up to 20 m to 25 m, while large recasting excavators can reach bench heights of over 30 m. Wheel and chain excavators can have bench heights of 40 m to 50 m. Therefore, the selection of bench height should be determined based on the type of excavator.

Bench slope angle constraint

The bench slope angle primarily depends on the stability of the rock mass, and its value increases as the rock mass stability improves. Furthermore, the inclination of the bedding plane in the rock mass directly affects the bench slope angle. When the inclination of the bedding plane aligns or closely resembles that of the bench slope, and the bedding plane has a steep dip angle, the bench slope angle equals the inclination of the bedding plane.

Mining belt width constraint

In mining operations, the benches are typically divided into sequential strips of certain widths based on the direction of rock layers, known as mining belt. The mining belt width represents the width of excavation carried out by the excavator in one pass, and it influences various aspects such as drilling and blasting, mining and loading, track relocation, and bench width. Consequently, the mining belt width plays a crucial role in determining the progress of stripping and mining operations. In cases where the ore and rock are soft and do not require blasting, the mining area width is equivalent to the mining belt width. However, for ore and rock that necessitate blasting, the mining belt width generally refers to the width of one mining operation after a single blast, commonly known as "one blasting, two mining" or "one blasting, one mining". The determination of the mining belt width is primarily based on the technical specifications of the excavator. It is important to ensure that the mining belt width does not exceed 1.5 times the radius of the maximum excavator at the standing level, denoted as A ≤ 1.5 R wp . Additionally, the minimum width of the mining belt should meet the following requirements:

where $e$ is the safe distance from the center line of the drilling rig to the top line of the bench slope, generally 2.5–3 m; $h$ is bench height, m; $\alpha$ is bench slope angle, °.

When using track transportation, due to its flexibility, small turning radius, and climbing ability, it can effectively cooperate with the mining equipment in the production to improve the mining efficiency. Simultaneously, the mining belt width can be set without considering the problem of blasting pile buried road after blasting. The mining belt width can be larger, which is only related to the technical specifications of the excavator, the shunting mode and the safe distance of the transportation channel.

Shovel-truck’s operation efficiency constraint

Equipment operation efficiency refers to the ability of equipment to achieve maximum production efficiency with minimal resource consumption, while ensuring quality during the production process 29 , 30 . In this study, the analysis and calculation will be carried out using the example of the coordinated operation between open-pit mining shovels and trucks. Furthermore, to align with practical applications, the matching operation of multiple equipment models between self-operated and outsourcing units is taken into consideration.

In a multi-link production system, the equipment within each production link is operated in a sequential and mutually constrained manner. The production system consists of M production links. The i link contains N i equipment with an average operation rate of p i . The actual number of units in the system will fluctuate around a specific mathematical expectation, causing corresponding changes in the overall system index. Probability analysis can be employed to study this distribution characteristic, facilitating the rational selection and utilization of equipment.

Probability distribution of simultaneous operation equipment quantity

The self-operated and outsourced units within the system collectively possess x devices, and the probability of simultaneous operation follows a binomial distribution:

where N is the total number of equipment, N S denotes the number of shovels, and N T represents the number of trucks; x is the number of devices operating simultaneously; p is the average operating rate of the equipment, p s is the average operating rate of the shovels, and p t is the average operating rate of the trucks.

For convenience, the following recursive formula can be utilized to calculate the probability values for different x :

From Eq. ( 3 ), the probability distribution matrix of the number of shovel-truck simultaneous operations can be obtained. Among them:

where $P\left( {i,j} \right)$ is the probability of simultaneous with i shovels and j trucks; $P_{s} \left( i \right)$ is the probability of i shovels operating simultaneously; $P_{t} \left( j \right)$ is the probability of j trucks operating simultaneously.

Assuming the system consists of: shovel N s = 1 and truck N T = 5. The shovel operation rate is p s = 0.8, and the truck operation rate is p t = 0.75. The probability distribution of simultaneous shovel-truck operations can be found in Table 1 .

By conducting an analysis of Table 1 , it becomes evident that the probability matrix can be effectively partitioned into three distinct regions:

No product area. That is, the number of working shovels or the trucks is zero.

Oversaturated area. It refers to the area where the truck to shovel ratio is too large and the truck cannot give full play to its efficiency. When the quantities of trucks and shovels are not zero and the shovel quantity is fixed, the probability values gradually increase as the number of trucks grows. Once a certain quantity is reached, the probability values start to decrease. The region where the probability values decrease is defined as the oversaturated area.

The research findings indicate that Fig. 4 illustrates the correlation between the efficiency of each shovel or truck and the truck to shovel ratio. When the truck to shovel ratio K is below a critical value K 0 , the efficiency Q s of each shovel demonstrates a proportional relationship with the truck to shovel ratio K . However, once K > K 0 , the growth rate of Q s becomes significantly sluggish. The efficiency Q t of each truck primarily remains constant when K < K 0 . However, when K > K 0 , the efficiency Q t becomes inversely proportional to K , indicating that as K increases, the efficiency of each truck decreases. The region where K > K 0 is referred to as the oversaturated area, signifying an excessive number of trucks that prevent the improvement of shovel efficiency and lead to a decrease in truck efficiency. This ultimately results in wasteful utilization of equipment. In Table 1 , the division is made based on K 0 = 4 to delineate the different areas.

Relationship between efficiency of truck and shovel and truck to shovel ratio.

General operating area: This classification encompasses the regions that are not part of the previously mentioned no product area and oversaturated area.

System production capacity and equipment efficiency

The total system production capacity can be obtained by Eq. ( 5 ):

where Q c is the total system production capacity; P ( i , j ) is the probability of i shovels and j trucks operating simultaneously; Q ( i , j ) is the system production capacity of i shovels and j trucks operating simultaneously; N s is the number of shovels in the system; N t is the number of trucks in the system.

Considering the simultaneous operation of both self-operated and outsourced equipment, a combination of various types of shovel and truck equipment is employed to work together. As a result, the total system production capacity can be determined:

where Q m is the production capacity of each set of shovel-truck; $N_{t}^{\prime}$ is the number of trucks calculated, when $j \le iK_{0}$ , $N_{t}^{\prime} = j$ , when $j > iK_{0}$ , $N_{t}^{\prime} = iK_{0}$ ; Q 0 is the efficiency of each truck when shovels and trucks are working continuously; K 0 is the truck to shovel ratio saturation value (round-off number).

The average efficiency of each equipment is:

where Q s is the average efficiency per shovel; Q t is the average efficiency per truck.

Suppose $Q_{0} = 10^{6}$ t/a, $K_{0} = 4$ ,the following results are obtained.

When $N_{s} = 1$ , $N_{t} = 5$ , $Q_{c} = 2.810264 \times 10^{6}$ t/a; $Q_{s} = 2.810264 \times 10^{6}$ t/a; $Q_{t} = 5.62053 \times 10^{5}$ t/a.

Block model size

The volume $V$ of a single block is determined by the daily production capacity of a shovel. The number of working days per year is calculated at 330. Once the mining technology and operational equipment model are determined, the bench height h , the bench slope angle α , and the bench width A , that is, Z -axis size $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z}$ and Y -axis size $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}$ of the block model can be determined. With these values known, the X -axis size $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{X}$ can be obtained using the volume calculation Eqs. ( 10 ) and ( 11 ).

where $\tilde{Q}$ is the shovel’s daily operation efficiency.

The specific process of the method proposed in this study is depicted in Fig. 5 .

Flow chart of the block model size determination method.

This study utilizes the Baorixile open-pit mine as a case study (as shown in Fig. 6 ). The Chenbaerhuqi coal field in Hulunbuir City, Inner Mongolia Autonomous Region of China, is where the Baorixile open-pit coal mine is situated 31 , 32 . The mine's external perimeter spans a 50.72 km 2 region, measuring 5.86 km in width from north to south and 10.98 km in length from east to west. The mine was constructed in 1998. The primary coal seams targeted for mining are coal B, coal 1 2 , coal 2 1 , and coal 3 1 . The original design’s annual production capacity was 1.80 million t. In 2014, the approved production capacity of the Baorixile Open-pit Coal Mine was 35 million t/a. The semi-continuous mining technology of shovel-single bucket truck-semi-fixed crushing station is adopted.

The Baorixile open-pit mine.

The self-operated and outsourcing units’ shovels and trucks of the open-pit mine simultaneously performs on-site coal mining and rock stripping operations 33 . The models of self-operated trucks and shovel are as follows:

Coal mining equipment models

The single bucket excavators of WK-35 (bucket capacity 35 m 3 ), WK-10B (bucket capacity 10 m 3 ) and WK-12 (bucket capacity 12 m 3 ) are selected for the coal mining equipment, and the dump trucks of MT4400AC-220T (load 220t) and TR100 (load 91t) are selected for the coal transportation equipment.

Rock stripping equipment models

The single bucket excavators of WK-35 (bucket capacity 35 m 3 ), WK-10B (bucket capacity 10 m 3 ) and WK-12 (bucket capacity 12 m 3 ) are selected for rock stripping equipment, and the dump trucks of MT4400AC-220T (load 220t) and TR100 (load 91t) are selected for rock transportation equipment.

The outsourcing equipment is mainly responsible for rock stripping, and their models are 4 m 3 hydraulic shovel and 60t dump truck.

Table 2 presents the models, quantities, and operational efficiencies of all shovels and trucks utilized in the open-pit mine.

Block model

Mining parameter.

The following mining parameters are obtained from the preliminary design report of the mine 33 .

Bench height

The bench height is determined by considering the properties of the stripping soil and rock, process characteristics, equipment specifications, mining requirements, and a comprehensive assessment aimed at enhancing equipment operating conditions and improving production efficiency 34 . Bench height stands as a significant mining parameter in open-pit mines. To determine the appropriate bench height for stripping and coal mining operations, several factors are considered, including the maximum excavation height of hydraulic excavators, production stripping ratio, and other relevant considerations.

Based on a careful evaluation of the physical and mechanical properties of the stripping material and its burial conditions, as well as considering the specifications of the mining equipment, the determined stripping step height for the mine is 15 m (that is $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z}_{r}$ = 15 m). The benches are horizontally divided to ensure effective and efficient mining operations.

The coal seam of this mine is a nearly horizontal and the dip angle is 1–3°. Hence, the standard coal bench height is set at 15 m, denoted as $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z}_{c}$ = 15 m.

Bench slope angle

The topsoil bench slope angle is 65°, that is α t = 65°; the coal and rock bench slope angle are 70°, that is α c = 70°, α r = 70°.

Mining belt width

Based on process characteristics, it is observed that a wider mining belt width leads to a reduced number of pit lines in the annual operation, resulting in higher system efficiency. However, an increase in the mining belt width will result in a slower working slope angle, leading to an increase in the amount of stripping work required. Considering the specifications of the working equipment, mining and loading conditions, and other relevant factors, the mining belt width for each stripping equipment is determined as follows:

The mining belt width of shovel WK-35 is 25 m, that is $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 35} = 25$ m;

The bench width of shovel WK-10B is 20 m, that is $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 10B} = 20$ m;

The bench width of shovel WK-12 is 20 m, that is $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 12} = 20$ m;

The mining belt width of 4 m 3 hydraulic backhoe is 10 m, that is $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{{4m^{3} }} = 10$ m.

Shovel-truck’s operation efficiency

Table 2 illustrates the operational efficiency of various shovel types in both self-operated and outsourcing units as follows: P WK-35 = 0.96, P WK-10B = 0.95, P WK-12 = 0.92, P 4m3 = 0.93. Operating efficiency of different types of trucks: P MT4400AC-220 T = 0.93, P TR100 = 0.94, P 60t = 0.80.

Based on Eq. ( 3 ), the probability distribution of the number of shovels and trucks operating simultaneously can be calculated, as demonstrated in Table 3 .

Due to the extensive volume of data, only a portion of the data is presented in Table 3 . Based on the data calculation presented in Table 3 , it can be concluded that the maximum probability of simultaneous operation for all self-operated and outsourced equipment is P = 2.8483E−04. Then the combination of utilizing 6 WK-35, 4 WK-10B, 2 WK-12, 28 MT4400AC-220T, 32 TR100, 106 4 m 3 hydraulic shovels, and 365 60t dump trucks exhibits the highest probability, as depicted in Table 4 . Simultaneously, Table 4 showcases the specific combinations of trucks and shovels employed in the actual production operations of the open-pit mine.

According to the specific saturation value K 0 of each combination of trucks and shovels in Table 4 , as well as the working efficiency of each type of truck when both the shovel and the truck are continuously operated, the production capacity of each combination can be calculated using Eq. ( 7 ) as follows:

WK-35 + MT4400AC-220T:

$Q_{WK - 35} = \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{j = 1}^{{iK_{0} }} {P\left( {i,j} \right) \cdot j \cdot Q_{MT400AC - 200T} } } = 1954.16 \cdot {1}0^{{4}} \;{\text{m}}^{{3}} {\text{/a}}$

WK-10B + TR100T:

$Q_{WK - 10B} = \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{j = 1}^{{iK_{0} }} {P\left( {i,j} \right) \cdot j \cdot Q_{TR100T} } } = 993.21 \cdot {1}0^{{4}} \;{\text{m}}^{{3}} {\text{/a}}$

WK-12 + TR100T:

$Q_{WK - 12} = \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{j = 1}^{{iK_{0} }} {P\left( {i,j} \right) \cdot j \cdot Q_{TR100T} } } + \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{{i = iK_{0} + 1}}^{{N_{t} }} {P\left( {i,j} \right) \cdot iK_{0} \cdot Q_{TR100T} } } = 608.29 \cdot {1}0^{{4}} \;{\text{m}}^{{3}} {\text{/a}}$

4 m 3 hydraulic backhoe + 60t dump truck:

$Q_{{4m^{3} }} = \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{j = 1}^{{iK_{0} }} {P\left( {i,j} \right) \cdot j \cdot Q_{60t} } } + \sum\limits_{i = 1}^{{N_{s} }} {\sum\limits_{{i = iK_{0} + 1}}^{{N_{t} }} {P\left( {i,j} \right) \cdot iK_{0} \cdot Q_{60t} } } = 10313.50 \cdot {1}0^{{4}} \;{\text{m}}^{{3}} {\text{/a}}$

The daily operation efficiency of different types of shovels can be calculated using Eq. ( 11 ).

$\tilde{Q}_{WK - 35} = \frac{1954.16}{{6 \times 330}} \times 10000 = 9869.50\;{\text{m}}^{{3}} {\text{/d;}}$

$\tilde{Q}_{WK - 10B} = \frac{993.21}{{4 \times 330}} \times 10000 = 7524.32\;{\text{m}}^{{3}} {\text{/d;}}$

$\tilde{Q}_{WK - 12} = \frac{608.29}{{2 \times 330}} \times 10000 = 9216.52\;{\text{m}}^{{3}} {\text{/d;}}$

$\tilde{Q}_{{4m^{3} }} = \frac{10313.50}{{106 \times 330}} \times 10000 = 2948.40\;{\text{m}}^{{3}} {\text{/d}}{.}$

Based on the mining parameters outlined in section " Mining parameter " and the daily operation efficiency of each shovel model calculated in section " Shovel-truck’s operation efficiency ", the X -axis size of the block model for each shovel's working area are calculated using Eq. ( 11 ) as follows:

One of the working areas of the shovel WK-35 is at the level of 565 m to 580 m. The X -axis size of the block model for this specific bench is: $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{X}_{WK - 35} = \frac{{\tilde{Q}_{WK - 35} }}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 35} \cdot \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z} }} = \frac{9869.50}{{25 \times 15}} = 26$ m;

One of the working areas of the shovel WK-10B is at the level of 505 m to 520 m. The X -axis size of the block model for this specific bench is: $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{X}_{WK - 10B} = \frac{{\tilde{Q}_{WK - 10B} }}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 10B} \cdot \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z} }} = \frac{7524.32}{{20 \times 15}} = 25$ m;

One of the working areas of the shovel WK-12 is at the level of 490 m to 505 m. The X -axis size of the block model for this specific bench is: $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{X}_{WK - 12} = \frac{{\tilde{Q}_{WK - 12} }}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{WK - 12} \cdot \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z} }} = \frac{9216.52}{{20 \times 15}} = 30$ m;

One of the working areas of the 4 m 3 hydraulic backhoes is at the level of 590 m to 610 m. The X -axis size of the block model for this specific bench is: $\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{X}_{{4m^{3} }} = \frac{{\tilde{Q}_{{4m^{3} }} }}{{\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Y}_{{4m^{3} }} \cdot \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{Z} }} = \frac{2948.40}{{10 \times 15}} = 20$ m;

The "Mining model" function module of the self-developed "Life Cycle Mining Software System" was developed using the C++ programming language. The module successfully realizes the construction of the block model for this study, as depicted in Fig. 7 .

The self-developed software system and the generation of the ‘Mining model’.

Accuracy comparative analysis

The closed shell model is a geometric representation defined by its surface or boundary, encompassing only the outer surface or boundary without considering the specific shape of the interior set. The solid model provides a higher level of detail as it encompasses the internal structure and geometry. In comparison to the block model, the solid model offers enhanced computational accuracy; however, it comes with a trade-off of significantly slower efficiency. If the block were to be divided based on mining process parameters and equipment efficiency using the solid model as a foundation, the limited computing power of the computer would hinder the completion of this operation. In the case of layered deposits, there is no disparity in volume accuracy between the closed shell model and the solid model. Additionally, the computer exhibits fast operation speed when performing segmentation on the shell model. Consequently, this study opts for the closed shell model to construct the “Mining model”. To assess the superiority of the proposed method in terms of volume accuracy, a comparative analysis was conducted using the four-year mining and stripping engineering models of Baorixile open-pit mine. The selected time periods for analysis encompassed the years 2021 to 2022, 2022 to 2023, 2023 to 2024, and 2023 to 2024. This study employed these specific models to facilitate a comprehensive evaluation. Figure 8 illustrates the closed shell model depicting the mining and stripping engineering operations over a span of four years. The annual stripping engineering model is constructed separately using block configurations of 3 * 3 * 3, 6 * 6 * 6, 12 * 12 * 12, as shown in Fig. 9 .

Closed shell model of 2021–2025 mining and stripping project in Baorixile open-pit mine.

Block models of mining engineering with different specifications in 2021–2025.

Table 5 illustrates the annual quantities of Coal B, Coal 1 2 , Coal 2 1 , and Coal 3 1 , as well as the total coal quantity, total volume, and error between the closed shell model and the block models, within the closed shell model and the corresponding 3 * 3 * 3, 6 * 6 * 6, 12 * 12 * 12 three different specifications of the block model of the mining and stripping project for the four years production plans.

To provide a clearer representation of the engineering quantity errors between the three different specifications of the block model and the closed shell model at each stage, two comparison diagrams (refer to Figs. 10 and 11 ) are presented. These diagrams aim to enhance the understanding and visual depiction of the variations in engineering quantity. After conducting a comprehensive comparison, it is evident that the 12 * 12 * 12 block model generally exhibits the highest error, while the 3 * 3 * 3 block model demonstrates the lowest error in general. Nevertheless, it is not necessarily that as the regular block model size decreases, the accuracy tends to improve. For instance, the 6 * 6 * 6 block model for the 2024–2025 period in Fig. 10 a and for the 2021–2022 and 2022–2023 periods in Fig. 10 c, exhibit higher accuracy compared to the 3 * 3 * 3 block model. The 12 * 12 * 12 block model exhibit higher accuracy compared to the 3 * 3 * 3 block model for the 2023–2024 period in Fig. 10 e. In the design of the production plan for the next few years, the open-pit mine will not always carry out the experiment of optimal block size selection. Therefore, the presence of interference factors resulting from human subjective consciousness continues to exert a significant influence on the accuracy. Upon error superposition, the block specification with the highest error in the 2021–2022 period, as depicted in Fig. 11 e, is 12 × 12 × 12, with a maximum error reaching 7.4252%. Similarly, in Fig. 11 f, the block specification with the highest error is 6 × 6 × 6, with a maximum error of 2.4798%. This highlights the challenge in selecting the optimal block size when utilizing a regular block model for constructing a three-dimensional geological body. Simultaneously, the findings also demonstrate that the block division method proposed in this study yields the highest level of accuracy in terms of volume precision. The method introduced in this study effectively mitigates volume errors associated with utilizing a regular block model to populate the three-dimensional geological model for production planning design. Moreover, it ensures the regularity of mining benches, aligning more closely with the practical construction conditions in open-pit mines.

The comparison of coal quantity of each layer, total coal quantity and total volume between the closed shell model and the corresponding block model for 4 years.

The accuracy error of 4 years of different specifications of mining and stripping engineering block model.

Discussions

In this study, the aim was to enhance the accuracy of the foundational model for production planning by expanding the application of the open-pit mine three-dimensional geological model. To achieve this, a departure from the conventional approach of employing a regular block model to populate the solid model was pursued. To achieve this, a departure from the conventional approach of employing a regular block model to populate the solid model was pursued. The closed shell model is directly discretized into a block model while incorporating the constraints imposed by mining parameters and shovel-truck’s operation efficiency. This study serves as a foundation for our future research on attribute assignment of the mining model, block model migration order, and migration path prediction. Our overarching objective is to develop a comprehensive optimization method for production planning throughout the life cycle of open-pit mines, and to create a prototype system for decision-making in open-pit mine life cycle mining planning. It is noteworthy that in this study, the daily equipment operation efficiency is utilized to determine the X-axis size of the block. However, it is also possible to partition the blocks based on weekly or monthly equipment operation efficiency, enabling the creation of long-term production plans. Based on the diverse equipment types and their respective layout positions, the block size of the equipment's operational area can be determined. This approach aligns more closely with the actual operational conditions. Simultaneously, it significantly reduces the number of blocks, minimizes the computer's processing time, and enhances overall work efficiency. However, it is important to note that this study focuses specifically on the shovel-truck intermittent mining technology, and there remains a dearth of research and development regarding other mining technologies. Subsequent research endeavors will aim to build upon and expand the findings of this study, with the goal of further improvement and in-depth exploration.

Conclusions

The following conclusions and comments can be made from the present study:

Based on the three-dimensional geological model of the closed shell type, the block model is generated considering mining parameters as constraints. Specifically, the bench height is employed as the Z -axis size of the block model, the mining belt width of is adopted as the Y -axis size of the block model, and the bench slope angle serves as the inclination angle of the block model.

Considering the random fluctuation of the number of equipment, compared with the method of calculating equipment efficiency and output by average index, the results of equipment operation efficiency obtained by probability analysis method are more in line with the actual situation. In this study, the daily equipment outburst performance of each type of excavator is calculated based on the maximum probability of simultaneous operation of self-operated and outsourced units. This calculation aids in determining the X -axis size of the block model in the operational area of different types of excavators.

In this paper, using Baorixile open-pit mine as a case study, a ‘Mining model’ function module is developed within the self-developed system. A closed shell block model of the mining and stripping project at the open-pit mine for a four-year period from 2021 to 2025 was created. The regular block models of 3 * 3 * 3, 6 * 6 * 6, and 12 * 12 * 12 were selected and compared to the block model used in this study. This highlights the challenge in selecting the optimal block size when utilizing a regular block model for constructing a three-dimensional geological body. Simultaneously, the findings also demonstrate that the block division method proposed in this study yields the highest level of accuracy in terms of volume precision. The method introduced in this study effectively mitigates volume errors associated with utilizing a regular block model to populate the three-dimensional geological model for production planning design. Moreover, it ensures the regularity of mining benches, aligning more closely with the practical construction conditions in open-pit mines.

Data availability

The data used and analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (51974144), the Youth Fund of the National Natural Science Foundation of China (52204158), 'Jie Bang Gua Shuai' (Take the Lead) of the Key Scientific and Technological Project for Liaoning Province (2021JH1/10400011), Discipline Innovation Team of Liaoning Technical University (LNTU20TD-07), and School level scientific research project of Yancheng Institute of Technology (xjr2020039).

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Guo, W., Liu, G., Li, J. et al. Research on the method of determining the block size for an open-pit mine integrating mining parameters and shovel-truck’s operation efficiency. Sci Rep 14 , 10119 (2024). https://doi.org/10.1038/s41598-024-52815-9

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Study identifies method to reduce extended-spectrum antibiotic use for UTI

In total, the study included 127,403 noncritically ill adult patients admitted to the hospital with UTI.

The method involves the use of a computerized provider order entry (CPOE) that provides real-time recommendations to prescribing physicians on the best antibiotic match for each patient. With the program, patients’ risk for an antibiotic-resistant infection is determined using information on patient characteristics from electronic medical records as well as hospital and location-specific data. For the study, physicians treating patients with a low estimated risk (less than 10%) of MDRO UTI were prompted to give standard-spectrum antibiotics.

Concurrently, the INSPIRE Pneumonia Trial was published in JAMA demonstrating the same approach in patients admitted with pneumonia. 2

“The right information at the right time can improve physician antibiotic selection. Many different bacteria can cause pneumonia or UTI, and picking the best matched antibiotic can be a challenge. Results from these trials show that giving physicians an alert informing them of their patient’s actual risk for antibiotic resistance can help them choose the best antibiotic and reduce extended-spectrum antibiotic use,” said Shruti Gohil, MD, MPH, in a news release on the findings. 3 Gohil is the lead author for both INSPIRE trials, as well as an assistant professor in the Division of Infectious Diseases at the University of California, Irvine School of Medicine.

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Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance: Subsea Christmas Tree System as A Case Study

Published: 02 May 2024
Volume 38 , pages 198–209, ( 2024 )

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Qi-bing Wu 1 ,
Bao-ping Cai 2 ,
Hong-yan Fan 2 ,
Guan-nan Wang 1 ,
Weifeng Ge 3 ,
Xiao-yan Shao 2 &
Yong-hong Liu 2

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CNOOC Safety Technology Services Co., Ltd., Tianjin, 300456, China

Qi-bing Wu, Guan-nan Wang & Xi Rao

College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao, 266580, China

Bao-ping Cai, Hong-yan Fan, Xiao-yan Shao & Yong-hong Liu

Safety & Environmental Protection Branch, CNOOC EnerTech, Tianjin, 300456, China

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Correspondence to Bao-ping Cai .

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Foundation item: This work was financially supported by the National Key Research and Development Program of China (Grant No. 2022YFC3004802), the National Natural Science Foundation of China (Grant Nos. 52171287 and 52325107), High Tech Ship Research Project of Ministry of Industry and Information Technology (Grant Nos. 2023GXB01-05-004-03 and GXBZH2022-293), the Science Foundation for Distinguished Young Scholars of Shandong Province (Grant No. ZR2022JQ25), the Taishan Scholars Project (Grant No. tsqn201909063), and the sub project of the major special project of CNOOC Development Technology, “Research on the Integrated Technology of Intrinsic Safety of Offshore Oil Facilities” (Phase I), “Research on Dynamic Quantitative Analysis and Control Technology of Risks in Offshore Production Equipment” (Grant No. HFKJ-2D2X-AQ-2021-03).

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Wu, Qb., Cai, Bp., Fan, Hy. et al. Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance: Subsea Christmas Tree System as A Case Study. China Ocean Eng 38 , 198–209 (2024). https://doi.org/10.1007/s13344-024-0017-y

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Received : 05 May 2023

Revised : 18 July 2023

Accepted : 10 August 2023

Published : 02 May 2024

Issue Date : April 2024

DOI : https://doi.org/10.1007/s13344-024-0017-y

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Machine learning approaches can benefit Parkinson’s research: Study

1 method found to help ID 'more potent' treatment candidates in lab

by Patricia Inácio, PhD | April 29, 2024

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A number of different oral medications are seen scattered together, along with two pill bottles.

Scientists exploring the potential of machine learning approaches in drug discovery for Parkinson’s disease and other neurodegenerative disorders — focusing on misfolded proteins that are the hallmark of such conditions — found that one such method identified compounds “two orders of magnitude more potent” than ones previously reported, per a new study.

Using this method allowed the researchers, from the U.K. and the U.S., to identify compounds that can effectively block the clumping, or aggregation, of alpha-synuclein protein, an underlying cause of Parkinson’s, the study reported.

“We anticipate that using machine learning approaches of the type described here could be of considerable benefit to researchers working in the field of protein misfolding diseases [such as Parkinson’s], and indeed early-stage drug discovery research in general,” the researchers wrote.

Their study, “ Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning ” was published in the journal Nature Chemical Biology.

An illustration provides a close-up view of protein clumps known as amyloid plaques, that are a hallmark of Alzheimer's disease.

Simulations uncover mechanisms in alpha-synuclein protein buildup

Machine learning approaches ‘speeding up the whole process’ of drug discovery.

Parkinson’s disease is marked by the toxic accumulation of misfolded forms of the alpha-synuclein protein within dopamine-producing nerve cells — those responsible for releasing the neurotransmitter dopamine. Dopamine is a signaling molecule that plays a role in controlling movement; Parkinson’s results from the progressive loss of these cells.

Despite efforts to identify compounds that stop this toxic accumulation, there are, to date, no disease-modifying treatments available for Parkinson’s.

Traditional strategies to identify novel therapies — which involve screening large chemical libraries looking for potential candidates prior to any testing in humans — are time-consuming, expensive, and often unsuccessful.

In the case of Parkinson’s, the development of effective therapies has been hampered by the lack of methods to identify the right molecular targets.

“One route to search for potential treatments for Parkinson’s requires the identification of small molecules that can inhibit the aggregation of alpha-synuclein. … But this is an extremely time-consuming process — just identifying a lead candidate for further testing can take months or even years,” Michele Vendruscolo, a professor at the University of Cambridge and the study’s lead author, said in a university press release .

Now, the researchers developed a method that was able to use machine learning to quickly screen chemical libraries containing literally millions of compounds. The goal was to identify small molecules able to block the clumping of alpha-synuclein.

From a list of small molecules predicted to have a good binding to the alpha-synuclein aggregates, the researchers chose a small number of the top-ranking compounds to test experimentally as potent inhibitors of aggregation.

The results from these experimental assays were then fed to the machine learning model, which identified those with the most promising effects. This process was repeated a few times, so that highly potent compounds were identified.

“Instead of screening experimentally, we screen computationally,” Vendruscolo said.

Machine learning is having a real impact on the drug discovery process — it’s speeding up the whole process of identifying the most promising candidates. For us this means we can start work on multiple drug discovery programs — instead of just one.

“By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules,” Vendruscolo said.

Using this method, the researchers optimized the initial compounds to target pockets on the surfaces of the alpha-synuclein clumps.

In lab tests using brain tissue samples from patients with Lewy body dementia (LBD) and multiple system atrophy (MSA), two forms of atypical parkinsonism , the compounds effectively blocked aggregation of alpha-synuclein.

“Machine learning is having a real impact on the drug discovery process — it’s speeding up the whole process of identifying the most promising candidates,” Vendruscolo said. “For us this means we can start work on multiple drug discovery programs — instead of just one.”

According to Vendruscolo, “so much is possible due to the massive reduction in both time and cost – it’s an exciting time.”

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Machine learning tool may help detect Parkinson’s disease: Study

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Machine learning aids distinguishing Parkinson’s, essential tremor voice

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AI analysis helps identify molecular subtypes of Parkinson’s

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Case Study Methods and Examples
By Janet Salmons, PhD Manager, Sage Research Methods Community. What is Case Study Methodology?. Case studies in research are both unique and uniquely confusing. The term case study is confusing because the same term is used multiple ways. The term can refer to the methodology, that is, a system of frameworks used to design a study, or the methods used to conduct it.
What Is a Case Study?
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 Study
Defnition: A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation. It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied.
Methodology or method? A critical review of qualitative case study
Definitions of qualitative case study research. Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995).Qualitative case study research, as described by Stake (), draws together "naturalistic, holistic, ethnographic, phenomenological, and biographic research methods" in a bricoleur design ...
What is a Case Study?
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.
Case Study
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.
The case study approach
A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the ...
Case Study Methodology of Qualitative Research: Key Attributes and
Case Study Methodology: Basic Definitions and Concepts ... In a case study research, multiple methods of data collection are used, as it involves an in-depth study of a phenomenon. It must be noted, as highlighted by Yin (2009), a case study is not a method of data collection, rather is a research strategy or design to study a social unit.
(PDF) Qualitative Case Study Methodology: Study Design and
The case study is a qualitative methodology that supports research on studying complex phenomena within their contexts (Baxter and Jack, 2008). The case study strategy was selected as contextual ...
Qualitative Case Study Methodology: Study Design and Implementation for
Qualitative case study methodology provides tools for researchers to study complex phenomena within their contexts. When the approach is applied correctly, it becomes a valuable method for health science ... treatment of case study methods we encourage you to read Hancock and Algozzine's, Doing case study research: A practical guide for ...
What the Case Study Method Really Teaches
What the Case Study Method Really Teaches. Summary. It's been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study ...
Continuing to enhance the quality of case study methodology in health
Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5,37 By seeking patterns within and across data ...
Case Study Research Method in Psychology
Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews). The case study research method originated in clinical medicine (the case history, i.e., the patient's personal history). In psychology, case studies are ...
What is the Case Study Method?
Overview. Simply put, the case method is a discussion of real-life situations that business executives have faced. On average, you'll attend three to four different classes a day, for a total of about six hours of class time (schedules vary). To prepare, you'll work through problems with your peers. Read More.
Research Methods
Qualitative analysis methods. Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected: From open-ended surveys and interviews, literature reviews, case studies, ethnographies, and other sources that use text rather than numbers. Using non-probability sampling methods.
Adaptive Case Study-Mixed Methods Design Practices for Researchers
Wave definitions for the current expanded case study-mixed methods Study of the COVID-19 pandemic in Alberta, Canada based on case count data. Note. The Y-axis represents case counts and the X-axis the date. Case count data from the Government of Alberta via its interactive COVID-19 data app (Government of Alberta, 2021).
Perceptual consequences of online group speech treatment for
Perceptual consequences of online group speech treatment for individuals with Parkinson's disease: A pilot study case series. ... Method . Seven speakers with hypokinetic dysarthria associated with PD participated in a university and community-based online group speech program for 10 weeks. Speech recordings occurred remotely 1 week before ...
Research on the method of determining the block size for an ...
This study utilizes the Baorixile open-pit mine as a case study (as shown in Fig. 6). The Chenbaerhuqi coal field in Hulunbuir City, Inner Mongolia Autonomous Region of China, is where the ...
Daily flow discharge prediction using integrated methodology based on
Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin. Author links open overlay panel Abinash Sahoo a, Swayamshu Satyapragnya Parida a, ... The hybrid ConvLSTM technique, a mix of deep learning methods, is used in this study's objective model. ConvLSTM is a hybrid form of the ...
International Surveys
Pew Research Center's cross-national studies are designed to be nationally representative using probability-based methods and target the non-institutional adult population (18 and older) in each country. The Center strives for samples that cover as much of the adult population as possible, given logistical, security and other constraints.
Study identifies method to reduce extended-spectrum antibiotic use for UTI
The method involves the use of a computerized provider order entry (CPOE) that provides real-time recommendations to prescribing physicians on the best antibiotic match for each patient. With the program, patients' risk for an antibiotic-resistant infection is determined using information on patient characteristics from electronic medical ...
Remaining Useful Life Prediction Method for Multi-Component ...
Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production, and is an indispensable part of prediction and health management. However, most of the existing remaining useful life (RUL) prediction methods assume that there is no maintenance or only perfect maintenance during ...
Machine learning approaches can benefit Parkinson's research: Study
Scientists exploring the potential of machine learning approaches in drug discovery for Parkinson's disease and other neurodegenerative disorders — focusing on misfolded proteins that are the hallmark of such conditions — found that one such method identified compounds "two orders of magnitude more potent" than ones previously reported, per a new study.
Toward Developing a Framework for Conducting Case Study Research
In order to further differentiate case study method from casework, case method, and case history (case records), she stresses its unique distinctive attributes: particularistic (it focuses on a particular situation, event, program, or phenomenon); descriptive (it yields a rich, thick description of the phenomenon under study); heuristic (it ...

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