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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved February 16, 2024, from https://www.scribbr.com/methodology/case-study/

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Research Method

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

Case Study Research

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

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  • Knowledge Base
  • Methodology
  • Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cite this Scribbr article

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

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 15 February 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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case study methods in research methodology

Case Study Research: Methods and Designs

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves…

Case Study Method

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves observing subjects, or cases, in their natural setting, with minimal interference from the researcher.

In the case study method , researchers pose a specific question about an individual or group to test their theories or hypothesis. This can be done by gathering data from interviews with key informants.

Here’s what you need to know about case study research design .

What Is The Case Study Method?

Main approaches to data collection, case study research methods, how case studies are used, case study model.

Case study research is a great way to understand the nuances of a matter that can get lost in quantitative research methods. A case study is distinct from other qualitative studies in the following ways:

  • It’s interested in the effect of a set of circumstances on an individual or group.
  • It begins with a specific question about one or more cases.
  • It focuses on individual accounts and experiences.

Here are the primary features of case study research:

  • Case study research methods typically involve the researcher asking a few questions of one person or a small number of people—known as respondents—to test one hypothesis.
  • Case study in research methodology may apply triangulation to collect data, in which the researcher uses several sources, including documents and field data. This is then analyzed and interpreted to form a hypothesis that can be tested through further research or validated by other researchers.
  • The case study method requires clear concepts and theories to guide its methods. A well-defined research question is crucial when conducting a case study because the results of the study depend on it. The best approach to answering a research question is to challenge the existing theories, hypotheses or assumptions.
  • Concepts are defined using objective language with no reference to preconceived notions that individuals might have about them. The researcher sets out to discover by asking specific questions on how people think or perceive things in their given situation.

They commonly use the case study method in business, management, psychology, sociology, political science and other related fields.

A fundamental requirement of qualitative research is recording observations that provide an understanding of reality. When it comes to the case study method, there are two major approaches that can be used to collect data: document review and fieldwork.

A case study in research methodology also includes literature review, the process by which the researcher collects all data available through historical documents. These might include books, newspapers, journals, videos, photographs and other written material. The researcher may also record information using video cameras to capture events as they occur. The researcher can also go through materials produced by people involved in the case study to gain an insight into their lives and experiences.

Field research involves participating in interviews and observations directly. Observation can be done during telephone interviews, events or public meetings, visits to homes or workplaces, or by shadowing someone for a period of time. The researcher can conduct one-on-one interviews with individuals or group interviews where several people are interviewed at once.

Let’s look now at case study methodology.

The case study method can be divided into three stages: formulation of objectives; collection of data; and analysis and interpretation. The researcher first makes a judgment about what should be studied based on their knowledge. Next, they gather data through observations and interviews. Here are some of the common case study research methods:

One of the most basic methods is the survey. Respondents are asked to complete a questionnaire with open-ended and predetermined questions. It usually takes place through face-to-face interviews, mailed questionnaires or telephone interviews. It can even be done by an online survey.

2. Semi-structured Interview

For case study research a more complex method is the semi-structured interview. This involves the researcher learning about the topic by listening to what others have to say. This usually occurs through one-on-one interviews with the sample. Semi-structured interviews allow for greater flexibility and can obtain information that structured questionnaires can’t.

3. Focus Group Interview

Another method is the focus group interview, where the researcher asks a few people to take part in an open-ended discussion on certain themes or topics. The typical group size is 5–15 people. This method allows researchers to delve deeper into people’s opinions, views and experiences.

4. Participant Observation

Participant observation is another method that involves the researcher gaining insight into an experience by joining in and taking part in normal events. The people involved don’t always know they’re being studied, but the researcher observes and records what happens through field notes.

Case study research design can use one or several of these methods depending on the context.

Case studies are widely used in the social sciences. To understand the impact of socio-economic forces, interpersonal dynamics and other human conditions, sometimes there’s no other way than to study one case at a time and look for patterns and data afterward.

It’s for the same reasons that case studies are used in business. Here are a few uses:

  • Case studies can be used as tools to educate and give examples of situations and problems that might occur and how they were resolved. They can also be used for strategy development and implementation.
  • Case studies can evaluate the success of a program or project. They can help teams improve their collaboration by identifying areas that need improvements, such as team dynamics, communication, roles and responsibilities and leadership styles.
  • Case studies can explore how people’s experiences affect the working environment. Because the study involves observing and analyzing concrete details of life, they can inform theories on how an individual or group interacts with their environment.
  • Case studies can evaluate the sustainability of businesses. They’re useful for social, environmental and economic impact studies because they look at all aspects of a business or organization. This gives researchers a holistic view of the dynamics within an organization.
  • We can use case studies to identify problems in organizations or businesses. They can help spot problems that are invisible to customers, investors, managers and employees.
  • Case studies are used in education to show students how real-world issues or events can be sorted out. This enables students to identify and deal with similar situations in their lives.

And that’s not all. Case studies are incredibly versatile, which is why they’re used so widely.

Human beings are complex and they interact with each other in their everyday life in various ways. The researcher observes a case and tries to find out how the patterns of behavior are created, including their causal relations. Case studies help understand one or more specific events that have been observed. Here are some common methods:

1. Illustrative case study

This is where the researcher observes a group of people doing something. Studying an event or phenomenon this way can show cause-and-effect relationships between various variables.

2. Cumulative case study

A cumulative case study is one that involves observing the same set of phenomena over a period. Cumulative case studies can be very helpful in understanding processes, which are things that happen over time. For example, if there are behavioral changes in people who move from one place to another, the researcher might want to know why these changes occurred.

3. Exploratory case study

An exploratory case study collects information that will answer a question. It can help researchers better understand social, economic, political or other social phenomena.

There are several other ways to categorize case studies. They may be chronological case studies, where a researcher observes events over time. In the comparative case study, the researcher compares one or more groups of people, places, or things to draw conclusions about them. In an intervention case study, the researcher intervenes to change the behavior of the subjects. The study method depends on the needs of the research team.

Deciding how to analyze the information at our disposal is an important part of effective management. An understanding of the case study model can help. With Harappa’s Thinking Critically course, managers and young professionals receive input and training on how to level up their analytic skills. Knowledge of frameworks, reading real-life examples and lived wisdom of faculty come together to create a dynamic and exciting course that helps teams leap to the next level.

Explore Harappa Diaries to learn more about topics such as Objectives Of Research , What are Qualitative Research Methods , How To Make A Problem Statement and How To Improve your Cognitive Skills to upgrade your knowledge and skills.

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Research-Methodology

Case Studies

Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”

Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.

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  • Published: 13 February 2024

Contextual effects: how to, and how not to, quantify them

  • Tobias Saueressig   ORCID: orcid.org/0000-0002-3332-3856 1 , 2 ,
  • Hugo Pedder   ORCID: orcid.org/0000-0002-7813-3749 3 ,
  • Patrick J Owen   ORCID: orcid.org/0000-0003-3924-9375 4 &
  • Daniel L Belavy   ORCID: orcid.org/0000-0002-9307-832X 1  

BMC Medical Research Methodology volume  24 , Article number:  35 ( 2024 ) Cite this article

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The importance of contextual effects and their roles in clinical care controversial. A Cochrane review published in 2010 concluded that placebo interventions lack important clinical effects overall, but that placebo interventions can influence patient-reported outcomes such as pain and nausea. However, systematic reviews published after 2010 estimated greater contextual effects than the Cochrane review, which stems from the inappropriate methods employed to quantify contextual effects. The effects of medical interventions (i.e., the total treatment effect) can be divided into three components: specific, contextual, and non-specific. We propose that the most effective method for quantifying the magnitude of contextual effects is to calculate the difference in outcome measures between a group treated with placebo and a non-treated control group. Here, we show that other methods, such as solely using the placebo control arm or calculation of a ‘proportional contextual effect,’ are limited and should not be applied. The aim of this study is to provide clear guidance on best practices for estimating contextual effects in clinical research.

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Introduction

The importance of contextual effects in clinical care remains contentious [ 1 , 2 ]. We define ‘contextual effect’ as the influence of contextual elements on the clinical outcome (see the ‘Definition’ section for more details). A Cochrane review published in 2010 concluded that placebo interventions do not achieve important clinical effects overall, but that placebo interventions can influence patient-reported outcomes such as pain and nausea [ 3 ]. When pain was assessed using a binary “yes-no” outcome, there was no discernible placebo effect (risk ratio: 0.92, 95% confidence interval (CI): 0.77,1.11) based on data from six trials involving 1207 participants. However, when pain was evaluated on a continuous scale, the authors found a modest effect (standardized mean difference (SMD): -0.28, 95% CI: -0.36, -0.19) from data gathered across 60 trials with 4154 participants [ 3 ]. In practical terms, this small effect corresponds to an approximately five-unit change on a pain scale ranging from zero to 100. This conversion was achieved by re-expressing the SMD of -0.28, onto a 0-100 pain scale with an assumed standard deviation of 20 points [ 1 , 4 ].Although this effect is statistically significant and may be meaningful across multiple patients, the size of the effect is such that it is unlikely to be noticeable by the average individual patient [ 1 ].

Notably, when relying on patient-reported outcomes, it is increasingly difficult to discriminate between the patient-reported effects of placebo and response bias [ 3 ]. Response bias is the tendency of people to answer questions in a way that is not accurate or truthful for some reason(s) (e.g., social desirability bias, acquiescence bias, demand characteristics, fear of judgement or stigma, recall bias, cultural bias, cognitive bias) [ 5 ]. Compared to a previous Cochrane review [ 3 ], more recent systematic reviews [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] estimated greater proportional contextual effects for pain that ranged from 50 to 75% of the total treatment effect. Naturally, this raises the question of how this discrepancy can be explained.

We contend that these differences in estimates arise from inappropriate meta-analytical methods employed to quantify contextual effects. The objective of this study is to elucidate contextual effects and offer best practices for robust estimation in comparison to the overall treatment effect. Furthermore, we provide insights into why certain methodologies may not be suitable for assessing the contextual effects. Discerning contextual effects in clinical practice is important to gain an understanding of their magnitude and causal pathways.

Definitions

The effects of medical interventions (i.e., total treatment effect) are commonly divided into three components: specific, contextual, and non-specific (Fig.  1 ) [ 3 , 18 , 19 ].

figure 1

Total treatment effect encompasses the specific effects of treatment, contextual effects, and nonspecific effects. The addition of contextual and non-specific effects is called the placebo response. The treatment vs. placebo/sham comparison controls for contextual and nonspecific effects to isolate the specific treatment effect. The placebo vs. no-treatment comparison controls for nonspecific effects to isolate contextual effects. The treatment vs. no-treatment comparison controls for non-specific effects to isolate contextual and specific treatment effects. Adapted from the study by Cashin et al. [ 18 ]

Specific effect

The specific effect stems from the treatment itself and arises from the physiological mechanism of action (e.g., the action of a drug on a specific receptor in the body). It is calculated by subtracting the contextual and non-specific effects from the total treatment effect [ 19 ].

Contextual effect

Contextual effects are changes in the clinical outcome that result from exposure to factors related to the context of the healthcare setting. These factors include patient-related aspects (e.g., treatment expectations), therapist-related factors (e.g., friendliness) [ 20 ], patient-therapist relationships [ 21 ], and intervention settings [ 22 ]. Contextual effects produce a treatment effect independent of the specific effects of the intervention and are synonymous with “placebo-related effects,” occurring even in the absence of the inert treatment [ 23 ]. For an in-depth exploration of the mechanisms underlying the contextual effects, refer to Enck et al. [ 24 ].

Non-specific effect

Non-specific effects are associated with the natural course of the disease, including natural fluctuations in disease severity [ 25 ], regression to the mean [ 26 ], measurement error [ 27 ], random error [ 27 ], spontaneous remission [ 28 , 29 , 30 , 31 , 32 , 33 ], and the Hawthorne effect [ 34 ]. Unlike specific effects, nonspecific effects are not inherent to treatment and occur naturally over time.

A placebo is an intervention that is presumed to lack a specific effect, i.e., an effect for which there is an empirically supported theory of its mechanism of action, on the condition of interest, but that has been shown to be superior to no intervention [ 35 ].

Placebo response

The placebo response is defined as “[…] health changes that result after the administration of an inactive treatment (i.e., differences in symptoms before and after treatment), encompassing natural history and regression to the mean ” [ 36 ]. Therefore, placebo response refers to contextual and non-specific effects. Some authors equate the placebo response with contextual effects [ 7 ], which is misleading given that contextual effects are generated through exposure to contextual factors (e.g., expectations and setting of the intervention) alone. Therefore, non-specific effects should be differentiated from contextual effects, as the former occur irrespective of the treatment provided. Contextual effects are intricately tied to the specific treatment provided, meaning that they are influenced by the unique characteristics and components of the intervention [ 37 ].

Proper estimation of contextual effects: comparing a no-treatment or a ‘placebo-control group’ with a placebo group

In a seminal paper, Gøtzsche [ 35 ] defined the contextual effect as “ the difference in outcome between a placebo treated group and an untreated control group in an unbiased experiment .” Notably, this definition is based on groups rather than individuals, as it is often not possible to observe the counterfactual at the individual level (i.e., results of the untreated individual). A randomized crossover design would be an exception in this case, as the individual is the unit of analysis. Gøtzsche stated that an untreated control group was required to adjust for non-specific effects when measuring contextual effects. It is assumed that by subtracting the results of the placebo group from the untreated control group, non-specific effects are negated; therefore, only the contextual effects associated with the placebo group remain (see Fig.  2 ). Gerdesmeyer et al. [ 38 ] contend that this design leads to biased estimates, as an untreated control group may increase the risk of bias (e.g. attrition bias, response bias, compensatory rivalry, resentful demoralization) when assessing outcome measures [ 38 ]. One way to mitigate this problem is to use a (modified) Zelen Design [ 24 , 39 , 40 ], which is a modification of the three-arm trial design described above. This design separates the recruitment of patients for an observational study from the recruitment of patients for an interventional trial and allows monitoring of the natural course of the disease without randomizing participants to a no-treatment control group [ 24 ]. We would like to emphasize that while the (modified) Zelen Design mitigates some of the inherent biases (e.g., attrition bias, response bias, compensatory rivalry, and resentful demoralization) associated with employing a three-arm trial design, it does not completely eliminate them.

figure 2

Proper estimation of contextual effects by calculating the difference between the placebo and no-treatment control groups. Non-specific effects cancel each other out and the effect size shows the magnitude of the contextual effect

Alternatively, examining two placebo-controlled groups may allow for quantification of contextual effects. In this example, participants in one group are told that they are receiving the “real” treatment, whereas the other group is told that they are receiving a placebo treatment that has no effect [ 38 ]. Gerdesmeyer et al. [ 38 ] contend that this is a better design to study the actual placebo effect, as an untreated control group may increase the risk of bias (e.g. attrition bias, response bias, compensatory rivalry, resentful demoralization) when measuring outcome measures [ 38 ]. We believe that the risk of these biases also applies to the randomized controlled trial design proposed by Gerdesmeyer et al. [ 38 ], as patients who were told that they would be treated with an actual placebo treatment that has no effect might also develop compensatory rivalry (i.e., participants in the group not receiving the experimental treatment feel disadvantaged, disappointed, or left out, and therefore seek similar or alternative treatments on their own) [ 41 ] and/or resentful demoralization (i.e., participants in the control group became resentful of not receiving the experimental treatment) [ 42 ]. Collectively, these factors may increase the risk of attrition bias, with a corresponding drop out in the placebo group, which was told the truth about their treatment. In this example, participants in one group are told they are receiving the “real” treatment (placebo active group), whereas the other group are told they are receiving a placebo treatment that has no effect (placebo control group) [ 34 ]. Another limitation of this design is that it does not allow the exclusion of non-specific effects. Although these effects cancel each other out, the contextual effect is not entirely isolated.

An alternative design that provides a thorough evaluation of contextual effects is a three-arm study as described in the literature [ 43 , 44 ]. This design includes two placebo groups, as proposed by Gerdesmeyer and colleagues [ 38 ]: one placebo active group where participants believed they were receiving the actual treatment, and a placebo control group where participants were informed that they were receiving a placebo with no therapeutic effect. Comparing these groups allows for consideration of any remaining physiological effects related to the sham treatment, such as the calming impact of a non-active cream. Additionally, a third group that received no treatment (the natural history group) was included to control for non-specific effects. This tripartite approach enables a detailed analysis of contextual effects, separating the placebo effect from other confounding factors linked to sham treatment, while also considering non-specific effects. However, this design is not immune to the biases present in other study designs. An overview of the study designs is presented in Table  1 .

Based on the model presented thus far, we assume that the effects of non-specific effects, placebo effect, and treatment effect are additive, indicating that they do not depend on or interact with each other [ 45 ]. Some authors [ 46 , 47 , 48 , 49 ] challenged the idea that the effects of (treatment) are additive. They argued that the placebo effect and non-specific effects can influence each other. This means that placebo can either enhance or reduce the effects of other factors, such as the natural healing of the body [ 45 ]. Following Senn [ 50 ], we recommend using a simpler model (i.e., additive model) unless (1) real evidence indicates that this assumption is untrue and (2) if continuing to naively assume that this assumption is true, it will cause a problem in the statistical analysis.

Overall, we contend that a no-treatment group or ‘placebo-control group’ (i.e., an unblinded group that is aware that they are receiving a placebo/sham intervention) should be used to measure contextual effects; however, there is a need to consider the potential risk of bias associated with this experimental design. For an overview of the different study designs used to measure contextual effects, please refer to Table  1 .

Inappropriate method: Four meta-analyses evaluated only the placebo arm

We identified four meta-analyses that inappropriately evaluated only the placebo arm [ 10 , 11 , 15 , 16 ]. Measuring the within-group changes of the placebo arm between baseline and follow-up does not measure contextual effects because the measurement contains non-specific effects (e.g. statistical factors, biological properties of the disease, and psychological aspects of receiving attention by clinical staff) [ 51 ]. Some researchers further dichotomize their continuous results using an arbitrary response threshold to identify responders and non-responders to placebo intervention [ 10 , 11 ], which is considered problematic by some statisticians, as it leads to the use of arbitrary thresholds that influence effect estimates [ 52 , 53 ]. Nevertheless, responder analysis is considered by some researchers as a valid method of analysis, especially in the realm of pain research [ 54 , 55 ]. We also contend that it is difficult to identify a clear biological/clinical rationale for dichotomous response versus non-response in the placebo group.

We reject the concept of considering the within-group changes in the placebo group as a placebo response. Specifically, there is no need to quantify the placebo response, as it has limited relevance regarding the magnitude of contextual effects. Rather, quantifying this response creates a potential for misunderstanding, whereby research consumers may mistakenly believe that contextual effects are a large component of the total treatment effect, and subsequently deviate from engagement with evidence-based treatments with established effectiveness.

Inappropriate method: attempting to quantify proportional contextual effect used in eight meta-analyses

Eight meta-analyses [ 6 , 7 , 8 , 9 , 12 , 13 , 14 , 17 ] attempted to quantify contextual effects via the proportional contextual effect (PCE). This method of measuring contextual effects was first proposed by Zhang et al. [ 9 , 56 ] and was derived by comparing an active treatment group with a placebo control group. The total treatment effect was measured as an effect size by the active treatment group, and the contextual effect was measured as an effect size by the placebo group. As an effect size, the mean change from baseline in standard deviation (SD) units was calculated for each group. The PCE is then calculated by dividing the effect size of the placebo group by the effect size of the active treatment group [PCE = \((\frac{Improvement\,of\,the\,outcome\,in\,the\,placeo\,group}{Improvement\,of\,the\,outcome\,in\,active\,group}= \frac{{d}_{placebo}}{{d}_{active}})\) ]. The standard error (SE) SE corresponding confidence interval were calculated according to the effect size of the response ratio [ 57 ]. The authors [ 9 ] stated that theoretically, the PCE should range from 0 (0% contribution of contextual effects) to 1 (100% contribution from contextual effects); however, the effect size occasionally exceeds 1, which is interpreted as a 100% contribution from contextual effects. Notably, this method excludes trials in which patients in either the treatment or placebo group worsened from the baseline from analysis [ 9 ], which subsequently introduces an inherent risk of bias. The PCE is log-transformed for each study, and the SE are calculated according to Hedges et al. [ 57 ]. The log-transformed PCE and log SE is then pooled via meta-analysis, and the summary effect is then back-transformed via exponentiation (for example, see Supplement 1 ).

To explain why we contend that PCE does not measure the proportion of the total treatment effect attributed to contextual effects, an understanding of the estimation of the treatment effects is warranted. Treatment effects are most often statistically modeled on an additive scale [ 58 , 59 ]. This means that an improvement on a quality-of-life scale (0-100 points) from 50 to 70 points is an additional 20 points and represents constant improvement. By contrast, a multiplicative or proportional treatment effects model may conclude that the quality-of-life scale improves by 15%. Percentage change is inherently limited by the reliance on baseline values whereby a change from 50 to 57.5 points or 70 to 80.5 points both represent a 15% improvement, despite differences in raw changes of 7.5 vs. 10.5 points. Notably, multiplicative modeling is often used when the underlying data require log-transformation or when there are other plausible biological reasons to use a multiplicative model (e.g., a quality of life scale that is composed of various domains that are summed together; if the treatment has an effect on multiple scales, then the overall effect will be multiplicative) [ 58 ]. PCE uses a multiplicative model based on the response ratio, which is also known as the ratio of means [ 57 , 60 , 61 ]. Here, we show that PCE only calculates a treatment effect (as a percentage/proportion) rather than the contextual effect relative to the total treatment effect.

The PCE is equal to \(\frac{{d}_{placebo}}{{d}_{active}}\) . This is then log-transformed for pooling purposes. This turns, according to the laws of logarithms (see Supplement 2 ), a division into subtraction: ln(PCE) = ln( \(\frac{{d}_{placebo}}{{d}_{active}}\) ) = ln( \({d}_{placebo}\) ) – ln( \({d}_{active}\) ). One can clearly see that one calculates a treatment effect between the placebo group and the active treatment group (see also Fig.  3 ). When calculating the treatment effect between a placebo group and a treatment group, the placebo group is used to precisely control for non-specific and contextual effects of the treatment group to obtain an unbiased estimate of the treatment effect [ 62 ]. The results obtained are specific treatment effects expressed on a multiplicative scale and not a proportion of the contextual effect of the intervention. A further problem with these calculations is that the response ratio is not suitable for change from baseline measures because they can be negative (i.e., the logarithm of a negative number is not defined) [ 4 ]. This is why negative changes from baseline are excluded from the calculation of the PCE, which increases the risk of bias due to study exclusion and overall reduces statistical power. Notably, it is possible to calculate a response ratio/ratio of means with change from baseline scores; however, this entails different calculations using the ratio of the ratio of means with appropriate standard errors [ 58 ]. One can also question standardization by the SD units of the mean changes because the response ratio/ratio of means is unitless [ 60 ] and is confounded by the method of standardization and the accuracy of the method used to estimate the (standardising) SD. Any standardization is superfluous in this case. PCE is also not (naturally) normed to a percentage value, which makes it nonsensical to interpret it as such. To see this, we can make the following example: we have two groups: one active group with a (standardized) mean change of \({d}_{active}\) (4 − 1.5)/1 = 2.5 and a placebo group with a mean change of \({d}_{placebo}\) (4.1 − 3)/1.1 = 1. The PCE is exp[(ln(2.5/1)] = 2.50, which implies that 150% of the total treatment effect is explained by the contextual effect. This shows that the measure is limited in that it does not measure what it purports to measure. In summary, PCE does not measure contextual effects, and we do not recommend that it be employed in future research that attempts to quantify contextual effects.

figure 3

Inappropriate method for measuring contextual effects using the proportional contextual effect method. In this case, a specific treatment effect (on a multiplicative scale) is calculated and not a “proportional contextual effect” because the comparison of treatment versus placebo group controls for non-specific and contextual effects. It should be noted that inappropriate formulas for the calculation of the treatment effect and its variance were used

The difference between a placebo group and a no-treatment group or an ‘placebo-control group’ can be used to measure contextual effects; however, the inherent biases associated with these designs should be considered. Using the placebo arm alone or calculating PCE represent inferior and therefore inappropriate methods for quantifying the contextual effect and should be retired from use in future studies.

Data availability

All the data and materials are available in this document.

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Authors’ contributions:Conceptualization: TS, HP. Data curation: Not applicable. Formal Analysis: Not applicable. Funding acquisition: Not applicable. Investigation: TS. Methodology: TS, HP. Project administration: TS. Resources: Not applicable. Software: Not applicable. Supervision: DLB, PJO. Validation: DLB. Visualization: TS. Writing – original draft: TS. Writing – review & editing: All. Approved final manuscript: All.

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Saueressig, T., Pedder, H., Owen, P.J. et al. Contextual effects: how to, and how not to, quantify them. BMC Med Res Methodol 24 , 35 (2024). https://doi.org/10.1186/s12874-024-02152-2

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BMC Medical Research Methodology

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case study methods in research methodology

College of Nursing

Driving change: a case study of a dnp leader in residence program in a gerontological center of excellence.

View as pdf A later version of this article appeared in Nurse Leader , Volume 21, Issue 6 , December 2023 . 

The American Association of Colleges of Nursing (AACN) published the Essentials of Doctoral Education for Advanced Practice Nursing in 2004 identifying the essential curriculum needed for preparing advanced practice nurse leaders to effectively assess organizations, identify systemic issues, and facilitate organizational changes. 1 In 2021, AACN updated the curriculum by issuing The Essentials: Core Competencies for Professional Nursing Education to guide the development of competency-based education for nursing students. 1 In addition to AACN’s competency-based approach to curriculum, in 2015 the American Organization of Nurse Leaders (AONL) released Nurse Leader Core Competencies (updated in 2023) to help provide a competency based model to follow in developing nurse leaders. 2

Despite AACN and AONL competency-based curriculum and model, it is still common for nurse leaders to be promoted to management positions based solely on their work experience or exceptional clinical skills, rather than demonstration of management and leadership competencies. 3 The importance of identifying, training, and assessing executive leaders through formal leadership development programs, within supportive organizational cultures has been discussed by national leaders. As well as the need for nurturing emerging leaders through fostering interprofessional collaboration, mentorship, and continuous development of leadership skills has been identified. 4 As Doctor of Nursing Practice (DNP) nurse leaders assume executive roles within healthcare organizations, they play a vital role within complex systems. Demonstration of leadership competence and participation in formal leadership development programs has become imperative for their success. However, models of competency-based executive leadership development programs can be hard to find, particularly programs outside of health care systems.

The implementation of a DNP Leader in Residence program, such as the one designed for The Barbara and Richard Csomay Center for Gerontological Excellence, addresses many of the challenges facing new DNP leaders and ensures mastery of executive leadership competencies and readiness to practice through exposure to varied experiences and close mentoring. The Csomay Center , based at The University of Iowa, was established in 2000 as one of the five original Hartford Centers of Geriatric Nursing Excellence in the country. Later funding by the Csomay family established an endowment that supports the Center's ongoing work. The current Csomay Center strategic plan and mission aims to develop future healthcare leaders while promoting optimal aging and quality of life for older adults. The Csomay Center Director created the innovative DNP Leader in Residence program to foster the growth of future nurse leaders in non-healthcare systems. The purpose of this paper is to present a case study of the development and implementation of the Leader in Residence program, followed by suggested evaluation strategies, and discussion of future innovation of leadership opportunities in non-traditional health care settings.

Development of the DNP Leader in Residence Program

The Plan-Do-Study-Act (PDSA) cycle has garnered substantial recognition as a valuable tool for fostering development and driving improvement initiatives. 5 The PDSA cycle can function as an independent methodology and as an integral component of broader quality enhancement approaches with notable efficacy in its ability to facilitate the rapid creation, testing, and evaluation of transformative interventions within healthcare. 6 Consequently, the PDSA cycle model was deemed fitting to guide the development and implementation of the DNP Leader in Residence Program at the Csomay Center.

PDSA Cycle: Plan

Existing resources. The DNP Health Systems: Administration/Executive Leadership Program offered by the University of Iowa is comprised of comprehensive nursing administration and leadership curriculum, led by distinguished faculty composed of national leaders in the realms of innovation, health policy, leadership, clinical education, and evidence-based practice. The curriculum is designed to cultivate the next generation of nursing executive leaders, with emphasis on personalized career planning and tailored practicum placements. The DNP Health Systems: Administration/Executive Leadership curriculum includes a range of courses focused on leadership and management with diverse topics such as policy an law, infrastructure and informatics, finance and economics, marketing and communication, quality and safety, evidence-based practice, and social determinants of health. The curriculum is complemented by an extensive practicum component and culminates in a DNP project with additional hours of practicum.

New program. The DNP Leader in Residence program at the Csomay Center is designed to encompass communication and relationship building, systems thinking, change management, transformation and innovation, knowledge of clinical principles in the community, professionalism, and business skills including financial, strategic, and human resource management. The program fully immerses students in the objectives of the DNP Health Systems: Administration/Executive Leadership curriculum and enables them to progressively demonstrate competencies outlined by AONL. The Leader in Residence program also includes career development coaching, reflective practice, and personal and professional accountability. The program is integrated throughout the entire duration of the Leader in Residence’s coursework, fulfilling the required practicum hours for both the DNP coursework and DNP project.

The DNP Leader in Residence program begins with the first semester of practicum being focused on completing an onboarding process to the Center including understanding the center's strategic plan, mission, vision, and history. Onboarding for the Leader in Residence provides access to all relevant Center information and resources and integration into the leadership team, community partnerships, and other University of Iowa College of Nursing Centers associated with the Csomay Center. During this first semester, observation and identification of the Csomay Center Director's various roles including being a leader, manager, innovator, socializer, and mentor is facilitated. In collaboration with the Center Director (a faculty position) and Center Coordinator (a staff position), specific competencies to be measured and mastered along with learning opportunities desired throughout the program are established to ensure a well-planned and thorough immersion experience.

Following the initial semester of practicum, the Leader in Residence has weekly check-ins with the Center Director and Center Coordinator to continue to identify learning opportunities and progression through executive leadership competencies to enrich the experience. The Leader in Residence also undertakes an administrative project for the Center this semester, while concurrently continuing observations of the Center Director's activities in local, regional, and national executive leadership settings. The student has ongoing participation and advancement in executive leadership roles and activities throughout the practicum, creating a well-prepared future nurse executive leader.

After completing practicum hours related to the Health Systems: Administration/Executive Leadership coursework, the Leader in Residence engages in dedicated residency hours to continue to experience domains within nursing leadership competencies like communication, professionalism, and relationship building. During residency hours, time is spent with the completion of a small quality improvement project for the Csomay Center, along with any other administrative projects identified by the Center Director and Center Coordinator. The Leader in Residence is fully integrated into the Csomay Center's Leadership Team during this phase, assisting the Center Coordinator in creating agendas and leading meetings. Additional participation includes active involvement in community engagement activities and presenting at or attending a national conference as a representative of the Csomay Center. The Leader in Residence must mentor a master’s in nursing student during the final year of the DNP Residency.

Implementation of the DNP Leader in Residence Program

PDSA Cycle: Do

Immersive experience. In this case study, the DNP Leader in Residence was fully immersed in a wide range of center activities, providing valuable opportunities to engage in administrative projects and observe executive leadership roles and skills during practicum hours spent at the Csomay Center. Throughout the program, the Leader in Residence observed and learned from multidisciplinary leaders at the national, regional, and university levels who engaged with the Center. By shadowing the Csomay Center Director, the Leader in Residence had the opportunity to observe executive leadership objectives such as fostering innovation, facilitating multidisciplinary collaboration, and nurturing meaningful relationships. The immersive experience within the center’s activities also allowed the Leader in Residence to gain a deep understanding of crucial facets such as philanthropy and community engagement. Active involvement in administrative processes such as strategic planning, budgeting, human resources management, and the development of standard operating procedures provided valuable exposure to strategies that are needed to be an effective nurse leader in the future.

Active participation. The DNP Leader in Residence also played a key role in advancing specific actions outlined in the center's strategic plan during the program including: 1) the creation of a membership structure for the Csomay Center and 2) successfully completing a state Board of Regents application for official recognition as a distinguished center. The Csomay Center sponsored membership for the Leader in Residence in the Midwest Nurse Research Society (MNRS), which opened doors to attend the annual MNRS conference and engage with regional nursing leadership, while fostering socialization, promotion of the Csomay Center and Leader in Residence program, and observation of current nursing research. Furthermore, the Leader in Residence participated in the strategic planning committee and engagement subcommittee for MNRS, collaborating directly with the MNRS president. Additional active participation by the Leader in Residence included attendance in planning sessions and completion of the annual report for GeriatricPain.org , an initiative falling under the umbrella of the Csomay Center. Finally, the Leader in Residence was involved in archiving research and curriculum for distinguished nursing leader and researcher, Dr. Kitty Buckwalter, for the Benjamin Rose Institute on Aging, the University of Pennsylvania Barbara Bates Center for the Study of the History of Nursing, and the University of Iowa library archives.

Suggested Evaluation Strategies of the DNP Leader in Residence Program

PDSA Cycle: Study

Assessment and benchmarking. To effectively assess the outcomes and success of the DNP Leader in Residence Program, a comprehensive evaluation framework should be used throughout the program. Key measures should include the collection and review of executive leadership opportunities experienced, leadership roles observed, and competencies mastered. The Leader in Residence is responsible for maintaining detailed logs of their participation in center activities and initiatives on a semester basis. These logs serve to track the progression of mastery of AONL competencies by benchmarking activities and identifying areas for future growth for the Leader in Residence.

Evaluation. In addition to assessment and benchmarking, evaluations need to be completed by Csomay Center stakeholders (leadership, staff, and community partners involved) and the individual Leader in Residence both during and upon completion of the program. Feedback from stakeholders will identify the contributions made by the Leader in Residence and provide valuable insights into their growth. Self-reflection on experiences by the individual Leader in Residence throughout the program will serve as an important measure of personal successes and identify gaps in the program. Factors such as career advancement during the program, application of curriculum objectives in the workplace, and prospects for future career progression for the Leader in Residence should be considered as additional indicators of the success of the program.

The evaluation should also encompass a thorough review of the opportunities experienced during the residency, with the aim of identifying areas for potential expansion and enrichment of the DNP Leader in Residence program. By carefully examining the logs, reflecting on the acquired executive leadership competencies, and studying stakeholder evaluations, additional experiences and opportunities can be identified to further enhance the program's efficacy. The evaluation process should be utilized to identify specific executive leadership competencies that require further immersion and exploration throughout the program.

Future Innovation of DNP Leader in Residence Programs in Non-traditional Healthcare Settings

PDSA Cycle: Act

As subsequent residents complete the program and their experiences are thoroughly evaluated, it is essential to identify new opportunities for DNP Leader in Residence programs to be implemented in other non-health care system settings. When feasible, expansion into clinical healthcare settings, including long-term care and acute care environments, should be pursued. By leveraging the insights gained from previous Leaders in Residence and their respective experiences, the program can be refined to better align with desired outcomes and competencies. These expansions will broaden the scope and impact of the program and provide a wider array of experiences and challenges for future Leaders in Residency to navigate, enriching their development as dynamic nurse executive leaders within diverse healthcare landscapes.

This case study presented a comprehensive overview of the development and implementation of the DNP Leader in Residence program developed by the Barbara and Richard Csomay Center for Gerontological Excellence. The Leader in Residence program provided a transformative experience by integrating key curriculum objectives, competency-based learning, and mentorship by esteemed nursing leaders and researchers through successful integration into the Center. With ongoing innovation and application of the PDSA cycle, the DNP Leader in Residence program presented in this case study holds immense potential to help better prepare 21 st century nurse leaders capable of driving positive change within complex healthcare systems.

Acknowledgements

         The author would like to express gratitude to the Barbara and Richard Csomay Center for Gerontological Excellence for the fostering environment to provide an immersion experience and the ongoing support for development of the DNP Leader in Residence program. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

  • American Association of Colleges of Nursing. The essentials: core competencies for professional nursing education. https://www.aacnnursing.org/Portals/42/AcademicNursing/pdf/Essentials-2021.pdf . Accessed June 26, 2023.
  • American Organization for Nursing Leadership. Nurse leader core competencies. https://www.aonl.org/resources/nurse-leader-competencies . Accessed July 10, 2023.
  • Warshawsky, N, Cramer, E. Describing nurse manager role preparation and competency: findings from a national study. J Nurs Adm . 2019;49(5):249-255. DOI:  10.1097/NNA.0000000000000746
  • Van Diggel, C, Burgess, A, Roberts, C, Mellis, C. Leadership in healthcare education. BMC Med. Educ . 2020;20(465). doi: 10.1186/s12909-020-02288-x
  • Institute for Healthcare Improvement. Plan-do-study-act (PDSA) worksheet. https://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx . Accessed July 4, 2023.
  • Taylor, M, McNicolas, C, Nicolay, C, Darzi, A, Bell, D, Reed, J. Systemic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Quality & Safety. 2014:23:290-298. doi: 10.1136/bmjqs-2013-002703

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Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

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 sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

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Performance of multisite streamflow stochastic generation approaches for a multi-reservoir system

  • ORIGINAL PAPER
  • Published: 16 February 2024

Cite this article

  • Yufei Ma 1 , 2 , 3 , 4 ,
  • Ping-an Zhong 5 ,
  • Guoqing Wang 1 , 2 , 3 , 4 &
  • Yao Xiao 6  

The streamflow process is an crucial information resource for the joint optimal operation of reservoirs. As the length and representativeness of historical streamflow samples are insufficient for practice projects, streamflow stochastic generation approaches are usually used to expand the streamflow series. For the joint operation and management of the multi-reservoir system, the multisite streamflow stochastic generation (MSSG) with high-dimensional temporal-spatial correlation poses a challenge. This paper develops the generative adversarial network as a novel MSSG model. In contrast to the existing literature on MSSG, which solely focuses on a specific case study and provides a comparatively one-sided assessment, this paper evaluates multiple characteristics of streamflow at various time scales from three MSSG models in two instances. Specifically, three MSSG models, namely the seasonal autoregression (SAR) model coupled with the master station method, the Copula model coupled with the master station method, and the deep convolutions generative adversarial network (DCGAN) model, are employed to generate monthly, ten-daily, and daily streamflow series of the two-reservoir and eight-reservoir systems. This study aims to examine the performance of three models and provide recommendations for implementing MSSG approaches in practice. Results show that: (1) the priority should be given to the maximum iterations on the DCGAN model at a large time scale, while at a smaller time scale, the training of the model is directly linked to the setting of batch size; (2) the Copula model is capable for better retaining statistical characteristics of streamflow series for similarity; (3) the SAR model excels in simulating the extremes of streamflow; and (4) the DCGAN model possesses a significant advantage in capturing the temporal-spatial higher-order correlation, especially in systems comprising more than two reservoirs and with small time scales (e.g., daily streamflow). Furthermore, this study presents comprehensive and multi-scale recommendations for selecting MSSG approaches, thereby providing a theoretical foundation and practical value for MSSG in diverse scenarios.

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case study methods in research methodology

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Acknowledgements

This study is supported by the National Key R&D Program of China (Grant No. 2023YFC3081000, 2021YFC3201100); the National Natural Science Foundation of China (Grant No. U2243228, 52121006).

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Research Center for Climate Change of Ministry of Water Resources, Nanjing, 210029, China

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Ping-an Zhong

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Yufei Ma wrote the original draft, Ping-an Zhong reviewed and edited the manuscript, Guoqing Wang prepared the software and formal analysis, and Yao Xiao prepared all figures. All authors reviewed the manuscript.

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Ma, Y., Zhong, Pa., Wang, G. et al. Performance of multisite streamflow stochastic generation approaches for a multi-reservoir system. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02672-9

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