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Case Study – Methods, Examples and Guide

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

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

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

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

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

Multiple-Case Study

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

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

Exploratory Case Study

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

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

Descriptive Case Study

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

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

Instrumental Case Study

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

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

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

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

Observations

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

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

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

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

How to conduct Case Study Research

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

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

Examples of Case Study

Here are some examples of case study research:

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

Application of Case Study

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

Business and Management

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

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

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

Social Sciences

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

Law and Ethics

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

Purpose of Case Study

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

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

Case studies can also serve other purposes, including:

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

Advantages of Case Study Research

There are several advantages of case study research, including:

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

Limitations of Case Study Research

There are several limitations of case study research, including:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Case study research: opening up research opportunities

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 December 2019

Issue publication date: 3 March 2020

The case study approach has been widely used in management studies and the social sciences more generally. However, there are still doubts about when and how case studies should be used. This paper aims to discuss this approach, its various uses and applications, in light of epistemological principles, as well as the criteria for rigor and validity.

Design/methodology/approach

This paper discusses the various concepts of case and case studies in the methods literature and addresses the different uses of cases in relation to epistemological principles and criteria for rigor and validity.

The use of this research approach can be based on several epistemologies, provided the researcher attends to the internal coherence between method and epistemology, or what the authors call “alignment.”

Originality/value

This study offers a number of implications for the practice of management research, as it shows how the case study approach does not commit the researcher to particular data collection or interpretation methods. Furthermore, the use of cases can be justified according to multiple epistemological orientations.

• Epistemology

Takahashi, A.R.W. and Araujo, L. (2020), "Case study research: opening up research opportunities", RAUSP Management Journal , Vol. 55 No. 1, pp. 100-111. https://doi.org/10.1108/RAUSP-05-2019-0109

Emerald Publishing Limited

Copyright © 2019, Adriana Roseli Wünsch Takahashi and Luis Araujo.

Published in RAUSP Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The case study as a research method or strategy brings us to question the very term “case”: after all, what is a case? A case-based approach places accords the case a central role in the research process ( Ragin, 1992 ). However, doubts still remain about the status of cases according to different epistemologies and types of research designs.

Despite these doubts, the case study is ever present in the management literature and represents the main method of management research in Brazil ( Coraiola, Sander, Maccali, & Bulgacov, 2013 ). Between 2001 and 2010, 2,407 articles (83.14 per cent of qualitative research) were published in conferences and management journals as case studies (Takahashi & Semprebom, 2013 ). A search on Spell.org.br for the term “case study” under title, abstract or keywords, for the period ranging from January 2010 to July 2019, yielded 3,040 articles published in the management field. Doing research using case studies, allows the researcher to immerse him/herself in the context and gain intensive knowledge of a phenomenon, which in turn demands suitable methodological principles ( Freitas et al. , 2017 ).

Our objective in this paper is to discuss notions of what constitutes a case and its various applications, considering epistemological positions as well as criteria for rigor and validity. The alignment between these dimensions is put forward as a principle advocating coherence among all phases of the research process.

This article makes two contributions. First, we suggest that there are several epistemological justifications for using case studies. Second, we show that the quality and rigor of academic research with case studies are directly related to the alignment between epistemology and research design rather than to choices of specific forms of data collection or analysis. The article is structured as follows: the following four sections discuss concepts of what is a case, its uses, epistemological grounding as well as rigor and quality criteria. The brief conclusions summarize the debate and invite the reader to delve into the literature on the case study method as a way of furthering our understanding of contemporary management phenomena.

2. What is a case study?

The debate over what constitutes a case in social science is a long-standing one. In 1988, Howard Becker and Charles Ragin organized a workshop to discuss the status of the case as a social science method. As the discussion was inconclusive, they posed the question “What is a case?” to a select group of eight social scientists in 1989, and later to participants in a symposium on the subject. Participants were unable to come up with a consensual answer. Since then, we have witnessed that further debates and different answers have emerged. The original question led to an even broader issue: “How do we, as social scientists, produce results and seem to know what we know?” ( Ragin, 1992 , p. 16).

An important step that may help us start a reflection on what is a case is to consider the phenomena we are looking at. To do that, we must know something about what we want to understand and how we might study it. The answer may be a causal explanation, a description of what was observed or a narrative of what has been experienced. In any case, there will always be a story to be told, as the choice of the case study method demands an answer to what the case is about.

A case may be defined ex ante , prior to the start of the research process, as in Yin’s (2015) classical definition. But, there is no compelling reason as to why cases must be defined ex ante . Ragin (1992 , p. 217) proposed the notion of “casing,” to indicate that what the case is emerges from the research process:

Rather than attempt to delineate the many different meanings of the term “case” in a formal taxonomy, in this essay I offer instead a view of cases that follows from the idea implicit in many of the contributions – that concocting cases is a varied but routine social scientific activity. […] The approach of this essay is that this activity, which I call “casing”, should be viewed in practical terms as a research tactic. It is selectively invoked at many different junctures in the research process, usually to resolve difficult issues in linking ideas and evidence.

In other words, “casing” is tied to the researcher’s practice, to the way he/she delimits or declares a case as a significant outcome of a process. In 2013, Ragin revisited the 1992 concept of “casing” and explored its multiple possibilities of use, paying particular attention to “negative cases.”

According to Ragin (1992) , a case can be centered on a phenomenon or a population. In the first scenario, cases are representative of a phenomenon, and are selected based on what can be empirically observed. The process highlights different aspects of cases and obscures others according to the research design, and allows for the complexity, specificity and context of the phenomenon to be explored. In the alternative, population-focused scenario, the selection of cases precedes the research. Both positive and negative cases are considered in exploring a phenomenon, with the definition of the set of cases dependent on theory and the central objective to build generalizations. As a passing note, it is worth mentioning here that a study of multiple cases requires a definition of the unit of analysis a priori . Otherwise, it will not be possible to make cross-case comparisons.

These two approaches entail differences that go beyond the mere opposition of quantitative and qualitative data, as a case often includes both types of data. Thus, the confusion about how to conceive cases is associated with Ragin’s (1992) notion of “small vs large N,” or McKeown’s (1999) “statistical worldview” – the notion that relevant findings are only those that can be made about a population based on the analysis of representative samples. In the same vein, Byrne (2013) argues that we cannot generate nomothetic laws that apply in all circumstances, periods and locations, and that no social science method can claim to generate invariant laws. According to the same author, case studies can help us understand that there is more than one ideographic variety and help make social science useful. Generalizations still matter, but they should be understood as part of defining the research scope, and that scope points to the limitations of knowledge produced and consumed in concrete time and space.

Thus, what defines the orientation and the use of cases is not the mere choice of type of data, whether quantitative or qualitative, but the orientation of the study. A statistical worldview sees cases as data units ( Byrne, 2013 ). Put differently, there is a clear distinction between statistical and qualitative worldviews; the use of quantitative data does not by itself means that the research is (quasi) statistical, or uses a deductive logic:

Case-based methods are useful, and represent, among other things, a way of moving beyond a useless and destructive tradition in the social sciences that have set quantitative and qualitative modes of exploration, interpretation, and explanation against each other ( Byrne, 2013 , p. 9).

Other authors advocate different understandings of what a case study is. To some, it is a research method, to others it is a research strategy ( Creswell, 1998 ). Sharan Merrian and Robert Yin, among others, began to write about case study research as a methodology in the 1980s (Merrian, 2009), while authors such as Eisenhardt (1989) called it a research strategy. Stake (2003) sees the case study not as a method, but as a choice of what to be studied, the unit of study. Regardless of their differences, these authors agree that case studies should be restricted to a particular context as they aim to provide an in-depth knowledge of a given phenomenon: “A case study is an in-depth description and analysis of a bounded system” (Merrian, 2009, p. 40). According to Merrian, a qualitative case study can be defined by the process through which the research is carried out, by the unit of analysis or the final product, as the choice ultimately depends on what the researcher wants to know. As a product of research, it involves the analysis of a given entity, phenomenon or social unit.

Thus, whether it is an organization, an individual, a context or a phenomenon, single or multiple, one must delimit it, and also choose between possible types and configurations (Merrian, 2009; Yin, 2015 ). A case study may be descriptive, exploratory, explanatory, single or multiple ( Yin, 2015 ); intrinsic, instrumental or collective ( Stake, 2003 ); and confirm or build theory ( Eisenhardt, 1989 ).

both went through the same process of implementing computer labs intended for the use of information and communication technologies in 2007;

both took part in the same regional program (Paraná Digital); and

they shared similar characteristics regarding location (operation in the same neighborhood of a city), number of students, number of teachers and technicians and laboratory sizes.

However, the two institutions differed in the number of hours of program use, with one of them displaying a significant number of hours/use while the other showed a modest number, according to secondary data for the period 2007-2013. Despite the context being similar and the procedures for implementing the technology being the same, the mechanisms of social integration – an idiosyncratic factor of each institution – were different in each case. This explained differences in their use of resource, processes of organizational learning and capacity to absorb new knowledge.

On the other hand, multiple case studies seek evidence in different contexts and do not necessarily require direct comparisons ( Stake, 2003 ). Rather, there is a search for patterns of convergence and divergence that permeate all the cases, as the same issues are explored in every case. Cases can be added progressively until theoretical saturation is achieved. An example is of a study that investigated how entrepreneurial opportunity and management skills were developed through entrepreneurial learning ( Zampier & Takahashi, 2014 ). The authors conducted nine case studies, based on primary and secondary data, with each one analyzed separately, so a search for patterns could be undertaken. The convergence aspects found were: the predominant way of transforming experience into knowledge was exploitation; managerial skills were developed through by taking advantages of opportunities; and career orientation encompassed more than one style. As for divergence patterns: the experience of success and failure influenced entrepreneurs differently; the prevailing rationality logic of influence was different; and the combination of styles in career orientation was diverse.

A full discussion of choice of case study design is outside the scope of this article. For the sake of illustration, we make a brief mention to other selection criteria such as the purpose of the research, the state of the art of the research theme, the time and resources involved and the preferred epistemological position of the researcher. In the next section, we look at the possibilities of carrying out case studies in line with various epistemological traditions, as the answers to the “what is a case?” question reveal varied methodological commitments as well as diverse epistemological and ontological positions ( Ragin, 2013 ).

3. Epistemological positioning of case study research

Ontology and epistemology are like skin, not a garment to be occasionally worn ( Marsh & Furlong, 2002 ). According to these authors, ontology and epistemology guide the choice of theory and method because they cannot or should not be worn as a garment. Hence, one must practice philosophical “self-knowledge” to recognize one’s vision of what the world is and of how knowledge of that world is accessed and validated. Ontological and epistemological positions are relevant in that they involve the positioning of the researcher in social science and the phenomena he or she chooses to study. These positions do not tend to vary from one project to another although they can certainly change over time for a single researcher.

Ontology is the starting point from which the epistemological and methodological positions of the research arise ( Grix, 2002 ). Ontology expresses a view of the world, what constitutes reality, nature and the image one has of social reality; it is a theory of being ( Marsh & Furlong, 2002 ). The central question is the nature of the world out there regardless of our ability to access it. An essentialist or foundationalist ontology acknowledges that there are differences that persist over time and these differences are what underpin the construction of social life. An opposing, anti-foundationalist position presumes that the differences found are socially constructed and may vary – i.e. they are not essential but specific to a given culture at a given time ( Marsh & Furlong, 2002 ).

Epistemology is centered around a theory of knowledge, focusing on the process of acquiring and validating knowledge ( Grix, 2002 ). Positivists look at social phenomena as a world of causal relations where there is a single truth to be accessed and confirmed. In this tradition, case studies test hypotheses and rely on deductive approaches and quantitative data collection and analysis techniques. Scholars in the field of anthropology and observation-based qualitative studies proposed alternative epistemologies based on notions of the social world as a set of manifold and ever-changing processes. In management studies since the 1970s, the gradual acceptance of qualitative research has generated a diverse range of research methods and conceptions of the individual and society ( Godoy, 1995 ).

The interpretative tradition, in direct opposition to positivism, argues that there is no single objective truth to be discovered about the social world. The social world and our knowledge of it are the product of social constructions. Thus, the social world is constituted by interactions, and our knowledge is hermeneutic as the world does not exist independent of our knowledge ( Marsh & Furlong, 2002 ). The implication is that it is not possible to access social phenomena through objective, detached methods. Instead, the interaction mechanisms and relationships that make up social constructions have to be studied. Deductive approaches, hypothesis testing and quantitative methods are not relevant here. Hermeneutics, on the other hand, is highly relevant as it allows the analysis of the individual’s interpretation, of sayings, texts and actions, even though interpretation is always the “truth” of a subject. Methods such as ethnographic case studies, interviews and observations as data collection techniques should feed research designs according to interpretivism. It is worth pointing out that we are to a large extent, caricaturing polar opposites rather characterizing a range of epistemological alternatives, such as realism, conventionalism and symbolic interactionism.

If diverse ontologies and epistemologies serve as a guide to research approaches, including data collection and analysis methods, and if they should be regarded as skin rather than clothing, how does one make choices regarding case studies? What are case studies, what type of knowledge they provide and so on? The views of case study authors are not always explicit on this point, so we must delve into their texts to glean what their positions might be.

Two of the cited authors in case study research are Robert Yin and Kathleen Eisenhardt. Eisenhardt (1989) argues that a case study can serve to provide a description, test or generate a theory, the latter being the most relevant in contributing to the advancement of knowledge in a given area. She uses terms such as populations and samples, control variables, hypotheses and generalization of findings and even suggests an ideal number of case studies to allow for theory construction through replication. Although Eisenhardt includes observation and interview among her recommended data collection techniques, the approach is firmly anchored in a positivist epistemology:

Third, particularly in comparison with Strauss (1987) and Van Maanen (1988), the process described here adopts a positivist view of research. That is, the process is directed toward the development of testable hypotheses and theory which are generalizable across settings. In contrast, authors like Strauss and Van Maanen are more concerned that a rich, complex description of the specific cases under study evolve and they appear less concerned with development of generalizable theory ( Eisenhardt, 1989 , p. 546).

This position attracted a fair amount of criticism. Dyer & Wilkins (1991) in a critique of Eisenhardt’s (1989) article focused on the following aspects: there is no relevant justification for the number of cases recommended; it is the depth and not the number of cases that provides an actual contribution to theory; and the researcher’s purpose should be to get closer to the setting and interpret it. According to the same authors, discrepancies from prior expectations are also important as they lead researchers to reflect on existing theories. Eisenhardt & Graebner (2007 , p. 25) revisit the argument for the construction of a theory from multiple cases:

A major reason for the popularity and relevance of theory building from case studies is that it is one of the best (if not the best) of the bridges from rich qualitative evidence to mainstream deductive research.

Although they recognize the importance of single-case research to explore phenomena under unique or rare circumstances, they reaffirm the strength of multiple case designs as it is through them that better accuracy and generalization can be reached.

Likewise, Robert Yin emphasizes the importance of variables, triangulation in the search for “truth” and generalizable theoretical propositions. Yin (2015 , p. 18) suggests that the case study method may be appropriate for different epistemological orientations, although much of his work seems to invoke a realist epistemology. Authors such as Merrian (2009) and Stake (2003) suggest an interpretative version of case studies. Stake (2003) looks at cases as a qualitative option, where the most relevant criterion of case selection should be the opportunity to learn and understand a phenomenon. A case is not just a research method or strategy; it is a researcher’s choice about what will be studied:

Even if my definition of case study was agreed upon, and it is not, the term case and study defy full specification (Kemmis, 1980). A case study is both a process of inquiry about the case and the product of that inquiry ( Stake, 2003 , p. 136).

Later, Stake (2003 , p. 156) argues that:

[…] the purpose of a case report is not to represent the world, but to represent the case. […] The utility of case research to practitioners and policy makers is in its extension of experience.

Still according to Stake (2003 , pp. 140-141), to do justice to complex views of social phenomena, it is necessary to analyze the context and relate it to the case, to look for what is peculiar rather than common in cases to delimit their boundaries, to plan the data collection looking for what is common and unusual about facts, what could be valuable whether it is unique or common:

Reflecting upon the pertinent literature, I find case study methodology written largely by people who presume that the research should contribute to scientific generalization. The bulk of case study work, however, is done by individuals who have intrinsic interest in the case and little interest in the advance of science. Their designs aim the inquiry toward understanding of what is important about that case within its own world, which is seldom the same as the worlds of researchers and theorists. Those designs develop what is perceived to be the case’s own issues, contexts, and interpretations, its thick descriptions . In contrast, the methods of instrumental case study draw the researcher toward illustrating how the concerns of researchers and theorists are manifest in the case. Because the critical issues are more likely to be know in advance and following disciplinary expectations, such a design can take greater advantage of already developed instruments and preconceived coding schemes.

The aforementioned authors were listed to illustrate differences and sometimes opposing positions on case research. These differences are not restricted to a choice between positivism and interpretivism. It is worth noting that Ragin’s (2013 , p. 523) approach to “casing” is compatible with the realistic research perspective:

In essence, to posit cases is to engage in ontological speculation regarding what is obdurately real but only partially and indirectly accessible through social science. Bringing a realist perspective to the case question deepens and enriches the dialogue, clarifying some key issues while sweeping others aside.

cases are actual entities, reflecting their operations of real causal mechanism and process patterns;

case studies are interactive processes and are open to revisions and refinements; and

social phenomena are complex, contingent and context-specific.

Ragin (2013 , p. 532) concludes:

Lurking behind my discussion of negative case, populations, and possibility analysis is the implication that treating cases as members of given (and fixed) populations and seeking to infer the properties of populations may be a largely illusory exercise. While demographers have made good use of the concept of population, and continue to do so, it is not clear how much the utility of the concept extends beyond their domain. In case-oriented work, the notion of fixed populations of cases (observations) has much less analytic utility than simply “the set of relevant cases,” a grouping that must be specified or constructed by the researcher. The demarcation of this set, as the work of case-oriented researchers illustrates, is always tentative, fluid, and open to debate. It is only by casing social phenomena that social scientists perceive the homogeneity that allows analysis to proceed.

In summary, case studies are relevant and potentially compatible with a range of different epistemologies. Researchers’ ontological and epistemological positions will guide their choice of theory, methodologies and research techniques, as well as their research practices. The same applies to the choice of authors describing the research method and this choice should be coherent. We call this research alignment , an attribute that must be judged on the internal coherence of the author of a study, and not necessarily its evaluator. The following figure illustrates the interrelationship between the elements of a study necessary for an alignment ( Figure 1 ).

In addition to this broader aspect of the research as a whole, other factors should be part of the researcher’s concern, such as the rigor and quality of case studies. We will look into these in the next section taking into account their relevance to the different epistemologies.

4. Rigor and quality in case studies

Traditionally, at least in positivist studies, validity and reliability are the relevant quality criteria to judge research. Validity can be understood as external, internal and construct. External validity means identifying whether the findings of a study are generalizable to other studies using the logic of replication in multiple case studies. Internal validity may be established through the theoretical underpinning of existing relationships and it involves the use of protocols for the development and execution of case studies. Construct validity implies defining the operational measurement criteria to establish a chain of evidence, such as the use of multiple sources of evidence ( Eisenhardt, 1989 ; Yin, 2015 ). Reliability implies conducting other case studies, instead of just replicating results, to minimize the errors and bias of a study through case study protocols and the development of a case database ( Yin, 2015 ).

Several criticisms have been directed toward case studies, such as lack of rigor, lack of generalization potential, external validity and researcher bias. Case studies are often deemed to be unreliable because of a lack of rigor ( Seuring, 2008 ). Flyvbjerg (2006 , p. 219) addresses five misunderstandings about case-study research, and concludes that:

[…] a scientific discipline without a large number of thoroughly executed case studies is a discipline without systematic production of exemplars, and a discipline without exemplars is an ineffective one.

theoretical knowledge is more valuable than concrete, practical knowledge;

the case study cannot contribute to scientific development because it is not possible to generalize on the basis of an individual case;

the case study is more useful for generating rather than testing hypotheses;

the case study contains a tendency to confirm the researcher’s preconceived notions; and

it is difficult to summarize and develop general propositions and theories based on case studies.

These criticisms question the validity of the case study as a scientific method and should be corrected.

The critique of case studies is often framed from the standpoint of what Ragin (2000) labeled large-N research. The logic of small-N research, to which case studies belong, is different. Cases benefit from depth rather than breadth as they: provide theoretical and empirical knowledge; contribute to theory through propositions; serve not only to confirm knowledge, but also to challenge and overturn preconceived notions; and the difficulty in summarizing their conclusions is because of the complexity of the phenomena studies and not an intrinsic limitation of the method.

Thus, case studies do not seek large-scale generalizations as that is not their purpose. And yet, this is a limitation from a positivist perspective as there is an external reality to be “apprehended” and valid conclusions to be extracted for an entire population. If positivism is the epistemology of choice, the rigor of a case study can be demonstrated by detailing the criteria used for internal and external validity, construct validity and reliability ( Gibbert & Ruigrok, 2010 ; Gibbert, Ruigrok, & Wicki, 2008 ). An example can be seen in case studies in the area of information systems, where there is a predominant orientation of positivist approaches to this method ( Pozzebon & Freitas, 1998 ). In this area, rigor also involves the definition of a unit of analysis, type of research, number of cases, selection of sites, definition of data collection and analysis procedures, definition of the research protocol and writing a final report. Creswell (1998) presents a checklist for researchers to assess whether the study was well written, if it has reliability and validity and if it followed methodological protocols.

In case studies with a non-positivist orientation, rigor can be achieved through careful alignment (coherence among ontology, epistemology, theory and method). Moreover, the concepts of validity can be understood as concern and care in formulating research, research development and research results ( Ollaik & Ziller, 2012 ), and to achieve internal coherence ( Gibbert et al. , 2008 ). The consistency between data collection and interpretation, and the observed reality also help these studies meet coherence and rigor criteria. Siggelkow (2007) argues that a case study should be persuasive and that even a single case study may be a powerful example to contest a widely held view. To him, the value of a single case study or studies with few cases can be attained by their potential to provide conceptual insights and coherence to the internal logic of conceptual arguments: “[…] a paper should allow a reader to see the world, and not just the literature, in a new way” ( Siggelkow, 2007 , p. 23).

Interpretative studies should not be justified by criteria derived from positivism as they are based on a different ontology and epistemology ( Sandberg, 2005 ). The rejection of an interpretive epistemology leads to the rejection of an objective reality: “As Bengtsson points out, the life-world is the subjects’ experience of reality, at the same time as it is objective in the sense that it is an intersubjective world” ( Sandberg, 2005 , p. 47). In this event, how can one demonstrate what positivists call validity and reliability? What would be the criteria to justify knowledge as truth, produced by research in this epistemology? Sandberg (2005 , p. 62) suggests an answer based on phenomenology:

This was demonstrated first by explicating life-world and intentionality as the basic assumptions underlying the interpretative research tradition. Second, based on those assumptions, truth as intentional fulfillment, consisting of perceived fulfillment, fulfillment in practice, and indeterminate fulfillment, was proposed. Third, based on the proposed truth constellation, communicative, pragmatic, and transgressive validity and reliability as interpretative awareness were presented as the most appropriate criteria for justifying knowledge produced within interpretative approach. Finally, the phenomenological epoché was suggested as a strategy for achieving these criteria.

From this standpoint, the research site must be chosen according to its uniqueness so that one can obtain relevant insights that no other site could provide ( Siggelkow, 2007 ). Furthermore, the view of what is being studied is at the center of the researcher’s attention to understand its “truth,” inserted in a given context.

The case researcher is someone who can reduce the probability of misinterpretations by analyzing multiple perceptions, searches for data triangulation to check for the reliability of interpretations ( Stake, 2003 ). It is worth pointing out that this is not an option for studies that specifically seek the individual’s experience in relation to organizational phenomena.

In short, there are different ways of seeking rigor and quality in case studies, depending on the researcher’s worldview. These different forms pervade everything from the research design, the choice of research questions, the theory or theories to look at a phenomenon, research methods, the data collection and analysis techniques, to the type and style of research report produced. Validity can also take on different forms. While positivism is concerned with validity of the research question and results, interpretivism emphasizes research processes without neglecting the importance of the articulation of pertinent research questions and the sound interpretation of results ( Ollaik & Ziller, 2012 ). The means to achieve this can be diverse, such as triangulation (of multiple theories, multiple methods, multiple data sources or multiple investigators), pre-tests of data collection instrument, pilot case, study protocol, detailed description of procedures such as field diary in observations, researcher positioning (reflexivity), theoretical-empirical consistency, thick description and transferability.

5. Conclusions

The central objective of this article was to discuss concepts of case study research, their potential and various uses, taking into account different epistemologies as well as criteria of rigor and validity. Although the literature on methodology in general and on case studies in particular, is voluminous, it is not easy to relate this approach to epistemology. In addition, method manuals often focus on the details of various case study approaches which confuse things further.

Faced with this scenario, we have tried to address some central points in this debate and present various ways of using case studies according to the preferred epistemology of the researcher. We emphasize that this understanding depends on how a case is defined and the particular epistemological orientation that underpins that conceptualization. We have argued that whatever the epistemological orientation is, it is possible to meet appropriate criteria of research rigor and quality provided there is an alignment among the different elements of the research process. Furthermore, multiple data collection techniques can be used in in single or multiple case study designs. Data collection techniques or the type of data collected do not define the method or whether cases should be used for theory-building or theory-testing.

Finally, we encourage researchers to consider case study research as one way to foster immersion in phenomena and their contexts, stressing that the approach does not imply a commitment to a particular epistemology or type of research, such as qualitative or quantitative. Case study research allows for numerous possibilities, and should be celebrated for that diversity rather than pigeon-holed as a monolithic research method.

The interrelationship between the building blocks of research

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Organizing Your Social Sciences Research Assignments

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

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

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

How to Approach Writing a Case Study Research Paper

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

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

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

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

Structure and Writing Style

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

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

I.  Introduction

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

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

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

II.  Literature Review

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

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

III.  Method

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

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

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

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

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

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

IV.  Discussion

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

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

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

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

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

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

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

V.  Conclusion

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

At Least Five Misconceptions about Case Study Research

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

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

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

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

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5.1 Assumptions underlying research

Learning objectives.

Learners will be able to…

• Ground your research project and working question in the philosophical assumptions of social science
• Define the terms ‘ ontology ‘ and ‘ epistemology ‘ and explain how they relate to quantitative and qualitative research methods

Pre-awareness check (Knowledge)

Thinking back on your practice experience, what types of things were dependent on a person’s own truth and more subjective? What types of things would you consider irrefutable truths and more objective?

Last chapter, we reviewed the ethical commitment that social work researchers have to protect the people and communities impacted by their research. Answering the practical questions of harm, conflicts of interest, and other ethical issues will provide clear foundation of what you can and cannot do as part of your research project. In this chapter, we will transition from the real world to the conceptual world. Together, we will discover and explore the theoretical and philosophical foundations of your project. You should complete this chapter with a better sense of how theoretical and philosophical concepts help you answer your working question, and in turn, how theory and philosophy will affect the research project you design.

Embrace philosophy

The single biggest barrier to engaging with philosophy of science, at least according to some of my students, is the word philosophy. I had one student who told me that as soon as that word came up, she tuned out because she thought it was above her head. As we discussed in Chapter 1, some students already feel like research methods is too complex of a topic, and asking them to engage with philosophical concepts within research is like asking them to tap dance while wearing ice skates.

For those students, I would first answer that this chapter is my favorite one to write because it was the most impactful for me to learn during my MSW program. Finding my theoretical and philosophical home was important for me to develop as a clinician and a researcher. Following our advice in Chapter 2, you’ve hopefully chosen a topic that is important to your interests as a social work practitioner, and consider this chapter an opportunity to find your personal roots in addition to revising your working question and designing your research study.

Exploring theoretical and philosophical questions will cause your working question and research project to become clearer. Consider this chapter as something similar to getting a nice outfit for a fancy occasion. You have to try on a lot of different theories and philosophies before you find the one that fits with what you’re going for. There’s no right way to try on clothes, and there’s no one right theory or philosophy for your project. You might find a good fit with the first one you’ve tried on, or it might take a few different outfits. You have to find ideas that make sense together because they fit with how you think about your topic and how you should study it.

As you read this section, try to think about which assumptions  feel right for your working question and research project. Which assumptions match what you think and believe about your topic? The goal is not to find the “right” answer, but to develop your conceptual understanding of your research topic by finding the right theoretical and philosophical fit.

Theoretical and philosophical fluency

In addition to self-discovery, theoretical and philosophical fluency is a skill that social workers must possess in order to engage in social justice work. That’s because theory and philosophy help sharpen your perceptions of the social world. Just as social workers use empirical data to support their work, they also use theoretical and philosophical foundations. More importantly, theory and philosophy help social workers build heuristics that can help identify the fundamental assumptions at the heart of social conflict and social problems. They alert you to the patterns in the underlying assumptions that different people make and how those assumptions shape their worldview, what they view as true, and what they hope to accomplish. In the next section, we will review feminist and other critical perspectives on research, and they should help inform you of how assumptions about research can reinforce existing oppression.

Understanding these deeper structures is a true gift of social work research. Because we acknowledge the usefulness and truth value of multiple philosophies and worldviews contained in this chapter, we can arrive at a deeper and more nuanced understanding of the social world. Methods can be closely associated with particular worldviews or ideologies. There are necessarily philosophical and theoretical aspects to this, and this can be intimidating at times, but it’s important to critically engage with these questions to improve the quality of research.

Building your ice float

Although it may not seem like it right now, your project will develop a from a strong connection to previous theoretical and philosophical ideas about your topic. It’s likely you already have some (perhaps unstated) philosophical or theoretical ideas that undergird your thinking on the topic. Moreover, the philosophical questions we review here should inform how you understand different theories and practice modalities in social work, as they deal with the bedrock questions about science and human knowledge.

Before we can dive into philosophy, we need to recall our conversation from Chapter 1 about objective truth and subjective truths. Let’s test your knowledge with a quick example. Is crime on the rise in the United States? A recent Five Thirty Eight article highlights the disparity between historical trends on crime that are at or near their lowest in the thirty years with broad perceptions by the public that crime is on the rise (Koerth & Thomson-DeVeaux, 2020). [1] Social workers skilled at research can marshal objective facts, much like the authors do, to demonstrate that people’s perceptions are not based on a rational interpretation of the world. Of course, that is not where our work ends. Subjective facts might seek to decenter this narrative of ever-increasing crime, deconstruct is racist and oppressive origins, or simply document how that narrative shapes how individuals and communities conceptualize their world.

Objective does not mean right, and subjective does not mean wrong. Researchers must understand what kind of truth they are searching for so they can choose a theory(ies), develop a theoretical framework (in quantitative research), select an appropriate methodology, and make sure the research question(s) matches them all. As we discussed in Chapter 1, researchers seeking objective truth (one of the philosophical foundations at the bottom of Figure 5.1) often employ quantitative methods (one of the methods at the top of Figure 5.1). Similarly, researchers seeking subjective truths (again, at the bottom of Figure 5.1) often employ qualitative methods (at the top of Figure 5.1). This chapter is about the connective tissue, and by the time you are done reading, you should have a first draft of a theoretical and philosophical (a.k.a. paradigmatic) framework for your study.

Ontology: Assumptions about what is real and true

In section 1.2, we reviewed the two types of truth that social work researchers seek— objective truth and subjective truths —and linked these with the methods—quantitative and qualitative—that researchers use to study the world. If those ideas aren’t fresh in your mind, you may want to navigate back to that section for an introduction.

These two types of truth rely on different assumptions about what is real in the social world—i.e., they have a different ontology . Ontology refers to the study of being (literally, it means “rational discourse about being”). In philosophy, basic questions about existence are typically posed as ontological, e.g.:

• What is there?
• What types of things are there?
• How can we describe existence?
• What kind of categories can things go into?
• Are the categories of existence hierarchical?

Objective vs. subjective ontologies

At first, it may seem silly to question whether the phenomena we encounter in the social world are real. Of course you exist, your thoughts exist, your computer exists, and your friends exist. You can see them with your eyes. This is the ontological framework of  realism , which simply means that the concepts we talk about in science exist independent of observation (Burrell & Morgan, 1979). [2] Obviously, when we close our eyes, the universe does not disappear. You may be familiar with the philosophical conundrum: “If a tree falls in a forest and no one is around to hear it, does it make a sound?”

The natural sciences, like physics and biology, also generally rely on the assumption of realism. Lone trees falling make a sound. We assume that gravity and the rest of physics are there, even when no one is there to observe them. Mitochondria are easy to spot with a powerful microscope, and we can observe and theorize about their function in a cell. The gravitational force is invisible, but clearly apparent from observable facts, such as watching an apple fall from a tree. Of course, our theories about gravity have changed over the years. Improvements were made when observations could not be correctly explained using existing theories and new theories emerged that provided a better explanation of the data.

As we discussed in section 1.2, culture-bound syndromes are an excellent example of where you might come to question realism. Of course, from a Western perspective as researchers in the United States, we think that the Diagnostic and Statistical Manual (DSM) classification of mental health disorders is real and that these culture-bound syndromes are aberrations from the norm. But what about if you were a person from Korea experiencing Hwabyeong? Wouldn’t you consider the Western diagnosis of somatization disorder to be incorrect or incomplete? This conflict raises the question–do either Hwabyeong   or DSM diagnoses like post-traumatic stress disorder (PTSD) really exist at all…or are they just social constructs that only exist in our minds?

If your answer is “no, they do not exist,” you are adopting the ontology of anti-realism ( or relativism ), or the idea that social concepts do not exist outside of human thought. Unlike the realists who seek a single, universal truth, the anti-realists perceive a sea of truths, created and shared within a social and cultural context. Unlike objective truth, which is true for all, subjective truths will vary based on who you are observing and the context in which you are observing them. The beliefs, opinions, and preferences of people are actually truths that social scientists measure and describe. Additionally, subjective truths do not exist independent of human observation because they are the product of the human mind. We negotiate what is true in the social world through language, arriving at a consensus and engaging in debate within our socio-cultural context.

These theoretical assumptions should sound familiar if you’ve studied social constructivism or symbolic interactionism in MSW courses, most likely in human behavior in the social environment (HBSE). [3] From an anti-realist perspective, what distinguishes the social sciences from natural sciences is human thought. When we try to conceptualize trauma from an anti-realist perspective, we must pay attention to the feelings, opinions, and stories in people’s minds. In their most radical formulations, anti-realists propose that these feelings and stories are all that truly exist.

What happens when a situation is incorrectly interpreted? Certainly, who is correct about what is a bit subjective. It depends on who you ask. Even if you can determine whether a person is actually incorrect, they think they are right. Thus, what may not be objectively true for everyone is nevertheless true to the individual interpreting the situation. Furthermore, they act on the assumption that they are right. We all do. Much of our behaviors and interactions are a manifestation of our personal subjective truth. In this sense, even incorrect interpretations are truths, even though they are true only to one person or a group of misinformed people. This leads us to question whether the social concepts we think about really exist. For researchers using subjective ontologies, they might only exist in our minds; whereas, researchers who use objective ontologies which assume these concepts exist independent of thought.

How do we resolve this dichotomy? As social workers, we know that often times what appears to be an either/or situation is actually a both/and situation. Let’s take the example of trauma. There is clearly an objective thing called trauma. We can draw out objective facts about trauma and how it interacts with other concepts in the social world such as family relationships and mental health. However, that understanding is always bound within a specific cultural and historical context. Moreover, each person’s individual experience and conceptualization of trauma is also true. Much like a client who tells you their truth through their stories and reflections, when a participant in a research study tells you what their trauma means to them, it is real even though only they experience and know it that way. By using both objective and subjective analytic lenses, we can explore different aspects of trauma—what it means to everyone, always, everywhere, and what is means to one person or group of people, in a specific place and time.

Epistemology: Assumptions about how we know things

Having discussed what is true, we can proceed to the next natural question—how can we come to know what is real and true? This is epistemology . Epistemology is derived from the Ancient Greek epistēmē which refers to systematic or reliable knowledge (as opposed to doxa, or “belief”). Basically, it means “rational discourse about knowledge,” and the focus is the study of knowledge and methods used to generate knowledge. Epistemology has a history as long as philosophy, and lies at the foundation of both scientific and philosophical knowledge.

Epistemological questions include:

• What is knowledge?
• How can we claim to know anything at all?
• What does it mean to know something?
• What makes a belief justified?
• What is the relationship between the knower and what can be known?

While these philosophical questions can seem far removed from real-world interaction, thinking about these kinds of questions in the context of research helps you target your inquiry by informing your methods and helping you revise your working question. Epistemology is closely connected to method as they are both concerned with how to create and validate knowledge. Research methods are essentially epistemologies – by following a certain process we support our claim to know about the things we have been researching. Inappropriate or poorly followed methods can undermine claims to have produced new knowledge or discovered a new truth. This can have implications for future studies that build on the data and/or conceptual framework used.

Research methods can be thought of as essentially stripped down, purpose-specific epistemologies. The knowledge claims that underlie the results of surveys, focus groups, and other common research designs ultimately rest on epistemological assumptions of their methods. Focus groups and other qualitative methods usually rely on subjective epistemological (and ontological) assumptions. Surveys and other quantitative methods usually rely on objective epistemological assumptions. These epistemological assumptions often entail congruent subjective or objective ontological assumptions about the ultimate questions about reality.

Objective vs. subjective epistemologies

One key consideration here is the status of ‘truth’ within a particular epistemology or research method. If, for instance, some approaches emphasize subjective knowledge and deny the possibility of an objective truth, what does this mean for choosing a research method?

We began to answer this question in Chapter 1 when we described the scientific method and objective and subjective truths. Epistemological subjectivism focuses on what people think and feel about a situation, while epistemological objectivism focuses on objective facts irrelevant to our interpretation of a situation (Lin, 2015). [4]

While there are many important questions about epistemology to ask (e.g., “How can I be sure of what I know?” or “What can I not know?” see Willis, 2007 [5] for more), from a pragmatic perspective most relevant epistemological question in the social sciences is whether truth is better accessed using numerical data or words and performances. Generally, scientists approaching research with an objective epistemology (and realist ontology) will use quantitative methods to arrive at scientific truth. Quantitative methods examine numerical data to precisely describe and predict elements of the social world. For example, while people can have different definitions for poverty, an objective measurement such as an annual income of “less than $25,100 for a family of four” provides a precise measurement that can be compared to incomes from all other people in any society from any time period, and refers to real quantities of money that exist in the world. Mathematical relationships are uniquely useful in that they allow comparisons across individuals as well as time and space. In this book, we will review the most common designs used in quantitative research: surveys and experiments. These types of studies usually rely on the epistemological assumption that mathematics can represent the phenomena and relationships we observe in the social world.

Although mathematical relationships are useful, they are limited in what they can tell you. While you can use quantitative methods to measure individuals’ experiences and thought processes, you will miss the story behind the numbers. To analyze stories scientifically, we need to examine their expression in interviews, journal entries, performances, and other cultural artifacts using qualitative methods . Because social science studies human interaction and the reality we all create and share in our heads, subjectivists focus on language and other ways we communicate our inner experience. Qualitative methods allow us to scientifically investigate language and other forms of expression—to pursue research questions that explore the words people write and speak. This is consistent with epistemological subjectivism’s focus on individual and shared experiences, interpretations, and stories.

It is important to note that qualitative methods are entirely compatible with seeking objective truth. Approaching qualitative analysis with a more objective perspective, we look simply at what was said and examine its surface-level meaning. If a person says they brought their kids to school that day, then that is what is true. A researcher seeking subjective truth may focus on how the person says the words—their tone of voice, facial expressions, metaphors, and so forth. By focusing on these things, the researcher can understand what it meant to the person to say they dropped their kids off at school. Perhaps in describing dropping their children off at school, the person thought of their parents doing the same thing. In this way, subjective truths are deeper, more personalized, and difficult to generalize.

Putting it all together

As you might guess by the structure of the next two parts of this textbook, the distinction between quantitative and qualitative is important. Because of the distinct philosophical assumptions of objectivity and subjectivity, it will inform how you define the concepts in your research question, how you measure them, and how you gather and interpret your raw data. You certainly do not need to have a final answer right now! But stop for a minute and think about which approach feels right so far. In the next section, we will consider another set of philosophical assumptions that relate to ethics and the role of research in achieving social justice.

Key Takeaways

• Philosophers of science disagree on the basic tenets of what is true and how we come to know what is true.
• Researchers searching for objective truth will likely have a different research design, and methods than researchers searching for subjective truths.
• These differences are due to different assumptions about what is real and true (ontology) and how we can come to understand what is real and true (epistemology).

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Does an objective or subjective epistemological/ontological framework make the most sense for your research project?

• Are you more concerned with how people think and feel about your topic, their subjective truths—more specific to the time and place of your project?
• Or are you more concerned with objective truth, so that your results might generalize to populations beyond the ones in your study?

Using your answer to the above question, describe how either quantitative or qualitative methods make the most sense for your project.

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

You are interested in researching bullying among school-aged children, and how this impacts students’ academic success.

• If you are using an objective epistemological/ontological framework, what types of research questions might you ask?
• If you are using a subjective epistemological/ontological framework, what types of research questions might you ask?
• Koerth, M. & Thomson-DeVeaux, A. (2020, August 3). Many Americans are convinced crime is rising in the U.S. They're wrong. FiveThirtyEight . Retrieved from: https://fivethirtyeight.com/features/many-americans-are-convinced-crime-is-rising-in-the-u-s-theyre-wrong ↵
• Burrell, G. & Morgan, G. (1979). Sociological paradigms and organizational analysis . Routledge. ↵
• Here are links to two HBSE open textbooks, if you are unfamiliar with social work theories. https://uark.pressbooks.pub/hbse1/ and https://uark.pressbooks.pub/humanbehaviorandthesocialenvironment2/ ↵
• Lin, C. T. (2016). A critique of epistemic subjectivity. Philosophia, 44 (3), 915-920. ↵
• Wills, J. W. (2007).  World views, paradigms and the practice of social science research. Thousand Oaks, CA: Sage. ↵

a single truth, observed without bias, that is universally applicable

one truth among many, bound within a social and cultural context

assumptions about what is real and true

assumptions about how we come to know what is real and true

quantitative methods examine numerical data to precisely describe and predict elements of the social world

qualitative methods interpret language and behavior to understand the world from the perspectives of other people

Doctoral Research Methods in Social Work Copyright © by Mavs Open Press. All Rights Reserved.

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Designing and Conducting Case Studies

This guide examines case studies, a form of qualitative descriptive research that is used to look at individuals, a small group of participants, or a group as a whole. Researchers collect data about participants using participant and direct observations, interviews, protocols, tests, examinations of records, and collections of writing samples. Starting with a definition of the case study, the guide moves to a brief history of this research method. Using several well documented case studies, the guide then looks at applications and methods including data collection and analysis. A discussion of ways to handle validity, reliability, and generalizability follows, with special attention to case studies as they are applied to composition studies. Finally, this guide examines the strengths and weaknesses of case studies.

Definition and Overview

Case study refers to the collection and presentation of detailed information about a particular participant or small group, frequently including the accounts of subjects themselves. A form of qualitative descriptive research, the case study looks intensely at an individual or small participant pool, drawing conclusions only about that participant or group and only in that specific context. Researchers do not focus on the discovery of a universal, generalizable truth, nor do they typically look for cause-effect relationships; instead, emphasis is placed on exploration and description.

Case studies typically examine the interplay of all variables in order to provide as complete an understanding of an event or situation as possible. This type of comprehensive understanding is arrived at through a process known as thick description, which involves an in-depth description of the entity being evaluated, the circumstances under which it is used, the characteristics of the people involved in it, and the nature of the community in which it is located. Thick description also involves interpreting the meaning of demographic and descriptive data such as cultural norms and mores, community values, ingrained attitudes, and motives.

Unlike quantitative methods of research, like the survey, which focus on the questions of who, what, where, how much, and how many, and archival analysis, which often situates the participant in some form of historical context, case studies are the preferred strategy when how or why questions are asked. Likewise, they are the preferred method when the researcher has little control over the events, and when there is a contemporary focus within a real life context. In addition, unlike more specifically directed experiments, case studies require a problem that seeks a holistic understanding of the event or situation in question using inductive logic--reasoning from specific to more general terms.

In scholarly circles, case studies are frequently discussed within the context of qualitative research and naturalistic inquiry. Case studies are often referred to interchangeably with ethnography, field study, and participant observation. The underlying philosophical assumptions in the case are similar to these types of qualitative research because each takes place in a natural setting (such as a classroom, neighborhood, or private home), and strives for a more holistic interpretation of the event or situation under study.

Unlike more statistically-based studies which search for quantifiable data, the goal of a case study is to offer new variables and questions for further research. F.H. Giddings, a sociologist in the early part of the century, compares statistical methods to the case study on the basis that the former are concerned with the distribution of a particular trait, or a small number of traits, in a population, whereas the case study is concerned with the whole variety of traits to be found in a particular instance" (Hammersley 95).

Case studies are not a new form of research; naturalistic inquiry was the primary research tool until the development of the scientific method. The fields of sociology and anthropology are credited with the primary shaping of the concept as we know it today. However, case study research has drawn from a number of other areas as well: the clinical methods of doctors; the casework technique being developed by social workers; the methods of historians and anthropologists, plus the qualitative descriptions provided by quantitative researchers like LePlay; and, in the case of Robert Park, the techniques of newspaper reporters and novelists.

Park was an ex-newspaper reporter and editor who became very influential in developing sociological case studies at the University of Chicago in the 1920s. As a newspaper professional he coined the term "scientific" or "depth" reporting: the description of local events in a way that pointed to major social trends. Park viewed the sociologist as "merely a more accurate, responsible, and scientific reporter." Park stressed the variety and value of human experience. He believed that sociology sought to arrive at natural, but fluid, laws and generalizations in regard to human nature and society. These laws weren't static laws of the kind sought by many positivists and natural law theorists, but rather, they were laws of becoming--with a constant possibility of change. Park encouraged students to get out of the library, to quit looking at papers and books, and to view the constant experiment of human experience. He writes, "Go and sit in the lounges of the luxury hotels and on the doorsteps of the flophouses; sit on the Gold Coast settees and on the slum shakedowns; sit in the Orchestra Hall and in the Star and Garter Burlesque. In short, gentlemen [sic], go get the seats of your pants dirty in real research."

But over the years, case studies have drawn their share of criticism. In fact, the method had its detractors from the start. In the 1920s, the debate between pro-qualitative and pro-quantitative became quite heated. Case studies, when compared to statistics, were considered by many to be unscientific. From the 1930's on, the rise of positivism had a growing influence on quantitative methods in sociology. People wanted static, generalizable laws in science. The sociological positivists were looking for stable laws of social phenomena. They criticized case study research because it failed to provide evidence of inter subjective agreement. Also, they condemned it because of the few number of cases studied and that the under-standardized character of their descriptions made generalization impossible. By the 1950s, quantitative methods, in the form of survey research, had become the dominant sociological approach and case study had become a minority practice.

Educational Applications

The 1950's marked the dawning of a new era in case study research, namely that of the utilization of the case study as a teaching method. "Instituted at Harvard Business School in the 1950s as a primary method of teaching, cases have since been used in classrooms and lecture halls alike, either as part of a course of study or as the main focus of the course to which other teaching material is added" (Armisted 1984). The basic purpose of instituting the case method as a teaching strategy was "to transfer much of the responsibility for learning from the teacher on to the student, whose role, as a result, shifts away from passive absorption toward active construction" (Boehrer 1990). Through careful examination and discussion of various cases, "students learn to identify actual problems, to recognize key players and their agendas, and to become aware of those aspects of the situation that contribute to the problem" (Merseth 1991). In addition, students are encouraged to "generate their own analysis of the problems under consideration, to develop their own solutions, and to practically apply their own knowledge of theory to these problems" (Boyce 1993). Along the way, students also develop "the power to analyze and to master a tangled circumstance by identifying and delineating important factors; the ability to utilize ideas, to test them against facts, and to throw them into fresh combinations" (Merseth 1991).

In addition to the practical application and testing of scholarly knowledge, case discussions can also help students prepare for real-world problems, situations and crises by providing an approximation of various professional environments (i.e. classroom, board room, courtroom, or hospital). Thus, through the examination of specific cases, students are given the opportunity to work out their own professional issues through the trials, tribulations, experiences, and research findings of others. An obvious advantage to this mode of instruction is that it allows students the exposure to settings and contexts that they might not otherwise experience. For example, a student interested in studying the effects of poverty on minority secondary student's grade point averages and S.A.T. scores could access and analyze information from schools as geographically diverse as Los Angeles, New York City, Miami, and New Mexico without ever having to leave the classroom.

The case study method also incorporates the idea that students can learn from one another "by engaging with each other and with each other's ideas, by asserting something and then having it questioned, challenged and thrown back at them so that they can reflect on what they hear, and then refine what they say" (Boehrer 1990). In summary, students can direct their own learning by formulating questions and taking responsibility for the study.

Types and Design Concerns

Researchers use multiple methods and approaches to conduct case studies.

Types of Case Studies

Under the more generalized category of case study exist several subdivisions, each of which is custom selected for use depending upon the goals and/or objectives of the investigator. These types of case study include the following:

Illustrative Case Studies These are primarily descriptive studies. They typically utilize one or two instances of an event to show what a situation is like. Illustrative case studies serve primarily to make the unfamiliar familiar and to give readers a common language about the topic in question.

Exploratory (or pilot) Case Studies These are condensed case studies performed before implementing a large scale investigation. Their basic function is to help identify questions and select types of measurement prior to the main investigation. The primary pitfall of this type of study is that initial findings may seem convincing enough to be released prematurely as conclusions.

Cumulative Case Studies These serve to aggregate information from several sites collected at different times. The idea behind these studies is the collection of past studies will allow for greater generalization without additional cost or time being expended on new, possibly repetitive studies.

Critical Instance Case Studies These examine one or more sites for either the purpose of examining a situation of unique interest with little to no interest in generalizability, or to call into question or challenge a highly generalized or universal assertion. This method is useful for answering cause and effect questions.

Identifying a Theoretical Perspective

Much of the case study's design is inherently determined for researchers, depending on the field from which they are working. In composition studies, researchers are typically working from a qualitative, descriptive standpoint. In contrast, physicists will approach their research from a more quantitative perspective. Still, in designing the study, researchers need to make explicit the questions to be explored and the theoretical perspective from which they will approach the case. The three most commonly adopted theories are listed below:

Individual Theories These focus primarily on the individual development, cognitive behavior, personality, learning and disability, and interpersonal interactions of a particular subject.

Organizational Theories These focus on bureaucracies, institutions, organizational structure and functions, or excellence in organizational performance.

Social Theories These focus on urban development, group behavior, cultural institutions, or marketplace functions.

Two examples of case studies are used consistently throughout this chapter. The first, a study produced by Berkenkotter, Huckin, and Ackerman (1988), looks at a first year graduate student's initiation into an academic writing program. The study uses participant-observer and linguistic data collecting techniques to assess the student's knowledge of appropriate discourse conventions. Using the pseudonym Nate to refer to the subject, the study sought to illuminate the particular experience rather than to generalize about the experience of fledgling academic writers collectively.

For example, in Berkenkotter, Huckin, and Ackerman's (1988) study we are told that the researchers are interested in disciplinary communities. In the first paragraph, they ask what constitutes membership in a disciplinary community and how achieving membership might affect a writer's understanding and production of texts. In the third paragraph they state that researchers must negotiate their claims "within the context of his sub specialty's accepted knowledge and methodology." In the next paragraph they ask, "How is literacy acquired? What is the process through which novices gain community membership? And what factors either aid or hinder students learning the requisite linguistic behaviors?" This introductory section ends with a paragraph in which the study's authors claim that during the course of the study, the subject, Nate, successfully makes the transition from "skilled novice" to become an initiated member of the academic discourse community and that his texts exhibit linguistic changes which indicate this transition. In the next section the authors make explicit the sociolinguistic theoretical and methodological assumptions on which the study is based (1988). Thus the reader has a good understanding of the authors' theoretical background and purpose in conducting the study even before it is explicitly stated on the fourth page of the study. "Our purpose was to examine the effects of the educational context on one graduate student's production of texts as he wrote in different courses and for different faculty members over the academic year 1984-85." The goal of the study then, was to explore the idea that writers must be initiated into a writing community, and that this initiation will change the way one writes.

The second example is Janet Emig's (1971) study of the composing process of a group of twelfth graders. In this study, Emig seeks to answer the question of what happens to the self as a result educational stimuli in terms of academic writing. The case study used methods such as protocol analysis, tape-recorded interviews, and discourse analysis.

In the case of Janet Emig's (1971) study of the composing process of eight twelfth graders, four specific hypotheses were made:

• Twelfth grade writers engage in two modes of composing: reflexive and extensive.
• These differences can be ascertained and characterized through having the writers compose aloud their composition process.
• A set of implied stylistic principles governs the writing process.
• For twelfth grade writers, extensive writing occurs chiefly as a school-sponsored activity, or reflexive, as a self-sponsored activity.

In this study, the chief distinction is between the two dominant modes of composing among older, secondary school students. The distinctions are:

• The reflexive mode, which focuses on the writer's thoughts and feelings.
• The extensive mode, which focuses on conveying a message.

Emig also outlines the specific questions which guided the research in the opening pages of her Review of Literature , preceding the report.

Designing a Case Study

After considering the different sub categories of case study and identifying a theoretical perspective, researchers can begin to design their study. Research design is the string of logic that ultimately links the data to be collected and the conclusions to be drawn to the initial questions of the study. Typically, research designs deal with at least four problems:

• What questions to study
• What data are relevant
• What data to collect
• How to analyze that data

In other words, a research design is basically a blueprint for getting from the beginning to the end of a study. The beginning is an initial set of questions to be answered, and the end is some set of conclusions about those questions.

Because case studies are conducted on topics as diverse as Anglo-Saxon Literature (Thrane 1986) and AIDS prevention (Van Vugt 1994), it is virtually impossible to outline any strict or universal method or design for conducting the case study. However, Robert K. Yin (1993) does offer five basic components of a research design:

• A study's questions.
• A study's propositions (if any).
• A study's units of analysis.
• The logic that links the data to the propositions.
• The criteria for interpreting the findings.

In addition to these five basic components, Yin also stresses the importance of clearly articulating one's theoretical perspective, determining the goals of the study, selecting one's subject(s), selecting the appropriate method(s) of collecting data, and providing some considerations to the composition of the final report.

Conducting Case Studies

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of approaches and methods. These approaches, methods, and related issues are discussed in depth in this section.

Method: Single or Multi-modal?

To obtain as complete a picture of the participant as possible, case study researchers can employ a variety of methods. Some common methods include interviews , protocol analyses, field studies, and participant-observations. Emig (1971) chose to use several methods of data collection. Her sources included conversations with the students, protocol analysis, discrete observations of actual composition, writing samples from each student, and school records (Lauer and Asher 1988).

Berkenkotter, Huckin, and Ackerman (1988) collected data by observing classrooms, conducting faculty and student interviews, collecting self reports from the subject, and by looking at the subject's written work.

A study that was criticized for using a single method model was done by Flower and Hayes (1984). In this study that explores the ways in which writers use different forms of knowing to create space, the authors used only protocol analysis to gather data. The study came under heavy fire because of their decision to use only one method.

Participant Selection

Case studies can use one participant, or a small group of participants. However, it is important that the participant pool remain relatively small. The participants can represent a diverse cross section of society, but this isn't necessary.

For example, the Berkenkotter, Huckin, and Ackerman (1988) study looked at just one participant, Nate. By contrast, in Janet Emig's (1971) study of the composition process of twelfth graders, eight participants were selected representing a diverse cross section of the community, with volunteers from an all-white upper-middle-class suburban school, an all-black inner-city school, a racially mixed lower-middle-class school, an economically and racially mixed school, and a university school.

Often, a brief "case history" is done on the participants of the study in order to provide researchers with a clearer understanding of their participants, as well as some insight as to how their own personal histories might affect the outcome of the study. For instance, in Emig's study, the investigator had access to the school records of five of the participants, and to standardized test scores for the remaining three. Also made available to the researcher was the information that three of the eight students were selected as NCTE Achievement Award winners. These personal histories can be useful in later stages of the study when data are being analyzed and conclusions drawn.

Data Collection

There are six types of data collected in case studies:

• Archival records.
• Interviews.
• Direct observation.
• Participant observation.

In the field of composition research, these six sources might be:

• A writer's drafts.
• School records of student writers.
• Transcripts of interviews with a writer.
• Transcripts of conversations between writers (and protocols).
• Videotapes and notes from direct field observations.
• Hard copies of a writer's work on computer.

Depending on whether researchers have chosen to use a single or multi-modal approach for the case study, they may choose to collect data from one or any combination of these sources.

Protocols, that is, transcriptions of participants talking aloud about what they are doing as they do it, have been particularly common in composition case studies. For example, in Emig's (1971) study, the students were asked, in four different sessions, to give oral autobiographies of their writing experiences and to compose aloud three themes in the presence of a tape recorder and the investigator.

In some studies, only one method of data collection is conducted. For example, the Flower and Hayes (1981) report on the cognitive process theory of writing depends on protocol analysis alone. However, using multiple sources of evidence to increase the reliability and validity of the data can be advantageous.

Case studies are likely to be much more convincing and accurate if they are based on several different sources of information, following a corroborating mode. This conclusion is echoed among many composition researchers. For example, in her study of predrafting processes of high and low-apprehensive writers, Cynthia Selfe (1985) argues that because "methods of indirect observation provide only an incomplete reflection of the complex set of processes involved in composing, a combination of several such methods should be used to gather data in any one study." Thus, in this study, Selfe collected her data from protocols, observations of students role playing their writing processes, audio taped interviews with the students, and videotaped observations of the students in the process of composing.

It can be said then, that cross checking data from multiple sources can help provide a multidimensional profile of composing activities in a particular setting. Sharan Merriam (1985) suggests "checking, verifying, testing, probing, and confirming collected data as you go, arguing that this process will follow in a funnel-like design resulting in less data gathering in later phases of the study along with a congruent increase in analysis checking, verifying, and confirming."

It is important to note that in case studies, as in any qualitative descriptive research, while researchers begin their studies with one or several questions driving the inquiry (which influence the key factors the researcher will be looking for during data collection), a researcher may find new key factors emerging during data collection. These might be unexpected patterns or linguistic features which become evident only during the course of the research. While not bearing directly on the researcher's guiding questions, these variables may become the basis for new questions asked at the end of the report, thus linking to the possibility of further research.

Data Analysis

As the information is collected, researchers strive to make sense of their data. Generally, researchers interpret their data in one of two ways: holistically or through coding. Holistic analysis does not attempt to break the evidence into parts, but rather to draw conclusions based on the text as a whole. Flower and Hayes (1981), for example, make inferences from entire sections of their students' protocols, rather than searching through the transcripts to look for isolatable characteristics.

However, composition researchers commonly interpret their data by coding, that is by systematically searching data to identify and/or categorize specific observable actions or characteristics. These observable actions then become the key variables in the study. Sharan Merriam (1988) suggests seven analytic frameworks for the organization and presentation of data:

• The role of participants.
• The network analysis of formal and informal exchanges among groups.
• Historical.
• Thematical.
• Ritual and symbolism.
• Critical incidents that challenge or reinforce fundamental beliefs, practices, and values.

There are two purposes of these frameworks: to look for patterns among the data and to look for patterns that give meaning to the case study.

As stated above, while most researchers begin their case studies expecting to look for particular observable characteristics, it is not unusual for key variables to emerge during data collection. Typical variables coded in case studies of writers include pauses writers make in the production of a text, the use of specific linguistic units (such as nouns or verbs), and writing processes (planning, drafting, revising, and editing). In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, researchers coded the participant's texts for use of connectives, discourse demonstratives, average sentence length, off-register words, use of the first person pronoun, and the ratio of definite articles to indefinite articles.

Since coding is inherently subjective, more than one coder is usually employed. In the Berkenkotter, Huckin, and Ackerman (1988) study, for example, three rhetoricians were employed to code the participant's texts for off-register phrases. The researchers established the agreement among the coders before concluding that the participant used fewer off-register words as the graduate program progressed.

Composing the Case Study Report

In the many forms it can take, "a case study is generically a story; it presents the concrete narrative detail of actual, or at least realistic events, it has a plot, exposition, characters, and sometimes even dialogue" (Boehrer 1990). Generally, case study reports are extensively descriptive, with "the most problematic issue often referred to as being the determination of the right combination of description and analysis" (1990). Typically, authors address each step of the research process, and attempt to give the reader as much context as possible for the decisions made in the research design and for the conclusions drawn.

This contextualization usually includes a detailed explanation of the researchers' theoretical positions, of how those theories drove the inquiry or led to the guiding research questions, of the participants' backgrounds, of the processes of data collection, of the training and limitations of the coders, along with a strong attempt to make connections between the data and the conclusions evident.

Although the Berkenkotter, Huckin, and Ackerman (1988) study does not, case study reports often include the reactions of the participants to the study or to the researchers' conclusions. Because case studies tend to be exploratory, most end with implications for further study. Here researchers may identify significant variables that emerged during the research and suggest studies related to these, or the authors may suggest further general questions that their case study generated.

For example, Emig's (1971) study concludes with a section dedicated solely to the topic of implications for further research, in which she suggests several means by which this particular study could have been improved, as well as questions and ideas raised by this study which other researchers might like to address, such as: is there a correlation between a certain personality and a certain composing process profile (e.g. is there a positive correlation between ego strength and persistence in revising)?

Also included in Emig's study is a section dedicated to implications for teaching, which outlines the pedagogical ramifications of the study's findings for teachers currently involved in high school writing programs.

Sharan Merriam (1985) also offers several suggestions for alternative presentations of data:

• Prepare specialized condensations for appropriate groups.
• Replace narrative sections with a series of answers to open-ended questions.
• Present "skimmer's" summaries at beginning of each section.
• Incorporate headlines that encapsulate information from text.
• Prepare analytic summaries with supporting data appendixes.
• Present data in colorful and/or unique graphic representations.

Issues of Validity and Reliability

Once key variables have been identified, they can be analyzed. Reliability becomes a key concern at this stage, and many case study researchers go to great lengths to ensure that their interpretations of the data will be both reliable and valid. Because issues of validity and reliability are an important part of any study in the social sciences, it is important to identify some ways of dealing with results.

Multi-modal case study researchers often balance the results of their coding with data from interviews or writer's reflections upon their own work. Consequently, the researchers' conclusions become highly contextualized. For example, in a case study which looked at the time spent in different stages of the writing process, Berkenkotter concluded that her participant, Donald Murray, spent more time planning his essays than in other writing stages. The report of this case study is followed by Murray's reply, wherein he agrees with some of Berkenkotter's conclusions and disagrees with others.

As is the case with other research methodologies, issues of external validity, construct validity, and reliability need to be carefully considered.

Commentary on Case Studies

Researchers often debate the relative merits of particular methods, among them case study. In this section, we comment on two key issues. To read the commentaries, choose any of the items below:

Strengths and Weaknesses of Case Studies

Most case study advocates point out that case studies produce much more detailed information than what is available through a statistical analysis. Advocates will also hold that while statistical methods might be able to deal with situations where behavior is homogeneous and routine, case studies are needed to deal with creativity, innovation, and context. Detractors argue that case studies are difficult to generalize because of inherent subjectivity and because they are based on qualitative subjective data, generalizable only to a particular context.

Flexibility

The case study approach is a comparatively flexible method of scientific research. Because its project designs seem to emphasize exploration rather than prescription or prediction, researchers are comparatively freer to discover and address issues as they arise in their experiments. In addition, the looser format of case studies allows researchers to begin with broad questions and narrow their focus as their experiment progresses rather than attempt to predict every possible outcome before the experiment is conducted.

Emphasis on Context

By seeking to understand as much as possible about a single subject or small group of subjects, case studies specialize in "deep data," or "thick description"--information based on particular contexts that can give research results a more human face. This emphasis can help bridge the gap between abstract research and concrete practice by allowing researchers to compare their firsthand observations with the quantitative results obtained through other methods of research.

Inherent Subjectivity

"The case study has long been stereotyped as the weak sibling among social science methods," and is often criticized as being too subjective and even pseudo-scientific. Likewise, "investigators who do case studies are often regarded as having deviated from their academic disciplines, and their investigations as having insufficient precision (that is, quantification), objectivity and rigor" (Yin 1989). Opponents cite opportunities for subjectivity in the implementation, presentation, and evaluation of case study research. The approach relies on personal interpretation of data and inferences. Results may not be generalizable, are difficult to test for validity, and rarely offer a problem-solving prescription. Simply put, relying on one or a few subjects as a basis for cognitive extrapolations runs the risk of inferring too much from what might be circumstance.

High Investment

Case studies can involve learning more about the subjects being tested than most researchers would care to know--their educational background, emotional background, perceptions of themselves and their surroundings, their likes, dislikes, and so on. Because of its emphasis on "deep data," the case study is out of reach for many large-scale research projects which look at a subject pool in the tens of thousands. A budget request of $10,000 to examine 200 subjects sounds more efficient than a similar request to examine four subjects.

Ethical Considerations

Researchers conducting case studies should consider certain ethical issues. For example, many educational case studies are often financed by people who have, either directly or indirectly, power over both those being studied and those conducting the investigation (1985). This conflict of interests can hinder the credibility of the study.

The personal integrity, sensitivity, and possible prejudices and/or biases of the investigators need to be taken into consideration as well. Personal biases can creep into how the research is conducted, alternative research methods used, and the preparation of surveys and questionnaires.

A common complaint in case study research is that investigators change direction during the course of the study unaware that their original research design was inadequate for the revised investigation. Thus, the researchers leave unknown gaps and biases in the study. To avoid this, researchers should report preliminary findings so that the likelihood of bias will be reduced.

Concerns about Reliability, Validity, and Generalizability

Merriam (1985) offers several suggestions for how case study researchers might actively combat the popular attacks on the validity, reliability, and generalizability of case studies:

• Prolong the Processes of Data Gathering on Site: This will help to insure the accuracy of the findings by providing the researcher with more concrete information upon which to formulate interpretations.
• Employ the Process of "Triangulation": Use a variety of data sources as opposed to relying solely upon one avenue of observation. One example of such a data check would be what McClintock, Brannon, and Maynard (1985) refer to as a "case cluster method," that is, when a single unit within a larger case is randomly sampled, and that data treated quantitatively." For instance, in Emig's (1971) study, the case cluster method was employed, singling out the productivity of a single student named Lynn. This cluster profile included an advanced case history of the subject, specific examination and analysis of individual compositions and protocols, and extensive interview sessions. The seven remaining students were then compared with the case of Lynn, to ascertain if there are any shared, or unique dimensions to the composing process engaged in by these eight students.
• Conduct Member Checks: Initiate and maintain an active corroboration on the interpretation of data between the researcher and those who provided the data. In other words, talk to your subjects.
• Collect Referential Materials: Complement the file of materials from the actual site with additional document support. For example, Emig (1971) supports her initial propositions with historical accounts by writers such as T.S. Eliot, James Joyce, and D.H. Lawrence. Emig also cites examples of theoretical research done with regards to the creative process, as well as examples of empirical research dealing with the writing of adolescents. Specific attention is then given to the four stages description of the composing process delineated by Helmoltz, Wallas, and Cowley, as it serves as the focal point in this study.
• Engage in Peer Consultation: Prior to composing the final draft of the report, researchers should consult with colleagues in order to establish validity through pooled judgment.

Although little can be done to combat challenges concerning the generalizability of case studies, "most writers suggest that qualitative research should be judged as credible and confirmable as opposed to valid and reliable" (Merriam 1985). Likewise, it has been argued that "rather than transplanting statistical, quantitative notions of generalizability and thus finding qualitative research inadequate, it makes more sense to develop an understanding of generalization that is congruent with the basic characteristics of qualitative inquiry" (1985). After all, criticizing the case study method for being ungeneralizable is comparable to criticizing a washing machine for not being able to tell the correct time. In other words, it is unjust to criticize a method for not being able to do something which it was never originally designed to do in the first place.

Annotated Bibliography

Armisted, C. (1984). How Useful are Case Studies. Training and Development Journal, 38 (2), 75-77.

This article looks at eight types of case studies, offers pros and cons of using case studies in the classroom, and gives suggestions for successfully writing and using case studies.

Bardovi-Harlig, K. (1997). Beyond Methods: Components of Second Language Teacher Education . New York: McGraw-Hill.

A compilation of various research essays which address issues of language teacher education. Essays included are: "Non-native reading research and theory" by Lee, "The case for Psycholinguistics" by VanPatten, and "Assessment and Second Language Teaching" by Gradman and Reed.

Bartlett, L. (1989). A Question of Good Judgment; Interpretation Theory and Qualitative Enquiry Address. 70th Annual Meeting of the American Educational Research Association. San Francisco.

Bartlett selected "quasi-historical" methodology, which focuses on the "truth" found in case records, as one that will provide "good judgments" in educational inquiry. He argues that although the method is not comprehensive, it can try to connect theory with practice.

Baydere, S. et. al. (1993). Multimedia conferencing as a tool for collaborative writing: a case study in Computer Supported Collaborative Writing. New York: Springer-Verlag.

The case study by Baydere et. al. is just one of the many essays in this book found in the series "Computer Supported Cooperative Work." Denley, Witefield and May explore similar issues in their essay, "A case study in task analysis for the design of a collaborative document production system."

Berkenkotter, C., Huckin, T., N., & Ackerman J. (1988). Conventions, Conversations, and the Writer: Case Study of a Student in a Rhetoric Ph.D. Program. Research in the Teaching of English, 22, 9-44.

The authors focused on how the writing of their subject, Nate or Ackerman, changed as he became more acquainted or familiar with his field's discourse community.

Berninger, V., W., and Gans, B., M. (1986). Language Profiles in Nonspeaking Individuals of Normal Intelligence with Severe Cerebral Palsy. Augmentative and Alternative Communication, 2, 45-50.

Argues that generalizations about language abilities in patients with severe cerebral palsy (CP) should be avoided. Standardized tests of different levels of processing oral language, of processing written language, and of producing written language were administered to 3 male participants (aged 9, 16, and 40 yrs).

Bockman, J., R., and Couture, B. (1984). The Case Method in Technical Communication: Theory and Models. Texas: Association of Teachers of Technical Writing.

Examines the study and teaching of technical writing, communication of technical information, and the case method in terms of those applications.

Boehrer, J. (1990). Teaching With Cases: Learning to Question. New Directions for Teaching and Learning, 42 41-57.

This article discusses the origins of the case method, looks at the question of what is a case, gives ideas about learning in case teaching, the purposes it can serve in the classroom, the ground rules for the case discussion, including the role of the question, and new directions for case teaching.

Bowman, W. R. (1993). Evaluating JTPA Programs for Economically Disadvantaged Adults: A Case Study of Utah and General Findings . Washington: National Commission for Employment Policy.

"To encourage state-level evaluations of JTPA, the Commission and the State of Utah co-sponsored this report on the effectiveness of JTPA Title II programs for adults in Utah. The technique used is non-experimental and the comparison group was selected from registrants with Utah's Employment Security. In a step-by-step approach, the report documents how non-experimental techniques can be applied and several specific technical issues can be addressed."

Boyce, A. (1993) The Case Study Approach for Pedagogists. Annual Meeting of the American Alliance for Health, Physical Education, Recreation and Dance. (Address). Washington DC.

This paper addresses how case studies 1) bridge the gap between teaching theory and application, 2) enable students to analyze problems and develop solutions for situations that will be encountered in the real world of teaching, and 3) helps students to evaluate the feasibility of alternatives and to understand the ramifications of a particular course of action.

Carson, J. (1993) The Case Study: Ideal Home of WAC Quantitative and Qualitative Data. Annual Meeting of the Conference on College Composition and Communication. (Address). San Diego.

"Increasingly, one of the most pressing questions for WAC advocates is how to keep [WAC] programs going in the face of numerous difficulties. Case histories offer the best chance for fashioning rhetorical arguments to keep WAC programs going because they offer the opportunity to provide a coherent narrative that contextualizes all documents and data, including what is generally considered scientific data. A case study of the WAC program, . . . at Robert Morris College in Pittsburgh demonstrates the advantages of this research method. Such studies are ideal homes for both naturalistic and positivistic data as well as both quantitative and qualitative information."

---. (1991). A Cognitive Process Theory of Writing. College Composition and Communication. 32. 365-87.

No abstract available.

Cromer, R. (1994) A Case Study of Dissociations Between Language and Cognition. Constraints on Language Acquisition: Studies of Atypical Children . Hillsdale: Lawrence Erlbaum Associates, 141-153.

Crossley, M. (1983) Case Study in Comparative and International Education: An Approach to Bridging the Theory-Practice Gap. Proceedings of the 11th Annual Conference of the Australian Comparative and International Education Society. Hamilton, NZ.

Case study research, as presented here, helps bridge the theory-practice gap in comparative and international research studies of education because it focuses on the practical, day-to-day context rather than on the national arena. The paper asserts that the case study method can be valuable at all levels of research, formation, and verification of theories in education.

Daillak, R., H., and Alkin, M., C. (1982). Qualitative Studies in Context: Reflections on the CSE Studies of Evaluation Use . California: EDRS

The report shows how the Center of the Study of Evaluation (CSE) applied qualitative techniques to a study of evaluation information use in local, Los Angeles schools. It critiques the effectiveness and the limitations of using case study, evaluation, field study, and user interview survey methodologies.

Davey, L. (1991). The Application of Case Study Evaluations. ERIC/TM Digest.

This article examines six types of case studies, the type of evaluation questions that can be answered, the functions served, some design features, and some pitfalls of the method.

Deutch, C. E. (1996). A course in research ethics for graduate students. College Teaching, 44, 2, 56-60.

This article describes a one-credit discussion course in research ethics for graduate students in biology. Case studies are focused on within the four parts of the course: 1) major issues, 2 )practical issues in scholarly work, 3) ownership of research results, and 4) training and personal decisions.

DeVoss, G. (1981). Ethics in Fieldwork Research. RIE 27p. (ERIC)

This article examines four of the ethical problems that can happen when conducting case study research: acquiring permission to do research, knowing when to stop digging, the pitfalls of doing collaborative research, and preserving the integrity of the participants.

Driscoll, A. (1985). Case Study of a Research Intervention: the University of Utah’s Collaborative Approach . San Francisco: Far West Library for Educational Research Development.

Paper presented at the annual meeting of the American Association of Colleges of Teacher Education, Denver, CO, March 1985. Offers information of in-service training, specifically case studies application.

Ellram, L. M. (1996). The Use of the Case Study Method in Logistics Research. Journal of Business Logistics, 17, 2, 93.

This article discusses the increased use of case study in business research, and the lack of understanding of when and how to use case study methodology in business.

Emig, J. (1971) The Composing Processes of Twelfth Graders . Urbana: NTCE.

This case study uses observation, tape recordings, writing samples, and school records to show that writing in reflexive and extensive situations caused different lengths of discourse and different clusterings of the components of the writing process.

Feagin, J. R. (1991). A Case For the Case Study . Chapel Hill: The University of North Carolina Press.

This book discusses the nature, characteristics, and basic methodological issues of the case study as a research method.

Feldman, H., Holland, A., & Keefe, K. (1989) Language Abilities after Left Hemisphere Brain Injury: A Case Study of Twins. Topics in Early Childhood Special Education, 9, 32-47.

"Describes the language abilities of 2 twin pairs in which 1 twin (the experimental) suffered brain injury to the left cerebral hemisphere around the time of birth and1 twin (the control) did not. One pair of twins was initially assessed at age 23 mo. and the other at about 30 mo.; they were subsequently evaluated in their homes 3 times at about 6-mo intervals."

Fidel, R. (1984). The Case Study Method: A Case Study. Library and Information Science Research, 6.

The article describes the use of case study methodology to systematically develop a model of online searching behavior in which study design is flexible, subject manner determines data gathering and analyses, and procedures adapt to the study's progressive change.

Flower, L., & Hayes, J. R. (1984). Images, Plans and Prose: The Representation of Meaning in Writing. Written Communication, 1, 120-160.

Explores the ways in which writers actually use different forms of knowing to create prose.

Frey, L. R. (1992). Interpreting Communication Research: A Case Study Approach Englewood Cliffs, N.J.: Prentice Hall.

The book discusses research methodologies in the Communication field. It focuses on how case studies bridge the gap between communication research, theory, and practice.

Gilbert, V. K. (1981). The Case Study as a Research Methodology: Difficulties and Advantages of Integrating the Positivistic, Phenomenological and Grounded Theory Approaches . The Annual Meeting of the Canadian Association for the Study of Educational Administration. (Address) Halifax, NS, Can.

This study on an innovative secondary school in England shows how a "low-profile" participant-observer case study was crucial to the initial observation, the testing of hypotheses, the interpretive approach, and the grounded theory.

Gilgun, J. F. (1994). A Case for Case Studies in Social Work Research. Social Work, 39, 4, 371-381.

This article defines case study research, presents guidelines for evaluation of case studies, and shows the relevance of case studies to social work research. It also looks at issues such as evaluation and interpretations of case studies.

Glennan, S. L., Sharp-Bittner, M. A. & Tullos, D. C. (1991). Augmentative and Alternative Communication Training with a Nonspeaking Adult: Lessons from MH. Augmentative and Alternative Communication, 7, 240-7.

"A response-guided case study documented changes in a nonspeaking 36-yr-old man's ability to communicate using 3 trained augmentative communication modes. . . . Data were collected in videotaped interaction sessions between the nonspeaking adult and a series of adult speaking."

Graves, D. (1981). An Examination of the Writing Processes of Seven Year Old Children. Research in the Teaching of English, 15, 113-134.

Hamel, J. (1993). Case Study Methods . Newbury Park: Sage. .

"In a most economical fashion, Hamel provides a practical guide for producing theoretically sharp and empirically sound sociological case studies. A central idea put forth by Hamel is that case studies must "locate the global in the local" thus making the careful selection of the research site the most critical decision in the analytic process."

Karthigesu, R. (1986, July). Television as a Tool for Nation-Building in the Third World: A Post-Colonial Pattern, Using Malaysia as a Case-Study. International Television Studies Conference. (Address). London, 10-12.

"The extent to which Television Malaysia, as a national mass media organization, has been able to play a role in nation building in the post-colonial period is . . . studied in two parts: how the choice of a model of nation building determines the character of the organization; and how the character of the organization influences the output of the organization."

Kenny, R. (1984). Making the Case for the Case Study. Journal of Curriculum Studies, 16, (1), 37-51.

The article looks at how and why the case study is justified as a viable and valuable approach to educational research and program evaluation.

Knirk, F. (1991). Case Materials: Research and Practice. Performance Improvement Quarterly, 4 (1 ), 73-81.

The article addresses the effectiveness of case studies, subject areas where case studies are commonly used, recent examples of their use, and case study design considerations.

Klos, D. (1976). Students as Case Writers. Teaching of Psychology, 3.2, 63-66.

This article reviews a course in which students gather data for an original case study of another person. The task requires the students to design the study, collect the data, write the narrative, and interpret the findings.

Leftwich, A. (1981). The Politics of Case Study: Problems of Innovation in University Education. Higher Education Review, 13.2, 38-64.

The article discusses the use of case studies as a teaching method. Emphasis is on the instructional materials, interdisciplinarity, and the complex relationships within the university that help or hinder the method.

Mabrito, M. (1991, Oct.). Electronic Mail as a Vehicle for Peer Response: Conversations of High and Low Apprehensive Writers. Written Communication, 509-32.

McCarthy, S., J. (1955). The Influence of Classroom Discourse on Student Texts: The Case of Ella . East Lansing: Institute for Research on Teaching.

A look at how students of color become marginalized within traditional classroom discourse. The essay follows the struggles of one black student: Ella.

Matsuhashi, A., ed. (1987). Writing in Real Time: Modeling Production Processes Norwood, NJ: Ablex Publishing Corporation.

Investigates how writers plan to produce discourse for different purposes to report, to generalize, and to persuade, as well as how writers plan for sentence level units of language. To learn about planning, an observational measure of pause time was used" (ERIC).

Merriam, S. B. (1985). The Case Study in Educational Research: A Review of Selected Literature. Journal of Educational Thought, 19.3, 204-17.

The article examines the characteristics of, philosophical assumptions underlying the case study, the mechanics of conducting a case study, and the concerns about the reliability, validity, and generalizability of the method.

---. (1988). Case Study Research in Education: A Qualitative Approach San Francisco: Jossey Bass.

Merry, S. E., & Milner, N. eds. (1993). The Possibility of Popular Justice: A Case Study of Community Mediation in the United States . Ann Arbor: U of Michigan.

". . . this volume presents a case study of one experiment in popular justice, the San Francisco Community Boards. This program has made an explicit claim to create an alternative justice, or new justice, in the midst of a society ordered by state law. The contributors to this volume explore the history and experience of the program and compare it to other versions of popular justice in the United States, Europe, and the Third World."

Merseth, K. K. (1991). The Case for Cases in Teacher Education. RIE. 42p. (ERIC).

This monograph argues that the case method of instruction offers unique potential for revitalizing the field of teacher education.

Michaels, S. (1987). Text and Context: A New Approach to the Study of Classroom Writing. Discourse Processes, 10, 321-346.

"This paper argues for and illustrates an approach to the study of writing that integrates ethnographic analysis of classroom interaction with linguistic analysis of written texts and teacher/student conversational exchanges. The approach is illustrated through a case study of writing in a single sixth grade classroom during a single writing assignment."

Milburn, G. (1995). Deciphering a Code or Unraveling a Riddle: A Case Study in the Application of a Humanistic Metaphor to the Reporting of Social Studies Teaching. Theory and Research in Education, 13.

This citation serves as an example of how case studies document learning procedures in a senior-level economics course.

Milley, J. E. (1979). An Investigation of Case Study as an Approach to Program Evaluation. 19th Annual Forum of the Association for Institutional Research. (Address). San Diego.

The case study method merged a narrative report focusing on the evaluator as participant-observer with document review, interview, content analysis, attitude questionnaire survey, and sociogram analysis. Milley argues that case study program evaluation has great potential for widespread use.

Minnis, J. R. (1985, Sept.). Ethnography, Case Study, Grounded Theory, and Distance Education Research. Distance Education, 6.2.

This article describes and defines the strengths and weaknesses of ethnography, case study, and grounded theory.

Nunan, D. (1992). Collaborative language learning and teaching . New York: Cambridge University Press.

Included in this series of essays is Peter Sturman’s "Team Teaching: a case study from Japan" and David Nunan’s own "Toward a collaborative approach to curriculum development: a case study."

Nystrand, M., ed. (1982). What Writers Know: The Language, Process, and Structure of Written Discourse . New York: Academic Press.

Owenby, P. H. (1992). Making Case Studies Come Alive. Training, 29, (1), 43-46. (ERIC)

This article provides tips for writing more effective case studies.

---. (1981). Pausing and Planning: The Tempo of Writer Discourse Production. Research in the Teaching of English, 15 (2),113-34.

Perl, S. (1979). The Composing Processes of Unskilled College Writers. Research in the Teaching of English, 13, 317-336.

"Summarizes a study of five unskilled college writers, focusing especially on one of the five, and discusses the findings in light of current pedagogical practice and research design."

Pilcher J. and A. Coffey. eds. (1996). Gender and Qualitative Research . Brookfield: Aldershot, Hants, England.

This book provides a series of essays which look at gender identity research, qualitative research and applications of case study to questions of gendered pedagogy.

Pirie, B. S. (1993). The Case of Morty: A Four Year Study. Gifted Education International, 9 (2), 105-109.

This case study describes a boy from kindergarten through third grade with above average intelligence but difficulty in learning to read, write, and spell.

Popkewitz, T. (1993). Changing Patterns of Power: Social Regulation and Teacher Education Reform. Albany: SUNY Press.

Popkewitz edits this series of essays that address case studies on educational change and the training of teachers. The essays vary in terms of discipline and scope. Also, several authors include case studies of educational practices in countries other than the United States.

---. (1984). The Predrafting Processes of Four High- and Four Low Apprehensive Writers. Research in the Teaching of English, 18, (1), 45-64.

Rasmussen, P. (1985, March) A Case Study on the Evaluation of Research at the Technical University of Denmark. International Journal of Institutional Management in Higher Education, 9 (1).

This is an example of a case study methodology used to evaluate the chemistry and chemical engineering departments at the University of Denmark.

Roth, K. J. (1986). Curriculum Materials, Teacher Talk, and Student Learning: Case Studies in Fifth-Grade Science Teaching . East Lansing: Institute for Research on Teaching.

Roth offers case studies on elementary teachers, elementary school teaching, science studies and teaching, and verbal learning.

Selfe, C. L. (1985). An Apprehensive Writer Composes. When a Writer Can't Write: Studies in Writer's Block and Other Composing-Process Problems . (pp. 83-95). Ed. Mike Rose. NMY: Guilford.

Smith-Lewis, M., R. and Ford, A. (1987). A User's Perspective on Augmentative Communication. Augmentative and Alternative Communication, 3, 12-7.

"During a series of in-depth interviews, a 25-yr-old woman with cerebral palsy who utilized augmentative communication reflected on the effectiveness of the devices designed for her during her school career."

St. Pierre, R., G. (1980, April). Follow Through: A Case Study in Metaevaluation Research . 64th Annual Meeting of the American Educational Research Association. (Address).

The three approaches to metaevaluation are evaluation of primary evaluations, integrative meta-analysis with combined primary evaluation results, and re-analysis of the raw data from a primary evaluation.

Stahler, T., M. (1996, Feb.) Early Field Experiences: A Model That Worked. ERIC.

"This case study of a field and theory class examines a model designed to provide meaningful field experiences for preservice teachers while remaining consistent with the instructor's beliefs about the role of teacher education in preparing teachers for the classroom."

Stake, R. E. (1995). The Art of Case Study Research. Thousand Oaks: Sage Publications.

This book examines case study research in education and case study methodology.

Stiegelbauer, S. (1984) Community, Context, and Co-curriculum: Situational Factors Influencing School Improvements in a Study of High Schools. Presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Discussion of several case studies: one looking at high school environments, another examining educational innovations.

Stolovitch, H. (1990). Case Study Method. Performance And Instruction, 29, (9), 35-37.

This article describes the case study method as a form of simulation and presents guidelines for their use in professional training situations.

Thaller, E. (1994). Bibliography for the Case Method: Using Case Studies in Teacher Education. RIE. 37 p.

This bibliography presents approximately 450 citations on the use of case studies in teacher education from 1921-1993.

Thrane, T. (1986). On Delimiting the Senses of Near-Synonyms in Historical Semantics: A Case Study of Adjectives of 'Moral Sufficiency' in the Old English Andreas. Linguistics Across Historical and Geographical Boundaries: In Honor of Jacek Fisiak on the Occasion of his Fiftieth Birthday . Berlin: Mouton de Gruyter.

United Nations. (1975). Food and Agriculture Organization. Report on the FAO/UNFPA Seminar on Methodology, Research and Country: Case Studies on Population, Employment and Productivity . Rome: United Nations.

This example case study shows how the methodology can be used in a demographic and psychographic evaluation. At the same time, it discusses the formation and instigation of the case study methodology itself.

Van Vugt, J. P., ed. (1994). Aids Prevention and Services: Community Based Research . Westport: Bergin and Garvey.

"This volume has been five years in the making. In the process, some of the policy applications called for have met with limited success, such as free needle exchange programs in a limited number of American cities, providing condoms to prison inmates, and advertisements that depict same-sex couples. Rather than dating our chapters that deal with such subjects, such policy applications are verifications of the type of research demonstrated here. Furthermore, they indicate the critical need to continue community based research in the various communities threatened by acquired immuno-deficiency syndrome (AIDS) . . . "

Welch, W., ed. (1981, May). Case Study Methodology in Educational Evaluation. Proceedings of the Minnesota Evaluation Conference. Minnesota. (Address).

The four papers in these proceedings provide a comprehensive picture of the rationale, methodology, strengths, and limitations of case studies.

Williams, G. (1987). The Case Method: An Approach to Teaching and Learning in Educational Administration. RIE, 31p.

This paper examines the viability of the case method as a teaching and learning strategy in instructional systems geared toward the training of personnel of the administration of various aspects of educational systems.

Yin, R. K. (1993). Advancing Rigorous Methodologies: A Review of 'Towards Rigor in Reviews of Multivocal Literatures.' Review of Educational Research, 61, (3).

"R. T. Ogawa and B. Malen's article does not meet its own recommended standards for rigorous testing and presentation of its own conclusions. Use of the exploratory case study to analyze multivocal literatures is not supported, and the claim of grounded theory to analyze multivocal literatures may be stronger."

---. (1989). Case Study Research: Design and Methods. London: Sage Publications Inc.

This book discusses in great detail, the entire design process of the case study, including entire chapters on collecting evidence, analyzing evidence, composing the case study report, and designing single and multiple case studies.

Related Links

Consider the following list of related Web sites for more information on the topic of case study research. Note: although many of the links cover the general category of qualitative research, all have sections that address issues of case studies.

• Sage Publications on Qualitative Methodology: Search here for a comprehensive list of new books being published about "Qualitative Methodology" http://www.sagepub.co.uk/
• The International Journal of Qualitative Studies in Education: An on-line journal "to enhance the theory and practice of qualitative research in education." On-line submissions are welcome. http://www.tandf.co.uk/journals/tf/09518398.html
• Qualitative Research Resources on the Internet: From syllabi to home pages to bibliographies. All links relate somehow to qualitative research. http://www.nova.edu/ssss/QR/qualres.html

Becker, Bronwyn, Patrick Dawson, Karen Devine, Carla Hannum, Steve Hill, Jon Leydens, Debbie Matuskevich, Carol Traver, & Mike Palmquist. (2005). Case Studies. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=60

Stating the Obvious: Writing Assumptions, Limitations, and Delimitations

During the process of writing your thesis or dissertation, you might suddenly realize that your research has inherent flaws. Don’t worry! Virtually all projects contain restrictions to your research. However, being able to recognize and accurately describe these problems is the difference between a true researcher and a grade-school kid with a science-fair project. Concerns with truthful responding, access to participants, and survey instruments are just a few of examples of restrictions on your research. In the following sections, the differences among delimitations, limitations, and assumptions of a dissertation will be clarified.

Delimitations

Delimitations are the definitions you set as the boundaries of your own thesis or dissertation, so delimitations are in your control. Delimitations are set so that your goals do not become impossibly large to complete. Examples of delimitations include objectives, research questions, variables, theoretical objectives that you have adopted, and populations chosen as targets to study. When you are stating your delimitations, clearly inform readers why you chose this course of study. The answer might simply be that you were curious about the topic and/or wanted to improve standards of a professional field by revealing certain findings. In any case, you should clearly list the other options available and the reasons why you did not choose these options immediately after you list your delimitations. You might have avoided these options for reasons of practicality, interest, or relativity to the study at hand. For example, you might have only studied Hispanic mothers because they have the highest rate of obese babies. Delimitations are often strongly related to your theory and research questions. If you were researching whether there are different parenting styles between unmarried Asian, Caucasian, African American, and Hispanic women, then a delimitation of your study would be the inclusion of only participants with those demographics and the exclusion of participants from other demographics such as men, married women, and all other ethnicities of single women (inclusion and exclusion criteria). A further delimitation might be that you only included closed-ended Likert scale responses in the survey, rather than including additional open-ended responses, which might make some people more willing to take and complete your survey. Remember that delimitations are not good or bad. They are simply a detailed description of the scope of interest for your study as it relates to the research design. Don’t forget to describe the philosophical framework you used throughout your study, which also delimits your study.

Limitations

Limitations of a dissertation are potential weaknesses in your study that are mostly out of your control, given limited funding, choice of research design, statistical model constraints, or other factors. In addition, a limitation is a restriction on your study that cannot be reasonably dismissed and can affect your design and results. Do not worry about limitations because limitations affect virtually all research projects, as well as most things in life. Even when you are going to your favorite restaurant, you are limited by the menu choices. If you went to a restaurant that had a menu that you were craving, you might not receive the service, price, or location that makes you enjoy your favorite restaurant. If you studied participants’ responses to a survey, you might be limited in your abilities to gain the exact type or geographic scope of participants you wanted. The people whom you managed to get to take your survey may not truly be a random sample, which is also a limitation. If you used a common test for data findings, your results are limited by the reliability of the test. If your study was limited to a certain amount of time, your results are affected by the operations of society during that time period (e.g., economy, social trends). It is important for you to remember that limitations of a dissertation are often not something that can be solved by the researcher. Also, remember that whatever limits you also limits other researchers, whether they are the largest medical research companies or consumer habits corporations. Certain kinds of limitations are often associated with the analytical approach you take in your research, too. For example, some qualitative methods like heuristics or phenomenology do not lend themselves well to replicability. Also, most of the commonly used quantitative statistical models can only determine correlation, but not causation.

Assumptions

Assumptions are things that are accepted as true, or at least plausible, by researchers and peers who will read your dissertation or thesis. In other words, any scholar reading your paper will assume that certain aspects of your study is true given your population, statistical test, research design, or other delimitations. For example, if you tell your friend that your favorite restaurant is an Italian place, your friend will assume that you don’t go there for the sushi. It’s assumed that you go there to eat Italian food. Because most assumptions are not discussed in-text, assumptions that are discussed in-text are discussed in the context of the limitations of your study, which is typically in the discussion section. This is important, because both assumptions and limitations affect the inferences you can draw from your study. One of the more common assumptions made in survey research is the assumption of honesty and truthful responses. However, for certain sensitive questions this assumption may be more difficult to accept, in which case it would be described as a limitation of the study. For example, asking people to report their criminal behavior in a survey may not be as reliable as asking people to report their eating habits. It is important to remember that your limitations and assumptions should not contradict one another. For instance, if you state that generalizability is a limitation of your study given that your sample was limited to one city in the United States, then you should not claim generalizability to the United States population as an assumption of your study. Statistical models in quantitative research designs are accompanied with assumptions as well, some more strict than others. These assumptions generally refer to the characteristics of the data, such as distributions, correlational trends, and variable type, just to name a few. Violating these assumptions can lead to drastically invalid results, though this often depends on sample size and other considerations.

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Short and sweet: multiple mini case studies as a form of rigorous case study research

• Original Article
• Open access
• Published: 15 May 2024

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• Sebastian Käss   ORCID: orcid.org/0000-0002-0640-3500 1 ,
• Christoph Brosig   ORCID: orcid.org/0000-0001-7809-0796 1 ,
• Markus Westner   ORCID: orcid.org/0000-0002-6623-880X 2 &
• Susanne Strahringer   ORCID: orcid.org/0000-0002-9465-9679 1  

Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.

Avoid common mistakes on your manuscript.

1 Introduction

Case study research has become a widely used research method in Information Systems (IS) research (Palvia et al. 2015 ) that allows for a comprehensive analysis of a contemporary phenomenon in its real-world context (Dubé and Paré, 2003 ). This research method is particularly useful due to its flexibility in covering complex phenomena with multiple contextual variables, different types of evidence, and a wide range of analytical options (Voss et al. 2002 ; Yin 2018 ). Although case study research is particularly useful for studying contemporary phenomena, some researchers feel that it lacks rigour, particularly in terms of the validity of findings (Lee and Hubona 2009 ). In response to these criticisms, Yin ( 2018 ) provides comprehensive methodological steps to conduct case studies rigorously. In addition, many other publications with a partly discipline-specific view on case study research, offer guidelines for achieving rigour in case study research, e.g., Benbasat et al. ( 1987 ), Dubé and Paré ( 2003 ), Pan and Tan ( 2011 ), or Voss et al. ( 2002 ). Most publications on case study methodology converge on four criteria for ensuring rigour in case study research: (1) construct validity, (2) internal validity, (3) external validity, and (4) reliability (Gibbert et al. 2008 ; Voss et al. 2002 ; Yin 2018 ).

A key element of rigour in case study research is to look at the unit of analysis of a case from multiple perspectives in order to draw informed conclusions (Dubois and Gadde 2002 ). Case study researchers refer to this as triangulation, for example, by using multiple sources of evidence per case to support findings (Benbasat et al. 1987 ; Yin 2018 ). However, in our own research experience, we have come across numerous IS publications with a limited number of sources of evidence per case, such as a single interview per case. Some researchers refer to these studies as mini case studies (e.g., McBride 2009 ; Weill and Olson 1989 ), while others refer to them as multiple mini cases (e.g., Eisenhardt 1989 ). We were unable to find a definition or conceptualisation of this type of case study. Therefore, we will refer to this type of case study as a multiple mini case study (MMCS). Interestingly, many researchers use these MMCSs to study emerging and innovative phenomena.

From a methodological perspective, multiple case study publications with limited sources of evidence, also known as MMCSs, may face criticism for their lack of rigour (Dubé and Paré 2003 ). Alternatively, they may be referred to as “marginal case studies” (Piekkari et al. 2009 , p. 575) if they fail to establish a connection between theory and empirical evidence, provide only limited context, or merely offer illustrative aspects (Piekkari et al. 2009 ). IS scholars advocate conducting case study research in a mindful manner by balancing methodological blueprints and justified design choices (Keutel et al. 2014 ). Consequently, we propose MMCSs as a mindful approach with the potential for rigour, distinguishing them from marginal case studies. The following research question guides our study:

RQ: How can researchers rigorously conduct MMCSs in the IS discipline?

As shown in Fig.  1 , we develop an analytical framework by synthesising methodological guidance on how to rigorously conduct multiple case study research. We then address three aspects of our research question: For aspect (1), we analyse published MMCSs in the IS discipline to derive a "Research in Practice" definition of MMCSs and research situations for MMCSs. For aspect (2), we use the analytical framework to analyse how researchers in the IS discipline ensure that existing MMCSs follow a rigorous methodology. For aspect (3), we discuss the methodological findings about rigorous MMCSs in order to derive methodological guidelines for MMCSs that researchers in the IS discipline can follow.

Overview of the research approach

We approach these aspects by introducing the conceptual foundation for case study research in Sect.  2 . We define commonly accepted criteria for ensuring validity in case study research, introduce the concept of MMCSs, and distinguish them from other types of case studies. Furthermore, as a basis for analysis, we present an analytical framework of methodological steps and options for the rigorous conduct of multiple case study research. Section  3 presents our methodological approach to identifying published MMCSs in the IS discipline. In Sect.  4 , we first define MMCSs from a research in practice perspective (Sect.  4.1 ). Second, we present an overview of methodological options for rigorous MMCSs based on our analytical framework (Sect.  4.2 ). In Sect.  5 , we differentiate MMCSs from other research approaches, identify research situations of MMCSs (i.e., to study emerging and innovative phenomena), and provide guidance on how to ensure rigour in MMCSs. In our conclusion, we clarify the limitations of our study and provide an outlook for future research with MMCSs.

2 Conceptual foundation

2.1 case study research.

Case study research is about understanding phenomena by studying one or multiple cases in their context. Creswell and Poth ( 2016 ) define it as an “approach in which the investigator explores a bounded system (a case) or multiple bounded systems (cases) over time, through detailed, in-depth data collection” (p. 73). Therefore, it is suitable for complex topics with little available knowledge, needing an in-depth investigation, or where the research subject is inseparable from its context (Paré 2004 ). Additionally, Yin ( 2018 ) states that case study research is useful if the research focuses on contemporary events where no control of behavioural events is required. Typically, this type of research is most suitable for how and why research questions (Yin 2018 ). Eventually, the inferences from case study research are based on analytic or logical generalisation (Yin 2018 ). Instead of drawing conclusions from a representative statistical sample towards the population, case study research builds on analytical findings from the observed cases (Dubois and Gadde 2002 ; Eisenhardt and Graebner 2007 ). Case studies can be descriptive, exploratory, or explanatory (Dubé and Paré 2003 ).

The contribution of research to theory can be divided into the steps of theory building , development and testing , which is a continuum (Ridder 2017 ; Welch et al. 2011 ), and case studies are useful at all stages (Ridder 2017 ). In theory building, there is no theory to explain a phenomenon, and the researcher identifies new concepts, constructs, and relationships based on the data (Ridder 2017 ). In theory development, a tentative theory already exists that is extended or refined (e.g., by adding new antecedents, moderators, mediators, and outcomes) (Ridder 2017 ). In theory testing, an existing theory is challenged through empirical investigation (Ridder 2017 ).

In case study research, there are different paradigms for obtaining research results, either positivist or interpretivist (Dubé and Paré 2003 ; Orlikowski and Baroudi 1991 ). The positivist paradigm assumes that a set of variables and relationships can be objectively identified by the researcher (Orlikowski and Baroudi 1991 ). In contrast, the interpretivist paradigm assumes that the results are inherently rooted in the researcher’s worldview (Orlikowski and Baroudi 1991 ). Nowadays, researchers find that there are similar numbers of positivist and interpretivist case studies in the IS discipline compared to almost 20 years ago when positivist research was perceived as dominant (Keutel et al. 2014 ; Klein and Myers 1999 ). As we aim to understand how to conduct MMCSs rigorously, we focus on methodological guidance for positivist case study research.

The literature proposes a four-phased approach to conducting a case study: (1) the definition of the research design, (2) the data collection, (3) the data analysis, and (4) the composition (Yin 2018 ). Table 1 provides an overview and explanation of the four phases.

Case studies can be classified based on their depth and breadth, as shown in Fig.  2 . We can distinguish five types of case studies: in-depth single case studies , marginal case studies , multiple case studies , MMCSs , and extensive in-depth multiple case studies . Each type has distinct characteristics, yet the boundaries between the different types of case studies is blurred. Except for the marginal case studies, the italic references in Fig.  2 are well-established publications that define the respective type and provide methodological guidance. The shading is to visualise the different types of case studies. The italic references in Fig.  2 for marginal case studies refer to publications that conceptualise them.

Simplistic conceptualisation of MMCS

In-depth single case studies focus on a single bounded system as a case (Creswell and Poth 2016 ; Paré 2004 ; Yin 2018 ). According to the literature, a single case study should only be used if a case meets one or more of the following five characteristics: it is a critical, unusual, common, revelatory, or longitudinal case (Benbasat et al. 1987 ; Yin 2018 ). Single case studies are more often used for descriptive research (Dubé and Paré 2003 ).

A second type of case studies are marginal case studies , which generally have low depth (Keutel et al. 2014 ; Piekkari et al. 2009 ). Marginal case studies lack a clear link between theory and empirical evidence, a clear contextualisation of the case, and are often used for illustration purposes (Keutel et al. 2014 ; Piekkari et al. 2009 ). Therefore, marginal case studies provide only marginal insights with a lack of generalisability.

In contrast, multiple case studies employ multiple cases to obtain a broader picture of the researched phenomenon from different perspectives (Creswell and Poth 2016 ; Paré 2004 ; Yin 2018 ). These multiple case studies are often considered to provide more robust results due to the multiplicity of their insights (Eisenhardt and Graebner 2007 ). However, often discussed criticisms of multiple case studies are high costs, difficult access to multiple sources of evidence for each case, and long duration (Dubé and Paré 2003 ; Meredith 1998 ; Voss et al. 2002 ). Eisenhardt ( 1989 ) considers four to ten in-depth cases as a suitable number of cases for multiple case study research. With fewer than four cases, the empirical grounding is less convincing, and with more than ten cases, researchers quickly get overwhelmed by the complexity and volume of data (Eisenhardt 1989 ). Therefore, methodological literature views extensive in-depth multiple case studies as almost infeasible due to their high complexity and resource demands, which can easily overwhelm the research team and the readers (Stake 2013 ). Hence, we could not find a methodological publication outlining the approach for this case study type.

To solve the complexity and resource issues for multiple case studies, a new phenomenon has emerged: MMCS . An MMCS is a special type of multiple case study that focuses on an investigation's breadth by using a relatively high number of cases while having a somewhat limited depth per case. We characterise breadth not only by the number of cases but also by the variety of the cases. Even though there is no formal conceptualisation of the term, we understand MMCSs as a type of multiple case study research with few sources of evidence per case. Due to the limited depth per case, one can overcome the resource and complexity issues of classical multiple case studies. However, having only some sources of evidence per case may be considered a threat to rigour. Therefore, in this publication, we provide suggestions on how to address these threats.

2.2 Rigour in case study research

Rigour is essential for case study research (Dubé and Paré 2003 ; Yin 2018 ) and, in the early 2000s, researchers criticised case study research for inadequate rigour (e.g., Dubé and Paré 2003 ; Gibbert et al. 2008 ). Based on this, various methodological publications provide guidance for rigorous case study research (e.g., Dubé and Paré 2003 ; Gibbert et al. 2008 ).

Methodological literature proposes four criteria to ensure rigour in case study research: Construct validity , internal validity , external validity , and reliability (Dubé and Paré 2003 ; Gibbert et al. 2008 ; Yin 2018 ). Table 2 outlines these criteria and states in which research phase they should be addressed (Yin 2018 ). Methodological literature agrees that all four criteria must be met for rigorous case study research (Dubé and Paré 2003 ).

The methodological literature discusses multiple options for achieving rigour in case study research (e.g., Benbasat et al. 1987 ; Dubé and Paré 2003 ; Eisenhardt 1989 ; Yin 2018 ). We aggregated guidance from multiple sources by conducting a cross-disciplinary literature review to build our analytical foundation (cf. Fig. 1 ). This literature review aims to identify the most relevant multiple case study methodology publications from a cross-disciplinary and IS-specific perspective. We focus on the most cited methodology publications, while being aware that this may over-represent disciplines with a higher number of case study publications. However, this approach helps to capture an implicit consensus among case study researchers on how to conduct multiple case studies rigorously. The literature review produced an analytical framework of methodological steps and options for conducting multiple case studies rigorously. Appendix A Footnote 1 provides a detailed documentation of the literature review process. The analytical framework derived from the set of methodological publications is presented in Table  3 . We identified required and optional steps for each research stage. The analytical framework is the basis for the further analysis of MMCS and an explanation of all methodological steps is provided in Appendix B. Footnote 2

3 Research methodology

For our research, we analysed published MMCSs in the IS discipline with the goal of understanding how these publications ensured rigour. This section outlines the methodology of how we identified our MMCS publications.

First, we searched bibliographic databases and citation indexing services (Vom Brocke et al. 2009 ; Vom Brocke et al. 2015 ) to retrieve IS-specific MMCSs (Hanelt et al. 2015 ). As shown in Fig.  3 , we used two sets of keywords, the first set focusing on multiple case studies and the second set explicitly on mini case studies. We decided to follow this approach as many MMCSs are positioned as multiple case studies, avoiding the connotation “mini” or “short”. We restricted our search to completed research publications written in English from litbaskets.io size “S”, a set of 29 highly ranked IS journals (Boell and Wang 2019 ) Footnote 3 and leading IS conference proceedings from AMCIS, ECIS, HICSS, ICIS, and PACIS (published until end of June 2023). We focused on these outlets, as they can be taken as a representative sample of high quality IS research (Gogan et al. 2014 ; Sørensen and Landau 2015 ).

The search process for published MMCSs in the IS discipline

Second, we screened the obtained set of IS publications to identify MMCSs. We only included publications with positivist multiple cases where the majority of cases was captured with only one primary source of evidence. Further, we excluded all publications which were interview studies rather than case studies (i.e., they do not have a clearly defined case). In some cases, it was unclear from the full text whether a publication fulfils this requirement. Therefore, we contacted the authors and clarified the research methodology with them. Eventually, our final set contained 50 publications using MMCSs.

For qualitative data analysis, we employed axial coding (Recker 2012 ) based on the pre-defined analytical framework shown in Table  3 . For the coding, we followed the explanations of the authors in the manuscripts. The coding was conducted and reviewed by two of the authors. We coded the first five publications of the set of IS MMCS publications together and discussed our decisions. After the initial coding was completed, we checked the reliability and validity by re-coding a sample of the other author’s set. In this sample, we achieved inter-coder reliability of 91% as a percent agreement in the decisions made (Nili et al. 2020 ). Hence, we consider our coding as highly consistent.

In the results section, we illustrate the chosen methodological steps for each MMCS type (descriptive, exploratory, and explanatory). For this purpose, we selected three publications based on two criteria: only journal publications, as they have more details about their methodological steps and publications which applied most of the analytical framework’s methodology steps. This led to three exemplary IS MMCS publications: (1) McBride ( 2009 ) for descriptive MMCSs, (2) Baker and Niederman ( 2014 ) for exploratory MMCSs, and (3) van de Weerd et al. ( 2016 ) for explanatory MMCSs.

4.1 MMCS from a “Research in Practice" perspective

In this section, we explain MMCSs from a "Research in Practice" perspective and identify different types based on our sample of 50 MMCS publications. As outlined in Sect.  2.1 , an MMCS is a special type of a multiple case study, which focuses on an investigation’s breadth by using a relatively high number of cases while having a limited depth per case. In the most extreme scenario, an MMCS only has one source of evidence per case. Moreover, breadth is not only characterised by the number of cases, but also by the variety of the cases. MMCSs have been used widely but hardly labelled as such, i.e., only 10 of our analysed 50 MMCS publications explicitly use the terms mini or short case in the manuscript . Multiple case study research distinguishes between descriptive, exploratory, and explanatory case studies (Dubé and Paré 2003 ). The MMCSs in our sample follow the same classification with three descriptive, 40 exploratory, and seven explanatory MMCSs. Descriptive and exploratory MMCSs are used in the early stages of research , and exploratory and explanatory MMCSs are used to corroborate findings .

Descriptive MMCSs provide little information on the methodological steps for the design, data collection, analysis, and presentation of results. They are used to illustrate novel phenomena and create research questions, not solutions, and can be useful for developing research agendas (e.g., McBride 2009 ; Weill and Olson 1989 ). The descriptive MMCS publications analysed contained between four to six cases, with an average of 4.6 cases per publication. Of the descriptive MMCSs analysed, one did not state research questions, one answered a how question and the third answered how and what questions. Descriptive MMCSs are illustrative and have a low depth per case, resulting in the highest risk of being considered a marginal case study.

Exploratory MMCSs are used to explore new phenomena quickly, generate first research results, and corroborate findings. Most of the analysed exploratory MMCSs answer what and how questions or combinations. However, six publications do not explicitly state a research question, and some MMCSs use why, which, or whether research questions. The analysed exploratory MMCSs have three to 27 cases, with an average of 10.2 cases per publication. An example of an exploratory MMCS is the study by Baker and Niederman ( 2014 ), who explore the impacts of strategic alignment during merger and acquisition (M&A) processes. They argue that previous research with multiple case studies (mostly with  three cases) shows some commonalities, but much remains unclear due to the low number of cases. Moreover, they justify the limited depth of their research with the “proprietary and sensitive nature of the questions” (Baker and Niederman 2014 , p. 123).

Explanatory MMCSs use an a priori framework with a relatively high number of cases to find groups of cases that share similar characteristics. Most explanatory MMCSs answer how questions, yet some publications answer what, why, or combinations of the three questions. The analysed explanatory MMCSs have three to 18 cases, with an average of 7.2 cases per publication. An example of an explanatory MMCS publication is van de Weerd et al. ( 2016 ), who researched the influence of organisational factors on the adoption of Software as a Service (SaaS) in Indonesia.

4.2 Applied MMCS methodology in IS publications

4.2.1 overarching.

In the following sections, we present the results of our analysis. For this purpose, we mapped our 50 IS MMCS publications to the methodological options (Table  3 ) and present one example per MMCS type. We extended some methodological steps with options from methodology-in-use. A full coding table can be found in Appendix D Footnote 4 . Tables 4 , 5 , 6 and 7 summarise the absolute and percentual occurrences of each methodological option in descriptive, exploratory, and explanatory IS MMCS publications. All tables are structured in the same way and show the number of absolute and, in parentheses, the percentual occurrences of each methodological option. The percentages may not add up to 100% due to rounding. The bold numbers show the most common methodological option for each MMCS type and step. Most publications were classified in previously identified options. Some IS MMCS publications lacked detail on methodological steps, so we classified them as "step not evident". Only 16% (8 out of 50) explained how they addressed validity and reliability threats.

4.2.2 Research design phase

There are six methodological steps in the research design phase, as shown in Table  4 . Descriptive MMCSs usually define the research question (2 out of 3, 67%), clarify the unit of analysis (2 out of 3, 67%), bound the case (2 out of 3, 67%), or specify an a priori theoretical framework (2 out of 3, 67%). The case replication logic is mostly not evident (2 out of 3, 67%). Descriptive MMCS use a criterion-based selection (1 out of 3, 33%), a maximum variation selection (1 out of 3, 33%), or do not specify the selection logic (1 out of 3, 33%). Descriptive MMCSs have a high risk of becoming a marginal case study due to their illustrative nature–our chosen example is not different. McBride ( 2009 ) does not define the research question, does not have a priori theoretical framework, nor does he justify the case replication and the case selection logic. However, he clarifies the unit of analysis and extensively bounds each case with significant context about the case organisation and its setup.

The majority of exploratory MMCSs define the research question (34 out of 40, 85%) clarify the unit of analysis (35 out of 40, 88%), and specify an a priori theoretical framework (33 out of 40, 83%). However, only a minority (6 out of 40, 15%) follow the instructions of bounding the case or justify the case replication logic (13 out of 40, 33%). The most used case selection logic is the criterion-based selection (23 out of 40, 58%), followed by step not evident (5 out of 40, 13%), other selection approaches (3 of 40, 13%), maximum variation selection (3 out of 40, 13%), a combination of approaches (2 out of 40, 5%), snowball selection (2 out of 40, 5%), typical case selection (1 out of 40, 3%), and convenience-based selection (1 out of 40, 3%). Baker and Niederman ( 2014 ) build their exploratory MMCS on previous multiple case studies with three cases that showed ambiguous results. Hence, Baker and Niederman ( 2014 ) formulate three research objectives instead of defining a research question. They clearly define the unit of analysis (i.e., the integration of the IS function after M&A) but lack the bounding of the case. The authors use a rather complex a priori framework, leading to a high number of required cases. This a priori framework is also used for the “theoretical replication logic [to choose] conforming and disconfirming cases” (Baker and Niederman 2014 , p. 116). A combination of maximum variation and snowball selection is used to select the cases (Baker and Niederman 2014 ). The maximum variation is chosen to get evidence for all elements of their rather complex a priori framework (i.e., the breadth), and the snowball sampling is chosen to get more details for each framework element.

All explanatory MMCS s define the research question, clarify the unit of analysis, and specify an a priori theoretical framework. However, only one (14%) bounds the case. The case replication logic is mostly a mixture of theoretical and literal replication (3 out of 7, 43%) and one (14%) MMCS does a literal replication. For 43% (3 out of 7) of the publications, the step is not evident. Most explanatory MMCSs use criterion-based selection (4 out of 7, 57%), followed by maximum variation selection (2 out of 7, 29%) and snowball selection (1 out of 7, 14%). In their publication, van de Weerd et al. ( 2016 ) define the research question and clarify the unit of analysis (i.e., the influence of organisational factors on SaaS adoption in Indonesian SMEs). Further, they specify an a priori framework (i.e., based on organisational size, organisational readiness, and top management support) to target the research (van de Weerd et al. 2016 ). A combination of theoretical (between the groups of cases) and literal (within the groups of cases) replication was used. To strengthen the findings, van de Weerd et al. ( 2016 ) find at least one other literally replicated case for each theoretically replicated case.

To summarize this phase, we see that in all three types of MMCSs, the majority of publications define the research question, clarify the unit of analysis, and specify an a priori theoretical framework. Moreover, descriptive MMCSs are more likely to bound the case than exploratory and explanatory MMCSs. However, only a minority across all MMCSs justify the case replication logic, whereas the majority does not. Most MMCSs justify the case selection logic, with criterion-based case selection being the most often applied methodological option.

4.2.3 Data collection phase

In the data collection phase, there are four methodological steps, as summarised in Table  5 .

One descriptive MMCS applies triangulation via multiple sources, whereas for the majority (2 out of 3, 67%), the step is not evident. One (33%) of the analysed descriptive MMCSs creates a full chain of evidence, none creates a case study database, and one (33%) uses a case study protocol. McBride ( 2009 ) applies triangulation via multiple sources, as he followed “up practitioner talks delivered at several UK annual conferences” (McBride 2009 , p. 237). Therefore, we view the follow-up interviews as the primary source of evidence per case, as dedicated questions to the unit of analysis can be asked per case. Triangulation via multiple sources was then conducted by combining practitioner talks and documents with follow-up interviews. McBride ( 2009 ) does not create a full chain of evidence, a case study database, nor a case study protocol. This design decision might be rooted in the objective of a descriptive MMCS to illustrate and open up new questions rather than find clear solutions (McBride 2009 ).

Most exploratory MMCSs triangulate via multiple sources (20 out of 40, 50%) or via multiple investigators (4 out of 40, 10%). Eight (20%) exploratory MMCSs apply multiple triangulation types and for eight (20%), no triangulation is evident. At first glance, a triangulation via multiple sources may seem contradictory to the definition of MMCSs–yet it is not. MMCSs that triangulate via multiple sources have one source per case as the primary, detailed evidence (e.g., an interview), which is combined with easily available supplementary sources of evidence (e.g., public reports and documents (Baker and Niederman 2014 ), press articles (Hahn et al. 2015 ), or online data (Kunduru and Bandi 2019 )). As this leads to multiple sources of evidence, we understand this as a triangulation via multiple sources; however, on a different level than triangulating via multiple in-depth interviews per case. Only a minority of exploratory MMCSs create a full chain of evidence (14 out of 40, 35%), and a majority (23 out of 40, 58%) use a case study database or a case study protocol (20 out of 40, 50%). Baker and Niederman ( 2014 ) triangulate with multiple sources (i.e., financial reports as supplementary sources) to increase the validity of their research. Further, the authors create a full chain of evidence from their research question through an identical interview protocol to the case study’s results. For every case, an individual case report is created and stored in the case study database (Baker and Niederman 2014 ).

All explanatory MMCSs triangulate during the data collection phase, either via multiple sources (2 out of 7, 29%) or a combination of multiple investigators and sources (5 out of 7, 71%). Interestingly, only three explanatory MMCSs (43%) create a full chain of evidence. All create a case study database (7 out of 7, 100%) and the majority creates a case study protocol (6 out of 7, 86%). In their explanatory MMCS, van de Weerd et al. ( 2016 ) use semi-structured interviews as the primary data collection method. The interview data is complemented “with field notes and (online) documentation” (van de Weerd et al. 2016 , p. 919), e.g., data from corporate websites or annual reports. Moreover, a case study protocol and a case study database in NVivo are created to increase reliability.

To summarise the data collection phase, we see that most (40 out of 50, 80%) of MMCSs apply some type of triangulation. However, only 36% (18 out of 50) of the analysed MMCSs create a full chain of evidence. Moreover, descriptive MMCSs are less likely to create a case study database (0 out of 3, 0%) or a case study protocol (1 out of 3, 33%). In contrast, most exploratory and explanatory MMCS publications create a case study database and case study protocol.

4.2.4 Data analysis phase

There are three methodological steps (cf. Table 6 ) for the data analysis phase, each with multiple methodological options.

One descriptive MMCS (33%) corroborates findings through triangulation, and two do not (67%). Further, one (33%) uses a rich description of findings as other corroboration approaches, whereas for the majority (2 out of 3, 67%), the corroboration with other approaches is not evident. Descriptive MMCSs mostly do not define their within-case analysis strategy (2 out of 3, 67%). However, pre-defined patterns are used to conduct a cross-case analysis (2 out of 3, 67%). In the data analysis, McBride ( 2009 ) triangulates via multiple sources of evidence (i.e., talks at practitioner conferences and resulting follow-up interviews), but does not apply other corroboration approaches or provides methodological explanations for the within or cross-case analysis. This design decision might be rooted in the illustrative nature of his descriptive MMCS and the focus on analysing each case standalone.

Exploratory MMCSs mostly corroborate findings through a combination of triangulation via multiple investigators and sources (15 out of 40, 38%) or triangulation via multiple sources (9 out of 40, 23%). However, for ten (25%) exploratory MMCSs, this step is not evident. For the other corroboration approaches, a combination of approaches is mostly used (15 out of 40, 38%), followed by rich description of findings (11 out of 40, 28%), peer review (6 out of 40, 15%), and prolonged field visits (1 out of 40, 3%). For five (13%) publications, other corroboration approaches are not evident. Pattern matching (17 out of 40, 43%) and explanation building (5 out of 40, 13%) are the most used methodological options for the within-case analysis. To conduct a cross-case analysis, 11 (28%) MMCSs use a comparison of pairs or groups of cases, nine (23%) pre-defined patterns, and six (15%) structure their data along themes. Interestingly, for 14 (35%) exploratory MMCSs, no methodological step to conduct the cross-case analysis is evident. Baker and Niederman ( 2014 ) use a combination of triangulation via multiple investigators (“The interviews were coded by both researchers independently […], with a subsequent discussion to reach complete agreement” (Baker and Niederman 2014 , p. 117)) and sources to increase internal validity. Moreover, the authors use a rich description of the findings. An explanation-building strategy is used for the within-case analysis, and the cross-case analysis is done based on pre-defined patterns (Baker and Niederman 2014 ). This decision for the cross-case analysis is justified by a citation of Dubé and Paré ( 2003 , p. 619), who see it as “a form of pattern-matching in which the analysis of the case study is carried out by building a textual explanation of the case.”

Explanatory MMCSs corroborate findings through a triangulation via multiple sources (4 out of 7, 57%) or a combination of multiple investigators and sources (3 out of 7, 43%). For the other corroboration approaches, a rich description of findings (3 out of 7, 43%), a combination of approaches (3 out of 7, 43%), or peer review (1 out of 7, 14%) are used. To conduct a within-case analysis, pattern matching (5 out of 7, 71%) or explanation building (1 out of 7, 14%) are used. For the cross-case analysis, pre-defined patterns (3 out of 7, 43%) and a comparison of pairs or groups of cases (2 out of 7, 29%) are used; yet, for two (29%) explanatory MMCSs a cross-case analysis step is not evident. van de Weerd et al. ( 2016 ) corroborate their findings through a triangulation via multiple sources, a combination of rich description of findings and solicitation of participants’ views (“summarizing the interview results of each case company for feedback and approval” (van de Weerd et al. 2016 , p. 920)) as other corroboration approaches. Moreover, for the within-case analysis, the authors “followed an explanation-building procedure to strengthen […] [the] internal validity” (van de Weerd et al. 2016 , p. 920). For the cross-case, the researchers compare groups of cases. They refer to this approach as an informal qualitative comparative analysis.

To summarize the results of the data analysis phase, we see that some type of triangulation is used by most of the MMCSs, with source triangulation (alone or in combination with another approach) being the most often used methodological option. For the within-case analysis, pattern matching (22 of 50, 44%) is the most often used methodological option. For the cross-case analysis, pre-defined patterns are most often used (14 out of 50, 28%). However, depending on the type of MMCS, there are differences in the options used and some methodological options are never used (e.g., time-series analysis and solicitation of participants’ views).

4.2.5 Composition phase

We can find two methodological steps for the composition phase, as summarized in Table  7 .

Descriptive MMCSs do not apply triangulation in the composition phase (3 out of 3, 100%), nor do they use the methodological step to let key informants review the draft of the case study report (3 of 3, 100%). Also, the descriptive MMCS by McBride ( 2009 ) does not apply any of the methodological steps.

Exploratory MMCSs mostly use triangulation via multiple sources (25 out of 40, 63%), a combination of multiple sources and theories (2 out of 40, 5%), triangulation via multiple investigators (1 out of 40, 3%), and a combination of multiple sources and methods (1 out of 40, 3%). However, for 11 (28%) exploratory MMCS publications, no triangulation step is evident. Moreover, the majority (24 out of 40, 85%) do not let key informants review a draft of the case study report. Baker and Niederman ( 2014 ) do not use triangulation in the composition phase nor let key informants review the draft of the case study report. An example of an exploratory publication that applies both methodological steps is the publication by Kurnia et al. ( 2015 ). The authors triangulate via multiple sources and let key informants review their interview transcripts and the case study report to increase construct validity.

Explanatory MMCSs mostly use triangulation via multiple sources (5 out of 7, 71%) and for two (29%), the step is not evident. Furthermore, only two MMCS (29%) publications let key informants review the draft of the case study report, whereas the majority (5 out of 7, 71%) do not. In their publication , van de Weerd et al. ( 2016 ) use both methodological steps of the composition phase. The authors triangulate via multiple sources by presenting interview snippets from different cases for each result in the case study manuscript. Moreover, each case and the final case study report were shared with key informants for review and approval to reduce the risk of misinterpretations and increase construct validity.

To summarize, most exploratory and explanatory MMCSs use triangulation in the composition phase, whereas descriptive MMCSs do not. Moreover, only a fraction of all MMCSs let key informants review a draft of the case study report (8 out of 50, 16%).

5 Discussion

5.1 mmcs from a “research in practice" perspective, 5.1.1 delineating mmcs from other research approaches.

In this section, we delineate MMCSs from related research approaches. In the subsequent sections, we outline research situations for which MMCSs can be used and the benefits MMCSs provide.

Closely related research approaches from which we delineate MMCSs are multiple case studies , interviews, and vignettes . As shown in Fig.  2 , MMCSs differ from multiple case studies in that they focus on breadth by using a high number of cases with limited depth per case. In the most extreme situation, an MMCS only has one primary source of evidence per case. Moreover, MMCSs can also consider a greater variety of cases. In contrast, multiple case studies have a high depth per case and multiple sources of evidence per case to allow for a source triangulation (Benbasat et al. 1987 ; Yin 2018 ). Moreover, multiple case studies mainly focus on how and why research questions (Yin 2018 ), whereas MMCSs can additionally answer what, whether, and which research questions. The rationale why MMCSs are used for more types of research questions is their breadth, allowing them to also answer rather explorative research questions.

Distinguishing MMCSs from interviews is more difficult . Yet, we see two differences. First, interview studies do not have a clear unit of analysis. Interview studies may choose interviewees based on expertise (expert interviews), whereas case study researchers select informants based on the ability to inform about the case (key informants) (Yin 2018 ). Most of the 50 analysed MMCS (88%) specify their unit of analysis. Second, MMCSs can use multiple data collection methods (e.g., observations, interviews, documents), while interviews only use one (the interview) (Lamnek and Krell 2010 ). An example showing these delineation difficulties between MMCSs and interviews is the publication of Demlehner and Laumer ( 2020 ). The authors claim to take “a multiple case study approach including 39 expert interviews” (Demlehner and Laumer 2020 , p. 1). However, our criteria classify this as an interview study. Demlehner and Laumer ( 2020 ) contend that the interviewees were chosen using a “purposeful sampling strategy” (p. 5). However, case study research selects cases based on replication logic, not sampling (Yin 2018 ). Moreover, the results are not presented on a per-case basis (as usual for case studies); instead, the findings are presented on an aggregated level, similar to expert interviews. Therefore, we would not classify this publication as an MMCS but find that it is a very good example to discuss this delineation.

MMCSs differ from vignettes, which are used for (1) data collection , (2) data analysis , and (3) research communication (Klotz et al. 2022 ; Urquhart 2001 ). Researchers use vignettes for data collection as stimuli to which participants react (Klotz et al. 2022 ), i.e., a carefully constructed description of a person, object, or situation (Atzmüller and Steiner 2010 ; Hughes and Huby 2002 ). We can delineate MMCS from vignettes for data collection based on this definition. First, MMCSs are not used as a stimulus to which participants can react, as in MMCSs, data is collected without the stimulus requirement. Furthermore, vignettes for data collection are carefully constructed, which contradicts the characteristics of MMCS, that are all based on collected empirical data and not constructed descriptions.

A data analysis vignette is used as a retrospective tool (Klotz et al. 2022 ) and is very short, which makes it difficult to analyse deeper relationships between constructs. MMCSs differ from vignettes for data analysis in two ways. First, MMCSs are a complete research methodology with four steps, whereas vignettes for data analysis cover only one step (the data analysis) (e.g., Zamani and Pouloudi 2020 ). Second, vignettes are too short to conduct a thorough analysis of relationships, whereas MMCSs foster a more comprehensive analysis, allowing for a deeper analysis of relationships.

Finally, a vignette used for research communication “(1) is bounded to a short time span, a location, a special situation, or one or a few key actors, (2) provides vivid, authentic, and evocative accounts of the events with a narrative flow, (3) is rather short, and (4) is rooted in empirical data, sometimes inspired by data or constructed.” (Klotz et al. 2022 , p. 347). Based on the four elements for the vignettes’ definition, we can delineate MMCS from vignettes used for research communication. First, MMCSs are not necessarily bounded to a short time span, location, special situation, or key actors; instead, with MMCSs, a clearly defined case bounded in its context is researched. Second, the focus of MMCSs is not on the narrative flow; instead, the focus is on describing (c.f., McBride ( 2009 )), exploring (c.f., Baker and Niederman ( 2014 )), or explaining (c.f., van de Weerd et al. ( 2016 )) a phenomenon. Third, while MMCSs do not have the depth of multiple case studies, they are much more comprehensive than vignettes (e.g., the majority of analysed publications (42 of 50, 84%) specify an a priori theoretical framework). Fourth, every MMCS must be based on empirical data, i.e., all of our 50 MMCSs collect data for their study and base their results on this data. This is a key difference from vignettes, which can be completely fictitious (Klotz et al. 2022 ).

5.1.2 MMCS research situations

The decision to use an MMCS as a research method depends on the research context. MMCSs can be used in the early stages of research (descriptive and exploratory MMCS) and to corroborate findings (exploratory and explanatory MMCS). Academic literature has yet to agree on a uniform categorisation of research questions. For instance, Marshall and Rossman ( 2016 ) distinguish between descriptive, exploratory, explanatory, and emancipatory research questions. In contrast, Yin ( 2018 ) distinguishes between who , what , where , how , and why questions, where he argues that the latter two are especially suitable for explanatory case study research. MMCSs can answer more types of research questions than Yin ( 2018 ) proposed. The reason for this is rooted in the higher breadth of MMCSs, which allows MMCSs to also answer rather exploratory what , whether , or which questions, besides the how and why questions that are suggested by Yin ( 2018 ).

For descriptive MMCSs , the main goal of the how and what questions is to describe the phenomenon. However, in our sample of analysed MMCSs, the analysis stops after the description of the phenomenon. The main goal of the five types of exploratory MMCS research questions is to investigate little-known aspects of a particular phenomenon. The how and why questions analyse operational links between different constructs (e.g., “How do different types of IS assets account for synergies between business units to create business value?” (Mandrella et al. 2016 , p. 2)). Exploratory what questions can be answered by case study research and other research methods (e.g., surveys or archival analysis) (Yin 2018 ). Nevertheless, all whether and which MMCS research questions can also be re-formulated as exploratory what questions. The reason why many MMCSs answer what , whether , or which research questions lies in the breadth (i.e., higher number and variety of cases) of MMCS, that allow them to answer these rather exploratory research questions to a satisfactory level. Finally, the research questions of the explanatory MMCSs aim to analyse operational links (i.e., how or why something is happening). This is also in line with the findings of Yin ( 2018 ) for multiple case study research. However, for MMCSs, this view must be extended, as explanatory MMCSs are also able to answer what questions. We explain this with the higher breadth of MMCS.

To discuss an MMCS’s contribution to theory, we use the idea of the theory continuum proposed by Ridder ( 2017 ) (cf. Section  2.1 ). Despite being used in the early phase of research (descriptive and exploratory), we do not recommend using MMCSs to build theory . We argue that for theory building, data with “as much depth as […] feasible” (Eisenhardt 1989 , p. 539) is required on a per-case basis. However, a key characteristic of MMCSs is the limited depth per case, which conflicts with the in-depth requirements of theory building. Moreover, a criterion for theory building is that there is no theory available which explains the phenomenon (Ridder 2017 ). Nevertheless, in our analysed MMCSs, 84% (42 out of 50) have an a priori theoretical framework. Furthermore, for theory building, the recommendation is to use between four to ten cases; with more, “it quickly becomes difficult to cope with the complexity and volume of the data” (Eisenhardt 1989 , p. 545). However, a characteristic of MMCSs is to have a relatively high number of cases, i.e., the analysed MMCSs often have more than 20 cases, which is significantly above the recommendation for theory building.

The next phase in the theory continuum is theory development , where a tentative theory is extended or refined (Ridder 2017 ). MMCSs should and are used for theory development, i.e., 84% (42 out of 50) of analysed MMCS publications have an a priori theoretical framework extended and refined using the MMCS. An MMCS example for theory development is the research of Karunagaran et al. ( 2016 ), who use a combination of the diffusion of innovation theory and technology organisation environment framework as tentative theories to research the adoption of cloud computing. As Ridder ( 2017 ) outlined, for theory development, literal replication and pattern matching should be used. Both methodological steps are used by Karunagaran et al. ( 2016 ) to identify the mechanisms of cloud adoption more precisely.

The next step in the theory continuum is theory testing , where existing theory is challenged by finding anomalies that existing theory cannot explain (Ridder 2017 ). The boundaries between theory development and testing are often blurred (Ridder 2017 ). In theory testing, the phenomenon is understood, and the research strategy focuses on testing if the theory also holds under different circumstances, i.e., hypotheses can be formed and tested based on existing theory (Ridder 2017 ). In multiple case study research, theory testing uses theoretical replication with pattern matching or addressing rival explanations (Ridder 2017 ). In our MMCS publications, no publication addresses rival explanations, and only a few apply theoretical replication and pattern matching–yet not for theory testing. A few publications claim to test propositions derived from an a priori theoretical framework (e.g., Schäfferling et al. 2011 ; Spiegel and Lazic 2010 ; Wagner and Ettrich-Schmitt 2009 ). However, these publications either do not state their replication logic (e.g., Spiegel and Lazic 2010 ; Wagner and Ettrich-Schmitt 2009 ) or use a literal replication (e.g., Schäfferling et al. 2011 ), both of which weaken the value of their theory testing.

5.1.3 MMCS research benefits

MMCSs are beneficial in multiple research situations and can be an avenue to address the frequent criticism of multiple case study research of being time-consuming and costly (Voss et al. 2002 ; Yin 2018 ).

Firstly, MMCSs can be used for time-critical topics where it is beneficial to publish results quicker and discuss them instead of conducting in-depth multiple case studies (e.g., COVID-19 (e.g., dos Santos Tavares et al. 2021 ) or emergent technology adoption (e.g., Bremser 2017 )). Especially with COVID-19, research publishing saw a significantly higher speed due to special issues of journals and faster review processes. Further, due to the fast technological advancements, there is a higher risk that the results are obsolete and of less practical use when researched with time-consuming multiple in-depth case studies.

Secondly, MMCSs can be used in research situations when it is challenging to gather in-depth data from multiple sources of evidence for each case due to the limited availability of sources of evidence or limited accessibility of sources of evidence. When researching novel phenomena (e.g., the adoption of new technologies in organisations), managers and decision-makers are usually interviewed as sources of evidence. However, in most organisations, only one (or very few) decision-makers have the ability to inform and should be interviewed, limiting the potential sources of evidence per case. These decision-makers often have limited availability for multiple in-depth interviews. Furthermore, the sources of evidence are often difficult to access, as professional organisations have regulations that prevent sharing documents with researchers.

Thirdly, MMCSs can be beneficial when the research framework is complex and requires many cases for validation (e.g., Baker and Niederman ( 2014 ) validate their rather complex a priori framework with 22 cases) or when previous research has led to contradictory results . Therefore, in both situations, a higher breadth of cases is required to also research combinatorial effects (e.g., van de Weerd et al. 2016 ). However, conducting an in-depth multiple case study would take time and effort. Therefore, MMCSs can be a mindful way to collect many cases, but in the same vein, being time and cost-efficient.

5.2 MMCS research rigour

Table 8 outlines two types of methodological steps for MMCSs. The first are methodological steps, where MMCSs should follow multiple case study methodological guidance (e.g., clarify the unit of analysis ), while the second is unique to MMCSs due to its characteristics. This section focuses on the latter, exploring MMCS characteristics, problems, validity threats, and proposed solutions.

The characteristics of MMCSs of having only one primary source of evidence per case prevents MMCSs from using source triangulation, which is often used in multiple case study research (Stake 2013 ; Voss et al. 2002 ; Yin 2018 ). By only having one source of evidence, researchers can fail to develop a sufficient set of operational measures and instead rely on subjective judgements, which threatens construct validity (Yin 2018 ). The threats to construct validity must be addressed throughout the MMCS research process. To do so, we propose to use easily accessible supplementary data or other triangulation approaches to increase construct validity in a MMCS. For the other triangulation approaches, we see that the majority of publications use supplementary data (e.g., publicly available documents) as further sources of evidence, multiple investigators, multiple methods (e.g., quantitative and qualitative), multiple theories, or combinations of these (cf. Tables 5 , 6 and 7 ). Having one or, in the best case, all of them reduces the risk of reporting spurious relationships and subjective judgements of the researchers, as a phenomenon is analysed from multiple perspectives. Besides the above-mentioned types of triangulation, we propose to apply a new type of triangulation, which is specific to MMCSs and triangulates findings across similar cases combined to groups instead of multiple sources per case. We propose that all reported findings have to be found in more than one case in a group of cases. This is also in line with previous methodological guidelines, which suggest that findings should only be reported if they have at least three confirmations (Stake 2013 ). To triangulate across multiple cases in one group, researchers have to identify multiple similar cases by applying a literal case replication logic to reinforce similar results. One should also apply a theoretical replication to compare different groups of literally replicated cases (i.e., searching for contrary results). Therefore, researchers have to justify their case replication logic . However, in our sample of MMCS, the majority (32 of 50, 64%) does not justify their replication logic, whereas the remaining publications use either literal replication (8 of 50, 16%), theoretical replication (6 of 50, 12%), or a combination (4 of 50, 8%). We encourage researchers to use a combination of literal and theoretical replication because it allows triangulation across different groups of cases. An exemplary MMCS that uses this approach is the publication of van de Weerd et al. ( 2016 ), who use theoretical replication to find cases with different outcomes (e.g., adoption and non-adoption) and use literal replication to find cases with similar characteristics and form groups of them.

Two further methodological steps, which are not exclusive to MMCS but recommended for increasing the construct validity, are creating a chain of evidence and letting key informants review a draft of the case study report . Only 36% (18 out of 50) of the analysed MMCS publications establish a chain of evidence. One reason for this lower usage may be that the majority (35 out of 50, 70%) of the publications analysed are conference proceedings. While we understand that these publications face space limitations, we note that no publication offers a supplementary appendix with in-depth insights. However, we encourage researchers to create a full chain of evidence with as much transparency as possible. Therefore, online directories for supplementary appendices could be a valuable addition. As opposed to a few years ago, these repositories today are widely available and using them for such purposes could become a good research practice for qualitative research. Interestingly, only 16% (8 of 50) analysed MMCS publications let key informants review the draft of the case study report . As MMCSs only have one source of evidence per case, misinterpretations and subjective judgement by the researcher have a significantly higher impact on the results compared to multiple case study research. Therefore, MMCS researchers should let key informants review the case study report before publishing.

MMCSs only have few (one) sources of evidence per case, so the risk of focusing on spurious relationships is higher, threatening internal validity (Dubé and Paré 2003 ). This threat to internal validity must be addressed in the data analysis phase. In the context of MMCSs, researchers may aggregate fewer data points to obtain a within-case overview. Therefore, having a clear perspective of the existing data points and rigorously applying the within-case analysis methodological steps (e.g., pattern matching) is even more critical. However, due to the limited depth of data at MMCSs, the within-case analysis must be combined with an analysis across groups of cases (to allow triangulation via multiple groups of cases). For MMCSs, we propose not doing the cross-case analysis on a per-case basis. Instead, we propose to build groups of similar cases across which researchers could conduct an analysis across groups of cases. This solidifies internal validity in case study research (Eisenhardt 1989 ) by viewing and synthesising insights from multiple perspectives (Paré 2004 ; Yin 2018 ).

Another risk of MMCSs is the relatively high number of cases (i.e., we found up to 27 for exploratory MMCSs) that is higher than Eisenhardt’s ( 1989 ) recommendation of maximal ten cases in multiple case study research. With more than ten in-depth cases, researchers struggled to manage the complexity and data volume, resulting in models with low generalisability and reduced external validity (Eisenhardt 1989 ). We propose to use two methodological steps to address the threat to external validity.

First, like Yin’s ( 2018 ) recommendation to use theory for single case studies, we suggest an a priori theoretical framework for MMCSs. 84% (42 out of 50) of the analysed MMCS publications use such a framework. An a priori theoretical framework has two advantages: it simplifies research by pre-defining constructs and relationships, and it enables analytical techniques like pattern matching. Second, instead of doing the within and then cross-case analysis on a per-case basis, for MMCSs, we propose first doing the within-case analysis and then forming groups of similar cases. Then, the cross-case analysis is performed on the formed groups of cases. To form case groups, replication logic (literal and theoretical) must be chosen carefully. Cross-group analysis (with at least two cases per group) can increase the generalisability of results.

To increase MMCS reliability, a case study database and protocol should be created, similar to multiple case studies. To ensure higher reliability, researchers should document MMCS design decisions in more detail. As outlined in the results section, the documentation on why design decisions were taken is often relatively short and should be more detailed. This call for better documentation is not exclusive to MMCSs, as Benbasat et al. ( 1987 ) and Dubé and Paré ( 2003 ) also criticised this for multiple case study research.To ensure rigour in MMCS, we suggest following the steps for multiple case study research. However, MMCSs have unique characteristics, such as an inability to source triangulate on a per-case level, a higher risk of marginal cases, and difficulty in managing a high number of cases. Therefore, for some methodological steps (cf. Table 8 ), we propose MMCS-specific methodological options. First, MMCS should include supplementary data per case (to increase construct validity). Second, instead of doing a cross-case analysis, we propose to form groups of similar cases and focus on the cross-group analysis (i.e., in each group, there must be at least two cases). Third, researchers should justify their case replication logic , i.e., a combination of theoretical replication (to form different groups) and literal replication (to find the same cases within groups) should be conducted to allow for this cross-group analysis.

6 Conclusion

Our publication contributes to case study research in the IS discipline and beyond by making four methodological contributions. First, we provide a conceptual definition of MMCSs and distinguish them from other research approaches. Second, we provide a contemporary collection of exemplary MMCS publications and their methodological choices. Third, we outline methodological guidelines for rigorous MMCS research and provide examples of good practice. Fourth, we identify research situations for which MMCSs can be used as a pragmatic and rigorous approach.

Our findings have three implications for research practice: First, we found that MMCSs can be descriptive, exploratory, or explanatory and can be considered as a type of multiple case study. Our set of IS MMCS publications shows that this pragmatic approach is advantageous in three situations. First, for time-sensitive topics, where rapid discussion of results, especially in the early stages of research, is beneficial. Second, when it is difficult to collect comprehensive data from multiple sources for each case, either because of limited availability or limited accessibility to the data source. Third, in situations where the research setting is complex, many cases are needed to validate effects (e.g., combinatorial effects) or previous research has produced conflicting results. It is important, however, that the pragmatism of the MMCS should not be misunderstood as a lack of methodological rigour.

Second, we have provided guidelines that researchers can follow to conduct MMCSs rigorously. As we observe an increasing number of MMCSs being published, we encourage their authors to clarify their methodological approach by referring to our analytical MMCS framework. Our analytical framework helps researchers to justify their approach and to distinguish it from approaches that lack methodological rigour.

Third, throughout our collection of MMCS publications, we contacted several authors to clarify their case study research methodology. In many cases, these publications lacked critical details that would be important to classify them as MMCS or marginal cases. Many researchers responded that some details were not mentioned due to space limitations. While we understand these constraints, we suggest that researchers still present these details, for example, by considering online appendices in research repositories.

Our paper has five limitations that could be addressed by future research. First, we focus exclusively on methodological guidelines for positivist multiple case study research. Therefore, we have not explicitly covered methodological approaches from other research paradigms.

Second, we aggregated methodological guidance on multiple case study research from the most relevant publications by citation count only. As a result, we did not capture evidence from publications with far fewer citations or that are relevant in specific niches. However, our design choice is still justified as the aim was to identify established and widely accepted methodological strategies to ensure rigour in case study research.

Third, the literature reviews were keyword-based. Therefore, concepts that fall within our understanding of MMCS but do not include the keywords used for the literature search could not be identified. However, due to the different search terms and versatile search approaches, our search should have captured the most relevant contributions.

Fourth, we selected publications from highly ranked IS MMCS publications and proceedings of leading IS conferences to analyse how rigour is ensured in MMCSs in the IS discipline. We therefore excluded all other research outlets. As with the limitations arising from the keyword-based search, we may have omitted IS MMCS publications that refer to short or mini case studies. However, the limitation of our search is justified as it helps us to ensure that all selected publications have undergone a substantial peer review process and qualify as a reference base in IS.

Fifth, we coded our variables based on the characteristics explicitly stated in the manuscript (i.e., if authors position their MMCS as exploratory, we coded it as exploratory). However, for some variables, researchers do not have a consistent understanding (e.g., the discussion of what constitutes exploratory research by cf., Sarker et al. ( 2018 )). Therefore, we took the risk that MMCS may have different understandings of the coded variables.

For the future, our manuscript on positivist MMCSs provides researchers with guidance for an emerging type of case study research. Based on our study, we can identify promising areas for future research. By limiting ourselves to the most established strategies for ensuring rigour, we also invite authors to enrich our methodological guidelines with other, less commonly used steps. In addition, future research could compare the use of MMCSs in IS with other disciplines in order to solidify our findings.

Data availability

Provided at https://doi.org/10.6084/m9.figshare.24916458

The information can be found in the online Appendix: https://doi.org/10.6084/m9.figshare.24916458 .

litbaskets.io is a web interface that allows searching for literature across the top 847 IS journals. It offers ranging from 2XS (Basket of Eight) to 3XL (847) essential IS journals and a full list of 29 journals which are the basis for this study can be found in Appendix C ( https://doi.org/10.6084/m9.figshare.24916458 ).

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Käss, S., Brosig, C., Westner, M. et al. Short and sweet: multiple mini case studies as a form of rigorous case study research. Inf Syst E-Bus Manage (2024). https://doi.org/10.1007/s10257-024-00674-2

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

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

Introduction

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

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

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

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

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

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

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

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

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

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

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

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

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

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

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

Analysing, interpreting and reporting case studies

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

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

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

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

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

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

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

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

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

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

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

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• Published: 14 May 2024

A burden of proof study on alcohol consumption and ischemic heart disease

• Sinclair Carr   ORCID: orcid.org/0000-0003-0421-3145 1 ,
• Dana Bryazka 1 ,
• Susan A. McLaughlin 1 ,
• Peng Zheng 1 , 2 ,
• Sarasvati Bahadursingh 3 ,
• Aleksandr Y. Aravkin 1 , 2 , 4 ,
• Simon I. Hay   ORCID: orcid.org/0000-0002-0611-7272 1 , 2 ,
• Hilary R. Lawlor 1 ,
• Erin C. Mullany 1 ,
• Christopher J. L. Murray   ORCID: orcid.org/0000-0002-4930-9450 1 , 2 ,
• Sneha I. Nicholson 1 ,
• Jürgen Rehm 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ,
• Gregory A. Roth 1 , 2 , 13 ,
• Reed J. D. Sorensen 1 ,
• Sarah Lewington 3 &
• Emmanuela Gakidou   ORCID: orcid.org/0000-0002-8992-591X 1 , 2  

Nature Communications volume  15 , Article number:  4082 ( 2024 ) Cite this article

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• Cardiovascular diseases
• Epidemiology
• Risk factors

Cohort and case-control data have suggested an association between low to moderate alcohol consumption and decreased risk of ischemic heart disease (IHD), yet results from Mendelian randomization (MR) studies designed to reduce bias have shown either no or a harmful association. Here we conducted an updated systematic review and re-evaluated existing cohort, case-control, and MR data using the burden of proof meta-analytical framework. Cohort and case-control data show low to moderate alcohol consumption is associated with decreased IHD risk – specifically, intake is inversely related to IHD and myocardial infarction morbidity in both sexes and IHD mortality in males – while pooled MR data show no association, confirming that self-reported versus genetically predicted alcohol use data yield conflicting findings about the alcohol-IHD relationship. Our results highlight the need to advance MR methodologies and emulate randomized trials using large observational databases to obtain more definitive answers to this critical public health question.

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Introduction.

It is well known that alcohol consumption increases the risk of morbidity and mortality due to many health conditions 1 , 2 , with even low levels of consumption increasing the risk for some cancers 3 , 4 . In contrast, a large body of research has suggested that low to moderate alcohol intake – compared to no consumption – is associated with a decreased risk of ischemic heart disease (IHD). This has led to substantial epidemiologic and public health interest in the alcohol-IHD relationship 5 , particularly given the high prevalence of alcohol consumption 6 and the global burden of IHD 7 .

Extensive evidence from experimental studies that vary short-term alcohol exposure suggests that average levels of alcohol intake positively affect biomarkers such as apolipoprotein A1, adiponectin, and fibrinogen levels that lower the risk of IHD 8 . In contrast, heavy episodic drinking (HED) may have an adverse effect on IHD by affecting blood lipids, promoting coagulation and thus thrombosis risk, and increasing blood pressure 9 . With effects likely to vary materially by patterns of drinking, alcohol consumption must be considered a multidimensional factor impacting IHD outcomes.

A recent meta-analysis of the alcohol-IHD relationship using individual participant data from 83 observational studies 4 found, among current drinkers, that – relative to drinking less than 50 g/week – any consumption above this level was associated with a lower risk of myocardial infarction (MI) incidence and consumption between >50 and <100 g/week was associated with lower risk of MI mortality. When evaluating other subtypes of IHD excluding MI, the researchers found that consumption between >100 and <250 g/week was associated with a decreased risk of IHD incidence, whereas consumption greater than 350 g/week was associated with an increased risk of IHD mortality. Roerecke and Rehm further observed that low to moderate drinking was not associated with reduced IHD risk when accompanied by occasional HED 10 .

The cohort studies and case-control studies (hereafter referred to as ‘conventional observational studies’) used in these meta-analyses are known to be subject to various types of bias when used to estimate causal relationships 11 . First, neglecting to separate lifetime abstainers from former drinkers, some of whom may have quit due to developing preclinical symptoms (sometimes labeled ‘sick quitters’ 12 , 13 ), and to account for drinkers who reduce their intake as a result of such symptoms may introduce reverse causation bias 13 . That is, the risk of IHD in, for example, individuals with low to moderate alcohol consumption may be lower when compared to IHD risk in sick quitters, not necessarily because intake at this level causes a reduction in risk but because sick quitters are at higher risk of IHD. Second, estimates can be biased because of measurement error in alcohol exposure resulting from inaccurate reporting, random fluctuation in consumption over time (random error), or intentional misreporting of consumption due, for example, to social desirability effects 14 (systematic error). Third, residual confounding may bias estimates if confounders of the alcohol-IHD relationship, such as diet or physical activity, have not been measured accurately (e.g., only via a self-report questionnaire) or accounted for. Fourth, because alcohol intake is a time-varying exposure, time-varying confounding affected by prior exposure must be accounted for 15 . To date, only one study that used a marginal structural model to appropriately adjust for time-varying confounding found no association between alcohol consumption and MI risk 16 . Lastly, if exposure to a risk factor, such as alcohol consumption, did not happen at random – even if all known confounders of the relationship between alcohol and IHD were perfectly measured and accounted for – the potential for unmeasured confounders persists and may bias estimates 11 .

In recent years, the analytic method of Mendelian randomization (MR) has been widely adopted to quantify the causal effects of risk factors on health outcomes 17 , 18 , 19 . MR uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for the exposure of interest. A valid IV should fulfill the following three assumptions: it must be associated with the risk factor (relevance assumption); there must be no common causes of the IV and the outcome (independence assumption); and the IV must affect the outcome only through the exposure (exclusion restriction or ‘no horizontal pleiotropy’ assumption) 20 , 21 . If all three assumptions are fulfilled, estimates derived from MR are presumed to represent causal effects 22 . Several MR studies have quantified the association between alcohol consumption and cardiovascular disease 23 , including IHD, using genes known to impact alcohol metabolism (e.g., ADH1B/C and ALDH2 24 ) or SNP combinations from genome-wide association studies 25 . In contrast to the inverse associations found in conventional observational studies, MR studies have found either no association or a harmful relationship between alcohol consumption and IHD 26 , 27 , 28 , 29 , 30 , 31 .

To advance the knowledge base underlying our understanding of this major health issue – critical given the worldwide ubiquity of alcohol use and of IHD – there is a need to systematically review and critically re-evaluate all available evidence on the relationship between alcohol consumption and IHD risk from both conventional observational and MR studies.

The burden of proof approach, developed by Zheng et al. 32 , is a six-step meta-analysis framework that provides conservative estimates and interpretations of risk-outcome relationships. The approach systematically tests and adjusts for common sources of bias defined according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria: representativeness of the study population, exposure assessment, outcome ascertainment, reverse causation, control for confounding, and selection bias. The key statistical tool to implement the approach is MR-BRT (meta-regression—Bayesian, regularized, trimmed 33 ), a flexible meta-regression tool that does not impose a log-linear relationship between the risk and outcome, but instead uses a spline ensemble to model non-linear relationships. MR-BRT also algorithmically detects and trims outliers in the input data, takes into account different reference and alternative exposure intervals in the data, and incorporates unexplained between-study heterogeneity in the uncertainty surrounding the mean relative risk (RR) curve (henceforth ‘risk curve’). For those risk-outcome relationships that meet the condition of statistical significance using conventionally estimated uncertainty intervals (i.e., without incorporating unexplained between-study heterogeneity), the burden of proof risk function (BPRF) is derived by calculating the 5th (if harmful) or 95th (if protective) quantile risk curve – inclusive of between-study heterogeneity – closest to the log RR of 0. The resulting BPRF is a conservative interpretation of the risk-outcome relationship based on all available evidence. The BPRF represents the smallest level of excess risk for a harmful risk factor or reduced risk for a protective risk factor that is consistent with the data, accounting for between-study heterogeneity. To quantify the strength of the evidence for the alcohol-IHD relationship, the BPRF can be summarized in a single metric, the risk-outcome score (ROS). The ROS is defined as the signed value of the average log RR of the BPRF across the 15th to 85th percentiles of alcohol consumption levels observed across available studies. The larger a positive ROS value, the stronger the alcohol-IHD association. For ease of interpretation, the ROS is converted into a star rating from one to five. A one-star rating (ROS < 0) indicates a weak alcohol-IHD relationship, and a five-star rating (ROS > 0.62) indicates a large effect size and strong evidence. Publication and reporting bias are evaluated with Egger’s regression and by visual inspection with funnel plots 34 . Further conceptual and technical details of the burden of proof approach are described in detail elsewhere 32 .

Using the burden of proof approach, we systematically re-evaluate all available eligible evidence from cohort, case-control, and MR studies published between 1970 and 2021 to conservatively quantify the dose-response relationship between alcohol consumption and IHD risk, calculated relative to risk at zero alcohol intake (i.e., current non-drinking, including lifetime abstinence or former use). We pool the evidence from all conventional observational studies combined, as well as individually for all three study designs, to estimate mean IHD risk curves. Based on patterns of results established by previous meta-analyses 4 , 35 , we also use data from conventional observational studies to estimate risk curves by IHD endpoint (morbidity or mortality) and further by sex, in addition to estimating risk curves for MI overall and by endpoint. We follow PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines 36 through all stages of this study (Supplementary Information section  1 , Fig.  S1 and Tables  S1 and S2 ) and comply with GATHER (Guidelines on Accurate and Transparent Health Estimates Reporting) recommendations 37 (Supplementary Information section  2 , Table  S3 ). The main findings and research implications of this work are summarized in Table  1 .

We updated the systematic review on the dose-response relationship between alcohol consumption and IHD previously conducted for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 1 . Of 4826 records identified in our updated systematic review (4769 from databases/registers and 57 by citation search and known literature), 11 were eligible based on our inclusion criteria and were included. In total, combined with the results of the previous systematic reviews 1 , 38 , information from 95 cohort studies 26 , 27 , 29 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 27 case-control studies 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , and five MR studies 26 , 27 , 28 , 29 , 31 was included in our meta-analysis (see Supplementary Information section  1 , Fig.  S1 , for the PRISMA diagram). Details on the extracted effect sizes, the design of each included study, underlying data sources, number of participants, duration of follow-up, number of cases and controls, and bias covariates that were evaluated and potentially adjusted for can be found in the Supplementary Information Sections  4 , 5 , and 6 .

Table  2 summarizes key metrics of each risk curve modeled, including estimates of mean RR and 95% UI (inclusive of between-study heterogeneity) at select alcohol exposure levels, the exposure level and RR and 95% UI at the nadir (i.e., lowest RR), the 85th percentile of exposure observed in the data and its corresponding RR and 95% UI, the BPRF averaged at the 15th and 85th percentile of exposure, the average excess risk or risk reduction according to the exposure-averaged BPRF, the ROS, the associated star rating, the potential presence of publication or reporting bias, and the number of studies included.

We found large variation in the association between alcohol consumption and IHD by study design. When we pooled the results of cohort and case-control studies, we observed an inverse association between alcohol at average consumption levels and IHD risk; that is, drinking average levels of alcohol was associated with a reduced IHD risk relative to drinking no alcohol. In contrast, we did not find a statistically significant association between alcohol consumption and IHD risk when pooling results from MR studies. When we subset the conventional observational studies to those reporting on IHD by endpoint, we found no association between alcohol consumption and IHD morbidity or mortality due to large unexplained heterogeneity between studies. When we further subset those studies that reported effect size estimates by sex, we found that average alcohol consumption levels were inversely associated with IHD morbidity in males and in females, and with IHD mortality in males but not in females. When we analyzed only the studies that reported on MI, we found significant inverse associations between average consumption levels and MI overall and with MI morbidity. Visualizations of the risk curves for morbidity and mortality of IHD and MI are provided in Supplementary Information Section  9 (Figs.  S2a –c, S3a –c, and S4a–c ). Among all modeled risk curves for which a BPRF was calculated, the ROS ranged from −0.40 for MI mortality to 0.20 for MI morbidity. In the Supplementary Information, we also provide details on the RR and 95% UIs with and without between-study heterogeneity associated with each 10 g/day increase in consumption for each risk curve (Table  S10 ), the parameter specifications of the model (Tables  S11 and S12 ), and each risk curve from the main analysis estimated without trimming 10% of the data (Fig.  S5a–l and Table  S13 ).

Risk curve derived from conventional observational study data

The mean risk curve and 95% UI were first estimated by combining all evidence from eligible cohort and case-control studies that quantified the association between alcohol consumption and IHD risk. In total, information from 95 cohort studies and 27 case-control studies combining data from 7,059,652 participants were included. In total, 243,357 IHD events were recorded. Thirty-seven studies quantified the association between alcohol consumption and IHD morbidity only, and 44 studies evaluated only IHD mortality. The estimated alcohol-IHD association was adjusted for sex and age in all but one study. Seventy-five studies adjusted the effect sizes for sex, age, smoking, and at least four other covariates. We adjusted our risk curve for whether the study sample was under or over 50 years of age, whether the study outcome was consistent with the definition of IHD (according to the International Classification of Diseases [ICD]−9: 410-414; and ICD-10: I20-I25) or related to specified subtypes of IHD, whether the outcome was ascertained by self-report only or by at least one other measurement method, whether the study accounted for risk for reverse causation, whether the reference group was non-drinkers (including lifetime abstainers and former drinkers), and whether effect sizes were adjusted (1) for sex, age, smoking, and at least four other variables, (2) for apolipoprotein A1, and (3) for cholesterol, as these bias covariates were identified as significant by our algorithm.

Pooling all data from cohort and case-control studies, we found that alcohol consumption was inversely associated with IHD risk (Fig.  1 ). The risk curve was J-shaped – without crossing the null RR of 1 at high exposure levels – with a nadir of 0.69 (95% UI: 0.48–1.01) at 23 g/day. This means that compared to individuals who do not drink alcohol, the risk of IHD significantly decreases with increasing consumption up to 23 g/day, followed by a risk reduction that becomes less pronounced. The average BPRF calculated between 0 and 45 g/day of alcohol intake (the 15th and 85th percentiles of the exposure range observed in the data) was 0.96. Thus, when between-study heterogeneity is accounted for, a conservative interpretation of the evidence suggests drinking alcohol across the average intake range is associated with an average decrease in the risk of IHD of at least 4% compared to drinking no alcohol. This corresponds to a ROS of 0.04 and a star rating of two, which suggests that the association – on the basis of the available evidence – is weak. Although we algorithmically identified and trimmed 10% of the data to remove outliers, Egger’s regression and visual inspection of the funnel plot still indicated potential publication or reporting bias.

The panels show the log(relative risk) function, the relative risk function, and a modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard error that includes the reported standard error and between-study heterogeneity on the y-axis. RR relative risk, UI uncertainty interval. Source data are provided as a Source Data file.

Risk curve derived from case-control study data

Next, we estimated the mean risk curve and 95% UI for the relationship between alcohol consumption and IHD by subsetting the data to case-control studies only. We included a total of 27 case-control studies (including one nested case-control study) with data from 60,914 participants involving 16,892 IHD cases from Europe ( n  = 15), North America ( n  = 6), Asia ( n  = 4), and Oceania ( n  = 2). Effect sizes were adjusted for sex and age in most studies ( n  = 25). Seventeen of these studies further adjusted for smoking and at least four other covariates. The majority of case-control studies accounted for the risk of reverse causation ( n  = 25). We did not adjust our risk curve for bias covariates, as our algorithm did not identify any as significant.

Evaluating only data from case-control studies, we observed a J-shaped relationship between alcohol consumption and IHD risk, with a nadir of 0.65 (0.50–0.85) at 23 g/day (Fig.  2 ). The inverse association between alcohol consumption and IHD risk reversed at an intake level of 61 g/day. In other words, alcohol consumption between >0 and 60 g/day was associated with a lower risk compared to no consumption, while consumption at higher levels was associated with increased IHD risk. However, the curve above this level is flat, implying that the association between alcohol and increased IHD risk is the same between 61 and 100 g/day, relative to not drinking any alcohol. The BPRF averaged across the exposure range between the 15th and 85th percentiles, or 0–45 g/day, was 0.87, which translates to a 13% average reduction in IHD risk across the average range of consumption. This corresponds to a ROS of 0.14 and a three-star rating. After trimming 10% of the data, no potential publication or reporting bias was found.

The panels show the log(relative risk) function, the relative risk function, and a modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard deviation that includes the reported standard deviation and between-study heterogeneity on the y-axis. RR relative risk, UI uncertainty interval. Source data are provided as a Source Data file.

Risk curve derived from cohort study data

We also estimated the mean risk curve and 95% UI for the relationship between alcohol consumption and IHD using only data from cohort studies. In total, 95 cohort studies – of which one was a retrospective cohort study – with data from 6,998,738 participants were included. Overall, 226,465 IHD events were recorded. Most data were from Europe ( n  = 43) and North America ( n  = 33), while a small number of studies were conducted in Asia ( n  = 14), Oceania ( n  = 3), and South America ( n  = 2). The majority of studies adjusted effect sizes for sex and age ( n  = 76). Fifty-seven of these studies also adjusted for smoking and at least four other covariates. Out of all cohort studies included, 88 accounted for the risk of reverse causation. We adjusted our risk curve for whether the study outcome was consistent with the definition of IHD or related to specified subtypes of IHD, and whether effect sizes were adjusted for apolipoprotein A1, as these bias covariates were identified as significant by our algorithm.

When only data from cohort studies were evaluated, we found a J-shaped relationship between alcohol consumption and IHD risk that did not cross the null RR of 1 at high exposure levels, with a nadir of 0.69 (0.47–1.01) at 23 g/day (Fig.  3 ). The shape of the risk curve was almost identical to the curve estimated with all conventional observational studies (i.e., cohort and case-control studies combined). When we calculated the average BPRF of 0.95 between the 15th and 85th percentiles of observed alcohol exposure (0–50 g/day), we found that alcohol consumption across the average intake range was associated with an average reduction in IHD risk of at least 5%. This corresponds to a ROS of 0.05 and a two-star rating. We identified potential publication or reporting bias after 10% of the data were trimmed.

Risk curve derived from Mendelian randomization study data

Lastly, we pooled evidence on the relationship between genetically predicted alcohol consumption and IHD risk from MR studies. Four MR studies were considered eligible for inclusion in our main analysis, with data from 559,708 participants from China ( n  = 2), the Republic of Korea ( n  = 1), and the United Kingdom ( n  = 1). Overall, 22,134 IHD events were recorded. Three studies used the rs671 ALDH2 genotype found in Asian populations, one study additionally used the rs1229984 ADH1B variant, and one study used the rs1229984 ADH1B Arg47His variant and a combination of 25 SNPs as IVs. All studies used the two-stage least squares (2SLS) method to estimate the association, and one study additionally applied the inverse-variance-weighted (IVW) method and multivariable MR (MVMR). For the study that used multiple methods to estimate effect sizes, we used the 2SLS estimates for our main analysis. Further details on the included studies are provided in Supplementary Information section  4 (Table  S6 ). Due to limited input data, we elected not to trim 10% of the observations. We adjusted our risk curve for whether the endpoint of the study outcome was mortality and whether the associations were adjusted for sex and/or age, as these bias covariates were identified as significant by our algorithm.

We did not find any significant association between genetically predicted alcohol consumption and IHD risk using data from MR studies (Fig.  4 ). No potential publication or reporting bias was detected.

As sensitivity analyses, we modeled risk curves with effect sizes estimated from data generated by Lankester et al. 28 using IVW and MVMR methods. We also used effect sizes from Biddinger et al. 31 , obtained using non-linear MR with the residual method, instead of those from Lankester et al. 28 in our main model (both were estimated with UK Biobank data) to estimate a risk curve. Again, we did not find a significant association between genetically predicted alcohol consumption and IHD risk (see Supplementary Information Section  10 , Fig.  S6a–c and Table  S14 ). To test for consistency with the risk curve we estimated using all included cohort studies, we also pooled the conventionally estimated effect sizes provided in the four MR studies. We did not observe an association between alcohol consumption and IHD risk due to large unexplained heterogeneity between studies (see Supplementary Information Section  10 , Fig.  S7, and Table  S14 ). Lastly, we pooled cohort studies that included data from China, the Republic of Korea, and the United Kingdom to account for potential geographic influences. Again, we did not find a significant association between alcohol consumption and IHD risk (see Supplementary Information Section  10 , Fig.  S8, and Table  S14 ).

Conventional observational and MR studies published to date provide conflicting estimates of the relationship between alcohol consumption and IHD. We conducted an updated systematic review and conservatively re-evaluated existing evidence on the alcohol-IHD relationship using the burden of proof approach. We synthesized evidence from cohort and case-control studies combined and separately and from MR studies to assess the dose-response relationship between alcohol consumption and IHD risk and to compare results across different study designs. It is anticipated that the present synthesis of evidence will be incorporated into upcoming iterations of GBD.

Our estimate of the association between genetically predicted alcohol consumption and IHD runs counter to our estimates from the self-report data and those of other previous meta-analyses 4 , 35 , 158 that pooled conventional observational studies. Based on the conservative burden of proof interpretation of the data, our results suggested an inverse association between alcohol and IHD when all conventional observational studies were pooled (alcohol intake was associated with a reduction in IHD risk by an average of at least 4% across average consumption levels; two-star rating). In evaluating only cohort studies, we again found an inverse association between alcohol consumption and IHD (alcohol intake was associated with a reduction in IHD risk by an average of at least 5% at average consumption levels; two-star rating). In contrast, when we pooled only case-control studies, we estimated that average levels of alcohol consumption were associated with at least a 13% average decrease in IHD risk (three-star rating), but the inverse association reversed when consumption exceeded 60 g/day, suggesting that alcohol above this level is associated with a slight increase in IHD risk. Our analysis of the available evidence from MR studies showed no association between genetically predicted alcohol consumption and IHD.

Various potential biases and differences in study designs may have contributed to the conflicting findings. In our introduction, we summarized important sources of bias in conventional observational studies of the association between alcohol consumption and IHD. Of greatest concern are residual and unmeasured confounding and reverse causation, the effects of which are difficult to eliminate in conventional observational studies. By using SNPs within an IV approach to predict exposure, MR – in theory – eliminates these sources of bias and allows for more robust estimates of causal effects. Bias may still occur, however, when using MR to estimate the association between alcohol and IHD 159 , 160 . There is always the risk of horizontal pleiotropy in MR – that is, the genetic variant may affect the outcome via pathways other than exposure 161 . The IV assumption of exclusion restriction is, for example, violated if only a single measurement of alcohol consumption is used in MR 162 ; because alcohol consumption varies over the life course, the gene directly impacts IHD through intake at time points other than that used in the MR analysis. To date, MR studies have not succeeded in separately capturing the multidimensional effects of alcohol intake on IHD risk (i.e., effects of average alcohol consumption measured through frequency-quantity, in addition to the effects of HED) 159 because the genes used to date only target average alcohol consumption that encompasses intake both at average consumption levels and HED. In other words, the instruments used are not able to separate out the individual effects of these two different dimensions of alcohol consumption on IHD risk using MR. Moreover, reverse causation may occur through cross-generational effects 160 , 163 , as the same genetic variants predispose both the individual and at least one of his or her parents to (increased) alcohol consumption. In this situation, IHD risk could be associated with the parents’ genetically predicted alcohol consumption and not with the individual’s own consumption. None of the MR studies included accounted for cross-generational effects, which possibly introduced bias in the effect estimates. It is important to note that bias by ancestry might also occur in conventional observational studies 164 . In summary, estimates of the alcohol-IHD association are prone to bias in all three study designs, limiting inferences of causation.

The large difference in the number of available MR versus conventional observational studies, the substantially divergent results derived from the different study types, and the rapidly developing field of MR clearly argue for further investigation of MR as a means to quantify the association between alcohol consumption and IHD risk. Future studies should investigate non-linearity in the relationship using non-linear MR methods. The residual method, commonly applied in non-linear MR studies such as Biddinger et al. 31 , assumes a constant, linear relationship between the genetic IV and the exposure in the study population; a strong assumption that may result in biased estimates and inflated type I error rates if the relationship varies by population strata 165 . However, by log-transforming the exposure, the relationships between the genetic IV and the exposure as expressed on a logarithmic scale may be more homogeneous across strata, possibly reducing the bias effect of violating the assumption of a constant, linear relationship. Alternatively, or in conjunction, the recently developed doubly ranked method, which obviates the need for this assumption, could be used 166 . Since methodology for non-linear MR is an active field of study 167 , potential limitations of currently available methods should be acknowledged and latest guidelines be followed 168 . Future MR studies should further (i) employ sensitivity analyses such as the MR weighted median method 169 to relax the exclusion restriction assumption that may be violated, as well as applying other methods such as the MR-Egger intercept test; (ii) use methods such as g-estimation of structural mean models 162 to adequately account for temporal variation in alcohol consumption in MR, and (iii) attempt to disaggregate the effects of alcohol on IHD by dimension in MR, potentially through the use of MVMR 164 . General recommendations to overcome common MR limitations are described in greater detail elsewhere 159 , 163 , 170 , 171 and should be carefully considered. With respect to prospective cohort studies used to assess the alcohol-IHD relationship, they should, at a minimum: (i) adjust the association between alcohol consumption and IHD for all potential confounders identified, for example, using a causal directed acyclic graph, and (ii) account for reverse causation introduced by sick quitters and by drinkers who changed their consumption. If possible, they should also (iii) use alcohol biomarkers as objective measures of alcohol consumption instead of or in addition to self-reported consumption to reduce bias through measurement error, (iv) investigate the association between IHD and HED, in addition to average alcohol consumption, and (v) when multiple measures of alcohol consumption and potential confounders are available over time, use g-methods to reduce bias through confounding as fully as possible within the limitations of the study design. However, some bias – due, for instance, to unmeasured confounding in conventional observational and to horizontal pleiotropy in MR studies – is likely inevitable, and the interpretation of estimates should be appropriately cautious, in accordance with the methods used in the study.

With the introduction of the Moderate Alcohol and Cardiovascular Health Trial (MACH15) 172 , randomized controlled trials (RCTs) have been revisited as a way to study the long-term effects of low to moderate alcohol consumption on cardiovascular disease, including IHD. In 2018, soon after the initiation of MACH15, the National Institutes of Health terminated funding 173 , reportedly due to concerns about study design and irregularities in the development of funding opportunities 174 . Although MACH15 was terminated, its initiation represented a previously rarely considered step toward investigating the alcohol-IHD relationship using an RCT 175 . However, while the insights from an RCT are likely to be invaluable, the implementation is fraught with potential issues. Due to the growing number of studies suggesting increased disease risk, including cancer 3 , 4 , associated with alcohol use even at very low levels 176 , the use of RCTs to study alcohol consumption is ethically questionable 177 . A less charged approach could include the emulation of target trials 178 using existing observational data (e.g., from large-scale prospective cohort studies such as the UK Biobank 179 , Atherosclerosis Risk in Communities Study 180 , or the Framingham Heart Study 181 ) in lieu of real trials to gather evidence on the potential cardiovascular effects of alcohol. Trials like MACH15 can be emulated, following the proposed trial protocols as closely as the observational dataset used for the analysis allows. Safety and ethical concerns, such as those related to eligibility criteria, initiation/increase in consumption, and limited follow-up duration, will be eliminated because the data will have already been collected. This framework allows for hypothetical trials investigating ethically challenging or even untenable questions, such as the long-term effects of heavy (episodic) drinking on IHD risk, to be emulated and inferences to broader populations drawn.

There are several limitations that must be considered when interpreting our findings. First, record screening for our systematic review was not conducted in a double-blinded fashion. Second, we did not have sufficient evidence to estimate and examine potential differential associations of alcohol consumption with IHD risk by beverage type or with MI endpoints by sex. Third, despite using a flexible meta-regression tool that overcame several limitations common to meta-analyses, the results of our meta-analysis were only as good as the quality of the studies included. We were able, however, to address the issue of varying quality of input data by adjusting for bias covariates that corresponded to core study characteristics in our analyses. Fourth, because we were only able to include one-sample MR studies that captured genetically predicted alcohol consumption, statistical power may be lower than would have been possible with the inclusion of two-sample MR studies, and studies that directly estimated gene-IHD associations were not considered 23 . Finally, we were not able to account for participants’ HED status when pooling effect size estimates from conventional observational studies. Given established differences in IHD risk for drinkers with and without HED 35 and the fact that more than one in three drinkers reports HED 6 , we would expect that the decreased average risk we found at moderate levels of alcohol consumption would be attenuated (i.e., approach the IHD risk of non-drinkers) if the presence of HED was taken into account.

Using the burden of proof approach 32 , we conservatively re-evaluated the dose-response relationship between alcohol consumption and IHD risk based on existing cohort, case-control, and MR data. Consistent with previous meta-analyses, we found that alcohol at average consumption levels was inversely associated with IHD when we pooled conventional observational studies. This finding was supported when aggregating: (i) all studies, (ii) only cohort studies, (iii) only case-control studies, (iv) studies examining IHD morbidity in females and males, (v) studies examining IHD mortality in males, and (vi) studies examining MI morbidity. In contrast, we found no association between genetically predicted alcohol consumption and IHD risk based on data from MR studies. Our confirmation of the conflicting results derived from self-reported versus genetically predicted alcohol use data highlights the need to advance methodologies that will provide more definitive answers to this critical public health question. Given the limitations of randomized trials, we advocate using advanced MR techniques and emulating target trials using observational data to generate more conclusive evidence on the long-term effects of alcohol consumption on IHD risk.

This study was approved by the University of Washington IRB Committee (study #9060).

The burden of proof approach is a six-step framework for conducting meta-analysis 32 : (1) data from published studies that quantified the dose-response relationship between alcohol consumption and ischemic heart disease (IHD) risk were systematically identified and obtained; (2) the shape of the mean relative risk (RR) curve (henceforth ‘risk curve’) and associated uncertainty was estimated using a quadratic spline and algorithmic trimming of outliers; (3) the risk curve was tested and adjusted for biases due to study attributes; (4) unexplained between-study heterogeneity was quantified, adjusting for within-study correlation and number of studies included; (5) the evidence for small-study effects was evaluated to identify potential risks of publication or reporting bias; and (6) the burden of proof risk function (BPRF) – a conservative interpretation of the average risk across the exposure range found in the data – was estimated relative to IHD risk at zero alcohol intake. The BPRF was converted to a risk-outcome score (ROS) that was mapped to a star rating from one to five to provide an intuitive interpretation of the magnitude and direction of the dose-response relationship between alcohol consumption and IHD risk.

We calculated the mean RR and 95% uncertainty intervals (UIs) for IHD associated with levels of alcohol consumption separately with all evidence available from conventional observational studies and from Mendelian randomization (MR) studies. For the risk curves that met the condition of statistical significance when the conventional 95% UI that does not include unexplained between-study heterogeneity was evaluated, we calculated the BPRF, ROS, and star rating. Based on input data from conventional observational studies, we also estimated these metrics by study design (cohort studies, case-control studies), and by IHD endpoint (morbidity, mortality) for both sexes (females, males) and sex-specific. For sex-stratified analyses, we only considered studies that reported effect sizes for both females and males to allow direct comparison of IHD risk across different exposure levels; however, we did not collect information about the method each study used to determine sex. We also estimated risk curves for myocardial infarction (MI), overall and by endpoint, using data from conventional observational studies. As a comparison, we also estimated each risk curve without trimming 10% of the input data. We did not consider MI as an outcome or disaggregate findings by sex or endpoint for MR studies due to insufficient data.

With respect to MR studies, several statistical methods are typically used to estimate the associations between genetically predicted exposure and health outcomes (e.g., two-stage least squares [2SLS], inverse-variance-weighted [IVW], multivariable Mendelian randomization [MVMR]). For our main analysis synthesizing evidence from MR studies, we included the reported effect sizes estimated using 2SLS if a study applied multiple methods because this method was common to all included studies. In sensitivity analyses, we used the effect sizes obtained by other MR methods (i.e., IVW, MVMR, and non-linear MR) and estimated the mean risk curve and uncertainty. We also pooled conventionally estimated effect sizes from MR studies to allow comparison with the risk curve estimated with cohort studies. Due to limited input data from MR studies, we elected not to trim 10% of the observations. Furthermore, we estimated the risk curve from cohort studies with data from countries that corresponded to those included in MR studies (China, the Republic of Korea, and the United Kingdom). Due to a lack of data, we were unable to estimate a risk curve from case-control studies in these geographic regions.

Conducting the systematic review

In step one of the burden of proof approach, data for the dose-response relationship between alcohol consumption and IHD risk were systematically identified, reviewed, and extracted. We updated a previously published systematic review 1 in PubMed that identified all studies evaluating the dose-response relationship between alcohol consumption and risk of IHD morbidity or mortality from January 1, 1970, to December 31, 2019. In our update, we additionally considered all studies up to and including December 31, 2021, for eligibility. We searched articles in PubMed on March 21, 2022, with the following search string: (alcoholic beverage[MeSH Terms] OR drinking behavior[MeSH Terms] OR “alcohol”[Title/Abstract]) AND (Coronary Artery Disease[Mesh] OR Myocardial Ischemia[Mesh] OR atherosclerosis[Mesh] OR Coronary Artery Disease[TiAb] OR Myocardial Ischemia[TiAb] OR cardiac ischemia[TiAb] OR silent ischemia[TiAb] OR atherosclerosis Outdent [TiAb] OR Ischemic heart disease[TiAb] OR Ischemic heart disease[TiAb] OR coronary heart disease[TiAb] OR myocardial infarction[TiAb] OR heart attack[TiAb] OR heart infarction[TiAb]) AND (Risk[MeSH Terms] OR Odds Ratio[MeSH Terms] OR “risk”[Title/Abstract] OR “odds ratio”[Title/Abstract] OR “cross-product ratio”[Title/Abstract] OR “hazards ratio”[Title/Abstract] OR “hazard ratio”[Title/Abstract]) AND (“1970/01/01”[PDat]: “2021/12/31”[PDat]) AND (English[LA]) NOT (animals[MeSH Terms] NOT Humans[MeSH Terms]). Studies were eligible for inclusion if they met all of the following criteria: were published between January 1, 1970, and December 31, 2021; were a cohort study, case-control study, or MR study; described an association between alcohol consumption and IHD and reported an effect size estimate (relative risk, hazard ratio, odds ratio); and used a continuous dose as exposure of alcohol consumption. Studies were excluded if they met any of the following criteria: were an aggregate study (meta-analysis or pooled cohort); utilized a study design not designated for inclusion in this analysis: not a cohort study, case-control study, or MR study; were a duplicate study: the underlying sample of the study had also been analyzed elsewhere (we always considered the analysis with the longest follow-up for cohort studies or the most recently published analysis for MR studies); did not report on the exposure of interest: reported on combined exposure of alcohol and drug use or reported alcohol consumption in a non-continuous way; reported an outcome that was not IHD or a composite outcome that included but was not limited to IHD, or outcomes lacked specificity, such as cardiovascular disease or all-cause mortality; were not in English; and were animal studies. All screenings of titles and abstracts of identified records, as well as full texts of potentially eligible studies, and extraction of included studies, were done by a single reviewer (SC or HL) independently. If eligible, studies were extracted for study characteristics, exposure, outcome, adjusted confounders, and effect sizes and their uncertainty. While the previous systematic review only considered cohort and case-control studies, our update also included MR studies. We chose to consider only ‘one-sample’ MR studies, i.e., those in which genes, risk factors, and outcomes were measured in the same participants, and not ‘two-sample’ MR studies in which two different samples were used for the MR analysis so that we could fully capture study-specific information. We re-screened previously identified records for MR studies to consider all published MR studies in the defined time period. We also identified and included in our sensitivity analysis an MR study published in 2022 31 which used a non-linear MR method to estimate the association between genetically predicted alcohol consumption and IHD. When eligible studies reported both MR and conventionally estimated effect sizes (i.e., for the association between self-reported alcohol consumption and IHD risk), we extracted both. If studies used the same underlying sample and investigated the same outcome in the same strata, we included the study that had the longest follow-up. This did not apply when the same samples were used in conventional observational and MR studies, because they were treated separately when estimating the risk curve of alcohol consumption and IHD. Continuous exposure of alcohol consumption was defined as a frequency-quantity measure 182 and converted to g/day. IHD was defined according to the International Classification of Diseases (ICD)−9, 410-414, and ICD-10, I20-I25.

The raw data were extracted with a standardized extraction sheet (see Supplementary Information Section  3 , Table  S4 ). For conventional observational studies, when multiple effect sizes were estimated from differently adjusted regression models, we used those estimated with the model reported to be fully adjusted or the one with the most covariates. In the majority of studies, alcohol consumption was categorized based on the exposure range available in the data. If the lower end of a categorical exposure range (e.g., <10 g/day) of an effect size was not specified in the input data, we assumed that this was 0 g/day. If the upper end was not specified (e.g., >20 g/day), it was calculated by multiplying the lower end of the categorical exposure range by 1.5. When the association between alcohol and IHD risk was reported as a linear slope, the average consumption level in the sample was multiplied by the logarithm of the effect size to effectively render it categorical. From the MR study which employed non-linear MR 31 , five effect sizes and their uncertainty were extracted at equal intervals across the reported range of alcohol exposure using WebPlotDigitizer. To account for the fact that these effect sizes were derived from the same non-linear risk curve, we adjusted the extracted standard errors by multiplying them by the square root of five (i.e., the number of extracted effect sizes). Details on data sources are provided in Supplementary Information Section  4 .

Estimating the shape of the risk-outcome relationship

In step two, the shape of the dose-response relationship (i.e., ‘signal’) between alcohol consumption and IHD risk was estimated relative to risk at zero alcohol intake. The meta-regression tool MR-BRT (meta-regression—Bayesian, regularized, trimmed), developed by Zheng et al. 33 , was used for modeling. To allow for non-linearity, thus relaxing the common assumption of a log-linear relationship, a quadratic spline with two interior knots was used for estimating the risk curve 33 . We used the following three risk measures from included studies: RRs, odds ratios (ORs), and hazard ratios (HRs). ORs were treated as equivalent to RRs and HRs based on the rare outcome assumption. To counteract the potential influence of knot placement on the shape of the risk curve when using splines, an ensemble model approach was applied. Fifty component models with random knot placements across the exposure domain were computed. These were combined into an ensemble by weighting each model based on model fit and variation (i.e., smoothness of fit to the data). To prevent bias from outliers, a robust likelihood-based approach was applied to trim 10% of the observations. Technical details on estimating the risk curve, use of splines, the trimming procedure, the ensemble model approach, and uncertainty estimation are described elsewhere 32 , 33 . Details on the model specifications for each risk curve are provided in Supplementary Information section  8 . We first estimated each risk curve without trimming input data to visualize the shape of the curve, which informed knot placement and whether to set a left and/or right linear tail when data were sparse at low or high exposure levels (see Supplementary Information Section  10 , Fig.  S5a–l ).

Testing and adjusting for biases across study designs and characteristics

In step three, the risk curve was tested and adjusted for systematic biases due to study attributes. According to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria 183 , the following six bias sources were quantified: representativeness of the study population, exposure assessment, outcome ascertainment, reverse causation, control for confounding, and selection bias. Representativeness was quantified by whether the study sample came from a location that was representative of the underlying geography. Exposure assessment was quantified by whether alcohol consumption was recorded once or more than once in conventional observational studies, or with only one or multiple SNPs in MR studies. Outcome ascertainment was quantified by whether IHD was ascertained by self-report only or by at least one other measurement method. Reverse causation was quantified by whether increased IHD risk among participants who reduced or stopped drinking was accounted for (e.g., by separating former drinkers from lifetime abstainers). Control for confounding factors was quantified by which and how many covariates the effect sizes were adjusted for (i.e., through stratification, matching, weighting, or standardization). Because the most adjusted effect sizes in each study were extracted in the systematic review process and thus may have been adjusted for mediators, we additionally quantified a bias covariate for each of the following potential mediators of the alcohol-IHD relationship: body mass index, blood pressure, cholesterol (excluding high-density lipoprotein cholesterol), fibrinogen, apolipoprotein A1, and adiponectin. Selection bias was quantified by whether study participants were selected and included based on pre-existing disease states. We also quantified and considered as possible bias covariates whether the reference group was non-drinkers, including lifetime abstainers and former drinkers; whether the sample was under or over 50 years of age; whether IHD morbidity, mortality, or both endpoints were used; whether the outcome mapped to IHD or referred only to subtypes of IHD; whether the outcome mapped to MI; and what study design (cohort or case-control) was used when conventional observational studies were pooled. Details on quantified bias covariates for all included studies are provided in Supplementary Information section  5 (Tables  S7 and S8 ). Using a Lasso approach 184 , the bias covariates were first ranked. They were then included sequentially, based on their ranking, as effect modifiers of the ‘signal’ obtained in step two in a linear meta-regression. Significant bias covariates were included in modeling the final risk curve. Technical details of the Lasso procedure are described elsewhere 32 .

Quantifying between-study heterogeneity, accounting for heterogeneity, uncertainty, and small number of studies

In step four, the between-study heterogeneity was quantified, accounting for heterogeneity, uncertainty, and small number of studies. In a final linear mixed-effects model, the log RRs were regressed against the ‘signal’ and selected bias covariates, with a random intercept to account for within-study correlation and a study-specific random slope with respect to the ‘signal’ to account for between-study heterogeneity. A Fisher information matrix was used to estimate the uncertainty associated with between-study heterogeneity 185 because heterogeneity is easily underestimated or may be zero when only a small number of studies are available. We estimated the mean risk curve with a 95% UI that incorporated between-study heterogeneity, and we additionally estimated a 95% UI without between-study heterogeneity as done in conventional meta-regressions (see Supplementary Information Section  7 , Table  S10 ). The 95% UI incorporating between-study heterogeneity was calculated from the posterior uncertainty of the fixed effects (i.e., the ‘signal’ and selected bias covariates) and the 95% quantile of the between-study heterogeneity. The estimate of between-study heterogeneity and the estimate of the uncertainty of the between-study heterogeneity were used to determine the 95% quantile of the between-study heterogeneity. Technical details of quantifying uncertainty of between-study heterogeneity are described elsewhere 32 .

Evaluating potential for publication or reporting bias

In step five, the potential for publication or reporting bias was evaluated. The trimming algorithm used in step two helps protect against these biases, so risk curves found to have publication or reporting bias using the following methods were derived from data that still had bias even after trimming. Publication or reporting bias was evaluated using Egger’s regression 34 and visual inspection using funnel plots. Egger’s regression tested for a significant correlation between residuals of the RR estimates and their standard errors. Funnel plots showed the residuals of the risk curve against their standard errors. We reported publication or reporting bias when identified.

Estimating the burden of proof risk function

In step six, the BPRF was calculated for risk-outcome relationships that were statistically significant when evaluating the conventional 95% UI without between-study heterogeneity. The BPRF is either the 5th (if harmful) or the 95th (if protective) quantile curve inclusive of between-study heterogeneity that is closest to the RR line at 1 (i.e., null); it indicates a conservative estimate of a harmful or protective association at each exposure level, based on the available evidence. The mean risk curve, 95% UIs (with and without between-study heterogeneity), and BPRF (where applicable) are visualized along with included effect sizes using the midpoint of each alternative exposure range (trimmed data points are marked with a red x), with alcohol consumption in g/day on the x-axis and (log)RR on the y-axis.

We calculated the ROS as the average log RR of the BPRF between the 15th and 85th percentiles of alcohol exposure observed in the study data. The ROS summarizes the association of the exposure with the health outcome in a single measure. A higher, positive ROS indicates a larger association, while a negative ROS indicates a weak association. The ROS is identical for protective and harmful risks since it is based on the magnitude of the log RR. For example, a mean log BPRF between the 15th and 85th percentiles of exposure of −0.6 (protective association) and a mean log BPRF of 0.6 (harmful association) would both correspond to a ROS of 0.6. The ROS was then translated into a star rating, representing a conservative interpretation of all available evidence. A star rating of 1 (ROS: <0) indicates weak evidence of an association, a star rating of 2 (ROS: 0–0.14) indicates a >0–15% increased or >0–13% decreased risk, a star rating of 3 (ROS: >0.14–0.41) indicates a >15–50% increased or >13–34% decreased risk, a star rating of 4 (ROS: >0.41–0.62) indicates a >50–85% increased or >34–46% decreased risk, and a star rating of 5 (ROS: >0.62) indicates a >85% increased or >46% decreased risk.

Statistics & reproducibility

The statistical analyses conducted in this study are described above in detail. No statistical method was used to predetermine the sample size. When analyzing data from cohort and case-control studies, we excluded 10% of observations using a trimming algorithm; when analyzing data from MR studies, we did not exclude any observations. As all data used in this meta-analysis were from observational studies, no experiments were conducted, and no randomization or blinding took place.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The findings from this study were produced using data extracted from published literature. The relevant studies were identified through a systematic literature review and can all be accessed online as referenced in the current paper 26 , 27 , 28 , 29 , 31 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 . Further details on the relevant studies can be found on the GHDx website ( https://ghdx.healthdata.org/record/ihme-data/gbd-alcohol-ihd-bop-risk-outcome-scores ). Study characteristics of all relevant studies included in the analyses are also provided in Supplementary Information Section  4 (Tables  S5 and S6 ). The template of the data collection form is provided in Supplementary Information section  3 (Table  S4 ). The source data includes processed data from these studies that underlie our estimates. Source data are provided with this paper.

Code availability

Analyses were carried out using R version 4.0.5 and Python version 3.10.9. All code used for these analyses is publicly available online ( https://github.com/ihmeuw-msca/burden-of-proof ).

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Research reported in this publication was supported by the Bill & Melinda Gates Foundation [OPP1152504]. S.L. has received grants or contracts from the UK Medical Research Council [MR/T017708/1], CDC Foundation [project number 996], World Health Organization [APW No 2021/1194512], and is affiliated with the NIHR Oxford Biomedical Research Centre. The University of Oxford’s Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) is supported by core grants from the Medical Research Council [Clinical Trial Service Unit A310] and the British Heart Foundation [CH/1996001/9454]. The CTSU receives research grants from industry that are governed by University of Oxford contracts that protect its independence and has a staff policy of not taking personal payments from industry. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The funders of the study had no role in study design, data collection, data analysis, data interpretation, writing of the final report, or the decision to publish.

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Carr, S., Bryazka, D., McLaughlin, S.A. et al. A burden of proof study on alcohol consumption and ischemic heart disease. Nat Commun 15 , 4082 (2024). https://doi.org/10.1038/s41467-024-47632-7

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Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study

• Jocelyn Schroeder 1 ,
• Barbara Pesut 1 , 2 ,
• Lise Olsen 2 ,
• Nelly D. Oelke 2 &
• Helen Sharp 2  

BMC Nursing volume  23 , Article number:  326 ( 2024 ) Cite this article

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Metrics details

Medical Assistance in Dying (MAiD) was legalized in Canada in 2016. Canada’s legislation is the first to permit Nurse Practitioners (NP) to serve as independent MAiD assessors and providers. Registered Nurses’ (RN) also have important roles in MAiD that include MAiD care coordination; client and family teaching and support, MAiD procedural quality; healthcare provider and public education; and bereavement care for family. Nurses have a right under the law to conscientious objection to participating in MAiD. Therefore, it is essential to prepare nurses in their entry-level education for the practice implications and moral complexities inherent in this practice. Knowing what nursing students think about MAiD is a critical first step. Therefore, the purpose of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context.

The design was a mixed-method, modified e-Delphi method that entailed item generation from the literature, item refinement through a 2 round survey of an expert faculty panel, and item validation through a cognitive focus group interview with nursing students. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

During phase 1, a 56-item survey was developed from existing literature that included demographic items and items designed to measure experience with death and dying (including MAiD), education and preparation, attitudes and beliefs, influences on those beliefs, and anticipated future involvement. During phase 2, an expert faculty panel reviewed, modified, and prioritized the items yielding 51 items. During phase 3, a sample of nursing students further evaluated and modified the language in the survey to aid readability and comprehension. The final survey consists of 45 items including 4 case studies.

Systematic evaluation of knowledge-to-date coupled with stakeholder perspectives supports robust survey design. This study yielded a survey to assess nursing students’ attitudes toward MAiD in a Canadian context.

The survey is appropriate for use in education and research to measure knowledge and attitudes about MAiD among nurse trainees and can be a helpful step in preparing nursing students for entry-level practice.

Peer Review reports

Medical Assistance in Dying (MAiD) is permitted under an amendment to Canada’s Criminal Code which was passed in 2016 [ 1 ]. MAiD is defined in the legislation as both self-administered and clinician-administered medication for the purpose of causing death. In the 2016 Bill C-14 legislation one of the eligibility criteria was that an applicant for MAiD must have a reasonably foreseeable natural death although this term was not defined. It was left to the clinical judgement of MAiD assessors and providers to determine the time frame that constitutes reasonably foreseeable [ 2 ]. However, in 2021 under Bill C-7, the eligibility criteria for MAiD were changed to allow individuals with irreversible medical conditions, declining health, and suffering, but whose natural death was not reasonably foreseeable, to receive MAiD [ 3 ]. This population of MAiD applicants are referred to as Track 2 MAiD (those whose natural death is foreseeable are referred to as Track 1). Track 2 applicants are subject to additional safeguards under the 2021 C-7 legislation.

Three additional proposed changes to the legislation have been extensively studied by Canadian Expert Panels (Council of Canadian Academics [CCA]) [ 4 , 5 , 6 ] First, under the legislation that defines Track 2, individuals with mental disease as their sole underlying medical condition may apply for MAiD, but implementation of this practice is embargoed until March 2027 [ 4 ]. Second, there is consideration of allowing MAiD to be implemented through advanced consent. This would make it possible for persons living with dementia to receive MAID after they have lost the capacity to consent to the procedure [ 5 ]. Third, there is consideration of extending MAiD to mature minors. A mature minor is defined as “a person under the age of majority…and who has the capacity to understand and appreciate the nature and consequences of a decision” ([ 6 ] p. 5). In summary, since the legalization of MAiD in 2016 the eligibility criteria and safeguards have evolved significantly with consequent implications for nurses and nursing care. Further, the number of Canadians who access MAiD shows steady increases since 2016 [ 7 ] and it is expected that these increases will continue in the foreseeable future.

Nurses have been integral to MAiD care in the Canadian context. While other countries such as Belgium and the Netherlands also permit euthanasia, Canada is the first country to allow Nurse Practitioners (Registered Nurses with additional preparation typically achieved at the graduate level) to act independently as assessors and providers of MAiD [ 1 ]. Although the role of Registered Nurses (RNs) in MAiD is not defined in federal legislation, it has been addressed at the provincial/territorial-level with variability in scope of practice by region [ 8 , 9 ]. For example, there are differences with respect to the obligation of the nurse to provide information to patients about MAiD, and to the degree that nurses are expected to ensure that patient eligibility criteria and safeguards are met prior to their participation [ 10 ]. Studies conducted in the Canadian context indicate that RNs perform essential roles in MAiD care coordination; client and family teaching and support; MAiD procedural quality; healthcare provider and public education; and bereavement care for family [ 9 , 11 ]. Nurse practitioners and RNs are integral to a robust MAiD care system in Canada and hence need to be well-prepared for their role [ 12 ].

Previous studies have found that end of life care, and MAiD specifically, raise complex moral and ethical issues for nurses [ 13 , 14 , 15 , 16 ]. The knowledge, attitudes, and beliefs of nurses are important across practice settings because nurses have consistent, ongoing, and direct contact with patients who experience chronic or life-limiting health conditions. Canadian studies exploring nurses’ moral and ethical decision-making in relation to MAiD reveal that although some nurses are clear in their support for, or opposition to, MAiD, others are unclear on what they believe to be good and right [ 14 ]. Empirical findings suggest that nurses go through a period of moral sense-making that is often informed by their family, peers, and initial experiences with MAID [ 17 , 18 ]. Canadian legislation and policy specifies that nurses are not required to participate in MAiD and may recuse themselves as conscientious objectors with appropriate steps to ensure ongoing and safe care of patients [ 1 , 19 ]. However, with so many nurses having to reflect on and make sense of their moral position, it is essential that they are given adequate time and preparation to make an informed and thoughtful decision before they participate in a MAID death [ 20 , 21 ].

It is well established that nursing students receive inconsistent exposure to end of life care issues [ 22 ] and little or no training related to MAiD [ 23 ]. Without such education and reflection time in pre-entry nursing preparation, nurses are at significant risk for moral harm. An important first step in providing this preparation is to be able to assess the knowledge, values, and beliefs of nursing students regarding MAID and end of life care. As demand for MAiD increases along with the complexities of MAiD, it is critical to understand the knowledge, attitudes, and likelihood of engagement with MAiD among nursing students as a baseline upon which to build curriculum and as a means to track these variables over time.

Aim, design, and setting

The aim of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context. We sought to explore both their willingness to be involved in the registered nursing role and in the nurse practitioner role should they chose to prepare themselves to that level of education. The design was a mixed-method, modified e-Delphi method that entailed item generation, item refinement through an expert faculty panel [ 24 , 25 , 26 ], and initial item validation through a cognitive focus group interview with nursing students [ 27 ]. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

Participants

A panel of 10 faculty from the two nursing education programs were recruited for Phase 2 of the e-Delphi. To be included, faculty were required to have a minimum of three years of experience in nurse education, be employed as nursing faculty, and self-identify as having experience with MAiD. A convenience sample of 5 fourth-year nursing students were recruited to participate in Phase 3. Students had to be in good standing in the nursing program and be willing to share their experiences of the survey in an online group interview format.

The modified e-Delphi was conducted in 3 phases: Phase 1 entailed item generation through literature and existing survey review. Phase 2 entailed item refinement through a faculty expert panel review with focus on content validity, prioritization, and revision of item wording [ 25 ]. Phase 3 entailed an assessment of face validity through focus group-based cognitive interview with nursing students.

Phase I. Item generation through literature review

The goal of phase 1 was to develop a bank of survey items that would represent the variables of interest and which could be provided to expert faculty in Phase 2. Initial survey items were generated through a literature review of similar surveys designed to assess knowledge and attitudes toward MAiD/euthanasia in healthcare providers; Canadian empirical studies on nurses’ roles and/or experiences with MAiD; and legislative and expert panel documents that outlined proposed changes to the legislative eligibility criteria and safeguards. The literature review was conducted in three online databases: CINAHL, PsycINFO, and Medline. Key words for the search included nurses , nursing students , medical students , NPs, MAiD , euthanasia , assisted death , and end-of-life care . Only articles written in English were reviewed. The legalization and legislation of MAiD is new in many countries; therefore, studies that were greater than twenty years old were excluded, no further exclusion criteria set for country.

Items from surveys designed to measure similar variables in other health care providers and geographic contexts were placed in a table and similar items were collated and revised into a single item. Then key variables were identified from the empirical literature on nurses and MAiD in Canada and checked against the items derived from the surveys to ensure that each of the key variables were represented. For example, conscientious objection has figured prominently in the Canadian literature, but there were few items that assessed knowledge of conscientious objection in other surveys and so items were added [ 15 , 21 , 28 , 29 ]. Finally, four case studies were added to the survey to address the anticipated changes to the Canadian legislation. The case studies were based upon the inclusion of mature minors, advanced consent, and mental disorder as the sole underlying medical condition. The intention was to assess nurses’ beliefs and comfort with these potential legislative changes.

Phase 2. Item refinement through expert panel review

The goal of phase 2 was to refine and prioritize the proposed survey items identified in phase 1 using a modified e-Delphi approach to achieve consensus among an expert panel [ 26 ]. Items from phase 1 were presented to an expert faculty panel using a Qualtrics (Provo, UT) online survey. Panel members were asked to review each item to determine if it should be: included, excluded or adapted for the survey. When adapted was selected faculty experts were asked to provide rationale and suggestions for adaptation through the use of an open text box. Items that reached a level of 75% consensus for either inclusion or adaptation were retained [ 25 , 26 ]. New items were categorized and added, and a revised survey was presented to the panel of experts in round 2. Panel members were again asked to review items, including new items, to determine if it should be: included, excluded, or adapted for the survey. Round 2 of the modified e-Delphi approach also included an item prioritization activity, where participants were then asked to rate the importance of each item, based on a 5-point Likert scale (low to high importance), which De Vaus [ 30 ] states is helpful for increasing the reliability of responses. Items that reached a 75% consensus on inclusion were then considered in relation to the importance it was given by the expert panel. Quantitative data were managed using SPSS (IBM Corp).

Phase 3. Face validity through cognitive interviews with nursing students

The goal of phase 3 was to obtain initial face validity of the proposed survey using a sample of nursing student informants. More specifically, student participants were asked to discuss how items were interpreted, to identify confusing wording or other problematic construction of items, and to provide feedback about the survey as a whole including readability and organization [ 31 , 32 , 33 ]. The focus group was held online and audio recorded. A semi-structured interview guide was developed for this study that focused on clarity, meaning, order and wording of questions; emotions evoked by the questions; and overall survey cohesion and length was used to obtain data (see Supplementary Material 2  for the interview guide). A prompt to “think aloud” was used to limit interviewer-imposed bias and encourage participants to describe their thoughts and response to a given item as they reviewed survey items [ 27 ]. Where needed, verbal probes such as “could you expand on that” were used to encourage participants to expand on their responses [ 27 ]. Student participants’ feedback was collated verbatim and presented to the research team where potential survey modifications were negotiated and finalized among team members. Conventional content analysis [ 34 ] of focus group data was conducted to identify key themes that emerged through discussion with students. Themes were derived from the data by grouping common responses and then using those common responses to modify survey items.

Ten nursing faculty participated in the expert panel. Eight of the 10 faculty self-identified as female. No faculty panel members reported conscientious objector status and ninety percent reported general agreement with MAiD with one respondent who indicated their view as “unsure.” Six of the 10 faculty experts had 16 years of experience or more working as a nurse educator.

Five nursing students participated in the cognitive interview focus group. The duration of the focus group was 2.5 h. All participants identified that they were born in Canada, self-identified as female (one preferred not to say) and reported having received some instruction about MAiD as part of their nursing curriculum. See Tables  1 and 2 for the demographic descriptors of the study sample. Study results will be reported in accordance with the study phases. See Fig.  1 for an overview of the results from each phase.

Fig. 1  Overview of survey development findings

Phase 1: survey item generation

Review of the literature identified that no existing survey was available for use with nursing students in the Canadian context. However, an analysis of themes across qualitative and quantitative studies of physicians, medical students, nurses, and nursing students provided sufficient data to develop a preliminary set of items suitable for adaptation to a population of nursing students.

Four major themes and factors that influence knowledge, attitudes, and beliefs about MAiD were evident from the literature: (i) endogenous or individual factors such as age, gender, personally held values, religion, religiosity, and/or spirituality [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], (ii) experience with death and dying in personal and/or professional life [ 35 , 40 , 41 , 43 , 44 , 45 ], (iii) training including curricular instruction about clinical role, scope of practice, or the law [ 23 , 36 , 39 ], and (iv) exogenous or social factors such as the influence of key leaders, colleagues, friends and/or family, professional and licensure organizations, support within professional settings, and/or engagement in MAiD in an interdisciplinary team context [ 9 , 35 , 46 ].

Studies of nursing students also suggest overlap across these categories. For example, value for patient autonomy [ 23 ] and the moral complexity of decision-making [ 37 ] are important factors that contribute to attitudes about MAiD and may stem from a blend of personally held values coupled with curricular content, professional training and norms, and clinical exposure. For example, students report that participation in end of life care allows for personal growth, shifts in perception, and opportunities to build therapeutic relationships with their clients [ 44 , 47 , 48 ].

Preliminary items generated from the literature resulted in 56 questions from 11 published sources (See Table  3 ). These items were constructed across four main categories: (i) socio-demographic questions; (ii) end of life care questions; (iii) knowledge about MAiD; or (iv) comfort and willingness to participate in MAiD. Knowledge questions were refined to reflect current MAiD legislation, policies, and regulatory frameworks. Falconer [ 39 ] and Freeman [ 45 ] studies were foundational sources for item selection. Additionally, four case studies were written to reflect the most recent anticipated changes to MAiD legislation and all used the same open-ended core questions to address respondents’ perspectives about the patient’s right to make the decision, comfort in assisting a physician or NP to administer MAiD in that scenario, and hypothesized comfort about serving as a primary provider if qualified as an NP in future. Response options for the survey were also constructed during this stage and included: open text, categorical, yes/no , and Likert scales.

Phase 2: faculty expert panel review

Of the 56 items presented to the faculty panel, 54 questions reached 75% consensus. However, based upon the qualitative responses 9 items were removed largely because they were felt to be repetitive. Items that generated the most controversy were related to measuring religion and spirituality in the Canadian context, defining end of life care when there is no agreed upon time frames (e.g., last days, months, or years), and predicting willingness to be involved in a future events – thus predicting their future selves. Phase 2, round 1 resulted in an initial set of 47 items which were then presented back to the faculty panel in round 2.

Of the 47 initial questions presented to the panel in round 2, 45 reached a level of consensus of 75% or greater, and 34 of these questions reached a level of 100% consensus [ 27 ] of which all participants chose to include without any adaptations) For each question, level of importance was determined based on a 5-point Likert scale (1 = very unimportant, 2 = somewhat unimportant, 3 = neutral, 4 = somewhat important, and 5 = very important). Figure  2 provides an overview of the level of importance assigned to each item.

Ranking level of importance for survey items

After round 2, a careful analysis of participant comments and level of importance was completed by the research team. While the main method of survey item development came from participants’ response to the first round of Delphi consensus ratings, level of importance was used to assist in the decision of whether to keep or modify questions that created controversy, or that rated lower in the include/exclude/adapt portion of the Delphi. Survey items that rated low in level of importance included questions about future roles, sex and gender, and religion/spirituality. After deliberation by the research committee, these questions were retained in the survey based upon the importance of these variables in the scientific literature.

Of the 47 questions remaining from Phase 2, round 2, four were revised. In addition, the two questions that did not meet the 75% cut off level for consensus were reviewed by the research team. The first question reviewed was What is your comfort level with providing a MAiD death in the future if you were a qualified NP ? Based on a review of participant comments, it was decided to retain this question for the cognitive interviews with students in the final phase of testing. The second question asked about impacts on respondents’ views of MAiD and was changed from one item with 4 subcategories into 4 separate items, resulting in a final total of 51 items for phase 3. The revised survey was then brought forward to the cognitive interviews with student participants in Phase 3. (see Supplementary Material 1 for a complete description of item modification during round 2).

Phase 3. Outcomes of cognitive interview focus group

Of the 51 items reviewed by student participants, 29 were identified as clear with little or no discussion. Participant comments for the remaining 22 questions were noted and verified against the audio recording. Following content analysis of the comments, four key themes emerged through the student discussion: unclear or ambiguous wording; difficult to answer questions; need for additional response options; and emotional response evoked by questions. An example of unclear or ambiguous wording was a request for clarity in the use of the word “sufficient” in the context of assessing an item that read “My nursing education has provided sufficient content about the nursing role in MAiD.” “Sufficient” was viewed as subjective and “laden with…complexity that distracted me from the question.” The group recommended rewording the item to read “My nursing education has provided enough content for me to care for a patient considering or requesting MAiD.”

An example of having difficulty answering questions related to limited knowledge related to terms used in the legislation such as such as safeguards , mature minor , eligibility criteria , and conscientious objection. Students were unclear about what these words meant relative to the legislation and indicated that this lack of clarity would hamper appropriate responses to the survey. To ensure that respondents are able to answer relevant questions, student participants recommended that the final survey include explanation of key terms such as mature minor and conscientious objection and an overview of current legislation.

Response options were also a point of discussion. Participants noted a lack of distinction between response options of unsure and unable to say . Additionally, scaling of attitudes was noted as important since perspectives about MAiD are dynamic and not dichotomous “agree or disagree” responses. Although the faculty expert panel recommended the integration of the demographic variables of religious and/or spiritual remain as a single item, the student group stated a preference to have religion and spirituality appear as separate items. The student focus group also took issue with separate items for the variables of sex and gender, specifically that non-binary respondents might feel othered or “outed” particularly when asked to identify their sex. These variables had been created based upon best practices in health research but students did not feel they were appropriate in this context [ 49 ]. Finally, students agreed with the faculty expert panel in terms of the complexity of projecting their future involvement as a Nurse Practitioner. One participant stated: “I certainly had to like, whoa, whoa, whoa. Now let me finish this degree first, please.” Another stated, “I'm still imagining myself, my future career as an RN.”

Finally, student participants acknowledged the array of emotions that some of the items produced for them. For example, one student described positive feelings when interacting with the survey. “Brought me a little bit of feeling of joy. Like it reminded me that this is the last piece of independence that people grab on to.” Another participant, described the freedom that the idea of an advance request gave her. “The advance request gives the most comfort for me, just with early onset Alzheimer’s and knowing what it can do.” But other participants described less positive feelings. For example, the mature minor case study yielded a comment: “This whole scenario just made my heart hurt with the idea of a child requesting that.”

Based on the data gathered from the cognitive interview focus group of nursing students, revisions were made to 11 closed-ended questions (see Table  4 ) and 3 items were excluded. In the four case studies, the open-ended question related to a respondents’ hypothesized actions in a future role as NP were removed. The final survey consists of 45 items including 4 case studies (see Supplementary Material 3 ).

The aim of this study was to develop and validate a survey that can be used to track the growth of knowledge about MAiD among nursing students over time, inform training programs about curricular needs, and evaluate attitudes and willingness to participate in MAiD at time-points during training or across nursing programs over time.

The faculty expert panel and student participants in the cognitive interview focus group identified a need to establish core knowledge of the terminology and legislative rules related to MAiD. For example, within the cognitive interview group of student participants, several acknowledged lack of clear understanding of specific terms such as “conscientious objector” and “safeguards.” Participants acknowledged discomfort with the uncertainty of not knowing and their inclination to look up these terms to assist with answering the questions. This survey can be administered to nursing or pre-nursing students at any phase of their training within a program or across training programs. However, in doing so it is important to acknowledge that their baseline knowledge of MAiD will vary. A response option of “not sure” is important and provides a means for respondents to convey uncertainty. If this survey is used to inform curricular needs, respondents should be given explicit instructions not to conduct online searches to inform their responses, but rather to provide an honest appraisal of their current knowledge and these instructions are included in the survey (see Supplementary Material 3 ).

Some provincial regulatory bodies have established core competencies for entry-level nurses that include MAiD. For example, the BC College of Nurses and Midwives (BCCNM) requires “knowledge about ethical, legal, and regulatory implications of medical assistance in dying (MAiD) when providing nursing care.” (10 p. 6) However, across Canada curricular content and coverage related to end of life care and MAiD is variable [ 23 ]. Given the dynamic nature of the legislation that includes portions of the law that are embargoed until 2024, it is important to ensure that respondents are guided by current and accurate information. As the law changes, nursing curricula, and public attitudes continue to evolve, inclusion of core knowledge and content is essential and relevant for investigators to be able to interpret the portions of the survey focused on attitudes and beliefs about MAiD. Content knowledge portions of the survey may need to be modified over time as legislation and training change and to meet the specific purposes of the investigator.

Given the sensitive nature of the topic, it is strongly recommended that surveys be conducted anonymously and that students be provided with an opportunity to discuss their responses to the survey. A majority of feedback from both the expert panel of faculty and from student participants related to the wording and inclusion of demographic variables, in particular religion, religiosity, gender identity, and sex assigned at birth. These and other demographic variables have the potential to be highly identifying in small samples. In any instance in which the survey could be expected to yield demographic group sizes less than 5, users should eliminate the demographic variables from the survey. For example, the profession of nursing is highly dominated by females with over 90% of nurses who identify as female [ 50 ]. Thus, a survey within a single class of students or even across classes in a single institution is likely to yield a small number of male respondents and/or respondents who report a difference between sex assigned at birth and gender identity. When variables that serve to identify respondents are included, respondents are less likely to complete or submit the survey, to obscure their responses so as not to be identifiable, or to be influenced by social desirability bias in their responses rather than to convey their attitudes accurately [ 51 ]. Further, small samples do not allow for conclusive analyses or interpretation of apparent group differences. Although these variables are often included in surveys, such demographics should be included only when anonymity can be sustained. In small and/or known samples, highly identifying variables should be omitted.

There are several limitations associated with the development of this survey. The expert panel was comprised of faculty who teach nursing students and are knowledgeable about MAiD and curricular content, however none identified as a conscientious objector to MAiD. Ideally, our expert panel would have included one or more conscientious objectors to MAiD to provide a broader perspective. Review by practitioners who participate in MAiD, those who are neutral or undecided, and practitioners who are conscientious objectors would ensure broad applicability of the survey. This study included one student cognitive interview focus group with 5 self-selected participants. All student participants had held discussions about end of life care with at least one patient, 4 of 5 participants had worked with a patient who requested MAiD, and one had been present for a MAiD death. It is not clear that these participants are representative of nursing students demographically or by experience with end of life care. It is possible that the students who elected to participate hold perspectives and reflections on patient care and MAiD that differ from students with little or no exposure to end of life care and/or MAiD. However, previous studies find that most nursing students have been involved with end of life care including meaningful discussions about patients’ preferences and care needs during their education [ 40 , 44 , 47 , 48 , 52 ]. Data collection with additional student focus groups with students early in their training and drawn from other training contexts would contribute to further validation of survey items.

Future studies should incorporate pilot testing with small sample of nursing students followed by a larger cross-program sample to allow evaluation of the psychometric properties of specific items and further refinement of the survey tool. Consistent with literature about the importance of leadership in the context of MAiD [ 12 , 53 , 54 ], a study of faculty knowledge, beliefs, and attitudes toward MAiD would provide context for understanding student perspectives within and across programs. Additional research is also needed to understand the timing and content coverage of MAiD across Canadian nurse training programs’ curricula.

The implementation of MAiD is complex and requires understanding of the perspectives of multiple stakeholders. Within the field of nursing this includes clinical providers, educators, and students who will deliver clinical care. A survey to assess nursing students’ attitudes toward and willingness to participate in MAiD in the Canadian context is timely, due to the legislation enacted in 2016 and subsequent modifications to the law in 2021 with portions of the law to be enacted in 2027. Further development of this survey could be undertaken to allow for use in settings with practicing nurses or to allow longitudinal follow up with students as they enter practice. As the Canadian landscape changes, ongoing assessment of the perspectives and needs of health professionals and students in the health professions is needed to inform policy makers, leaders in practice, curricular needs, and to monitor changes in attitudes and practice patterns over time.

Availability of data and materials

The datasets used and/or analysed during the current study are not publicly available due to small sample sizes, but are available from the corresponding author on reasonable request.

Abbreviations

British Columbia College of Nurses and Midwives

Medical assistance in dying

Nurse practitioner

Registered nurse

University of British Columbia Okanagan

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Acknowledgements

We would like to acknowledge the faculty and students who generously contributed their time to this work.

JS received a student traineeship through the Principal Research Chairs program at the University of British Columbia Okanagan.

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JS made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. JS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. BP made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. BP has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. LO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. LO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. NDO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. NDO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. HS made substantial contributions to drafting and substantively revising the work. HS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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JS conducted this study as part of their graduate requirements in the School of Nursing, University of British Columbia Okanagan.

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Schroeder, J., Pesut, B., Olsen, L. et al. Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study. BMC Nurs 23 , 326 (2024). https://doi.org/10.1186/s12912-024-01984-z

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Received : 24 October 2023

Accepted : 28 April 2024

Published : 14 May 2024

DOI : https://doi.org/10.1186/s12912-024-01984-z

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• Medical assistance in dying (MAiD)
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Enhancing data integrity in Electronic Health Records: Review of methods for handling missing data

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Introduction Electronic Health Records (EHRs) are vital repositories of patient information for medical research, but the prevalence of missing data presents an obstacle to the validity and reliability of research. This study aimed to review and category ise methods for handling missing data in EHRs, to help researchers better understand and address the challenges related to missing data in EHRs.

Materials and Methods This study employed scoping review methodology. Through systematic searches on EMBASE up to October 2023, including review articles and original studies, relevant literature was identified. After removing duplicates, titles and abstracts were screened against inclusion criteria, followed by full-text assessment. Additional manual searches and reference list screenings were conducted. Data extraction focused on imputation techniques, dataset characteristics, assumptions about missing data, and article types. Additionally, we explored the availability of code within widely used software applications.

Results We reviewed 101 articles, with two exclusions as duplicates. Of the 99 remaining documents, 21 underwent full-text screening, with nine deemed eligible for data extraction. These articles introduced 31 imputation approaches classified into ten distinct methods, ranging from simple techniques like Complete Case Analysis to more complex methods like Multiple Imputation, Maximum Likelihood, and Expectation-Maximization algorithm. Additionally, machine learning methods were explored. The different imputation methods, present varying reliability. We identified a total of 32 packages across the four software platforms (R, Python, SAS, and Stata) for imputation methods. However, it’s significant that machine learning methods for imputation were not found in specific packages for SAS and Stata. Out of the 9 imputation methods we investigated, package implementations were available for 7 methods in all four software platforms.

Conclusions Several methods to handle missing data in EHRs are available. These methods range in complexity and make different assumptions about the missing data mechanisms. Knowledge gaps remain, notably in handling non-monotone missing data patterns and implementing imputation methods in real-world healthcare settings under the Missing Not at Random assumption. Future research should prioritize refining and directly comparing existing methods.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Amin Vahdati, Pre-Doctoral Fellow (NIHR 129296), is funded by the National Institute for Health and Care Research (NIHR) for this research project.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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