case study significance

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What the Case Study Method Really Teaches

• Nitin Nohria

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

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

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

• Nitin Nohria is the George F. Baker Professor of Business Administration, Distinguished University Service Professor, and former dean of Harvard Business School.

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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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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

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

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

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

Access your free e-book today.

What Is the Harvard Business School Case Study Method?

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

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

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

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

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

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

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

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

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

5 Benefits of Learning Through Case Studies

1. take new perspectives.

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

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

2. Hone Your Decision-Making Skills

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

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

3. Become More Open-Minded

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

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

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

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

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

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

4. Enhance Your Curiosity

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

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

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

5. Build Your Self-Confidence

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

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

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

How to Experience the Case Study Method

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

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

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

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

About the Author

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
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• 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

Definition and Introduction

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

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

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

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

How to Approach Writing a Case Analysis Paper

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

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

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

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

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

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

Structure and Writing Style

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

Introduction

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

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

Background Analysis

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

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

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

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

Identification of Problems

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

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

Alternative Solutions

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

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

Recommended Courses of Action

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

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

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

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

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

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

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• Published: 25 February 2020

Writing impact case studies: a comparative study of high-scoring and low-scoring case studies from REF2014

• Bella Reichard   ORCID: orcid.org/0000-0001-5057-4019 1 ,
• Mark S Reed 1 ,
• Jenn Chubb 2 ,
• Ged Hall   ORCID: orcid.org/0000-0003-0815-2925 3 ,
• Lucy Jowett   ORCID: orcid.org/0000-0001-7536-3429 4 ,
• Alisha Peart 4 &
• Andrea Whittle 1  

Palgrave Communications volume  6 , Article number:  31 ( 2020 ) Cite this article

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• Language and linguistics

This paper reports on two studies that used qualitative thematic and quantitative linguistic analysis, respectively, to assess the content and language of the largest ever sample of graded research impact case studies, from the UK Research Excellence Framework 2014 (REF). The paper provides the first empirical evidence across disciplinary main panels of statistically significant linguistic differences between high- versus low-scoring case studies, suggesting that implicit rules linked to written style may have contributed to scores alongside the published criteria on the significance, reach and attribution of impact. High-scoring case studies were more likely to provide specific and high-magnitude articulations of significance and reach than low-scoring cases. High-scoring case studies contained attributional phrases which were more likely to attribute research and/or pathways to impact, and they were written more coherently (containing more explicit causal connections between ideas and more logical connectives) than low-scoring cases. High-scoring case studies appear to have conformed to a distinctive new genre of writing, which was clear and direct, and often simplified in its representation of causality between research and impact, and less likely to contain expressions of uncertainty than typically associated with academic writing. High-scoring case studies in two Main Panels were significantly easier to read than low-scoring cases on the Flesch Reading Ease measure, although both high-scoring and low-scoring cases tended to be of “graduate” reading difficulty. The findings of our work enable impact case study authors to better understand the genre and make content and language choices that communicate their impact as effectively as possible. While directly relevant to the assessment of impact in the UK’s Research Excellence Framework, the work also provides insights of relevance to institutions internationally who are designing evaluation frameworks for research impact.

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Research impact evaluation and academic discourse

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Aspiring to greater intellectual humility in science

Introduction.

Academics are under increasing pressure to engage with non-academic actors to generate “usable” knowledge that benefits society and addresses global challenges (Clark et al., 2016 ; Lemos, 2015 ; Rau et al., 2018 ). This is largely driven by funders and governments that seek to justify the societal value of public funding for research (Reed et al., 2020 ; Smith et al., 2011 ) often characterised as ‘impact’. While this has sometimes been defined narrowly as reflective of the need to demonstrate a return on public investment in research (Mårtensson et al., 2016 ; Tsey et al., 2016 ; Warry, 2006 ), there is also a growing interest in the evaluation of “broader impacts” from research (cf. Bozeman and Youtie, 2017 ; National Science Foundation, 2014 ), including less tangible but arguably equally relevant benefits for society and culture. This shift is exemplified by the assessment of impact in the UK’s Research Excellence Framework (REF) in 2014 and 2021, the system for assessing the quality of research in UK higher education institutions, and in the rise of similar policies and evaluation systems in Australia, Hong Kong, the United States, Horizon Europe, The Netherlands, Sweden, Italy, Spain and elsewhere (Reed et al., 2020 ).

The evaluation of research impact in the UK has been criticised by scholars largely for its association with a ‘market logic’ (Olssen and Peters, 2005 ; Rhoads and Torres, 2005 ). Critics argue that a focus of academic performativity can be seen to “destabilise” professional identities (Chubb and Watermeyer, 2017 ), which in the context of research impact evaluation can further “dehumanise and deprofessionalise” academic performance (Watermeyer, 2019 ), whilst leading to negative unintended consequences (which Derrick et al., 2018 , called “grimpact”). MacDonald ( 2017 ), Chubb and Reed ( 2018 ) and Weinstein et al. ( 2019 ) reported concerns from researchers that the impact agenda may be distorting research priorities, “encourag[ing] less discovery-led research” (Weinstein et al., 2019 , p. 94), though these concerns were questioned by University managers in the same study who were reported to “not have enough evidence to support that REF was driving specific research agendas in either direction” (p. 94), and further questioned by Hill ( 2016 ).

Responses to this critique have been varied. Some have called for civil disobedience (Watermeyer, 2019 ) and organised resistance (Back, 2015 ; MacDonald, 2017 ) against the impact agenda. In a review of Watermeyer ( 2019 ), Reed ( 2019 ) suggested that attitudes towards the neoliberal political roots of the impact agenda may vary according to the (political) values and beliefs of researchers, leading them to pursue impacts that either support or oppose neoliberal political and corporate interests. Some have defended the benefits of research impact evaluation. For example, Weinstein et al. ( 2019 ) found that “a focus on changing the culture outside of academia is broadly valued” by academics and managers. The impact agenda might enhance stakeholder engagement (Hill, 2016 ) and give “new currency” to applied research (Chubb, 2017 ; Watermeyer, 2019 ). Others have highlighted the long-term benefits for society of incentivising research impact, including increased public support and funding for a more accountable, outward-facing research system (Chubb and Reed, 2017 ; Hill, 2016 ; Nesta, 2018 ; Oancea, 2010 , 2014 ; Wilsdon et al., 2015 ).

In the UK REF, research outputs and impact are peer reviewed at disciplinary level in ‘Units of Assessment’ (36 in 2014, 34 in 2021), grouped into four ‘Main Panels’. Impact is assessed through case studies that describe the effects of academic research and are given a score between 1* (“recognised but modest”) and 4* (“outstanding”). The case studies follow a set structure of five sections: 1—Summary of the impact; 2—Underpinning research; 3—References to the research; 4—Details of the impact; 5—Sources to corroborate the impact (HEFCE, 2011 ). The publication of over 6000 impact case studies in 2014 Footnote 1 by Research England (formerly Higher Education Funding Council for England, HEFCE) was unique in terms of its size, and unlike the recent selective publication of high-scoring case studies from Australia’s 2018 Engagement and Impact Assessment, both high-scoring and low-scoring case studies were published. This provides a unique opportunity to evaluate the construction of case studies that were perceived by evaluation panels to have successfully demonstrated impact, as evidenced by a 4* rating, and to compare these to case studies that were judged as less successful.

The analysis of case studies included in this research is based on the definition of impact used in REF2014, as “an effect on, change or benefit to the economy, society, culture, public policy or services, health, the environment or quality of life, beyond academia” (HEFCE, 2011 , p. 26). According to REF2014 guidance, the primary functions of an impact case study were to articulate and evidence the significance and reach of impacts arising from research beyond academia, clearly demonstrating the contribution that research from a given institution contributed to those impacts (HEFCE, 2011 ).

In addition to these explicit criteria driving the evaluation of impact in REF2014, a number of analyses have emphasised the role of implicit criteria and subjectivity in shaping the evaluation of impact. For example, Pidd and Broadbent ( 2015 ) emphasised the implicit role a “strong narrative” plays in high-scoring case studies (p. 575). This was echoed by the fears of one REF2014 panellist interviewed by Watermeyer and Chubb ( 2018 ) who said, “I think with impact it is literally so many words of persuasive narrative” as opposed to “giving any kind of substance” (p. 9). Similarly, Watermeyer and Hedgecoe ( 2016 ), reporting on an internal exercise at Cardiff University to evaluate case studies prior to submission, emphasised that “style and structure” were essential to “sell impact”, and that “case studies that best sold impact were those rewarded with the highest evaluative scores” (p. 651).

Recent research based on interviews with REF2014 panellists has also emphasised the subjectivity of the peer-review process used to evaluate impact. Derrick’s ( 2018 ) research findings based on panellist interviews and participant observation of REF2014 sub-panels argued that scores were strongly influenced by who the evaluators were and how the group assessed impact together. Indeed, a panellist interviewed by Watermeyer and Chubb ( 2018 ) concurred that “the panel had quite an influence on the criteria” (p. 7), including an admission that some types of (more intangible) evidence were more likely to be overlooked than other (more concrete) forms of evidence, “privileg[ing] certain kinds of impact”. Other panellists interviewed spoke of their emotional and intellectual vulnerability in making judgements about an impact criterion that they had little prior experience of assessing (Watermeyer and Chubb, 2018 ). Derrick ( 2018 ) argued that this led many evaluators to base their assessments on more familiar proxies for excellence linked to scientific excellence, which led to biased interpretations and shortcuts that mimicked “groupthink” (p. 193).

This paper will for the first time empirically assess the content and language of the largest possible sample of research impact case studies that received high versus low scores from assessment panels in REF2014. Combining qualitative thematic and quantitative linguistic analysis, we ask:

How do high-scoring versus low-scoring case studies articulate and evidence impacts linked to underpinning research?

Do high-scoring and low-scoring case studies have differences in their linguistic features or styles?

Do high-scoring and low-scoring case studies have lexical differences (words and phrases that are statistically more likely to occur in high- or low-scoring cases) or text-level differences (including reading ease, narrative clarity, use of cohesive devices)?

By answering these questions, our goal is to provide evidence for impact case study authors and their institutions to reflect on in order to optimally balance the content and to use language that communicates their impact as effectively as possible. While directly relevant to the assessment of impact in the UK’s REF, the work also provides insights of relevance to institutions internationally who are designing evaluation frameworks for research impact.

Research design and sample

The datasets were generated by using published institutional REF2014 impact scores to deduce the scores of some impact case studies themselves. Although scores for individual case studies were not made public, we were able to identify case studies that received the top mark of 4* based on the distribution of scores received by some institutions, where the whole submission by an institution in a given Unit of Assessment was awarded the same score. In those 20 Units of Assessment (henceforth UoA) where high-scoring case studies could be identified in this way, we also accessed all case studies known to have scored either 1* or 2* in order to compare the features of high-scoring case studies to those of low-scoring case studies.

We approached our research questions with two separate studies, using quantitative linguistic and qualitative thematic analysis respectively. The thematic analysis, explained in more detail in the section “Qualitative thematic analysis” below, allowed us to find answers to research question 1 (see above). The quantitative linguistic analysis was used to extract and compare typical word combinations for high-scoring and low-scoring case studies, as well as assessing their readability. It mainly addressed research questions 2 and 3.

The quantitative linguistic analysis was based on a sample of all identifiable high-scoring case studies in any UoA ( n  = 124) and all identifiable low-scoring impact case studies in those UoAs where high-scoring case studies could be identified ( n  = 93). As the linguistic analysis focused on identifying characteristic language choices in running text, only those sections designed to contain predominantly text were included (1—Summary of the impact; 2—Underpinning research; 4—Details of the impact). Figure 1 shows the distribution of case studies across Main Panels in the quantitative analysis. Table 1 summarises the number of words included in the analysis.

Distribution of case studies across Main Panels used for the linguistic analysis sample.

In order to detect patterns of content in high-scoring and low-scoring case studies across all four Main Panels, a sub-sample of case studies was selected for a qualitative thematic analysis. This included 60% of high-scoring case studies and 97% of low-scoring case studies from the quantitative analysis, such that only UoAs were included where both high-scoring and low-scoring case studies are available (as opposed to the quantitative sample, which includes all available high-scoring case studies). Further selection criteria were then designed to create a greater balance in the number of high-scoring and low-scoring case studies across Main Panels. Main Panels A (high) and C (low) were particularly over-represented, so a lower proportion of those case studies were selected and 10 additional high-scoring case studies were considered in Panel B, including institutions where at least 85% of the case studies scored 4* and the remaining scores were 3*. As this added a further UoA, we could also include 14 more low-scoring case studies in Main Panel B. This resulted in a total of 85 high-scoring and 90 low-scoring case studies. Figure 2 shows the distribution of case studies across Main Panels in the thematic analysis, illustrating the greater balance compared to the sample used in the quantitative analysis. The majority (75%) of the case studies analysed are included in both samples (Table 2 ).

Distribution of case studies across Main Panels used for the thematic analysis sample.

Quantitative linguistic analysis

Quantitative linguistic analysis can be used to make recurring patterns in language use visible and to assess their significance. We treated the dataset of impact case studies as a text collection (the ‘corpus’) divided into two sections, namely high-scoring and low-scoring case studies (the two ‘sub-corpora’), in order to explore the lexical profile and the readability of the case studies.

One way to explore the lexical profile of groups of texts is to generate frequency-based word lists and compare these to word lists from a reference corpus to determine which words are characteristic of the corpus of interest (“keywords”, cf. Scott, 1997 ). Another way is to extract word combinations that are particularly frequent. Such word combinations, called “lexical bundles”, are “extended collocations” (Hyland, 2008 , p. 41) that appear across a set range of texts (Esfandiari and Barbary, 2017 ). We merged these two approaches in order to uncover meanings that could not be made visible through the analysis of single-word frequencies, comparing lexical bundles from each sub-corpus to the other. Lexical bundles of 2–4 words were extracted with AntConc (specialist software developed by Anthony, 2014 ) firstly from the corpus of all high-scoring case studies and then separately from the sub-corpora of high-scoring case studies in Main Panel A, C and D. Footnote 2 The corresponding lists were extracted from low-scoring case studies overall and separated by panel. The lists of lexical bundles for each of the high-scoring corpus parts were then compared to the corresponding low-scoring parts (High-Overall vs. Low-Overall, High-Main Panel A vs. Low-Main Panel A, etc.) to detect statistically significant over-use and under-use in one set of texts relative to another.

Two statistical measures were used in the analysis of lexical bundles. Log Likelihood was used as a measure of the statistical significance of frequency differences (Rayson and Garside, 2000 ), with a value of >3.84 corresponding to p  < 0.05. This measure had the advantage, compared to the more frequently used chi-square test, of not assuming a normal distribution of data (McEnery et al., 2006 ). The Log Ratio (Hardie, 2014 ) was used as a measure of effect size, which quantifies the scale, rather than the statistical significance, of frequency differences between two datasets. The Log Ratio is technically the binary log of the relative risk, and a value of >0.5 or <−0.5 is considered meaningful in corpus linguistics (Hardie, 2014 ), with values further removed from 0 reflecting a bigger difference in the relative frequencies found in each corpus. There is currently no agreed standard effect size measure for keywords (Brezina, 2018 , p. 85) and the Log Ratio was chosen because it is straightforward to interpret. Each lexical bundle that met the ‘keyness’ threshold (Log Likelihood > 3.84 in the case of expected values > 12, with higher significance levels needed for expected values < 13—see Rayson et al., 2004 , p. 8) was then assigned a code according to its predominant meaning in the texts, as reflected in the contexts captured in the concordance lines extracted from the corpus.

In the thematic analysis, it appeared that high-scoring case studies were easier to read. In order to quantify the readability of the texts, we therefore analysed them using the Coh-Metrix online tool (www.cohmetrix.com, v3.0) developed by McNamara et al. ( 2014 ). This tool provides 106 descriptive indices of language features, including 8 principal component scores developed from combinations of the other indices (Graesser et al., 2011 ). We selected these principal component scores as comprehensive measures of “reading ease” because they assess multiple characteristics of the text, up to whole-text discourse level (McNamara et al., 2014 , p. 78). This was supplemented by the traditional and more wide-spread Flesch Reading Ease score of readability measuring the lengths of words and sentences, which are highly correlated with reading speed (Haberlandt and Graesser, 1985 ). The selected measures were compared across corpus sections using t -tests to evaluate significance. The effect size was measured using Cohen’s D , following Brezina ( 2018 , p. 190), where D  > 0.3 indicates a small, D  > 0.5 a medium, and D  > 0.8 a high effect size. As with the analysis of lexical bundles, comparisons were made between high- and low-scoring case studies in each of Main Panels A, C and D, as well as between all high-scoring and all low-scoring case studies across Main Panels.

Qualitative thematic analysis

While a quantitative analysis as described above can make differences in the use of certain words visible, it does not capture the narrative or content of the texts under investigation. In order to identify common features of high-scoring and low-scoring case studies, thematic analysis was chosen to complement the quantitative analysis by identifying patterns and inferring meaning from qualitative data (Auerbach and Silverstein, 2003 ; Braun and Clarke, 2006 ; Saldana, 2009 ). To familiarise themselves with the data and for inter-coder reliability, two research team members read a selection of REF2014 impact case studies from different Main Panels, before generating initial codes for each of the five sections of the impact case study template. These were discussed with the full research team, comprising three academic and three professional services staff who had all read multiple case studies themselves. They were piloted prior to defining a final set of themes and questions against which the data was coded (based on the six-step process outlined by Braun and Clarke, 2006 ) (Table 3 ). An additional category was used to code stylistic features, to triangulate elements of the quantitative analysis (e.g. readability) and to include additional stylistic features difficult to assess in quantitative terms (e.g. effective use of testimonials). In addition to this, 10 different types of impact were coded for, based on Reed’s ( 2018 ) typology: capacity and preparedness, awareness and understanding, policy, attitudinal change, behaviour change and other forms of decision-making, other social, economic, environmental, health and wellbeing, and cultural impacts. There was room for coders to include additional insights arising in each section of the case study that had not been captured in the coding system; and there was room to summarise other key factors they thought might account for high or low scores.

Coders summarised case study content pertaining to each code, for example by listing examples of effective or poor use of structure and formatting as they arose in each case study. Coders also quoted the original material next to their summaries so that their interpretation could be assessed during subsequent analysis. This initial coding of case study text was conducted by six coders, with intercoder reliability (based on 10% of the sample) assessed at over 90%. Subsequent thematic analysis within the codes was conducted by two of the co-authors. This involved categorising coded material into themes as a way of assigning meaning to features that occurred across multiple case studies (e.g. categorising types of corroborating evidence typically used in high-scoring versus low-scoring case studies).

Results and discussion

In this section, we integrate findings from the quantitative linguistic study and the qualitative analysis of low-scoring versus high-scoring case studies. The results are discussed under four headings based on the key findings that emerged from both analyses. Taken together, these findings provide the most comprehensive evidence to date of the characteristics of a top-rated (4*) impact case study in REF2014.

Highly-rated case studies provided specific, high-magnitude and well-evidenced articulations of significance and reach

One finding from our qualitative thematic analysis was that 84% of high-scoring cases articulated benefits to specific groups and provided evidence of their significance and reach, compared to 32% of low-scoring cases which typically focused instead on the pathway to impact, for example describing dissemination of research findings and engagement with stakeholders and publics without citing the benefits arising from dissemination or engagement. One way of conceptualising this difference is using the content/process distinction: whereas low-scoring cases tended to focus on the process through which impact was sought (i.e. the pathway used), the high-scoring cases tended to focus on the content of the impact itself (i.e. what change or improvement occurred as a result of the research).

Examples of global reach were evidenced across high-scoring case studies from all panels (including Panel D for Arts and Humanities research), but were less often claimed or evidenced in low-scoring case studies. Where reach was more limited geographically, many high-scoring case studies used context to create robust arguments that their reach was impressive in that context, describing reach for example in social or cultural terms or arguing for the importance of reaching a narrow but hard-to-reach or otherwise important target group.

Table 4 provides examples of evidence from high-scoring cases and low-scoring cases that were used to show significance and reach of impacts in REF2014.

Findings from the quantitative linguistic analysis in Table 5 show how high-scoring impact case studies contained more phrases that specified reach (e.g. “in England and”, “in the US”), compared to low-scoring case studies that used the more generic term “international”, leaving the reader in doubt about the actual reach. They also include more phrases that implicitly specified the significance of the impact (e.g. “the government’s” or “to the House of Commons”), compared to low-scoring cases which provided more generic phrases, such as “policy and practice”, rather than detailing specific policies or practices that had been changed.

The quantitative linguistics analysis also identified a number of words and phrases pertaining to engagement and pathways, which were intended to deliver impact but did not actually specify impact (Table 6 ). A number of phrases contained the word “dissemination”, and there were several words and phrases specifying types of engagement that could be considered more one-way dissemination than consultative or co-productive (cf. Reed et al.’s ( 2018 ) engagement typology), e.g. “the book” and “the event”. The focus on dissemination supports the finding from the qualitative thematic analysis that low-scoring case tended to focus more on pathways or routes than on impact. Although it is not possible to infer this directly from the data, it is possible that this may represent a deeper epistemological position underpinning some case studies, where impact generation was seen as one-way knowledge or technology transfer, and research findings were perceived as something that could be given unchanged to publics and stakeholders through dissemination activities, with the assumption that this would be understood as intended and lead to impact.

It is worth noting that none of the four UK countries appear significantly more often in either high-scoring or low-scoring case studies (outside of the phrase “in England and”). Wales ( n  = 50), Scotland ( n  = 71) and Northern Ireland ( n  = 32) appear slightly more often in high-scoring case studies, but the difference is not significant (England: n  = 162). An additional factor to take into account is that our dataset includes only submissions that are either high-scoring or low-scoring, and the geographical spread of the submitting institutions was not a factor in selecting texts. There was a balanced number of high-scoring and low-scoring case studies in the sample from English, Scottish and Welsh universities, but no guaranteed low-scoring submissions from Northern Irish institutions. The REF2014 guidance made it clear that impacts in each UK country would be evaluated equally in comparison to each other, the UK and other countries. While the quantitative analysis of case studies from our sample only found a statistically significant difference for the phrase “in England and”, this, combined with the slightly higher number of phrases containing the other countries of the UK in high-scoring case studies, might indicate that this panel guidance was implemented as instructed.

Figures 3 – 5 shows which types of impact could be identified in high-scoring or low-scoring case studies, respectively, in the qualitative thematic analysis (based on Reed’s ( 2018 ) typology of impacts). Note that percentages do not add up to 100% because it was possible for each case study to claim more than one type of impact (high-scoring impact case studies described on average 2.8 impacts, compared to an average of 1.8 impacts described by low-scoring case studies) Footnote 3 . Figure 3 shows the number of impacts per type as a percentage of the total number of impacts claimed in high-scoring versus low-scoring case studies. This shows that high-scoring case studies were more likely to claim health/wellbeing and policy impacts, whereas low-scoring case studies were more likely to claim understanding/awareness impacts. Looking at this by Main Panel, over 50% of high-scoring case studies in Main Panel A claimed health/wellbeing, policy and understanding/awareness impacts (Fig. 4 ), whereas over 50% of low-scoring case studies in Main Panel A claimed capacity building impacts (Fig. 5 ). There were relatively high numbers of economic and policy claimed in both high-scoring and low-scoring case studies in Main Panels B and C, respectively, with no impact type dominating strongly in Main Panel D (Figs. 4 and 5 ).

Number of impacts claimed in high- versus low-scoring case studies by impact type.

Percentage of high-scoring case studies that claimed different types of impact.

Percentage of low-scoring case studies that claimed different types of impact.

Highly-rated case studies used distinct features to establish links between research (cause) and impact (effect)

Findings from the quantitative linguistic analysis show that high-scoring case studies were significantly more likely to include attributional phrases like “cited in”, “used to” and “resulting in”, compared to low-scoring case studies (Table 7 provides examples for some of the 12 phrases more frequent in high-scoring case studies). However, there were some attributional phrases that were more likely to be found in low-scoring case studies (e.g. “from the”, “of the research” and “this work has”—total of 9 different phrases).

To investigate this further, all 564 and 601 instances Footnote 4 of attributional phrases in high-scoring and low-scoring case studies, respectively, were analysed to categorise the context in which they were used, to establish the extent to which these phrases in each corpus were being used to establish attribution to impacts. The first word or phrase preceding or succeeding the attributional content was coded. For example, if the attributional content was “used the”, followed by “research to generate impact”, the first word succeeding the attributional content (in this case “research”) was coded rather than the phrase it subsequently led to (“generate impact”). According to a Pearson Chi Square test, high-scoring case studies were significantly more likely to establish attribution to impact than low-scoring cases ( p  < 0.0001, but with a small effect size based on Cramer’s V  = 0.22; bold in Table 8 ). 18% ( n  = 106) of phrases in the low-scoring corpus established attribution to impact, compared to 37% ( n  = 210) in the high-scoring corpus, for example, stating that research, pathway or something else led to impact. Instead, low-scoring case studies were more likely to establish attribution to research (40%; n  = 241) compared to high-scoring cases (28%; n  = 156; p  < 0.0001, but with a small effect size based on Cramer’s V  = 0.135). Both high- and low-scoring case studies were similarly likely to establish attribution to pathways (low: 32%; n  = 194; high: 31% n  = 176).

Moreover, low-scoring case studies were more likely to include ambiguous or uncertain phrases. For example, the phrase “a number of” can be read to imply that it is not known how many instances there were. This occurred in all sections of the impact case studies, for example in the underpinning research section as “The research explores a number of themes” or in the summary or details of the impact section as “The work has also resulted in a number of other national and international impacts”, or “has influenced approaches and practices of a number of partner organisations”. Similarly, “an impact on” could give the impression that the nature of the impact is not known. This phrase occurred only in summary and details of the impact sections, for example, “These activities have had an impact on the professional development”, “the research has had an impact on the legal arguments”, or “there has also been an impact on the work of regional agency”.

In the qualitative thematic analysis, we found that only 50% of low-scoring case studies clearly linked the underpinning research to claimed impacts (compared to 97% of high-scoring cases). This gave the impression of over-claimed impacts in some low-scoring submissions. For example, one case study claimed “significant impacts on [a country’s] society” based on enhancing the security of a new IT system in the department responsible for publishing and archiving legislation. Another claimed “economic impact on a worldwide scale” based on billions of pounds of benefits, calculated using an undisclosed method by an undisclosed evaluator in an unpublished final report by the research team. One case study claimed attribution for impact based on similarities between a prototype developed by the researchers and a product subsequently launched by a major corporation, without any evidence that the product as launched was based on the prototype. Similar assumptions were made in a number of other case studies that appeared to conflate correlation with causation in their attempts to infer attribution between research and impact. Table 9 provides examples of different ways in which links between research and impact were evidenced in the details of the research section.

Table 10 shows how corroborating sources were used to support these claims. 82% of high-scoring case studies compared to 7% of low-scoring cases were identified in the qualitative thematic analysis as having generally high-quality corroborating evidence. In contrast, 11% of high-scoring case studies, compared to 71% of low-scoring cases, were identified as having corroborating evidence that was vague and/or poorly linked to claimed impacts. Looking at only case studies that claim policy impact, 11 out of 26 high-scoring case studies in the sample described both policy and implementation (42%), compared to just 5 out of 29 low-scoring case studies that included both policy and implementation (17%; the remainder described policy impacts only with no evidence of benefits arising from implementation). High- scoring case studies were more likely to cite evidence of impacts rather than just citing evidence pertaining to the pathway (which was more common in low-scoring cases). High-scoring policy case studies also provided evidence pertaining to the pathway, but because they typically also included evidence of policy change, this evidence helped attribute policy impacts to research.

Highly-rated case studies were easy to understand and well written

In preparation for the REF, many universities invested heavily in writing assistance (Coleman, 2019 ) to ensure that impact case studies were “easy to understand and evaluation-friendly” (Watermeyer and Chubb, 2018 ) for the assessment panels, which comprised academics and experts from other sectors (HEFCE, 2011 , p. 6). With this in mind, we investigated readability and style, both in the quantitative linguistic and in the qualitative thematic analysis.

High-scoring impact case studies scored more highly on the Flesch Reading Ease score, a readability measure based on the length of words and sentences. The scores in Table 11 are reported out of 100, with a higher score indicating that a text is easier to read. While the scores reveal a significant difference between 4* and 1*/2* impact case studies, they also indicate that impact case studies are generally on the verge of “graduate” difficulty (Hartley, 2016 , p. 1524). As such our analysis should not be understood as suggesting that these technical documents should be adjusted to the readability of a newspaper article, but they should be maintained at interested and educated non-specialist level.

Interestingly, there were differences between the main panels. Footnote 5 In Social Science and Humanities case studies (Main Panels C and D), high-scoring impact case studies scored significantly higher on reading ease than low-scoring ones. There was no significant difference in Main Panel A between 4* and 1*/2* cases. However, all Main Panel A case studies showed, on average, lower reading ease scores than the low-scoring cases in Main Panels C and D. This means that their authors used longer words and sentences, which may be explained in part by more and longer technical terms needed in Main Panel A disciplines; the difference between high- and low-scoring case studies in Main Panels C and D may be explained by the use of more technical jargon (confirmed in the qualitative analysis).

The Flesch Reading Ease measure assesses the sentence- and word-level, rather than capturing higher-level text-processing difficulty. While this is recognised as a reliable indicator of comparative reading ease, and the underlying measures of sentence-length and word-length are highly correlated with reading speed (Haberlandt and Graesser, 1985 ), Hartley ( 2016 ) is right in his criticism that the tool takes neither the meaning of the words nor the wider text into account. The Coh-Metrix tool (McNamara et al., 2014 ) provides further measures for reading ease based on textual cohesion in these texts compared to a set of general English texts. Of the eight principal component scores computed by the tool, most did not reveal a significant difference between high- and low-scoring case studies or between different Main Panels. Moreover, in most measures, impact case studies overall were fairly homogenous compared to the baseline of general English texts. However, there were significant differences between high- and low-scoring impact case studies in two of the measures: “deep cohesion” and “connectivity” (Table 12 ).

“Deep cohesion” shows whether a text makes causal connections between ideas explicit (e.g. “because”, “so”) or leaves them for the reader to infer. High-scoring case studies had a higher level of deep cohesion compared to general English texts (Graesser et al., 2011 ), while low-scoring case studies tended to sit below the general English average. In addition, Main Panel A case studies (Life Sciences), which received the lowest scores in Flesch Reading Ease, on average scored higher on deep cohesion than case studies in more discursive disciplines (Main Panel C—Social Sciences and Main Panel D—Arts and Humanities). “Connectivity” measures the level of explicit logical connectives (e.g. “and”, “or” and “but”) to show relations in the text. Impact case studies were low in connectivity compared to general English texts, but within each of the Main Panels, high-scoring case studies had more explicit connectivity than low-scoring case studies. This means that Main Panel A case studies, while using on average longer words and sentences as indicated by the Flesch Reading Ease scores, compensated for this by making causal and logical relationships more explicit in the texts. In Main Panels C and D, which on average scored lower on these measures, there was a clearer difference between high- and low-scoring case studies than in Main Panel A, with high-scoring case studies being easier to read.

Linked to this, low-scoring case studies across panels were more likely than high-scoring case studies to contain phrases linked to the research process (suggesting an over-emphasis on the research rather than the impact, and a focus on process over findings or quality; Table 18 ) and filler-phrases (Table 13 ).

High-scoring case studies were more likely to clearly identify individual impacts via subheadings and paragraph headings ( p  < 0.0001, with effect size measure Log Ratio 0.54). The difference is especially pronounced in Main Panel D (Log Ratio 1.53), with a small difference in Main Panel C and no significant difference in Main Panel A. In Units of Assessment combined in Main Panel D, a more discursive academic writing style is prevalent (see e.g. Hyland, 2002 ) using fewer visual/typographical distinctions such as headings. The difference in the number of headings used in case studies from those disciplines suggests that high-scoring case studies showed greater divergence from disciplinary norms than low-scoring case studies. This may have allowed them to adapt the presentation of their research impact to the audience of panel members to a greater extent than low-scoring case studies.

The qualitative thematic analysis of Impact Case Studies indicates that it is not simply the number of subheadings that matters, although this comparison is interesting especially in the context of the larger discrepancy in Main Panel D. Table 14 summarises formatting that was considered helpful and unhelpful from the qualitative analysis.

The observations in Tables 11 – 13 stem from quantitative linguistic analysis, which, while enabling statistical testing, does not show directly the effect of a text on the reader. When conducting the qualitative thematic analysis, we collected examples of formatting and stylistic features from the writing and presentation of high and low-scoring case studies that might have affected clarity of the texts (Tables 14 and 15 ). Specifically, 38% of low-scoring case studies made inappropriate use of adjectives to describe impacts (compared to 20% of high-scoring; Table 16 ). Inappropriate use of adjectives may have given an impression of over-claiming or created a less factual impression than case studies that used adjectives more sparingly to describe impacts. Some included adjectives to describe impacts in testimonial quotes, giving third-party endorsement to the claims rather than using these adjectives directly in the case study text.

Highly-rated case studies were more likely to describe underpinning research findings, rather than research processes

To be eligible, case studies in REF2014 had to be based on underpinning research that was “recognised internationally in terms of originality, significance and rigour” (denoted by a 2* quality profile, HEFCE, 2011 , p. 29). Ineligible case studies were excluded from our sample (i.e. those in the “unclassifiable” quality profile), so all the case studies should have been based on strong research. Once this research quality threshold had been passed, scores were based on the significance and reach of impact, so case studies with higher-rated research should not, in theory, get better scores on the basis of their underpinning research. However, there is evidence that units whose research outputs scored well in REF2014 also performed well on impact (unpublished Research England analysis cited in Hill, 2016 ). This observation only shows that high-quality research and impact were co-located, rather than demonstrating a causal relationship between high-quality research and highly rated impacts. However, our qualitative thematic analysis suggests that weaker descriptions of research (underpinning research was not evaluated directly) may have been more likely to be co-located with lower-rated impacts at the level of individual case studies. We know that the majority of underpinning research in the sample was graded 2* or above (because we excluded unclassifiable case studies from the analysis) but individual ratings for outputs in the underpinning research section are not provided in REF2014. Therefore, the qualitative analysis looked for a range of indicators of strong or weak research in four categories: (i) indicators of publication quality; (ii) quality of funding sources; (iii) narrative descriptions of research quality; and (iv) the extent to which the submitting unit (versus collaborators outside the institution) had contributed to the underpinning research. As would be expected (given that all cases had passed the 2* threshold), only a small minority of cases in the sample gave grounds to doubt the quality of the underpinning research. However, both our qualitative and quantitative analyses identified research-related differences between high- and low-scoring impact case studies.

Based on our qualitative thematic analysis of indicators of research quality, a number of low-scoring cases contained indications that underpinning research may have been weak. This was very rare in high-scoring cases. In the most extreme case, one case study was not able to submit any published research to underpin the impact, relying instead on having secured grant funding and having a manuscript under review. Table 17 describes indicators that underpinning research may have been weaker (presumably closer to the 2* quality threshold for eligibility). It also describes the indications of higher quality research (which were likely to have exceeded the 2* threshold) that were found in the rest of the sample. High-scoring case studies demonstrated the quality of the research using a range of direct and indirect approaches. Direct approaches included the construction of arguments that articulated the originality, significance and rigour of the research in the “underpinning research” section of the case study (sometimes with reference to outputs that were being assessed elsewhere in the exercise to provide a quick and robust check on quality ratings). In addition to this, a wide range of indirect proxies were used to infer quality, including publication venue, funding sources, reviews and awards.

These indicators are of particular interest given the stipulation in REF2021 that case studies must provide evidence of research quality, with the only official guidance suggesting that this is done via the use of indicators. The indicators identified in Table 17 overlap significantly with example indicators proposed by panels in the REF2021 guidance. However, there are also a number of additional indicators, which may be of use for demonstrating the quality of research in REF2021 case studies. In common with proposed REF2021 research quality indicators, many of the indicators in Table 17 are highly context dependent, based on subjective disciplinary norms that are used as short-cuts to assessments of quality by peers within a given context. Funding sources, publication venues and reviews that are considered prestigious in one disciplinary context are often perceived very differently in other disciplinary contexts. While REF2021 does not allow the use of certain indicators (e.g. journal impact factors), no comment is given on the appropriateness of the suggested indicators. While this may be problematic, given that an indicator by definition sign-posts, suggests or indicates by proxy rather than representing the outcome of any rigorous assessment, we make no comment on whether it is appropriate to judge research quality via such proxies. Instead, Table 17 presents a subjective, qualitative identification of indicators of high or low research quality, which were as far as possible considered within the context of disciplinary norms in the Units of Assessments to which the case studies belonged.

The quantitative linguistic analysis also found differences between the high-scoring and low-scoring case studies relating to underpinning research. There were significantly more words and phrases in low-scoring case studies compared to high-scoring cases relating to research outputs (e.g. “the paper”, “peer-reviewed”, “journal of”, “et al”), the research process (e.g. “research project”, “the research”, “his work”, “research team”) and descriptions of research (“relationship between”, “research into”, “the research”) (Table 18 ). The word “research” itself appears frequently in both (high: 91× per 10,000 words; low: 110× per 10,000 words), which is nevertheless a small but significant over-use in the low-scoring case studies (effect size measure log ratio = 0.27, p  < 0.0001).

There are two alternative ways to interpret these findings. First, the qualitative research appears to suggest a link between higher-quality underpinning research and higher impact scores. However, the causal mechanism is not clear. An independent review of REF2014 commissioned by the UK Government (Stern, 2016 ) proposed that underpinning research should only have to meet the 2* threshold for rigour, as the academic significance and novelty of the research is not in theory a necessary precursor to significant and far-reaching impact. However, a number of the indications of weaker research in Table 17 relate to academic significance and originality, and many of the indicators that suggested research exceeded the 2* threshold imply academic significance and originality (e.g. more prestigious publication venues often demand stronger evidence of academic significance and originality in addition to rigour). As such, it may be possible to posit two potential causal mechanisms related to the originality and/or significance of research. First, it may be argued that major new academic breakthroughs may be more likely to lead to impacts, whether directly in the case of applied research that addresses societal challenges in new and important ways leading to breakthrough impacts, or indirectly in the case of major new methodological or theoretical breakthroughs that make new work possible that addresses previously intractable challenges. Second, the highest quality research may have sub-consciously biased reviewers to view associated impacts more favourably. Further research would be necessary to test either mechanism.

However, these mechanisms do not explain the higher frequency of words and phrases relating to research outputs and process in low-scoring case studies. Both high-scoring and low-scoring cases described the underpinning research, and none of the phrases that emerged from the analysis imply higher or lower quality of research. We hypothesised that this may be explained by low-scoring case studies devoting more space to underpinning research at the expense of other sections that may have been more likely to contribute towards scores. Word limits were “indicative”, and the real limit of “four pages” in REF2014 (extended to five pages in REF2021) was operationalised in various way. However, a t -test found no significant difference between the underpinning research word counts (mean of 579 and 537 words in high and low-scoring case studies, respectively; p  = 0.11). Instead, we note that words and phrases relating to research in the low-scoring case studies focused more on descriptions of research outputs and processes rather than descriptions of research findings or the quality of research, as requested in REF2014 guidelines. Given that eligibility evidenced in this section is based on whether the research findings underpin the impacts and the quality of the research (HEFCE, 2011 ), we hypothesise that the focus of low-scoring case studies on research outputs and processes was unnecessary (at best) or replaced or obscured research findings (at worst). This could be conceptualised as another instance of the content/process distinction, whereby high-scoring case studies focused on what the research found and low-scoring case studies focused on the process through which the research was conducted and disseminated. It could be concluded that this tendency may have contributed towards lower scores if unnecessary descriptions of research outputs and process, which would not have contributed towards scores, used up space that could otherwise have been used for material that may have contributed towards scores.

Limitations

These findings may be useful in guiding the construction and writing of case studies for REF2021 but it is important to recognise that our analyses are retrospective, showing examples of what was judged to be ‘good’ and ‘poor’ practice in the authorship of case studies for REF2014. Importantly, the findings of this study should not be used to infer a causal relationship between the linguistic features we have identified and the judgements of the REF evaluation panel. Our quantitative analysis has identified similarities and differences in their linguistic features, but there are undoubtedly a range of considerations taken into account by evaluation panels. It is also not possible to anticipate how REF2021 panels will interpret guidance and evaluate case studies, and there is already evidence that practice is changing significantly across the sector. This shift in expectations regarding impact is especially likely to be the case in research concerned with public policy, which are increasingly including policy implementation as well as design in their requirements, and research involving public engagement, which is increasingly being expected to provide longitudinal evidence of benefits and provide evidence of cause and effect. We are unable to say anything conclusive from our sample about case studies that focused primarily on public engagement and pedagogy because neither of these types of impact were common enough in either the high-scoring or low-scoring sample to infer reliable findings. While this is the largest sample of known high-scoring versus low-scoring case studies ever analysed, it is important to note that this represents <3% of the total case studies submitted to REF2014. Although the number of case studies was fairly evenly balanced between Main Panels in the thematic analysis, the sample only included a selection of Units of Assessment from each Main Panel, where sufficient numbers of high and low-scoring cases could be identified (14 and 20 out of 36 Units of Assessment in the qualitative and quantitative studies, respectively). As such, caution should be taken when generalising from these findings.

This paper provides empirical insights into the linguistic differences in high-scoring and low-scoring impact case studies in REF2014. Higher-scoring case studies were more likely to have articulated evidence of significant and far-reaching impacts (rather than just presenting the activities used to reach intended future impacts), and they articulated clear evidence of causal links between the underpinning research and claimed impacts. While a cause and effect relationship between linguistic features, styles and the panel’s evaluation cannot be claimed, we have provided a granularity of analysis that shows how high-scoring versus low-scoring case studies attempted to meet REF criteria. Knowledge of these features may provide useful lessons for future case study authors, submitting institutions and others developing impact assessments internationally. Specifically, we show that high-scoring case studies were more likely to provide specific and high-magnitude articulations of significance and reach, compared to low-scoring cases, which were more likely to provide less specific and lower-magnitude articulations of significance and reach. Lower-scoring case studies were more likely to focus on pathways to impact rather than articulating clear impact claims, with a particular focus on one-way modes of knowledge transfer. High-scoring case studies were more likely to provide clear links between underpinning research and impacts, supported by high-quality corroborating evidence, compared to low-scoring cases that often had missing links between research and impact and were more likely to be underpinned by corroborating evidence that was vague and/or not clearly linked to impact claims. Linked to this, high-scoring case studies were more likely to contain attributional phrases, and these phrases were more likely to attribute research and/or pathways to impact, compared to low-scoring cases, which contained fewer attributional phrases, which were more likely to provide attribution to pathways rather than impact. Furthermore, there is evidence that high-scoring case studies had more explicit causal connections between ideas and more logical connective words (and, or, but) than low-scoring cases.

However, in addition to the explicit REF2014 rules, which appear to have been enacted effectively by sub-panels, there is evidence that implicit rules, particularly linked to written style, may also have played a role. High-scoring case studies appear to have conformed to a distinctive new genre of writing, which was clear and direct, often simplified in its representation of causality between research and impact, and less likely to contain expressions of uncertainty than might be normally expected in academic writing (cf. e.g. Vold, 2006 ; Yang et al., 2015 ). Low-scoring case studies were more likely to contain filler phrases that could be described as “academese” (Biber and Gray, 2019 , p. 1), more likely to use unsubstantiated or vague adjectives to describe impacts, and were less likely to signpost readers to key points using sub-headings and paragraph headings. High-scoring case studies in two Main Panels (out of the three that could be analysed in this way) were significantly easier to read, although both high- and low-scoring case studies tended to be of “graduate” (Hartley, 2016 ) difficulty.

These findings suggest that aspects of written style may have contributed towards or compromised the scores of some case studies in REF2014, in line with previous research emphasising the role of implicit and subjective factors in determining the outcomes of impact evaluation (Derrick, 2018 ; Watermeyer and Chubb, 2018 ). If this were the case, it may raise questions about whether case studies are an appropriate way to evaluate impact. However, metric-based approaches have many other limitations and are widely regarded as inappropriate for evaluating societal impact (Bornmann et al., 2018 ; Pollitt et al., 2016 ; Ravenscroft et al., 2017 ; Wilsdon et al., 2015 ). Comparing research output evaluation systems across different countries, Sivertsen ( 2017 ) presents the peer-review-based UK REF as “best practice” compared to the metrics-based systems elsewhere. Comparing the evaluation of impact in the UK to impact evaluations in USA, the Netherlands, Italy and Finland, Derrick ( 2019 ) describes REF2014 and REF2021 as “the world’s most developed agenda for evaluating the wider benefits of research and its success has influenced the way many other countries define and approach the assessment of impact”.

We cannot be certain about the extent to which linguistic features or style shaped the judgement of REF evaluators, nor can such influences easily be identified or even consciously recognised when they are at work (cf. research on sub-conscious bias and tacit knowledge; the idea that “we know more than we can say”—Polanyi, 1958 cited in Goodman, 2003 , p. 142). Nonetheless, we hope that the granularity of our findings proves useful in informing decisions about presenting case studies, both for case study authors (in REF2021 and other research impact evaluations around the world) and those designing such evaluation processes. In publishing this evidence, we hope to create a more “level playing field” between institutions with and without significant resources available to hire dedicated staff or consultants to help write their impact case studies.

Data availability

The dataset analysed during the current study corresponds to the publicly available impact case studies defined through the method explained in Section “Research design and sample” and Table 2 . A full list of case studies included can be obtained from the corresponding author upon request.

https://impact.ref.ac.uk/casestudies/search1.aspx

For Main Panel B, only six high-scoring and two low-scoring case studies are clearly identifiable and available to the public (cf. Fig. 1 ). The Main Panel B dataset is therefore too small for separate statistical analysis, and no generalisations should be made on the basis of only one high-scoring and one low-scoring submission.

However, in the qualitative analysis, there were a similar number of high-scoring case studies that were considered to have reached this score due to a clear focus on one single, highly impressive impact, compared to those that were singled out for their impressive range of different impacts.

Note that there were more instances of the smaller number of attributional phrases in the low-scoring corpus.

For Main Panel B, only six high-scoring and two low-scoring case studies are clearly identifiable and available to the public. The Main Panel B dataset is therefore too small for separate statistical analysis, and no generalisations should be made on the basis of only one high-scoring and one low-scoring submission.

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Acknowledgements

Thanks to Dr. Adam Mearns, School of English Literature, Language & Linguistics at Newcastle University for help with statistics and wider input to research design as a co-supervisor on the Ph.D. research upon which this article is based.

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Reichard, B., Reed, M.S., Chubb, J. et al. Writing impact case studies: a comparative study of high-scoring and low-scoring case studies from REF2014. Palgrave Commun 6 , 31 (2020). https://doi.org/10.1057/s41599-020-0394-7

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• Conduct evaluations: As you learn more about how to write a case study, remember that you can also use a case study to conduct evaluations of a specific situation. A case study is a great way to learn more about complex situations, and you can evaluate how various people responded in that situation. By conducting a case study evaluation, you can learn more about what has worked well, what has not, and what you might want to change in the future.
• Identify potential solutions: A case study can also help you identify solutions to potential problems. If you have an issue in your business that you are trying to solve, you may be able to take a look at a case study where someone has dealt with a similar situation in the past. For example, you may uncover data bias in a specific solution that you would like to address when you tackle the issue on your own. If you need help solving a difficult problem, a case study may be able to help you.

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

What are the benefits of writing a business case study?

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

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

What are the limitations of case studies?

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

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

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

Types of case studies

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

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

Explanatory case studies

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

Exploratory case studies

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

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

Collective case studies

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

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

Intrinsic case studies

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

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

Instrumental case studies

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

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

Tips for writing a case study

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

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

Here are some helpful tips for writing your case studies:

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

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

Administrative summary

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

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

Key problem statement

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

Problem exploration

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

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

Proposed resolution

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

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

Overview of monetary consideration

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

Execution timeline

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

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

2. Make it clear and comprehensive

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

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

3. Don't rush through the process

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

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

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

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

4. Be confident in your theory development

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

5. Case studies and all qualitative research are long

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

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

Case study examples

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

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

Case study FAQs

When should you do a case study.

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

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

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

How are case studies different from other research methodologies?

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

Writing multiple case studies for your business

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

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

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

• Open access
• Published: 27 June 2011

The case study approach

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

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

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

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Introduction

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

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

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

What is a case study?

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

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

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

What are case studies used for?

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

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

How are case studies conducted?

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

Defining the case

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

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

Selecting the case(s)

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

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

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

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

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

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

Collecting the data

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

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

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

Analysing, interpreting and reporting case studies

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

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

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

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

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

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

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

Conclusions

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

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Acknowledgements

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

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

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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The Importance of Case Studies

Case studies are, arguably, one of the most essential components of any marketing or communications strategy. They may take time and effort, but the payoffs are well worth their effort and have been seen time and again, from organizations both big and small.

As Business Analysts, we’re often involved in creating or contributing to case studies, so it is important we understand what they are, the purpose they serve, and the benefits that they can bring. 

What Are Case Studies?

In essence, a case study is a report published by a company that highlights its approach to a challenge, the strategy used to solve the issue(s), and the effectiveness of the solution. It presents a holistic, in-depth review with valuable information about the costs involved, the expertise of the company employed, and the benefits achieved.

The Many Benefits of Case Studies

Case studies have a huge range of benefits, from insight to influence. They are used in nearly every industry, from finance to health care and they offer organizations the ability to convert observations into useable data.

But that barely scratches the surface of the many benefits of case studies. Let’s dig a little deeper and look at some of the long-lasting effects that case studies can have.

1. Boost Customer Confidence

Case studies are an excellent way to display your team’s skill and expertise in action. It’s one thing to propose a plan for improvement, or a solution to a challenge, but some customers may be skeptical of your team’s ability to follow through or produce the appealing results you’re promising. 

Case studies are exactly the type of proof they need to help push them past skepticism and into belief, and possibly, across the finish line to approval of your partnership. If it’s an internal project, case studies could be enough to convince decision-makers to pursue your solution to a challenge.

2. Improve Brand Awareness and Reputation

The reality of the world today is that every company in every industry has competition. Consumers are also becoming smarter about marketing tactics and messaging, so providing proof and backing up claims is essential for presenting your company as genuine in its promises. That’s where case studies come in. 

Case studies are social proof that the promises and claims that your company is making are true. They provide facts and statistics to show how your company improved productivity for your clients by ‘x’ amount, increased profits by ‘x’ amount, or implemented ‘x’ solution to combat a specific issue. 

Providing such proof of expertise bolsters your reputation and can increase brand awareness. Case studies can be published and distributed to current customers to remind them of why they partnered with you initially, and can help increase your company’s reputation and brand image as new potential customers discover you. 

3. Provide a Strategic Solution

No customer wants to partner with a company that acts erratically or thoughtlessly. Case studies provide insight into real challenges and an in-depth look at how your company goes about solving them. They give you a chance to display your strategic thinking and approach, your expertise and innovation, and your ability to follow through on your promised solution. 

4. Assist with Furthering Research

A case study helps show how different aspects of your business work together. They highlight a problem and how the executed solution provided value. They can also play a role in broader research. As you explore and analyze new ideas, other theories and methods can surface. This additional information can help you refine your strategies and further grow the value you provide. Case studies make great reference points that can show, not only how innovative, productive, and proficient your company and teams have become, but how trends have unfolded within your customers and services.

Final Thoughts

As you can see, case studies play a crucial role in the communication of how your team and you, as a Business Analyst, face challenging situations head-on and provide strategic, value-driven solutions for your customers. Once you have a better understanding of case studies and the deep benefits they provide to you and your brand, it’s easy to justify the time and effort that goes into creating them. As you create your next case study, use it as a reminder to take pride in the hard work you’ve done and the results you’ve achieved.

– Written by Jeremy Aschenbrenner, The BA Guide

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Home » Significance of the Study – Examples and Writing Guide

Significance of the Study – Examples and Writing Guide

Table of Contents

Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

About the author

Muhammad Hassan

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Importance of a Case Study

Aug 1, 2018 | coursework writing , case study , research paper , Writing

A case study is research method that involves an up-close, in-depth and detailed investigation of a subject of study and its related contextual position. They can be produced following a form of research. A case study helps in bringing the understanding of a complex issue or object. It can extend experience or add strength to the existing knowledge through previous research. Their contextual analysis revolves around a limited number of events or conditions and how they relate. The case study has been used by researchers for a long time and has been applied in different disciplines. It has been widely used in social sciences as a qualitative research method to investigate contemporary real-life situations and has provided a foundation of application of ideas and extension of methods.

It has been defined as an empirical inquiry that examines a contemporary phenomenon within the context of its real life. However, some people have disagreed with this research method arguing that the study of a small number of cases does not offer enough ground to establish reliability or generality of findings. Others have argued that a case study is only used when applied as an exploratory tool, yet most researchers continue using it successfully in carefully planned studies that concern real-life situations, problems, and issues.

Case studies will more often than not appear in journals or professional conferences instead of popular works. A case study may be an individual, organization, action, event existing in a given time and place. For instance, there are case studies of individuals and clinical practices. When the term “case” is used in a claim, an argument, or a proposition; it can be the subject of a litany of research methods. A case study will involve quantitative and qualitative methods of research.

Researchers, on the other hand, are always spoilt for choice when they are determining the tools to use in dealing with their research question. This is because there is an array of both qualitative and quantitative research tools. They can be based on in-depth case studies or desk-based literature reviews. When using case study, the researcher will get an in-depth investigation of a phenomenon, individual, or an event. They help in investigating and understanding the underlying principles in an occurrence within a real-life context.

• They are comprehensive Case studies enable a holistic review. A researcher can use a range of tools which he would otherwise not apply when using other stand-alone research techniques. This gives his time to develop an in-depth understanding of the topic and establish a credible platform to investigate the factors that affect a case study in extensive detail.
• Case studies reduce bias They give room to the diversity of perspectives as opposed to when one is using a single view of a person you get with a survey response or an interview. It eliminates chances of potential bias by giving an opportunity to gain a greater understanding of the subject under investigation. Lack of bias dilutes the agenda of a given individual.
• Broad relevance One of the criticisms that case study method gets is that the findings cannot be generalized. However, when a case study is part of broader research can explore common problems in detail.
• Permissions The identity of the research participants is crucial in painting a real picture of whatever that is taking place. Many researchers have found out that participants are more comfortable in situation s where they are sure that the identity will remain anonymous. However, this presents a challenge given the comprehensive nature of the study. In-depth case studies will require one to seek confirmation that the leading research participant agrees that the material is accurate and anonymous. This enables the confidence on the part of the researcher as well as the participant. Gaining permission can take quite some time and could culminate to additional restatements of the published research.
• Time Case studies consume time. You have to plan for multiple interviews, waiting for the data to come in; coordinating focus groups can take quite a substantial, amount of time. For instance, if you are depending on a voluntary case study participant who is going on with his daily business, that might present a challenge. You can overcome these issues by offering incentives to your participants and then outline what you expect from each of them from the onset and sending deadline notifications in advance. This helps in receiving the data early enough.
• Decide and define the research questions
• Select your case studies and determine the techniques for data collection and analysis
• Prepare to engage in data collection
• Collect data in the field
• Cary out data evaluation and analysis
• Write your report

You have to decide on the questions you want to use in your research . They are a referral for the researcher as he seeks to provide answers to them. The researcher has to establish the focus of the student by coming up with questions that concern the problem or the situation being studied and to determine the purpose of the study. The case study here might involve a program, an entity, a person, or a group of people. Each object has a relationship that is connected to social, political, historical, and personal issues. This provides wide-ranging possibilities for questions and adds complexities to the case study.

The case study must answer questions that begin with “why” or “how.” These questions are directed to a limited number of events or conditions and their inter-relationships. One way that enables researchers to formulate these questions is by conducting a literature review. This allows them to establish what previous researchers found out, and they enable in formulating insightful questions necessary for the examination of the problem. Well defined questions from the onset direct one on where to get more evidence and also helps in determining analysis method for the study, The definition of case study purpose, the literature review and the early decision on the potential audience for the final report will help in providing guidance on how the study will be conducted and published.

The design phase of the case study research gives the researcher an opportunity to decide what the approach will be when it comes to selecting the single or multiple real-life cases for examination. It will also help in deciding the data gathering approaches and the instruments used. When working with multiple cases, each case is treated individually. The conclusion of the individual case can be used as information that contributes to the whole study.

Excellent case studies often select and carefully examine the existing choices in the research tools at disposal with the objective of raising the study validity. You can create boundaries when you do careful discrimination when you are carrying out the selection. One of the strengths of case study method is by the use of multiple techniques and sources in the process of gathering data. The researcher makes an early determination of the evidence he has to gather and the analysis method he will apply for him to answer the research questions. The data gathered may be largely qualitative but can also be quantitative. One can use surveys, interviews, observation, documentation review, or the collection of physical artifacts as tools of data collection. The researcher ought to distribute the data gathering tools systematically when collecting the evidence. The researcher must ensure that the research is constructed to achieve, internal validity, external validity, construct validity and reliability. This should be achieved during the design phase.

Case study often generates big amounts of data from multiple sources. As such, it is important to organize your data systematically to prevent cases of confusion or getting overwhelmed by the incoming data. This helps the researcher to maintain sight of the original research purpose and questions.

One can prepare databases to help in sorting, categorizing, storing, and retrieving data for analysis. Some of the best case studies carries out training for the researchers to establish clear protocols and procedures early enough before the fieldwork kicks off. They also conduct a pilot study well in advance to remove barriers and problems in the field. Once the training is done, the last step is to select a pilot site where each data gathering method is put to the test to uncover problem areas and correct them early.

The researcher must ensure that the evidence and the issue under investigation are have maintained their relationship. It is possible for the researcher to enter data into a database and physically store it. However, he has to document, classify, and cross-reference all evidence for it to be efficiently recalled for examination and sorting as the study continues.

The researcher must collect and store data comprehensively and systematically. This should be done in formats that are easy to reference and sort to enable him in identifying possible lines of inquiry. Successful case studies utilize field notes and databases in categorizing and referencing data, so that is it readily available for interpretation. Field notes take records of feelings, intuitive hunches; pose questions, document work in progress. Stories, testimonies, and illustrations are useful in later reports. Some techniques require the researcher to place information into arrays, matrices of categories, flow charts or other displays as well as tabulations of event frequency. If there is conflicting evidence, the researcher must probe the differences deeper and identify the source of conflict. The researcher must provide answers to the “how” and “why” research questions.

The researcher has to examine raw data using different interpretations. This enables him to draw linkages between the outcomes and the research object bearing in mind the research questions. The researcher must have an open mind during the data evaluation and analysis process. The researcher can strengthen the research findings and conclusion thanks to the multiple data collection methods and analysis techniques he had applied.

The kind of tactics used by the researcher during the analysis compels him to go beyond the initial impressions to improve chances of reliable and accurate findings.

• Preparing the report

Excellent case studies interpret data in ways that they make it easy to understand a problem hitherto complex. It allows the reader to question and examine the study and arrive at an understanding that the researcher was independence. The written report aims to convey to the reader a simplified experience of the issue that was once complex. With case studies, the reader can access the information publicly in ways that may lead him to utilize the experience in his real-life situation.

The report can be written in a manner that handles each case on its separate chapter or giving it a chronological recounting. The researchers at times use the report writing process to do a critical examination of the document to identify ways through which the report might be incomplete. The researcher can use the representative audience to carry out a review and present comments on the same. The comments are the premise upon which the revisions of the documents are made. Sometimes it is recommended to have a journalist in the review audience whereas others argue that the participants should review the document. Those are the steps used in a case study research.

With case studies, the researcher will get a more concrete and unbiased understanding of a given complex situation. This is achieved using a range of search tools. With a real-life view, the research can give leeway for the recommendation of practical solutions to challenges. Case studies are important, and the challenges involved can be surmounted planning, background research and an informed selection of all the participants. If the case study approach works for you, utilize it.

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• Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana
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• Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana Megan Moore ; Elizabeth Covelli Metcalf ; Alexander Metcalf ( September 2023 , Journal of rural and community development) Post-industrial communities across the world are transitioning from industrial economies and identities to an uncertain future. Their successful transitions depend on communities’ abilities to navigate change and maintain a quality of life, or their community’s resilience. Previous scholarship offers resources and capabilities that facilitate or inhibit community resilience such as leadership, social capital, and information. However, collective memory is not well integrated within the community resilience literature. Drawing on data from interviews with 33 community leaders in the town of Anaconda, Montana, we illuminate the impact of collective memory on community resilience. The Anaconda Smelter Stack stands out as a specific landmark and prominent feature of the built environment that perpetuates particular collective memories in Anaconda. We find that collective memory is an integral part of community resilience, where memories can aid in a community’s recovery and rebuilding or constrain thinking and divide viewpoints. We argue that ignoring collective memory’s connections to resilience can undermine efforts to face changes in these communities.  more » « less
• The FEWSION for Community Resilience (F4R) Process: Building Local Technical and Social Capacity for Critical Supply Chain Resilience https://doi.org/10.3389/fenvs.2021.601220   Ryan, Sean M. ; Roberts, Elisabeth ; Hibbett, Emma ; Bloom, Nena ; Haden, Carol ; Rushforth, Richard R. ; Pfeiffer, Kyle ; Ruddell, Benjamin L. ( March 2021 , Frontiers in Environmental Science) null (Ed.) Local business leaders, policy makers, elected officials, city planners, emergency managers, and private citizens are responsible for, and deeply affected by, the performance of critical supply chains and related infrastructures. At the center of critical supply chains is the food-energy-water nexus (FEW); a nexus that is key to a community’s wellbeing, resilience, and sustainability. In the 21st century, managing a local FEW nexus requires accurate data describing the function and structure of a community’s supply chains. However, data is not enough; we need data-informed conversation and technical and social capacity building among local stakeholders to utilize the data effectively. There are some resources available at the mesoscale and for food, energy, or water, but many communities lack the data and tools needed to understand connections and bridge the gaps between these scales and systems. As a result, we currently lack the capacity to manage these systems in small and medium sized communities where the vast majority of people, decisions, and problems reside. This study develops and validates a participatory citizen science process for FEW nexus capacity building and data-driven problem solving in small communities at the grassroots level. The FEWSION for Community Resilience (F4R) process applies a Public Participation in Scientific Research (PPSR) framework to map supply chain data for a community’s FEW nexus, to identify the social network that manages the nexus, and then to generate a data-informed conversation among stakeholders. F4R was piloted and co-developed with participants over a 2-year study, using a design-based research process to make evidence-based adjustments as needed. Results show that the F4R model was successful at improving volunteers’ awareness about nexus and supply chain issues, at creating a network of connections and communication with stakeholders across state, regional, and local organizations, and in facilitating data-informed discussion about improvements to the system. In this paper we describe the design and implementation of F4R and discuss four recommendations for the successful application of the F4R model in other communities: 1) embed opportunities for co-created PPSR, 2) build social capital, 3) integrate active learning strategies with user-friendly digital tools, and 4) adopt existing materials and structure.  more » « less

Sustainable management of common pool resources requires local information and participation. We develop a framework for managing commons based on threats, consequences, and solutions (TCS). The status of the community’s interaction with their local commons is critical in developing viable solutions to avoiding the loss of natural resources, enhancing the benefits they provide, and sustaining the functions they perform. Threats to natural resources, the consequences of their depletion, and the solutions local communities perceive as most effective to prevent this loss are assessed as related to socioeconomic and landscape factors to develop strategies for the resilience of commons. Communities and representative stakeholders (224 respondents) participated in a survey in Honduras’s Lake Yojoa watershed. The community’s perception was also evaluated for impacts of changes in land use and climate on local commons. An ordinal logistic regression analysis was used to determine the effect of land use, geographic, and demographic factors on community perceptions. Distance to the lake, landcover percentages, slope, type of work, age, and importance of tourism were significant in influencing community interaction and perception of TCS. The involvement of communities in deriving knowledge on TCS is critical to increasing the resilience of local commons to emerging threats.

• Policy diffusion in community-scale flood risk management. Noonan, D. S. ; Richardson, L. E. ; Sadiq, A. A. ( January 2018 , WIT transactions on engineering sciences) This study analyzes which communities adopted flood risk management practices during the past 25 years. In particular, we focus on community-scale flood management efforts undertaken voluntarily in towns and counties across the United States. In 1990, the US Federal Emergency Management Agency created the Community Rating System (CRS) to provide incentives to local governments to improve flood resilience. About 1,300 counties and cities voluntarily participate in the CRS, but most eligible communities do not participate. Here, we explore the factors shaping community CRS participation, such as flood risk, socio-economic characteristics, and economic resources, and we assess the competing phenomena of policy diffusion versus free riding. Previous models of community-scale flood mitigation activities have all considered each community’s decision as independent of one another. Yet one community’s flood management activities might directly or indirectly influence its neighbors’ mitigation efforts. Spillover effects or “contagion” may arise if neighboring communities learn from or seek to emulate or outcompete early adopting neighbors. Conversely, stricter regulation in one community may allow its neighbors to capitalize on looser regulation either by attracting more development or enjoying reduced “downstream” flood risks. This paper presents a conceptual model that allows for multiple forces affecting diffusion, such as copycatting and learning from neighboring communities, free-riding on neighbors’ efforts, and competing with neighbors to provide valuable amenities. We empirically test for these alternative diffusion pathways after controlling for the spatially correlated extant flood risks, building patterns, and demographics. The analysis integrates several large datasets to predict community flood risk management for all cities and counties in the US since 1990. Controls for local flood risk combined with a spatial lag regression model allow separate identification of alternative diffusion pathways. The results indicate strong evidence of copycatting and also suggest possible free-riding.  more » « less

Arctic communities are experienced with severe weather, but impacts can still be serious, particularly when the intensity or persistence of hazardous conditions is extreme. Such was the case for the community of Clyde River (Kangiqtugaapik), Nunavut, Canada, which experienced 33 blizzard days during winter 2021/22—likely the most at Clyde River since at least 1978/79. Blizzard conditions resulted from unusually frequent high winds rather than excessive snowfall. The most severe stretch included eight blizzard days over an 11-day period, with top wind gusts of 98 km h −1 . Winds caused severe drifting, covering homes and blocking streets. Broken heavy equipment, including snow-clearing machines, compounded the impacts, leaving homes without essential services like water delivery and sewage pump-out for days. Residents reported the storms and resulting impacts as some of the worst in memory. The drifting and volume of snow, combined with the lack of available resources to manage it, obliged the community to declare a state of emergency. Projections of increased Arctic precipitation and extreme weather events points to the need for communities to have proper resources and supports, including preparedness and adaptation and mitigation strategies, so they can be better equipped to handle storm and blizzard impacts such as those experienced at Clyde River in the winter of 2021/22. Additional steps that can be implemented to better support and prepare communities include investing in preparedness planning, expanded and enhanced weather information and services, community land-based programming to transfer Inuit knowledge and skills, assessing the usefulness of current forecasts, and new approaches to community planning.

In this study, we consider the winter of 2021/22, during which the community of Clyde River (Kangiqtugaapik), Nunavut experienced 33 days with blizzard conditions—more than any other year since at least 1978/79. Blizzards are characterized by strong winds and blowing snow. Low visibility impedes travel, and drifting snow blocks roads and can bury equipment and buildings. In this case, broken snow-clearing equipment and other infrastructure challenges also hampered the community’s ability to respond, and residents went days without essential services. Several studies suggest that extreme winds will become more common in the Baffin Bay region in the future. This study demonstrates the need for proper resourcing of communities for preparedness, response, and adaptation strategies, especially with the possibility of extreme winter weather becoming more common.

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• MLA × Cite: MLA Format Moore, M., Metcalf, E., and Metcalf, A. Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana . Retrieved from https://par.nsf.gov/biblio/10502065. Journal of Rural and Community Development 18.3 Close
• APA × Cite: APA Format Moore, M., Metcalf, E., & Metcalf, A. Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana . Journal of Rural and Community Development , 18 (3). Retrieved from https://par.nsf.gov/biblio/10502065. Close
• Chicago × Cite: Chicago Format Moore, M., Metcalf, E., and Metcalf, A. "Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana". Journal of Rural and Community Development 18 (3). Country unknown/Code not available: Brandon University, Rural Development Institute. https://par.nsf.gov/biblio/10502065 . Close
• BibTeX × Cite: BibTeX Format @article{osti_10502065, place = {Country unknown/Code not available}, title = {Connecting Collective Memory and Community Resilience: A Case Study of Anaconda, Montana}, url = {https://par.nsf.gov/biblio/10502065}, abstractNote = {economies and identities to an uncertain future. Their successful transitions depend on communities’ abilities to navigate change and maintain a quality of life, or their community’s resilience. Previous scholarship offers resources and capabilities that facilitate or inhibit community resilience such as leadership, social capital, and information. However, collective memory is not well integrated within the community resilience literature. Drawing on data from interviews with 33 community leaders in the town of Anaconda, Montana, we illuminate the impact of collective memory on community resilience. The Anaconda Smelter Stack stands out as a specific landmark and prominent feature of the built environment that perpetuates particular collective memories in Anaconda. We find that collective memory is an integral part of community resilience, where memories can aid in a community’s recovery and rebuilding or constrain thinking and divide viewpoints. We argue that ignoring collective memory’s connections to resilience can undermine efforts to face changes in these communities.}, journal = {Journal of Rural and Community Development}, volume = {18}, number = {3}, publisher = {Brandon University, Rural Development Institute}, author = {Moore, M. and Metcalf, E. and Metcalf, A.}, } Close
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Investigating the Significance of Fluid Load Effects on Wet Surfaces in Concrete Dam Analysis: An Examination of Structural Behavior and Performance using the Pine Flat Dam as a Case Study

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• Published: 25 April 2024

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• H. Mirzabozorg   ORCID: orcid.org/0000-0003-0029-8137 1  

This study delves into the intricate interplay of reservoir–foundation interaction on the structural comportment exhibited by concrete gravity dams, employing the Pine Flat dam as a representative case study. In the context of seismic excitation sourced from established FEMA standards, a comprehensive investigation was conducted, encompassing both linear and nonlinear mass concrete behaviors. The coupled problem of fluid–structure interaction was solved simultaneously using implicit finite element technique. The findings of this research show the significant augmentation of tensile strength, moderation of compressive stress, and the introduction of precarious failure mechanisms within the dam structure when the reservoir–foundation interaction is considered. These observations underscore the necessity of accounting for this interaction in dam engineering, whereas disregard for this interaction in dam design culminates in inherently unsafe and unreliable results.

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Mirzabozorg, H. Investigating the Significance of Fluid Load Effects on Wet Surfaces in Concrete Dam Analysis: An Examination of Structural Behavior and Performance using the Pine Flat Dam as a Case Study. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08993-9

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Science Update: Steroid treatment in late pregnancy does not appear to affect children’s neurodevelopment, NICHD-funded study suggests

Children who were exposed to a steroid at 34 to 36 weeks of pregnancy are no more likely to have cognitive effects than children whose mother did not receive a steroid, suggests a study funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The findings help to allay concerns that fetal exposure to a steroid in the uterus—given to speed lung development in case of preterm birth—could affect a child’s neurodevelopment.

The study was conducted by Cynthia Gyamfi-Bannerman, M.D., M.S., and colleagues in the NICHD Maternal-Fetal Medicine Units Network. It appears in the Journal of the American Medical Association .

A previous study concluded that giving a single dose of the steroid betamethasone to pregnant people at risk of giving birth at 34 to 36 weeks of pregnancy significantly reduced the risk of respiratory complications in their newborns. However, the study also found that these infants were more likely to develop hypoglycemia (low blood sugar). Prolonged hypoglycemia in newborns is associated with brain injury . Other research suggests that multiple doses of steroids before birth could affect a child’s neurodevelopment.

For the current study, researchers evaluated children of the previous study’s participants when the children were six years old or older. A psychologist evaluated each child using a variety of tests that measured verbal and nonverbal reasoning and comprehension. A total of 949 children completed the testing (479 in the betamethasone group and 470 in the placebo group).

Both groups of children scored similarly across all measures of the test, called the Differential Ability Scales. A total of 17.1% in the betamethasone group received a score of less than 85, which did not differ significantly from the 18.5% of the placebo group. Similarly, the average score was 96.6 for both groups (compared to a national average of 100). Also similar between the groups were scores for verbal ability, nonverbal ability, spatial ability, social responsiveness, gross motor function, and behavior.

Significance

The authors conclude that giving a steroid to pregnant people at risk for late preterm birth to reduce potential respiratory complications in their infants is not associated with adverse neurodevelopmental outcomes at age 6 or older. The results help support the prescribing of corticosteroids to pregnant people at risk for late preterm birth.

Gyamfi-Bannerman, C, et al. Neurodevelopmental Outcomes After late preterm antenatal corticosteroids: The ALPS follow-up study. The Journal of the American Medical Association. 2024. doi:10.1001/jama.2024.4303

• Open access
• Published: 19 April 2024

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

• Xinrui Liu 1 , 2 ,
• Mingxiu Wang 1 ,
• Jie Qin 1 ,
• Yaxin Liu 1 ,
• Shikai Wang 1 ,
• Shiyu Wu 1 ,
• Ming Zhang 1 ,
• Jincheng Zhong 1 &
• Jiabo Wang 1  

BMC Genomics volume  25 , Article number:  386 ( 2024 ) Cite this article

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The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

Conclusions

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Peer Review reports

Genetic by environmental interaction (G × E) is crucial of explaining individual traits and has gained increasing attention in research. It refers to the influence of genetic factors on susceptibility to environmental factors. In-depth study of G × E contributes to a deeper understanding of the relationship between individual growth, living environment and phenotypes. Genetic factors play a role in most human diseases at the molecular or cellular level, but environmental factors also contribute significantly. Researchers aim to uncover the mechanisms behind complex diseases and quantitative traits by investigating the interactions between organisms and their environment. Common, complex, or rare human diseases are often considered as outcomes resulting from the interplay of genes, environmental factors, and their interactions. Analyzing the joint effects of genes and the environment can provide valuable insights into the underlying pathway mechanisms of diseases. For instance, researchers have successfully identified potential loci associated with asthma risk through G × E interactions [ 1 ], and have explored predisposing factors for challenging-to-treat diseases like cancer [ 2 , 3 ], rhinitis [ 4 ], and depression [ 5 ].

However, two main methods are currently being used by breeders in agricultural production to increase crop yields and livestock productivity [ 6 ]. The first is to develop varieties with relatively low G × E effect to ensure stable production performance in different environments. The second is to use information from different environments to improve the statistical power of genome-wide association study (GWAS) to reveal potential loci of complex traits. The first method requires long-term commitment, while the second method clearly has faster returns. In GWAS, the use of multiple environments or phenotypes for association studies has become increasingly important. This not only improves the statistical power of environmental susceptibility traits[ 7 ], but also allows to detect signaling loci for G × E. There are significant challenges when using multiple environments or phenotypes for GWAS, mainly because most diseases and quantitative traits have numerous associated loci with minimal impact [ 8 ], and thus it is impossible to determine the effect size regulated by environment in these loci. The current detection strategy for G × E is based on complex statistical model, often requiring the use of a large number of samples to detect important signals [ 9 , 10 ]. In GS, breeders can use whole genome marker data to identify and select target strains in the early stages of animal and plant production [ 11 , 12 , 13 ]. Initially, GS models, similar to GWAS models, could only analyze a single environment or phenotype [ 14 ]. To improve the predictive accuracy of the models, higher marker densities are often required, allowing the proportion of genetic variation explained by these markers to be increased, indirectly obtaining higher predictive accuracy. It is worth mentioning that the consideration of G × E and multiple phenotypes in GS models [ 15 ] has been widely studied in different plant and animal breeding [ 16 ]. GS models that allow G × E have been developed [ 17 ] and most of them have modeled and interpreted G × E using structured covariates [ 18 ]. In these studies, most of the GS models provided more predictive accuracy when combined with G × E compared to single environment (or phenotype) analysis. Hence, there is need to develop models that leverage G × E information for GWAS and GS studies.

This study developed a novel genotype-by-environment method based on R, termed GbyE, which leverages the interaction among multiple environments or phenotypes to enhance the association study and prediction performance of environmental susceptibility traits. The method enables the identification of mutation sites that exhibit G × E interactions in specific environments. To evaluate the performance of the method, simulation experiments were conducted using a dataset comprising 282 corn samples. Importantly, this method can be seamlessly integrated into any GWAS and GS analysis.

Materials and methods

Support packages.

The development purpose of GbyE is to apply it to GWAS and GS research, therefore it uses the genome association and prediction integrated tool (GAPIT) [ 19 ], Bayesian Generalized Linear Regression (BGLR) [ 20 ], and Ridge Regression Best Linear Unbiased Prediction (rrBLUP) [ 21 ]package as support packages, where GbyE only provides conversion of interactive formats and file generation. In order to simplify the operation of the GbyE function package, the basic calculation package is attached to this package to support the operation of GbyE, including four function packages GbyE.Simulation.R (Dual environment phenotype simulation based on heritability, genetic correlation, and QTL quantity), GbyE.Calculate.R (For numerical genotype and phenotype data, this package can be used to process interactive genotype files of GbyE), GbyE.Power.FDR.R (Calculate the statistical power and false discovery rate (FDR) of GWAS), and GbyE.Comparison.Pvalue.R (GbyE generates redundant calculations in GWAS calculations, and SNP effect loci with minimal p -values can be filtered by this package).

Samples and sequencing data

In this study, a small volume of data was used for software simulation analysis, which is widely used in testing tasks of software such as GAPIT, TASSEL, and rMPV. The demonstration data comes from 282 inbred lines of maize, including 4 phenotypic data. In any case, there are no missing phenotypes in these data, and this dataset can be obtained from the website of GAPIT ( https://zzlab.net/GAPIT/index.html , accessed on May 1, 2022). Among them, our phenotype data was simulated using a self-made R simulation function, and the Mean and GbyE phenotype files were calculated. Convert this format to HapMap format using PLINK v1.09 and scripts written by oneself.

Simulated traits

Phenotype simulation was performed by modifying the GAPIT.Phenotype.Simulation function in the GAPIT. Based on the input parameter NQTN, the random selected markers’ genotype from whole genome were used to simulate genetic effect in the simulated trait. The genotype effects of these selected QTNs were randomly sampled from a multivariate normal distribution, the correlation value between these normal distribution was used to define the genetic relationship between each environments. The additive heritability ( \({{\text{h}}}_{{\text{g}}}^{2}\) ) was used to scale the relationship between additive genetic variance and phenotype variance. The simulated phenotype conditions in this paper are set as follows: 1) The three levels of \({{\text{h}}}_{{\text{g}}}^{2}\) were set at 0.8, 0.5, and 0.2, representing high ( \({{\text{h}}}_{{\text{h}}}^{2}\) ), median ( \({{\text{h}}}_{{\text{m}}}^{2}\) ) and low ( \({{\text{h}}}_{{\text{l}}}^{2}\) ) heritability; 2) Genetic correlation were set three levels 0.8, 0.5, 0.2 representing high ( \({{\text{R}}}_{{\text{h}}}\) ), medium ( \({{\text{R}}}_{{\text{m}}}\) ) and low ( \({{\text{R}}}_{{\text{l}}}\) ) genetic correlation; 3) 20 pre-set effect loci of QTL. The phenotype values in each environment were simulated together following above parameters.

Genetic by environment interaction model

The pipeline analysis process of GbyE includes three steps: data preprocessing, production converted, Association analysis. Normalize the phenotype data matrix Y of the dual environment and perform GbyE conversion to generate phenotype data in GbyE.Y format. The genotype data format, such as hapmap, vcf, bed and other formats firstly need to be converted into numerical genotype format (homozygotes were coded as 0 or 2, heterozygotes were coded as 1) using software or scripts such as GAPIT, PLINK, etc. The environment (E) matrix is environment index matrix. The G (n × m) originally of genotype matrix was converted as GbyE.GD(2n × 2 m) \(\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]\) during the Kronecker product, and the Y vector (n × 1) was also converted as the GbyE.Y vector (2n × 1) after normalization. The duplicated data format indicated different environments, genetic effect, and populations. The genomic data we used in the analysis was still retained the whole genome information. The first column of E is the additive effect, which was the average genetic effect among environments. The others columns of E are the interactive effect, which should be less one column than the number of environments. Because it need to avoid the linear dependent in the model. In the GbyE algorithm, we coded the first environment as background as default, that means the genotype in the first environment are 0, the others are 1. Then the Kronecker product of G and environment index matrix was named as GbyE.GD. The interactive effect part of the GbyE.GD matrix in the GWAS and GS were the relative values based on the first environment (Fig.  1 ). The GbyE environmental interaction matrix can be easily obtained by constructing the interaction matrix E (e.g., Eq. 1 ) such that the genotype matrix G is Kronecker-product with the design interaction matrix E (e.g., Eq. 2 ), in which \(\left[\begin{array}{c}G\\ G\end{array}\right]\) matrix is defined as additive effect and \(\left[\begin{array}{c}0\\ G\end{array}\right]\) matrix is defined as interactive effect. \(\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]\) matrix is called gene by environment interaction matrix, hereinafter referred to as the GbyE matrix. The phenotype file (GbyE.Y) and genotype file (GbyE.GD) after transformation by GbyE will be inputted into the GWAS and GS models and computed as standard phenotype and genotype files.

where G is the matrix of whole genotype and E is the design matrix for exploring interactive effects. GbyE mainly uses the Kronecker product of the genetic matrix (G) and the environmental matrix (E) as the genotype for subsequent GWAS as a way to distinguish between additive and interactive effects.

The workflow pipeline of GbyE. The GbyE contains three main steps. (Step 1) Preprocessing of phenotype and genotype data,. The phenotype values in each environment was normalized respectively. Meanwhile, all genotype from HapMap, VCF, BED, and other types were converted to numeric genotype; (Step 2) Generate GbyE phenotype and interactive genotype matrix through the transformation of GbyE. In GbyE.GD matrix, the blue characters indicate additive effect, and red ones indicate interactive effect; (Step 3) The MLM and rrBLUP and BGLR were used to perform GWAS and GS

Association analysis model

The mixed linear model (MLM) of GAPIT is used as the basic model for GWAS analysis, and the principal component analysis (PCA) parameter is set to 3. Then the p -values of detection results are sorted and their power and FDR values are calculated. General expression of MLM (Fig.  1 ):

where Y is the vector of phenotypic measures (2n × 1); PCA and SNP i were defined as fixed effects, with a size of (2n × 2 m); Z is the incidence matrix of random effects; μ is the random effect vector, which follows the normal distribution μ ~ N(0, \({\delta }_{G}^{2}\) K) with mean vector of 0 and variance covariance matrix of \({\delta }_{G}^{2}\) K, where the \({\delta }_{G}^{2}\) is the total genetic variance including additive variance and interactive variance, the K is the kinship matrix built with all genotype including additive genotype and interactive genotype; e is a random error vector, and its elements need not be independent and identically distributed, e ~ N(0, \({\delta }_{e}^{2}\) I), where the \({\delta }_{e}^{2}\) is the residual and environment variance, the I is the design matrix.

Detectivity of GWAS

In the GWAS results, the list of markers following the order of P-values was used to evaluate detectivity of GWAS methods. When all simulated QTNs were detected, the power of the GWAS method was considered as 1 (100%). From the list of markers, following increasing of the criterion of real QTN, the power values will be increasing. The FDR indicates the rate between the wrong criterion of real QTNs and the number of all un-QTNs. The mean of 100 cycles was used to consider as the reference value for statistical power comparison. Here, we used a commonly used method in GWAS research with multiple traits or environmental phenotypes as a comparison[ 22 ]. This method obtains the mean of phenotypic values under different conditions as the phenotypic values for GWAS analysis, called the Mean value method, Compare the calculation results of GbyE with the additive and interactive effects of the mean method to evaluate the detection power of the GbyE strategy. Through the comprehensive analysis of these evaluation indicators, we aim to comprehensively evaluate the statistical power of the GbyE strategy in GWAS and provide a reference for future optimization research.

Among them, the formulae for calculating Power and FDR are as follows:

where \({{\text{n}}}_{{\text{i}}}\) indicates whether the i-th detection is true, true is 1, false is 0; \({{\text{m}}}_{{\text{r}}}\) is the total number of all true QTLs in the sample size; the maximum value of Power is 1.

where \({{\text{N}}}_{{\text{i}}}\) represents the i-th true value detected in the pseudogene, true is 1, false is 0. and cumulative calculation; \({{\text{M}}}_{{\text{f}}}\) is the number of all labeled un-QTNs in the total samples; the maximum value of FDR is 1.

Genomic prediction

To comparison the prediction accuracy of different GS models using GbyE, we performed rrBLUP, Bayesian methods using R packages. All phenotype of reference population and genotype of all population were used to train the model and predict genomic estimated breeding value (gEBV) of all individuals. The correlation between real phenotypes and gEBV of inference population was considered as prediction accuracy. fivefold cross-validation and 100 times repeats was performed to avoid over prediction and reduce bias. In order to distinguish the additive and interactive effects in GbyE, we designed two lists of additive and interactive effects in the "ETA" of BGLR, and put the additive and interactive effects into the model as two kinships for random objects. However, it was not possible to load the gene effects of the two lists in rrBLUP, so the additive and interactive genotypes together were used to calculate whole genetic kinship in rrBLUP (Fig.  1 ). Relevant parameters in BGLR are set as follows: 1) model set to "RRB"; 2) nIter is set to "12000"; 3) burnIn is set to "10000". The results of the above operations are averaged over 100 cycles. We also validated the GbyE method using four other Bayesian methods (BayesA, BayesB, BayesCpi, and Bayesian LASSO) in addition to RRB in BGLR.

Partial missing phentoype in the prediction

In this study, we artificially missed phenotype values in the single and double environments in the whole population from 281 inbred maize datasets. In the missing single environment case, the inference set in the cross-validation was selected from whole population, and each individual in the inference were only missed phenotypes in the one environment. The phenotype in the other environment was kept. The genotypes were always kept. In the case of missing double environments, both phenotypes and genotypes of environment 1 and environment 2 are missing, and the model can only predict phenotypic values in the two missing environments through the effects of other markers. In addition, the data were standardized and unstandardized to assess whether standardization had an effect on the estimation of the model. This experiment was tested using the "ML" method in rrBLUP to ensure the efficiency of the model.

GWAS statistical power of models at different heritabilities and genetic correlations

Power-FDR plots were used to demonstrate the detection efficiency of GbyE at three genetic correlation and three genetic power levels, with a total of nine different scenarios simulated (from left to right for high and low genetic correlation and from top to bottom for high and low genetic power). In order to distinguish whether the effect of improving the detection ability of genome-wide association analysis in GbyE is an additive effect or an effect of environmental interactions, we plotted their Power-FDR curves separately and added the traditional Mean method for comparative analysis. As shown in Fig.  2 , GbyE algorithm can detect more statistically significant genetic loci with lower FDR under any genetic background. However, in the combination with low heritability (Fig.  2 A, B, C), the interactive effect detected more real loci than GbyE under low FDR, but with the continued increase of FDR, GbyE detected more real loci than other groups. Under the combination with high heritability, all groups have high statistical power at low FDR, but with the increase of FDR, the statistical effect of GbyE gradually highlights. From the analysis of heritability combinations at all levels, the effect of heritability on interactive effect is not obvious, but GbyE always maintains the highest statistical power. The average detection power of GWAS in GbyE can be increased by about 20%, and with the decrease of genetic correlation, the effect of GbyE gradually highlights, indicating that the G × E plays a role.

The power-FDR testing in simulated traits. Comparing the efficacy of the GbyE algorithm with the conventional mean method in terms of detection power and FDR. From left to right, the three levels of genetic correlation are indicated in order of low, medium and high. From top to bottom, the three levels of heritability, low, medium and high, are indicated in order. (1) Inter: Interactive section extracted from GbyE; (2) AddE: Additive section extracted from GbyE; (3) \({{\text{h}}}_{{\text{l}}}^{2}\) , \({{\text{h}}}_{{\text{m}}}^{2}\) , \({{\text{h}}}_{{\text{g}}}^{2}\) : Low, medium, high heritability; (4) \({{\text{R}}}_{{\text{l}}}\) , \({{\text{R}}}_{{\text{m}}}\) , \({{\text{R}}}_{{\text{l}}}\) : where R stands for genetic correlation, represents three levels of low, medium and high

Resolution of additive and interactive effect

The output results of GbyE could be understood as resolution of additive and interactive genetic effect. Hence, we created a combined Manhattan plots with Mean result from MLM, additive, and interactive results from GbyE. As shown in Fig.  3 , true marker loci were detected on chromosomes 1, 6 and 9 in Mean, and the same loci were detected on chromosomes 1 and 6 for the additive result in GbyE (the common loci detected jointly by the two results were marked as solid gray lines in the figure). All known pseudo QTNs were labeled with gray dots in the circle. Total 20 pseudo QTNs were simulated in such trait (The heritability is set to 0.9, and the genetic correlation is set to 0.1). Although the additive section in GbyE did not catch the locus on chromosome 9 yet (those p-values of markers did not show above the significance threshold (p-value < 3.23 × 10 –6 )), it has shown high significance relative to other markers of the same chromosome. In the reciprocal effect of GbyE, we detected more significant loci on chromosomes 1, 2, 3 and 10, and these loci were not detected in either of the two previous sections. An integrate QQ plot (Fig.  3 D) shows that the overall statistical power of the additive section in Mean and GbyE are close, nevertheless, the interactive section in the GbyE provided a bit of inflation.

Manhattan statistical comparison plot. Manhattan comparison plots of mean ( A ), additive ( B ) and gene-environment interactive sections ( C ) at a heritability of 0.9 and genetic correlation of 0.1. Different colors are used in the diagram to distinguish between different chromosomes (X-axis). Loci with reinforcing circles and centroids are set up as real QTN loci. Consecutive loci found in both parts are shown as id lines, and loci found separately in the reciprocal effect only are shown as dashed lines. Parallel horizontal lines indicate significance thresholds ( p -value < 3.23 × 10 –6 ). D Quantile–quantile plots of simulated phenotypes for demo data from genome-wide association studies. x-axis indicates expected values of log p -values and y-axis is observed values of log p -values. The diagonal coefficients in red are 1. GbyE-inter is the interactive section in GbyE; GbyE-AddE is the additive section in GbyE

Genomic selection in assumption codistribution

The prediction accuracy of GbyE was significantly higher than the Mean value method by model statistics of rrBLUP in most cases of heritability and genetic correlation (Fig.  4 ). The prediction accuracy of the additive effect was close to that of Mean value method, which was consistent with the situation under the low hereditary. The prediction accuracy of interactive sections in GbyE remains at the same level as in GbyE, and interactive section plays an important role in the model. We observed that in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig.  4 C), \({{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig.  4 F), \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig.  4 G), the prediction accuracy of GbyE was slightly higher than the Mean value method, but there was no significant difference overall. In addition, we only observed that the prediction accuracy of GbyE was slightly lower than the Mean value method in \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig.  4 H), but there was still no significant difference between GbyE and Mean value methods. Under the combination of low heritability and genetic correlation, the prediction accuracy of Mean value method and additive effect model remained at a similar level. However, with the continuous increase of heritability and genetic correlation, the difference in prediction accuracy between the two gradually increases. In summary, the GbyE algorithm can improve the accuracy of GS by capturing information on multiple environment or trait effects under the rrBLUP model.

Box-plot of model prediction accuracy. The prediction accuracy (pearson's correlation coefficient) of the GbyE algorithm was compared with the tradition al Mean value method in a simulation experiment of genomic selection under the rrBLUP operating environment. The effect of different levels of heritability and genetic correlation on the prediction accuracy of genomic selection was simulated in this experiment. Each row from top to bottom represents low heritability ( \({{\text{h}}}_{{\text{l}}}^{2}\) ), medium heritability ( \({{\text{h}}}_{{\text{m}}}^{2}\) ) and high heritability ( \({{\text{h}}}_{{\text{h}}}^{2}\) ), respectively; each column from left to right represents low genetic correlation ( \({{\text{R}}}_{{\text{l}}}\) ), medium genetic correlation ( \({{\text{R}}}_{{\text{m}}}\) ) and high genetic correlation ( \({{\text{R}}}_{{\text{h}}}\) ), respectively; The X-axis shows the different test methods and effects, and the Y-axis shows the prediction accuracy

Genomic selection in assumption un-codistribution

The overall performance of GbyE under the 'BRR' statistical model based on the BGLR package remained consistent with rrBLUP, maintaining high predictive accuracy in most cases of heritability and genetic relatedness (Fig. S1 ). However, when the heritability is set to low and medium, the difference between the prediction accuracy of GbyE algorithm and Mean value method gradually decreases with the continuous increase of genetic correlation, and there is no statistically significant difference between the two. The prediction accuracy of the model by GbyE in \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 G) and \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig. S1 I) is significantly higher than that by Mean value method when the heritability is set to be high. On the contrary, when the genetic correlation is set to medium, there is no significant difference between GbyE and Mean value method in improving the prediction accuracy of the model, and the overall mean of GbyE is lower than Mean. When GbyE has relatively high heritability and low genetic correlation, its prediction accuracy is significantly higher than the mean method, such as \({{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 D), \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 G), and \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{m}}}\) (Fig. S1 H). Therefore, GbyE is more suitable for situations with high heritability and low genetic correlation.

Adaptability of Bayesian models

Next, we tested a more complex Bayesian model. The GbyE algorithm and Mean value method were combined with five Bayesian algorithms in BGLR for GS analysis, and the computing R script was used for phenotypic simulation test, where heritability and genetic correlation were both set to 0.5. The results indicate that among the three Bayesian models of RRB, BayesA, and BayesLASSO, the predictive accuracy of GbyE is significantly higher than that of Mean value method (Fig.  5 ). In contrast, under the Bayesian models of BayesB and BayesCpi, the prediction accuracy of GbyE is lower than that of the Mean value method. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from G × E and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. However, the predictive accuracy of the BayesB model decreased by 11.3%, while the BayescCpi model decreased by 6%.

Relative prediction accuracy histogram for different Bayesian models. The X-axis is the Bayesian approach based on BGLR, and the Y-axis is the relative prediction accuracy. Where we normalize the prediction accuracy of Mean (the prediction accuracy is all adjusted to 1); the prediction accuracy of GbyE is the increase or decrease value relative to Mean in the same group of models

Impact of all and partial environmental missing

We tested missing the environmental by using simulated data. In the case of the simulated data, we simulated a total of nine situations with different heritability and genetic correlations (Fig.  6 ) and conducted tests on single and dual environment missing. The improvement in prediction accuracy by the GbyE algorithm was found to be significantly higher than the Mean value method in single environment deletion, regardless of the combination of heritability and genetic correlation. In the case of \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}\) , the prediction accuracy of GbyE is higher than 0.5, which is the highest value among all simulated combinations. When GbyE estimates the phenotypic values of Environment 1 and Environment 2 separately, its predictive accuracy seems too accurate. On the other hand, when the phenotypic values of both environments are missing on the same genotype, the predictive accuracy of GbyE does not show a significant decrease, and even maintains accuracy comparable to that of a single environment missing. However, when GbyE estimates Environment 1 and Environment 2 separately, the prediction accuracy significantly decreases compared to when a single environment is missing, and the prediction accuracy of Environment 1 and Environment 2 in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{m}}}\) is extremely low (Fig.  6 B). In addition, the prediction accuracy of GbyE is lower than Mean values only in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}\) , whether it is missing in a single or dual environment.

Prediction accuracy of simulated data in single and dual environment absence. The prediction effect of GbyE was divided into two parts, environment 1 and environment 2, to compare the prediction accuracy of GbyE when predicting these two parts separately. This includes simulations with missing phenotypes and genotypes in environment 1 only ( A ) and simulations with missing in both environments ( B ). The horizontal coordinates of the graph indicate the different combinations of heritabilities and genetic correlations of the simulations

The phenotype of organisms is usually controlled by multiple factors, mainly genetic [ 23 ] and environmental factors [ 24 ], and their interactive factors. The phenotype of quantitative traits is often influenced by these three factors [ 25 , 26 ]. However, based on the computing limitation and lack of special tool, the interactive effect always was ignored in most GWAS and GS research, and it is difficult to distinguish additive and interactive effects. The rate between all additive genetic variance and phenotype variance was named as narrow sense heritability. The accuracy square of prediction of additive GS model is considered that can not surpass narrow sense heritability. In this study, the additive effects in GbyE are essentially equivalent to the detectability of traditional models, the key advantage of GbyE is the interactive section. More significant markers with interactive effects were detected. Detecting two genetic effects (additive and interactive sections) in GWAS and GS is a boost to computational complexity, while obtaining genotypes for genetic interactions by Kronecker product is an efficient means. This allows the estimation of additive and interactive genetic effects separately during the analysis, and ultimately the estimated genetic effects for each GbyE genotype (including additive and interactive genetic effect markers) are placed in a t-distribution for p -value calculation, and the significance of each genotype is considered by multiple testing. The GbyE also expanded the estimated heritability as generalized heritability which could be explained as the rate between total genetics variance and phenotype variance.

The genetic correlation among traits in multiple environments is the major immanent cause of GbyE. When the genetic correlation level is high, then additive genetic effects will play primary impact in the total genetic effect, and interactive genetic effects with different traits or environments are often at lower levels [ 27 ]. Therefore, the statistical power of the GbyE algorithm did not improve significantly compared with the traditional method (Mean value) when simulating high levels of genetic correlation. On the contrary, in the case of low levels of genetic correlation, the genetic variance of additive effects is relatively low and the genetic variance of interactive effects is major. At this time, GbyE utilizes multiple environments or traits to highlight the statistical power. Since the GbyE algorithm obtains additive, environmental, and interactive information by encoding numerical genotypes, it only increases the volume of SNP data and can be applied to any traditional GWAS association statistical model. However, this may slightly increase the correlation operation time of the GWAS model, but compared to other multi environment or trait models [ 28 , 29 ], GbyE only needs to perform a complete traditional GWAS once to obtain the results.

In GS, rrBLUP algorithm is a linear mixed model-based prediction method that assumes all markers provide genetic effects and their values following a normal distribution [ 30 ]. In contrast, the BGLR model is a linear mixed model, which assumes that gene effects are randomly drawn from a multivariate normal distribution and genotype effects are randomly drawn from a multivariate Gaussian process, which takes into account potential pleiotropy and polygenic effects and allows inferring the effects of single gene while estimating genomic values [ 31 ]. The algorithm typically uses Markov Chain Monte Carlo methods for estimation of the ratio between genetic variances and residual variances [ 32 , 33 ]. The model has been able to take into account more biological features and complexity, and therefore the overall improvement of the GbyE algorithm under BGLR is smaller than Mean method. In addition, the length of the Markov chain set on the BGLR package is often above 20,000 to obtain stable parameters and to undergo longer iterations to make the chain stable [ 34 ]. GbyE is effective in improving the statistical power of the model under most Bayesian statistical models. In the case of the phenotypes we simulated, more iterations cannot be provided for the BayesB and BayesCpi models because of the limitation of computation time, which causes low prediction accuracy. It is worth noting that the prediction accuracy of BayesCpi may also be influenced by the number of QTLs [ 35 ], and the prediction accuracy of BayesB is often related to the distribution of different allele frequencies (from rare to common variants) at random loci [ 36 ].

The overall statistical power of GbyE was significantly higher in missing single environment than in missing double environment, because in the case of missing single environment, GbyE can take full advantage of the information from the phenotype in the second environment. And the correlation between two environments can also affect the detectability of the GbyE algorithm in different ways. On the one hand, a high correlation between two environments can improve the predictive accuracy of the GbyE algorithm by using the information from one environment to predict the breeding values in the other environment, even if there is only few relationship with that environment [ 37 , 38 ]. On the other hand, when two environments are extremely uncorrelated, GbyE algorithm trained in one environment may not export well to another environment, which may lead to a decrease in prediction accuracy [ 39 ]. In the testing, we found that when the GbyE algorithm uses a GS model trained in one environment and tested in another environment, the high correlation between environments may result to the model capturing similarities between environments unrelated to G × E information [ 40 ]. However, when estimating the breeding values for each environment separately, GbyE still made effective predictions using the genotypes in that environment and maintained high prediction accuracy. As expected, the additive effect calculates the average genetic effect between environments, and its predictive effect does not differ much from the mean method. The interactive effect, however, has one less column than the number of environments, and it calculates the relative values between environments, a component that has a direct impact on the predictive effect. The correlation between the two environments may have both positive and negative effects on the detectability of the GbyE, so it is important to carefully consider the relationship between the two environments in subsequent in development and testing.

A key advantage of the GbyE algorithm is that it can be applied to almost all current genome-wide association and prediction. However, the focus of GbyE is still on estimating additive and interactive effects separately, so that it is easy to determine which portion of the is playing a role in the computational estimation.. The GbyE algorithm may have implications for the design of future GS studies. For example, the model could be used to identify the best environments or traits to include in GS studies in order to maximize prediction accuracy. It is particularly important to test the model on large datasets with different genetic backgrounds and environmental conditions to ensure that it can accurately predict genome-wide effects in a variety of contexts.

GbyE can simulate the effects of gene-environment interactions by building genotype files for multiple environments or multiple traits, normalizing the effects of multiple environments and multiple traits on marker effects. It also enables higher statistical power and prediction accuracy for GWAS and GS. The additive and interactive effects of genes under genetic roles could be revealed clearly, which makes it possible to utilize environmental information to improve the statistical power and prediction accuracy of traditional models, thus helping us to better understand the interactions between genes and the environment.

Availability of data and materials

The GbyE source code, demo script, and demo data are freely available on the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Abbreviations

• Genome-widely association study

Genome selection

Genetic by environmental interaction

Genome association and prediction integrated tool

Mixed linear model

Bayesian generalized linear regression

Ridge regression best linear unbiased prediction

False discovery rate

Principal component analysis

Genomic estimated breeding value

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Acknowledgements

Thank you to all colleagues in the laboratory for their continuous help.

This project was partially funded by the National Key Research and Development Project of China, China (2022YFD1601601), the Heilongjiang Province Key Research and Development Project, China (2022ZX02B09), the Qinghai Science and Technology Program, China (2022-NK-110), Sichuan Science and Technology Program, China (Award #s 2021YJ0269 and 2021YJ0266), the Program of Chinese National Beef Cattle and Yak Industrial Technology System, China (Award #s CARS-37), and Fundamental Research Funds for the Central Universities, China (Southwest Minzu University, Award #s ZYN2023097).

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Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China

Xinrui Liu, Mingxiu Wang, Jie Qin, Yaxin Liu, Shikai Wang, Shiyu Wu, Ming Zhang, Jincheng Zhong & Jiabo Wang

Nanchong Academy of Agricultural Sciences, Nanchong, 637000, China

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JW and XL conceived and designed the project. XL managed the entire trial, conducted software code development, software testing, and visualization. MW, JQ, YL, SW, MZ and SW helped with data collection and analysis. JQ, and YL assisted with laboratory analyses. JW, and XL had primary responsibility for the content in the final manuscript. JZ supervised the research. JW designed software and project methodology. All authors approved the final manuscript. All authors have reviewed the manuscript.

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Liu, X., Wang, M., Qin, J. et al. GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction. BMC Genomics 25 , 386 (2024). https://doi.org/10.1186/s12864-024-10310-5

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Received : 27 December 2023

Accepted : 15 April 2024

Published : 19 April 2024

DOI : https://doi.org/10.1186/s12864-024-10310-5

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