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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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• 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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Sarah Crowe & Anthony Avery

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

Kathrin Cresswell, Ann Robertson & Aziz Sheikh

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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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Received : 29 November 2010

Accepted : 27 June 2011

Published : 27 June 2011

DOI : https://doi.org/10.1186/1471-2288-11-100

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What is case study research?

Last updated

8 February 2023

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Cathy Heath

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Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

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• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

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

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

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

Table of contents

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

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

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

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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What the Case Study Method Really Teaches

• Nitin Nohria

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

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

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

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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Case study research for better evaluations of complex interventions: rationale and challenges

Sara paparini.

1 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Judith Green

2 Wellcome Centre for Cultures & Environments of Health, University of Exeter, Exeter, UK

Chrysanthi Papoutsi

Jamie murdoch.

3 School of Health Sciences, University of East Anglia, Norwich, UK

Mark Petticrew

4 Public Health, Environments and Society, London School of Hygiene & Tropical Medicin, London, UK

Trish Greenhalgh

Benjamin hanckel.

5 Institute for Culture and Society, Western Sydney University, Penrith, Australia

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 – 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 – 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

Acknowledgements

Not applicable

Abbreviations

Authors’ contributions

JG, MP, SP, JM, TG, CP and SS drafted the initial paper; all authors contributed to the drafting of the final version, and read and approved the final manuscript.

This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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The authors declare that they have no competing interests.

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Studia Universitatis Babeş-Bolyai Negotia (Sep 2024)

What Makes a Brand Successful? A Case-study of Musette Brand

• Erika KULCSÁR,
• Borostyán Viktória FILIP

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The study of gender differences has recently become a growing focus. Nothing is more evidence of this than the fact that in 2023 Claudia Goldin (who studied the position of women in relation to the labour market) was awarded the Nobel Prize in Economics. Furthermore, internationally, there are numerous studies that examine the differences between women and men as leaders and managers. The question arises as to what factors play a significant role from the perspective of women managers in starting and managing a successful business. The present research focuses on the analysis of a Romanian brand - Musette - which is co-founded by a woman (through Cristina Bâtlan) and is represented internationally. Despite the considerable success of the Musette brand, there is a lack of literature in Romania on the study of this brand. Consequently, the objectives of our study are: (1) to identify the conditions that should be the pillars of a start-up business, (2) to identify the factors without which there is no possibility of further development and lasting success, and (3) to examine the brand personality characteristics of Musette. JEL classification: M10, M31 Article History: Received: March 5, 2024; Reviewed: July 10, 2024; Accepted: August 7, 2024; Available online: September 23, 2024

• female managers
• communication
• brand personality

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• Published: 29 September 2024

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

• Ravi Kant 1   na1 ,
• S. P. Maurya 1 ,
• K. H. Singh 2   na1 ,
• Kottakkaran Sooppy Nisar 3   na1 &
• Anoop Kumar Tiwari 4   na1  

Scientific Reports volume  14 , Article number:  22581 ( 2024 ) Cite this article

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• Energy science and technology
• Solid Earth sciences

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone ( \(>20\%\) ) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s \(*\) g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires \(88\%\) more time than the latter.

Introduction

In combined exploration and reservoir investigations, reservoir characterization from seismic data is essential because it provides very detailed geological information and characteristics of the reservoir. According to Li and Zhang 1 , the oil and gas industries now extensively rely on the estimation of reservoir features using geophysical techniques for prospect appraisal, reservoir characterization, and geological modeling. When employing conventional methodologies for reservoir characterization, the problem of revealing deeper reservoirs in exploratory operations has always been challenging and expensive. A remedy for this problem has been found in coupled seismic inversion and reservoir characterization since it provides essential information about exploration and production operations. As a result, combined seismic inversion and reservoir characterization are used by the oil industry as a tool for exploration and production processes all over the world 2 , 3 , 4 . From seismic and well-log data, petrophysical models are extracted via seismic inversion. The subsurface models can also be established using the inversion of seismic data alone in the absence of well-log data 5 . The oil and gas business commonly uses seismic inversion techniques to identify subterranean reservoirs. The elastic parameters like impedance ( Z ), P-wave velocity ( \(V_s\) ), S-wave velocity ( \(V_s\) ), and density ( \(\rho\) ) can all be calculated using seismic inversion 6 . The lame parameters, which are affected by fluid saturation in rocks, can also be found using inverted impedance models. The fundamental challenge with seismic inversions is that there could be multiple solutions for a given situation. By incorporating additional information, which is frequently derived from the well-log data, the uncertainty of non-uniqueness can be reduced. Seismic inversion can be made more efficient by using both local and global optimization techniques, such as simulated annealing, branch and bound, Bayesian search (partition), genetic algorithms (GAs), and others. Local optimization techniques include least-squares optimization, Pattern Search, conjugate gradient, steepest descent, quasi-Newton, and Newton 7 . A wide range of optimization techniques, such as model-based inversion, colored inversion, sparse spike inversion (SSI), band-limited inversion (BLI), etc., are used in numerous examples of reservoir characterization on post-stack seismic data and local well-log data 1 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . Post-stack seismic data was employed in this study to characterize the Blackfoot reservoir, using seismic inversion based on particle swarm optimization and genetic algorithm.

The main goals of this study are to evaluate and compare the effectiveness of seismic inversion based on global optimization of particle swarm optimization (PSO) & genetic algorithm (GA) and to identify potentially productive zones using impedance and porosity in the Blackfoot field, Alberta, Canada. One of the bio-inspired algorithms is particle swarm optimization (PSO), which searches for the best solution in the problem space. It doesn’t depend on the gradient or any other differential form of the objective, in contrast to other optimization techniques, and only needs the objective function. Initially, PSO forms a starting swarm by randomly placing a collection of particles within a feasible solution space, with each particle assessed by the objective function 13 . Subsequently, each particle moves at a specific velocity within the search space, its velocity dynamically adjusted based on its movement and that of other particles. Typically, particles tend to gravitate towards the path of the optimal particle, converging towards the best solution over successive iterations 14 . During each iteration, every particle tracks two key extremes, its personal best, representing the optimal position it has achieved thus far, and the global best, indicating the best position across all particles in the swarm 15 . By adhering to these principles, each particle progressively converges to the same position over iterations, representing the optimal solution to the optimization problem 13 , 14 . With less parameters to change, particle swarm optimization (PSO) is a novel approach with an easy-to-implement principle. Since J. Kennedy and R. C. Eberhart first proposed the algorithm in 1995, the approach has drawn the attention of numerous academics. Its application in seismic inversion is still in the research stage, despite its widespread use. PSO seismic inversion is not now directly applied in the majority of commercial applications. Scholars have made significant strides from the early numerical modeling of PSO impedance inversion to the current implementation of PSO and its improved algorithm for real seismic data. Previous authors 16 have used PSO for the noise-corrupted synthetic sounding data sets over a multilayered 1D earth model by using DC, induced polarization (IP), and magnetotelluric (MT) methods. Authors 17 have used Inversion of seismic refraction data using particle swarm optimization where they have confirmed the ability and reliability of PSO in inverting seismic refraction data to model 1D P-wave velocity structures with acceptable misfit and convergence speed.

On the other hand, the genetic algorithm (GA), which is a subset of the larger class of evolutionary algorithms (EA), is a metaheuristic technique used in computer science and operations research that draws inspiration from the process of natural selection 18 . When solving optimization and search issues, genetic algorithms commonly use biologically inspired operators like mutation, crossover, and selection. This is accomplished by determining the model’s (objective function’s) least feasible mismatch with the seismic data. Because the observed seismic data may be thought of as a forward model in which the seismic wavelet is convolved with the earth’s reflectivity series, the inversion process is model-driven 19 . This process can be described by stochastic or deterministic methods. With no information loss, these techniques can generate quantifiable genuine model parameters of subsurface rock attributes (earth models), such as impedance, porosity, compressional velocity ( \(V_P\) ), and reflectivity, resulting in a practical global optimum solution. These methods help to improve thin-bed stratigraphy recovery, volumetric estimation, wavelet effects removal, sand body quantitative characterization, tuning problems, and seismic data de-noising, in general 20 . The results of global inversion procedures can lead to greater resolvability and a more favorable correlation between seismic data and lithology. Specifically, Markov chain analysis is used 21 to demonstrate that both the GA and the simulated annealing (SA) approach are capable of finding an optimal solution. The GA is one of the most significant algorithms among the stochastic methods discussed above and it has been used to solve inversion problems in a variety of scientific fields, including geophysics. The GA is applied to invert for many sets of synthetic seismic refraction data 22 and also applied to amplitude variation with offset inversion and prestack full-waveform inversion 23 , 24 , 25 . Further, the GA is used 26 , 27 to invert for reservoir properties. Nevertheless, the poor performance of stochastic algorithms, such as the GA, in local searches restricts the pace of the search. Several stochastic algorithms can find an optimal solution, as demonstrated in 21 , 28 .

Another important factor in inverse problems is the selection of the inversion algorithm. Simple inversion issues can be solved with local search techniques 29 . However, these techniques are insufficient to achieve the global optimum in complex circumstances, such as the inversion of reservoir characteristics. However, global geometry optimization problems can be solved by stochastic algorithms 30 , 31 , but it takes a long time for these algorithms to leave the local optimum and arrive at the global optimum. This places additional restrictions on how quickly stochastic algorithms can search. By analyzing previous comparative studies by authors such as 32 which have explored the advantages and disadvantages of swarm intelligence algorithms over GA, by conducting experiments on synthetic data only. Also, the authors 33 applied PSO and GA for the inversion of Rayleigh wave dispersion curves. However, our study not only shows the PSO’s superior performance in achieving global minima but also provides a detailed comparative analysis of GA and PSO, highlighting their performance differences in terms of iteration efficiency and resolution accuracy. For reservoir characterization, a thorough numerical simulation of PSO and GA wave impedance inversion is therefore performed in this work, along with a qualitative and quantitative comparison of the two approaches. In the discipline of geophysics, GA is a well-established method, whereas PSO is still in its infancy. As such, comparing the two becomes crucial to determining which is the best in the competition for convergence time using post-stack seismic data from Blackfoot, Canada.

The post-stack seismic data from the Blackfoot field in Alberta, Canada, coupled with well log data, are utilized to show how GA and PSO can be used to invert seismic data. In the Canadian province of Alberta, Blackfoot Field is situated southeast of Strathmore City 34 . To better understand the clastic Beaver Hill Lake carbonates and the reef-prone Glauconitic channel, the data was gathered in two overlapping patches 35 . 708 shots (seismic source), distributed across 690 fixed recording channels, make up the collection 36 . The data has a vertical bandwidth of 5–90 Hz and a horizontal bandwidth of 5–50 Hz. Data from the first patch i.e. reef-prone Glauconitic channel is used in this study. Simin 37 provides a detailed description of the processing techniques applied to the vertical and horizontal component data.

Methodology

• Genetic algorithm

Holland (1975) developed genetic algorithms (GAs), which were the first global optimization techniques used in this investigation. The more prevalent category of evolutionary algorithms includes genetic algorithms (GAs), which are adaptive heuristic search engines. On the concepts of natural selection and genetics, genetic algorithms are built. Natural selection, the mechanism that propels biological evolution, is the foundation of the genetic algorithm, which resolves both constrained and unconstrained optimization issues. GA works by repeatedly changing each unique solution in a population. The genetic algorithm chooses at random each unique solution, known as a parent, and utilizes it to create a new solution, known as children. After a significant number of generations, the problem’s solution approaches the optimum 38 . The evolutionary algorithm is used in many study domains to optimize discontinuous, non-differentiable, stochastic, or highly nonlinear conditions by decreasing errors between observed and modeled data. In this study, a genetic algorithm is employed to determine the subsurface acoustic impedance and porosity by minimizing the discrepancy between the observed and modeled seismic traces. To build a new population from the present population, the genetic algorithm performs three main processes, selection, crossover, and mutation 22 .

Using a selection technique based on fitness scores, individual models are coupled. Whether or not they are selected depends on the model’s fitness value. The likelihood of selecting a model with very high fitness values is much higher than selecting a model with low fitness values. This is because more often the models that suit the data will be chosen for replication. The model’s fitness value determines the selection probability 39 . The crossover genetic operator is applied after the models have been chosen and coupled. Transferring genetic data between paired models is the basic tenet of a crossover. The crossover could also be seen as a means of information sharing between paired models, leading to the creation of new models 40 . Geophysical difficulties might involve either single-point crossover or multipoint crossover. In the context of GA being applied to geophysical problems, the choice between single-point crossover and multipoint crossover depends on several factors related to the specific characteristics of the problem being solved and the goals of the optimization process. If the problem involves a relatively simple or smooth search space, a single-point crossover may be sufficient to navigate the space effectively. Since, our goal is to explore a diverse set of solutions and avoid premature convergence to local optima, So, multipoint crossover is more suitable here. The fundamental idea behind the single-point crossover is the use of a uniform probability distribution to choose the model’s one-bit location at random. The right side of the selected bit is then exchanged between two models, resulting in the creation of a new model. On the other hand, the multipoint crossover method randomly chooses a bit location and then shifts all bits up to this bit throughout the matched models. A further bit location is chosen at random from the second model parameter, and any bits that immediately follow this bit are again exchanged. Every model parameter goes through this process 41 .

The mutation, which adds randomization to the crossover, is adopted in the final stage. The mutation process often happens during the crossover phase. The number of walks in the model space is controlled by the mutation rate, which is established by a uniform probability distribution. Because there won’t be many walks in the model space due to the low mutation frequency, the problem will be transformed shortly. On the other hand, the high mutation probability will result in a lot of space-based random walks, but this can delay the algorithm’s convergence 42 . In the process of mutation, one new solution (children) from the crossing is chosen and employed as a parent. In order to produce unpredictability in the result, two mutation sites are selected and switched between one another. The following is how to apply the genetic algorithm.

Step 1: Generate the initial Populations

Step 2: Define search space by deciding lower and upper limit.

Step 3: Calculates the reflection coefficients.

whereas \(R_i\) is reflectivity at \(i^{th}\) interface, \(Z_{i+1}\) is impedance at \((i+1)^{th}\) layer, \(Z_i\) is impedance at \(i^{th}\) layer. Step 4: Creates traces synthetic using following formula.

Where \(S_{mod}\) is synthetic trace, R is reflectivity series, W is wavelet and \(*\) is convolution operator.

Step 5: Evaluates the merit function

Where e is the difference between model and observation, \(S_{mod\,i}\) is modeled data at the ith sample, \(S_{obj\,i}\) is observed data at the ith sample, and \(Z_{mod\,i}\) is the modeled impedance at ith time sample n is the total number of sample points in the data and \(Z_{obj\,i}\) is the observed impedance at ith sample. \(W_1\) and \(W_2\) are weights applied to the two terms, respectively. In most of the cases, these weights are chosen as unity i.e. \(W_1= W_2= 1\) . In the context of single data inversion, such as seismic data, these parameters are typically assigned a value of 1. However, when dealing with the inversion of multiple geophysical datasets, these parameters may vary depending on the data type and associated parameters. For instance, in joint inversion, which aims to integrate various geophysical data types like gravity, magnetotelluric, and seismic data, these parameters cannot be uniformly set to one. Instead, their values depend on the relative importance or weight assigned to each type of data.

Step 6: Selects the individuals to be combined based on their merit score

Step 7: Combines the selected individuals using the crossing point c

Step 8: Introduces a mutation be means of the operator M

Step 9: Selects the individuals that will follow to the next generation by means of the operator S

Step 10: Creates the next generation

Step 11: Final Result ( \(Z_p\) )

• Particle swarm optimization

The foraging and social activities of the swarm serve as the inspiration for particle swarm optimization (PSO). PSO imitate the action or the phenomenon, such as a bird flock or a school of fish. The algorithm of PSO was developed by Eberhart and Kennely 43 . The PSO is used to optimize continuous non-linear function by direct search approach and does not need any gradient information like other optimization needed. The benefit of not using gradient information is that, it can be used for discontinuous function. PSO begins with a randomly initialised population, much like a genetic algorithm (GA). Solutions, unlike GA operators, are given randomised velocities to explore the search space. In PSO, a particle is a solution of any kind. Three distinct features of PSO are given below.

\(p_{best\,i}\) : the best outcome (fitness) that particle i has so far attained

\(g_{best}\) : the most effective outcome (fitness) that any swarm particle has yet attained.

For exploring and utilising the search space to find the best answer, use velocity and position updates.

Now we will start with the position update first because it will behave like a variation operator that will help to change the current position of a particle to its position.

The following updates to particle (i) velocity are made.

Where w is an inertial weighting factor that linearly decreases with the number of iterations to give the algorithm strong global search performance at the beginning and strong local search performance at the end. The numbers \(c_1\) and \(c_2\) , also known as learning factors or acceleration factors, are positive constants. The particle’s step length to reach its current best position is controlled by \(c_1\) , while the particle’s step length to reach the world’s best position is controlled by \(c_2\) . Kennedy 13 conducted a number of parameter experiments and found that when the algorithm behaves better, the sum of \(c_1\) and \(c_2\) should be around 4.0. The random values \(r_1\) and \(_2\) fall between [0, 1], while t is the iteration number. In order to prevent particle velocities that are insufficient for a particle to fly over the ideal position, the maximum speed of particles must be limited. We refer to each particle’s top speed as having a maximum of \(V_{max}\) , and if \(V_{max} < v_{id}\) , we set \(V_{max}=v_{id}\) ; if \(V_{max}>v_{id}\) , we set \(-V_{max}=v_{id}\) 44 . The particle’s initial positions and speeds are produced at random, and the above two equations are iterated until a workable solution is obtained. Equation 12 involves three components,

Momentum part ( \(wv_{id}(t)\) ) : the first component is the particle’s earlier velocity, which guarantees the particle’s flight.

Congnitive part \(c_1 r_1 (p_{id} (t)-x_{id} (t))\) the second component is a vector that points from the particle’s present position to its own best position.

Social part \(c_2 r_2 (g_{id} (t)-x_{id} (t))\) : final component is a vector from its current location to the swarm’s best position 45 .

Each particle adjusted its speed in accordance with Eq.( 11 ) throughout each iteration. Fly to the new position in accordance to Eq.( 12 ) 17 .

Algorithm test

1d synthetic model.

The GA and PSO approach is used to estimate acoustic impedance first using synthetic data followed by real data application. For this, an acoustic impedance-based seventeen-layer Earth model is used with a range of AI is \(7040m/s*g/cc\) , \(9065m/s*g/cc\) , \(5740m/s*g/cc\) , \(7425m/s*g/cc\) , \(10040m/s*g/cc\) , \(8225m/s*g/cc\) , \(9500m/s*g/cc\) , \(11700m/s*g/cc\) , \(6300m/s*g/cc\) , \(10455m/s*g/cc\) , \(7425m/s*g/cc\) , \(13500m/s*g/cc\) , \(11700m/s*g/cc\) , \(10000m/s*g/cc\) , \(14144m/s*g/cc\) , \(15568m/s*g/cc\) , \(12720m/s*g/cc\) . We employed genetic algorithms and particle swarm optimization approaches to minimize the error between observed and simulated data after creating synthetic data sets.

The resulting 1-D post-stack seismic data were inverted to get the impedance values for each layer. This study uses a forward model for a 17-layered scenario. Each layer was thought to be homogeneous, with uniform velocity and density throughout. Each layer’s depth, velocity, and density were all model parameters. After that, the same model was inverted using GA and PSO, and model parameters were computed. The related results are presented in Fig. 1 . Tracks 1 and 2 of Fig.  1 illustrate the velocity and density of the subsurface geology model. In contrast, tracks 3 show the comparison of original impedance (black) and predicted impedances from the genetic method (green) and particle swarm optimization(red), respectively. By minimizing the error from eq. ( 4 ), the best outcomes are estimated. The calculated P-impedances agree well with their real models, and the correlation coefficients for GA and PSO are estimated to be 0.99 and 0.99, respectively. Also in Fig.  1 , tracks 4 and 5 compare synthetic traces from modeled impedance (black) and reproduced synthetic traces from inverted impedance estimated from GA (green) and PSO (red) show a very close match with each other with 0.99 and 0.98 correlation respectively.

Show the results of the 1D convolution model using synthetic data and seismic inversion.

As a quality check of inverted data, a cross plot between inverted and original impedance is constructed and exhibited in Fig. 2 . The image of track one depicts the best-fit line as a solid blue line, and the red and blue circles show inverted impedance which is the outcome of applying GA and PSO algorithms. From this figure, we see that the circles are too close to the best-fitting line. The proximity of the scatter points to the best-fit line indicates that the inverted results are extremely near to the true value, confirming the algorithm’s effectiveness. Table 1 compares observed and modeled impedance for each layer estimated by genetic algorithms and particle swarm optimization. Figure  2 b depicts error analysis for the genetic method (top) and particle swarm optimization (bottom). In the entire process of inversion, PSO takes 34 sec and GA takes 54 sec to archive the optimal solution. According to the observations and numerical analysis, the PSO produces better outcomes than the GA approaches.

a Displays a cross plot of the modeled and actual impedance, and b for the inversion of synthetic data, it displays the variation in error with iteration.

Synthetic wedge model

The wedge model is a key component of the interpreter’s toolkit for coal seams. It is widely used to comprehend the geologic significance of seismic amplitudes below a reservoir’s tuning thickness. Tuning describes the modification of seismic amplitudes brought on by beneficial and harmful interference from overlapping seismic reflections. When numerous closely spaced contacts reflect a downward wave, this occurs. If the resulting upgoing reflections coincide, the reflected seismic energy will interact with and change the amplitude response of the true geology.

Further, we examine this using a constructed zero-offset synthetic wedge model (Fig.  3 ). Linearly, the seam thickness rises from 70 to 90 m. We can observe that the wedge’s amplitude response is constant. This shows that the top and bottom of the wedge have distinct reflections with no interference. Figure  3 a, b depict a theoretical wedge model of a pinch-out coal seam and a forward seismic profile based on the convolution model. Figure  3 c, d show the impedance section estimated using a genetic algorithm and particle swarm optimization method respectively recognizing two distinct events that are thinner than the tuning thickness. The analysis demonstrates that the PSO estimates superior resolution than the genetic algorithms.

Represents a a wedge model, b synthetic data generated from the wedge model, c inverted impedance from GA, and d inverted impedance section from PSO.

Blackfoot data application

To predict the acoustic impedance and porosity in the inter-well zone, seismic data from the Blackfoot field in Canada is subjected to particle swarm optimization and the Genetic Algorithm. Although the entire volume of seismic data is accessible, only a cross-section at inline 1 and crosslines 1 to 50 are utilized to invert them by taking into account the long duration of convergence. Before performing inversion, first need to calculate the relationship between porosity reflectivity ( \(R_{\phi }\) ) and impedance reflectivity ( \(R_Z\) ), and the following formula is used 46 .

Where \(z_{i+1}\) and \(\phi _{i+1}\) is (i+1)th layer impedance and porosity whereas, \(z_i\) and \(\phi _i\) is the ith layer impedance and porosity respectively 47 . These equations (4 and 5) are used to estimate the correlation factor ( g ) which is nothing but the slope of the fitted line. Figure 4 a shows the cross plot of porosity reflectivity and acoustic reflectivity and the best-fit line gives a slope of −0.14. This correlation factor is used to generate a porosity wavelet by multiplying the g factor with the impedance wavelet which is extracted directly from the seismic data. Figure  4 b compares the porosity wavelet and the impedance wavelet and one can see that both are in reverse polarity and that porosity and impedance are inversely proportional to one another.

a Crossplot between acoustic reflectivity and porosity reflectivity for well \(01-08\_logs\) is marked by a best linear trend which gives the correlation factor ( \(g = -0.14\) ), and b compares statistical wavelet (black) and porosity wavelet (red) generated by multiplication of correlation factor to the statistical wavelet.

The application to real data is performed in two steps, first, a composite trace close to the well locations is derived from the seismic section, and both algorithms (GA and PSO) are applied to it. According to the theory, the composite trace is quite close to the well, and the same stratigraphy may be expected in the well as well as the composite trace. Both techniques explored begin with low-frequency acoustic impedance and porosity from the well-log and utilize it as a model to address constraints. The main advantage of beginning with a model based on well-log data is that it shortens the lengthy convergence process to these global optimization methods. In addition, a lower bound and an upper bound are used to further limit the search space inside the desired range. However, in this study, we used upper and lower bounds based on the original model built using well-log data. The process is as follows. The phenotype comprises one binary strings. Every individual is depicted by an Nx101 matrix, where N signifies the number of layers, 101 bytes indicates the chain’s length, and the parameter Zp and porosity is represented. For generating the initial population, uniform probability functions were linked to the ranges of the initial models, including impedance within ± 1000m/s*g/cc and porosity within ( \(\pm 10\%\) ) . Figure  5 depicts the inversion analysis result for the composite trace near wells 01-08, as calculated using the genetic algorithm and particle swarm optimization, respectively. Figure  5 depicts the original model (blue solid lines) as well as the lower and upper bounds (dotted blue lines). The first and second panels of Fig.  5 show a comparison of the original (black solid line) and inverted acoustic impedance (red solid line) whereas panels 3 and 4 compare the original (black solid line) and inverted porosity trace (red solid line). The AI and porosity created from well-defined data and inverted data have a good agreement with each other. The peak-to-peak of acoustic impedances and porosity do not match since well log data has a frequency range of 20 to 40 kHz, whereas seismic frequency typically ranges from 10 to 80 Hz. The correlation between well-log impedance and inverted impedance illustrated by GA and PSO is 0.71 and 0.66 and well-log porosity and inverted porosity are 0.63 and 0.63 respectively. Table  2 contains the other statistical comparisons between well data and inverted data. According to the analysis, the inverted outcomes of PSO and GA are extremely near to the original, so the method performance is good. From the Fig. 5 we highlighted the target zone is between 1040 and 1065 ms which shows low impedance and high porosity values from original as well as inverted data.

Represents four panels, the first and second panels compare inverted and well log impedance whereas the third and fourth panels compare inverted and well log porosity estimated from GA and PSO respectively.

After that, the global optimization techniques (GA and PSO) are applied trace by trace to the CDP stack section to determine the impedance and porosity volume. Figure  6 a displays a cross-section at inline 1 and crosslines 1 to 50 with a two-way travel time of \(900-1100ms\) of Blackfoot seismic data. Inversion is done trace by trace, and once all seismic traces have been inverted using GA and PSO, the results are plotted against two-way travel time and shown in Fig.  6 b, c, respectively. Figure  6 b, c clearly show that the 01–08 log impedance and inverted impedance have a good match and the low impedance zone is between 1040 and 1065 ms. The boundary at 1040 ms is designated as a high reflecting layer because the high impedance layer ( \(>11{,}000 m/s*g/cc\) ) surrounds the low impedance zone (6500–8500 m/s*g/cc). The visual analysis depicts that the impedance estimated by the PSO methods has a higher resolution as compared with GA. The layers are more clearly visible in PSO-derived impedance as compared with GA.

a Shows input seismic section, b depicts inverted impedance section estimated from GA, and c shows inverted impedance section estimated using PSO

Following that, Fig.  7 shows the porosity volume that was created by projecting porosity throughout the whole seismic section. Figure  7 a depicts post-stack seismic data, Fig. 7 b, c show the variation of the porosity section at inline 1 with a two-way travel time of 900–1100 milliseconds derived using GA and PSO techniques respectively. The inverted results show a very high resolution of the subsurface with layer information whereas input seismic data only provides interface information. It is noted that the porosity varies from 1 to 30 percent in the study region and the high porosity zone ( \(>20\%\) ) is between 1040 and 1065ms two-way travel time. The well-log porosity also shows a very good agreement with inverted porosity in both cases (GA and PSO).

a Shows input seismic section, b depicts inverted porosity section estimated from GA, and c shows inverted porosity section estimated using PSO.

From the analysis of Figs.  6 and  7 , one can notice that the low impedance zone found in the inverted impedance section also shows a very high porosity zone. This anomalous zone is already interpreted in the composite trace as well as well-log data between 1040 to 1065ms two-way travel time. This anomalous zone is characterized as a reservoir (sand channel). Additionally, we have shown inverted impedance and porosity using a standard inversion technique such as model-based inversion (MBI) in Fig.  8 . MBI employs the least squares method, a local optimization approach that converges faster than global optimization methods. However, MBI’s performance is highly dependent on the accuracy of the initial model, if the initial model is inaccurate, MBI may not reach the global minimum, resulting in a less accurate subsurface earth model. In Fig.  8 , the anomalous zone exhibits patches of low impedance and high porosity that are not uniform, lacking continuous definition. In contrast, global optimization techniques present the low impedance and high porosity zone more continuously.

Shows inversion results from MBI, a depicts inverted impedance, and c shows inverted porosity section.

For the quality check, the inverted synthetic is generated from the inverted impedance and porosity using the forward modeling technique and compared with Blackfoot seismic data. Figure  9 compares and presents Blackfoot seismic and replicated synthetic data. Figure  9 is divided into 4 panels, the first compares Blackfoot seismic to replicated synthetic seismic generated by inverted impedance using a genetic algorithm whereas the second panel compares seismic and inverted synthetic estimated from particle swarm optimization. Both curves (in the case of GA and PSO), match with each other very well with a very high correlation of 0.90 and 0.99, respectively. The third panel compares the seismic section with inverted synthetic derived from inverted porosity from GA and the fourth panel compares seismic data with inverted synthetic derived from the porosity section estimated using the PSO technique. These two seismic sections (in the case of GA and PSO) match with each other very well and the correlation coefficient is estimated to be 0.87 and 0.92, respectively. These qualitative and quantitative evaluations demonstrate how well the algorithm performs in this scenario.

The first and second panel compares Blackfoot seismic and reproduced synthetic sections from the inverted impedance estimated using GA and PSO, respectively. The third and fourth panel compares Blackfoot seismic and reproduced synthetic sections from the inverted porosity estimated using GA and PSO, respectively.

The GA and PSO optimization involves error minimization between observed (seismic) data and modeled (synthetic) data. The variation of error with iteration number is presented in Fig.  10 . The average error decreases from 1.0 to 0.8 when the genetic algorithm is used, whereas it reaches 0.3 when particle swarm optimization is used. From the figure, one can notice that the error decreases exponentially and major error decreases in between 1-100 iterations in both the cases (GA and PSO). The analysis also shows that the error minimization is fast and the resolution of the inverted section estimated from PSO is better in comparison with GA.

Variation of error with iteration estimated for the impedance inversion (top), and porosity inversion (bottom).

The time required to perform the optimization procedure relies on the complexity of the geological model, the amount of data, and the algorithm utilized for seismic inversion. As a result, making optimal use of time, memory, and computer resources is critical to ensuring that the optimization process is completed in a fair amount of time. Figure  11 compares the convergence time of the genetic algorithms and particle swarm optimization for synthetic as well as real data inversion cases. From the figure, it can be noticed that the PSO is less time-consuming as compared with GA in both cases. Using a PC(Personal Computer) with \(11th\,\, Gen\,\, Intel(R)\,\, Core(TM)\,\, i7-11700 @ 2.50GHz\, 2.50 GHz\) , the convergence time of GA is 670680sec. whereas the PSO takes 356400sec. to invert a total of 3240 traces. This indicates \(88\%\) less convergence time of PSO as compared with GA.

Convergence time comparison for a synthetic as well as b real data case.

An attempt was made to juxtapose the estimated impedance and porosity section with the findings presented by Maurya and Singh (2018), revealing a striking similarity between both sets of results. The study also calculated an anomalous zone with a two-way travel time of 1040–1065 ms and an extremely low acoustic impedance as well as a high porosity zone, which is likewise determined in the current investigation, and validated the results. Maurya and Singh, 2018 utilized the local optimization technique whereas the current study uses the global optimization technique which is proven to be a very powerful tool to minimize error and produces very high-resolution subsurface information that can not be retrieved using local optimization techniques.

Conclusions

Modeling and reservoir characterization are thought to be efficient ways to reduce subsurface uncertainty and improve reservoir prediction accuracy. In this study, qualitative as well as quantitative reservoir characterization has been performed using seismic inversion based on global optimization of algorithms such as genetic algorithm and particle swarm optimization. These global optimizations algorithms are not so common as they need high computing and expertise to implement but are very powerful tools to get very high-resolution subsurface information. This study demonstrated that if prior information (well log) is included in these inversion methods, it can reduce convergence time and generate very high-resolution subsurface information. An algorithm of seismic inversion based on PSO and GA is presented in this study and tested with synthetic as well as real data. In synthetic data, the inverted impedance trace follows the trend of real impedance very well for GA and PSO-based inversion. The correlation coefficient is 0.993 and 0.986 have been achieved from GA and PSO inversion, respectively. The error variation with the iteration of particle swarm optimization is substantially lower than that of genetic algorithms. In real data, the inverted impedance and porosity show very high-resolution subsurface information in both cases (GA and PSO), with impedance variation from 6000 to 16,000 \(m/s *g/cc\) and porosity from 1 to 30%. The interpretation of inverted sections depicts a low impedance (6000–8500 \(m/s*g/cc\) ) and high porosity (>20%) anomaly in the time interval 1040–1065 ms two-way travel time. This anomalous zone is also interpreted in well-log data and characterized as a reservoir (sand channel). The fitness error in this inversion process decreases from 1 to 0.88 with GA optimization after 400 iterations as opposed to 1 to 0.25 using PSO. The convergence time for PSO optimization is 356,400 s, but the convergence time for GA is 670,680 s, demonstrating that the former takes 88% longer to reach convergence.

Data availability

The data that support the findings of this study are available from https://www.geosoftware.com/ but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the author (S.P. Maurya) upon reasonable request and with permission from https://www.geosoftware.com/ .

Code availability

The code used in this study is available at: https://github.com/ravigeop/Global-optimization-GA-and-PSO-.git .

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Acknowledgements

We thank GeoSoftware for providing Hampson Russell software, particularly Emerge, Strata, and Geoview. One of the authors, Dr. S.P. Maurya, expresses gratitude to the funding organizations UGC-BSR (M-14-0585) and IoE BHU (Dev. Scheme No. 6031B) for their financial assistance. In addition, we acknowledge the academic licenses for Matlab (2022b) and Norsar (full package), respectively, from www.mathworks.com and www.norsar.no respectively. This work couldn’t be done without their help. This study is also supported via funding from Prince Sattam bin Abdulaziz University with project number PSAU/2023/R/1444.

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These authors contributed equally: Ravi Kant, K. H. Singh, Kottakkaran Sooppy Nisar and Anoop Kumar Tiwari.

Authors and Affiliations

Department of Geophysics, Banaras Hindu University, Varanasi, 221005, India

Ravi Kant & S. P. Maurya

Department of Earth Sciences, IIT Bombay, Mumbai, 400076, India

K. H. Singh

Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia

Kottakkaran Sooppy Nisar

Department of Computer Science and Information Technology, Central University of Haryana, Haryana, India

Anoop Kumar Tiwari

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Ravi Kant perform the experiment and analyzed results, S.P. Maurya, Anoop Kumar Tiwari and Ravi Kant developed methodology and code and K.H. Singh and Kottakkaran Sooppy Nisar verified results. Finally, Ravi Kant drafted manuscript and finalized by S.P. Maurya and K.H. Singh.

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Kant, R., Maurya, S.P., Singh, K.H. et al. Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study. Sci Rep 14 , 22581 (2024). https://doi.org/10.1038/s41598-024-72278-2

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Mechanical approach for creating different molecular adducts and regulating salt polymorphs: A case study of the anti-inflammatory medication Ensifentrine

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Ananya Kar , Lopamudra Giri , Gowtham Kenguva , Smruti Rekha Rout and Rambabu Dandela

First published on 20th September 2024

An intriguing technique for crystal engineering is mechanochemistry, which frequently yields solid forms (salt, cocrystal and polymorphs, etc.) that are challenging to acquire by traditional solution-based approaches. However, generating new and potentially beneficial solid forms remains an ongoing task in this field. Moving forward in this demanding arena, several molecular adducts (salts and salt polymorphs) of the model drug, Ensifentrine (ENSE) with GRAS (Generally Recognized as Safe) co-former, were synthesised for the first time using a mechanochemical technique followed by a slow evaporation crystallisation procedure. All the newly obtained solid forms were characterized by Single Crystal X-ray Diffraction (SCXRD), Powder X-Ray Diffraction (PXRD), Thermogravimetric analysis (TGA) and Differential Scanning Calorimetry (DSC)). Crystal structure analysis verified salt generation, revealing proton transfer from the carboxylic acid group of salt formers to the mesitylimino nitrogen atom of ENSE. Additionally, the phase transition behaviour of the produced salt polymorphs was examined by Variable Temperature PXRD (VT-PXRD) analysis. Furthermore, a detailed discussion of the physicochemical features of these recently produced entities was carried out and their solubility in pH 1.2 and pH 7 environments was examined. The results demonstrate that, as compared to the parent drug, the binary adduct's solubility rate has significantly increased at pH 7. Moreover, a thorough examination of the residue recovered after solubility confirmed that the majority of the molecular adducts were stable at pH 7 and did not show any phase change or dissociation, whereas at pH 1.2, the majority of the adducts were stable, with the exception of those generated with malonic acid, which moved into a new stable form, and a comprehensive study revealed that it converted into ENSE.Cl salt. To the best of our knowledge, this is the first work to investigate various forms of ENSE, and mechanical energy may be employed as a powerful control parameter to produce novel solid forms with superior physicochemical features. We hope that the current discovery will offer some valuable outlook prior to the ENSE drug formulation.

A 25-Year-Old Woman With Recurrent UTIs and History of Fluid Restriction to Avoid Gaining Weight

Heidi moawad, md.

September 27, 2024

Editor's Note : The Case Challenge series includes difficult-to-diagnose conditions, some of which are not frequently encountered by most clinicians, but are nonetheless important to accurately recognize. Test your diagnostic and treatment skills using the following patient scenario and corresponding questions. If you have a case that you would like to suggest for a future Case Challenge, please email us at [email protected] with the subject line "Case Challenge Suggestion." We look forward to hearing from you.

A 25-year-old woman presents to urgent care with concerns about urinary frequency and a burning sensation with urination. She says that she has a history of about three separate urinary tract infections (UTIs) over the past several years, each treated with antibiotics.

She says that she exercises regularly, watches her weight and diet, and that she is otherwise in good health. She is not sexually active and explains that she has never been. This patient does not smoke and drinks alcohol a few times a week socially.

The patient is a healthcare professions graduate student who says she has been doing well academically and that she balances a demanding study schedule with a social life and many family activities.

Her parents both have irritable bowel syndrome (IBS), diagnosed when they were in their mid-30s. Both of her parents occasionally follow a liquid diet for weight loss. This patient also worries about whether she could be at risk for IBS, although she does not have symptoms of abdominal discomfort or irregular stools.

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Cite this: Heidi Moawad. A 25-Year-Old Woman With Recurrent UTIs and History of Fluid Restriction to Avoid Gaining Weight -  Medscape  - Sep 27, 2024.

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Lecturer, Case Western Reserve University, Cleveland, Ohio Disclosure: Heidi Moawad, MD, has disclosed no relevant financial relationships.

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Case study research for better evaluations of complex interventions: rationale and challenges

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The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear.

Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports.

Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

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The need for methodological development to address the most urgent challenges in health research has been well-documented. Many of the most pressing questions for public health research, where the focus is on system-level determinants [ 1 , 2 ], and for health services research, where provisions typically vary across sites and are provided through interlocking networks of services [ 3 ], require methodological approaches that can attend to complexity. The need for methodological advance has arisen, in part, as a result of the diminishing returns from randomised controlled trials (RCTs) where they have been used to answer questions about the effects of interventions in complex systems [ 4 , 5 , 6 ]. In conditions of complexity, there is limited value in maintaining the current orientation to experimental trial designs in the health sciences as providing ‘gold standard’ evidence of effect.

There are increasing calls for methodological pluralism [ 7 , 8 ], with the recognition that complex intervention and context are not easily or usefully separated (as is often the situation when using trial design), and that system interruptions may have effects that are not reducible to linear causal pathways between intervention and outcome. These calls are reflected in a shifting and contested discourse of trial design, seen with the emergence of realist [ 9 ], adaptive and hybrid (types 1, 2 and 3) [ 10 , 11 ] trials that blend studies of effectiveness with a close consideration of the contexts of implementation. Similarly, process evaluation has now become a core component of complex healthcare intervention trials, reflected in MRC guidance on how to explore implementation, causal mechanisms and context [ 12 ].

Evidence about the context of an intervention is crucial for questions of external validity. As Woolcock [ 4 ] notes, even if RCT designs are accepted as robust for maximising internal validity, questions of transferability (how well the intervention works in different contexts) and generalisability (how well the intervention can be scaled up) remain unanswered [ 5 , 13 ]. For research evidence to have impact on policy and systems organisation, and thus to improve population and patient health, there is an urgent need for better methods for strengthening external validity, including a better understanding of the relationship between intervention and context [ 14 ].

Policymakers, healthcare commissioners and other research users require credible evidence of relevance to their settings and populations [ 15 ], to perform what Rosengarten and Savransky [ 16 ] call ‘careful abstraction’ to the locales that matter for them. They also require robust evidence for understanding complex causal pathways. Case study research, currently under-utilised in public health and health services evaluation, can offer considerable potential for strengthening faith in both external and internal validity. For example, in an empirical case study of how the policy of free bus travel had specific health effects in London, UK, a quasi-experimental evaluation (led by JG) identified how important aspects of context (a good public transport system) and intervention (that it was universal) were necessary conditions for the observed effects, thus providing useful, actionable evidence for decision-makers in other contexts [ 17 ].

The overall approach of case study research is based on the in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings. Empirical case studies typically enable dynamic understanding of complex challenges rather than restricting the focus on narrow problem delineations and simple fixes. Case study research is a diverse and somewhat contested field, with multiple definitions and perspectives grounded in different ways of viewing the world, and involving different combinations of methods. In this paper, we raise awareness of such plurality and highlight the contribution that case study research can make to the evaluation of complex system-level interventions. We review some of the challenges in exploiting the current evidence base from empirical case studies and conclude by recommending that further guidance and minimum reporting criteria for evaluation using case studies, appropriate for audiences in the health sciences, can enhance the take-up of evidence from case study research.

Case study research offers evidence about context, causal inference in complex systems and implementation

Well-conducted and described empirical case studies provide evidence on context, complexity and mechanisms for understanding how, where and why interventions have their observed effects. Recognition of the importance of context for understanding the relationships between interventions and outcomes is hardly new. In 1943, Canguilhem berated an over-reliance on experimental designs for determining universal physiological laws: ‘As if one could determine a phenomenon’s essence apart from its conditions! As if conditions were a mask or frame which changed neither the face nor the picture!’ ([ 18 ] p126). More recently, a concern with context has been expressed in health systems and public health research as part of what has been called the ‘complexity turn’ [ 1 ]: a recognition that many of the most enduring challenges for developing an evidence base require a consideration of system-level effects [ 1 ] and the conceptualisation of interventions as interruptions in systems [ 19 ].

The case study approach is widely recognised as offering an invaluable resource for understanding the dynamic and evolving influence of context on complex, system-level interventions [ 20 , 21 , 22 , 23 ]. Empirically, case studies can directly inform assessments of where, when, how and for whom interventions might be successfully implemented, by helping to specify the necessary and sufficient conditions under which interventions might have effects and to consolidate learning on how interdependencies, emergence and unpredictability can be managed to achieve and sustain desired effects. Case study research has the potential to address four objectives for improving research and reporting of context recently set out by guidance on taking account of context in population health research [ 24 ], that is to (1) improve the appropriateness of intervention development for specific contexts, (2) improve understanding of ‘how’ interventions work, (3) better understand how and why impacts vary across contexts and (4) ensure reports of intervention studies are most useful for decision-makers and researchers.

However, evaluations of complex healthcare interventions have arguably not exploited the full potential of case study research and can learn much from other disciplines. For evaluative research, exploratory case studies have had a traditional role of providing data on ‘process’, or initial ‘hypothesis-generating’ scoping, but might also have an increasing salience for explanatory aims. Across the social and political sciences, different kinds of case studies are undertaken to meet diverse aims (description, exploration or explanation) and across different scales (from small N qualitative studies that aim to elucidate processes, or provide thick description, to more systematic techniques designed for medium-to-large N cases).

Case studies with explanatory aims vary in terms of their positioning within mixed-methods projects, with designs including (but not restricted to) (1) single N of 1 studies of interventions in specific contexts, where the overall design is a case study that may incorporate one or more (randomised or not) comparisons over time and between variables within the case; (2) a series of cases conducted or synthesised to provide explanation from variations between cases; and (3) case studies of particular settings within RCT or quasi-experimental designs to explore variation in effects or implementation.

Detailed qualitative research (typically done as ‘case studies’ within process evaluations) provides evidence for the plausibility of mechanisms [ 25 ], offering theoretical generalisations for how interventions may function under different conditions. Although RCT designs reduce many threats to internal validity, the mechanisms of effect remain opaque, particularly when the causal pathways between ‘intervention’ and ‘effect’ are long and potentially non-linear: case study research has a more fundamental role here, in providing detailed observational evidence for causal claims [ 26 ] as well as producing a rich, nuanced picture of tensions and multiple perspectives [ 8 ].

Longitudinal or cross-case analysis may be best suited for evidence generation in system-level evaluative research. Turner [ 27 ], for instance, reflecting on the complex processes in major system change, has argued for the need for methods that integrate learning across cases, to develop theoretical knowledge that would enable inferences beyond the single case, and to develop generalisable theory about organisational and structural change in health systems. Qualitative Comparative Analysis (QCA) [ 28 ] is one such formal method for deriving causal claims, using set theory mathematics to integrate data from empirical case studies to answer questions about the configurations of causal pathways linking conditions to outcomes [ 29 , 30 ].

Nonetheless, the single N case study, too, provides opportunities for theoretical development [ 31 ], and theoretical generalisation or analytical refinement [ 32 ]. How ‘the case’ and ‘context’ are conceptualised is crucial here. Findings from the single case may seem to be confined to its intrinsic particularities in a specific and distinct context [ 33 ]. However, if such context is viewed as exemplifying wider social and political forces, the single case can be ‘telling’, rather than ‘typical’, and offer insight into a wider issue [ 34 ]. Internal comparisons within the case can offer rich possibilities for logical inferences about causation [ 17 ]. Further, case studies of any size can be used for theory testing through refutation [ 22 ]. The potential lies, then, in utilising the strengths and plurality of case study to support theory-driven research within different methodological paradigms.

Evaluation research in health has much to learn from a range of social sciences where case study methodology has been used to develop various kinds of causal inference. For instance, Gerring [ 35 ] expands on the within-case variations utilised to make causal claims. For Gerring [ 35 ], case studies come into their own with regard to invariant or strong causal claims (such as X is a necessary and/or sufficient condition for Y) rather than for probabilistic causal claims. For the latter (where experimental methods might have an advantage in estimating effect sizes), case studies offer evidence on mechanisms: from observations of X affecting Y, from process tracing or from pattern matching. Case studies also support the study of emergent causation, that is, the multiple interacting properties that account for particular and unexpected outcomes in complex systems, such as in healthcare [ 8 ].

Finally, efficacy (or beliefs about efficacy) is not the only contributor to intervention uptake, with a range of organisational and policy contingencies affecting whether an intervention is likely to be rolled out in practice. Case study research is, therefore, invaluable for learning about contextual contingencies and identifying the conditions necessary for interventions to become normalised (i.e. implemented routinely) in practice [ 36 ].

The challenges in exploiting evidence from case study research

At present, there are significant challenges in exploiting the benefits of case study research in evaluative health research, which relate to status, definition and reporting. Case study research has been marginalised at the bottom of an evidence hierarchy, seen to offer little by way of explanatory power, if nonetheless useful for adding descriptive data on process or providing useful illustrations for policymakers [ 37 ]. This is an opportune moment to revisit this low status. As health researchers are increasingly charged with evaluating ‘natural experiments’—the use of face masks in the response to the COVID-19 pandemic being a recent example [ 38 ]—rather than interventions that take place in settings that can be controlled, research approaches using methods to strengthen causal inference that does not require randomisation become more relevant.

A second challenge for improving the use of case study evidence in evaluative health research is that, as we have seen, what is meant by ‘case study’ varies widely, not only across but also within disciplines. There is indeed little consensus amongst methodologists as to how to define ‘a case study’. Definitions focus, variously, on small sample size or lack of control over the intervention (e.g. [ 39 ] p194), on in-depth study and context [ 40 , 41 ], on the logic of inference used [ 35 ] or on distinct research strategies which incorporate a number of methods to address questions of ‘how’ and ‘why’ [ 42 ]. Moreover, definitions developed for specific disciplines do not capture the range of ways in which case study research is carried out across disciplines. Multiple definitions of case study reflect the richness and diversity of the approach. However, evidence suggests that a lack of consensus across methodologists results in some of the limitations of published reports of empirical case studies [ 43 , 44 ]. Hyett and colleagues [ 43 ], for instance, reviewing reports in qualitative journals, found little match between methodological definitions of case study research and how authors used the term.

This raises the third challenge we identify that case study reports are typically not written in ways that are accessible or useful for the evaluation research community and policymakers. Case studies may not appear in journals widely read by those in the health sciences, either because space constraints preclude the reporting of rich, thick descriptions, or because of the reported lack of willingness of some biomedical journals to publish research that uses qualitative methods [ 45 ], signalling the persistence of the aforementioned evidence hierarchy. Where they do, however, the term ‘case study’ is used to indicate, interchangeably, a qualitative study, an N of 1 sample, or a multi-method, in-depth analysis of one example from a population of phenomena. Definitions of what constitutes the ‘case’ are frequently lacking and appear to be used as a synonym for the settings in which the research is conducted. Despite offering insights for evaluation, the primary aims may not have been evaluative, so the implications may not be explicitly drawn out. Indeed, some case study reports might properly be aiming for thick description without necessarily seeking to inform about context or causality.

Acknowledging plurality and developing guidance

We recognise that definitional and methodological plurality is not only inevitable, but also a necessary and creative reflection of the very different epistemological and disciplinary origins of health researchers, and the aims they have in doing and reporting case study research. Indeed, to provide some clarity, Thomas [ 46 ] has suggested a typology of subject/purpose/approach/process for classifying aims (e.g. evaluative or exploratory), sample rationale and selection and methods for data generation of case studies. We also recognise that the diversity of methods used in case study research, and the necessary focus on narrative reporting, does not lend itself to straightforward development of formal quality or reporting criteria.

Existing checklists for reporting case study research from the social sciences—for example Lincoln and Guba’s [ 47 ] and Stake’s [ 33 ]—are primarily orientated to the quality of narrative produced, and the extent to which they encapsulate thick description, rather than the more pragmatic issues of implications for intervention effects. Those designed for clinical settings, such as the CARE (CAse REports) guidelines, provide specific reporting guidelines for medical case reports about single, or small groups of patients [ 48 ], not for case study research.

The Design of Case Study Research in Health Care (DESCARTE) model [ 44 ] suggests a series of questions to be asked of a case study researcher (including clarity about the philosophy underpinning their research), study design (with a focus on case definition) and analysis (to improve process). The model resembles toolkits for enhancing the quality and robustness of qualitative and mixed-methods research reporting, and it is usefully open-ended and non-prescriptive. However, even if it does include some reflections on context, the model does not fully address aspects of context, logic and causal inference that are perhaps most relevant for evaluative research in health.

Hence, for evaluative research where the aim is to report empirical findings in ways that are intended to be pragmatically useful for health policy and practice, this may be an opportune time to consider how to best navigate plurality around what is (minimally) important to report when publishing empirical case studies, especially with regards to the complex relationships between context and interventions, information that case study research is well placed to provide.

The conventional scientific quest for certainty, predictability and linear causality (maximised in RCT designs) has to be augmented by the study of uncertainty, unpredictability and emergent causality [ 8 ] in complex systems. This will require methodological pluralism, and openness to broadening the evidence base to better understand both causality in and the transferability of system change intervention [ 14 , 20 , 23 , 25 ]. Case study research evidence is essential, yet is currently under exploited in the health sciences. If evaluative health research is to move beyond the current impasse on methods for understanding interventions as interruptions in complex systems, we need to consider in more detail how researchers can conduct and report empirical case studies which do aim to elucidate the contextual factors which interact with interventions to produce particular effects. To this end, supported by the UK’s Medical Research Council, we are embracing the challenge to develop guidance for case study researchers studying complex interventions. Following a meta-narrative review of the literature, we are planning a Delphi study to inform guidance that will, at minimum, cover the value of case study research for evaluating the interrelationship between context and complex system-level interventions; for situating and defining ‘the case’, and generalising from case studies; as well as provide specific guidance on conducting, analysing and reporting case study research. Our hope is that such guidance can support researchers evaluating interventions in complex systems to better exploit the diversity and richness of case study research.

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Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

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This work was funded by the Medical Research Council - MRC Award MR/S014632/1 HCS: Case study, Context and Complex interventions (TRIPLE C). SP was additionally funded by the University of Oxford's Higher Education Innovation Fund (HEIF).

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Paparini, S., Green, J., Papoutsi, C. et al. Case study research for better evaluations of complex interventions: rationale and challenges. BMC Med 18 , 301 (2020). https://doi.org/10.1186/s12916-020-01777-6

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Risk factors for cesarean section in women of urban Puducherry, India: A matched case–control study

Cesarean section (CS) is generally performed either to ensure maternal and child safety when vaginal delivery is not possible. The WHO has indicated that CS rates of more than 10% are considered overutilization. Increased CS rates can cause an increase in postpartum antibiotic treatment and longer hospital stay. In this research, we conducted a matched case–control study, including all women who gave birth through CS and resided in the study area over a 3-year period before the survey as cases and similar age- and year-matched women who had normal vaginal delivery during the same period as controls. The data were collected using a semi-structured pro forma through personal interviews and verified with discharge cards. We obtained a sample of 140 women (70 matched cases and controls) as study participants. Our results show that unadjusted analysis revealed socioeconomic status, history of gestational diabetes mellitus, previous lower segment CS (LSCS), and malpresentation emerged as risk factors, whereas in the adjusted analysis, we observed that previous LSCS (aOR 45.4 [4.3 – 483.6]), malpresentation (aOR 11.0 [1.6 – 73.8]), and belonging to middle (aOR 3.3 [1.0 – 10.8]) and upper class (aOR 23.55 [CI: 1.2 – 463.8]) remained as independent risk factors. Our study identified independent risk factors for CS that needs to be tackled for bringing down the CS rates.

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Title: gait switching and enhanced stabilization of walking robots with deep learning-based reachability: a case study on two-link walker.

Abstract: Learning-based approaches have recently shown notable success in legged locomotion. However, these approaches often lack accountability, necessitating empirical tests to determine their effectiveness. In this work, we are interested in designing a learning-based locomotion controller whose stability can be examined and guaranteed. This can be achieved by verifying regions of attraction (RoAs) of legged robots to their stable walking gaits. This is a non-trivial problem for legged robots due to their hybrid dynamics. Although previous work has shown the utility of Hamilton-Jacobi (HJ) reachability to solve this problem, its practicality was limited by its poor scalability. The core contribution of our work is the employment of a deep learning-based HJ reachability solution to the hybrid legged robot dynamics, which overcomes the previous work's limitation. With the learned reachability solution, first, we can estimate a library of RoAs for various gaits. Second, we can design a one-step predictive controller that effectively stabilizes to an individual gait within the verified RoA. Finally, we can devise a strategy that switches gaits, in response to external perturbations, whose feasibility is guided by the RoA analysis. We demonstrate our method in a two-link walker simulation, whose mathematical model is well established. Our method achieves improved stability than previous model-based methods, while ensuring transparency that was not present in the existing learning-based approaches.

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Working out just 1 or 2 days each week may lower your risk of over 200 diseases, new study finds

When you feel like you’ve barely got enough time in the day as it is, getting at least two and a half hours of moderate-to-vigorous physical activity per week can feel almost impossible. That number comes from the CDC’s recommendations for all adults, which suggests breaking up the 150 minutes into 30 minutes a day, five days a week, in addition to two days of strength training for all major muscle groups.

Working out five days a week might not be realistic for parents juggling full-time jobs and kids’ busy schedules, or people working shifts demanding 12 hours at a time. Many barely have the energy to cook dinner at the end of a long day.

Those people might be inclined to become “weekend warriors”—people who save their workouts for the weekend. And there’s good news for those weekend warriors: A new study published in Circulation journal indicates one to two days of exercise might be just as beneficial as exercising throughout the week, if you are still hitting those overall physical activity guidelines.

A case for ‘weekend warriors’

“It’s hard to get somebody to engage multiple times per week, if it’s a large time commitment or a spread out time commitment,” says Dr. Shaan Khurshid, lead author of the study and a faculty member in the Demoulas Center for Cardiac Arrhythmias at Massachusetts General Hospital.

Khurshid tells Fortune that he observed as busy lifestyles are becoming more common, more people are concentrating their exercise into one or two days. That set him and his team out to answer the question: Do those who exercise 20–30 minutes most days reap more health benefits than those who opt for longer exercise sessions on one or two days of the week?

Not necessarily, it seems.

Weekend warriors and regular exercisers had an almost equally lowered risk of developing 264 diseases, especially hypertensio n, diabetes , obesity, and sleep apnea.

Khurshid and his colleagues examined data on 89,573 individuals wearing physical activity trackers on their wrists for a full week. 30,228 participants were classified as the inactive group (exercising less than 150 minutes per week), 37,872 were in the weekend warrior group (exercising for at least 150 minutes, one to two days per week), and 21,473 were in the regular group (exercising for at least 150 minutes dispersed throughout the week).

All participants were engaging in moderate-to-vigorous exercise—what Khurshid defines as activity that gets your heart rate up to the point where speaking is hard, and singing is almost impossible. That includes activities like jogging or playing a sport, he says.

Both weekend warrior and regular activity patterns had similarly reduced health risks compared to the inactive group for all disease categories tested, including: heart attack (27% and 35% reduced risk respectively), stroke (21% and 17% lower risk), and diabetes (43% and 46% lower risks, respectively). 

“We didn’t see any diseases where one [workout] pattern was better than the other,” Khurshid tells Fortune .

150 minutes of exercise is still the magic number

If you’re working out just two days out of the week, you’ll probably have to concentrate a good amount of exercise into that short period. Weekend warrior and regular activity patterns had similar benefits because the participants exercised for a similar total volume during the week.

The regular during-the-week exercisers had a median volume of 418 minutes of moderate-to-vigorous physical activity, while the weekend warriors had a median volume of 288 minutes. What’s most important here is they all were well above the 150 minutes per week guideline from the CDC.

Khurshid says the bottom line is “however works for you best to get those guideline recommended levels.”

He acknowledged that a limitation of the study was that they only tracked participants for one week; however, Khurshid says, one week of tracking still seems to be indicative of people’s regular activity habits.

Empowered exercisers

Khurshid says people who are struggling to work out more than a day or two per week can see this study as validating their chosen routines and busy schedules.

“It’s empowering to be able to say, ‘Get the volume that you need to get, but it doesn’t matter how you do it. It’s important that you do it,’” Khurshid says.

“We don’t need to unnecessarily put constraints on how somebody should get their activity or make it harder for somebody to get their activity by saying, ‘You’ve got to do five days a week, you’ve got to do 30 minutes at a time,’” Khusrhid says. “It empowers you to find a routine that works for you and stick with it.”

Khurshid is hoping that these findings will catapult him into more research on the topic, such as how many weeks in a year you need to hit that 150-minute threshold to see health benefits. Ideally, participants will wear activity trackers for years, he says, to have more long-term data to analyze.

More on working out:

• Just how much exercise you need each week , according to experts
• How to stay in shape in your 30s, 40s, and 50s
• How 30-second micro-workouts can boost your energy and help you get fit

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