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What Is a Case Study?
When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to learn all about case studies.
Deep Dive into a Topic
At face value, a case study is a deep dive into a topic. Case studies can be found in many fields, particularly across the social sciences and medicine. When you conduct a case study, you create a body of research based on an inquiry and related data from analysis of a group, individual or controlled research environment.
As a researcher, you can benefit from the analysis of case studies similar to inquiries you’re currently studying. Researchers often rely on case studies to answer questions that basic information and standard diagnostics cannot address.
Study a Pattern
One of the main objectives of a case study is to find a pattern that answers whatever the initial inquiry seeks to find. This might be a question about why college students are prone to certain eating habits or what mental health problems afflict house fire survivors. The researcher then collects data, either through observation or data research, and starts connecting the dots to find underlying behaviors or impacts of the sample group’s behavior.
During the study period, the researcher gathers evidence to back the observed patterns and future claims that’ll be derived from the data. Since case studies are usually presented in the professional environment, it’s not enough to simply have a theory and observational notes to back up a claim. Instead, the researcher must provide evidence to support the body of study and the resulting conclusions.
As the study progresses, the researcher develops a solid case to present to peers or a governing body. Case study presentation is important because it legitimizes the body of research and opens the findings to a broader analysis that may end up drawing a conclusion that’s more true to the data than what one or two researchers might establish. The presentation might be formal or casual, depending on the case study itself.
Once the body of research is established, it’s time to draw conclusions from the case study. As with all social sciences studies, conclusions from one researcher shouldn’t necessarily be taken as gospel, but they’re helpful for advancing the body of knowledge in a given field. For that purpose, they’re an invaluable way of gathering new material and presenting ideas that others in the field can learn from and expand upon.
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A case study evaluation approach can be an incredibly powerful tool for monitoring and evaluating complex programs and policies. By identifying common themes and patterns, this approach allows us to better understand the successes and challenges faced by the program. In this article, we’ll explore the benefits of using a case study evaluation approach in the monitoring and evaluation of projects, programs, and public policies.
Table of Contents
Introduction to Case Study Evaluation Approach
The advantages of a case study evaluation approach, types of case studies, potential challenges with a case study evaluation approach, guiding principles for successful implementation of a case study evaluation approach.
- Benefits of Incorporating the Case Study Evaluation Approach in the Monitoring and Evaluation of Projects and Programs
A case study evaluation approach is a great way to gain an in-depth understanding of a particular issue or situation. This type of approach allows the researcher to observe, analyze, and assess the effects of a particular situation on individuals or groups.
An individual, a location, or a project may serve as the focal point of a case study’s attention. Quantitative and qualitative data are frequently used in conjunction with one another.
It also allows the researcher to gain insights into how people react to external influences. By using a case study evaluation approach, researchers can gain insights into how certain factors such as policy change or a new technology have impacted individuals and communities. The data gathered through this approach can be used to formulate effective strategies for responding to changes and challenges. Ultimately, this monitoring and evaluation approach helps organizations make better decision about the implementation of their plans.
This approach can be used to assess the effectiveness of a policy, program, or initiative by considering specific elements such as implementation processes, outcomes, and impact. A case study evaluation approach can provide an in-depth understanding of the effectiveness of a program by closely examining the processes involved in its implementation. This includes understanding the context, stakeholders, and resources to gain insight into how well a program is functioning or has been executed. By evaluating these elements, it can help to identify areas for improvement and suggest potential solutions. The findings from this approach can then be used to inform decisions about policies, programs, and initiatives for improved outcomes.
It is also useful for determining if other policies, programs, or initiatives could be applied to similar situations in order to achieve similar results or improved outcomes. All in all, the case study monitoring evaluation approach is an effective method for determining the effectiveness of specific policies, programs, or initiatives. By researching and analyzing the successes of previous cases, this approach can be used to identify similar approaches that could be applied to similar situations in order to achieve similar results or improved outcomes.
A case study evaluation approach offers the advantage of providing in-depth insight into a particular program or policy. This can be accomplished by analyzing data and observations collected from a range of stakeholders such as program participants, service providers, and community members. The monitoring and evaluation approach is used to assess the impact of programs and inform the decision-making process to ensure successful implementation. The case study monitoring and evaluation approach can help identify any underlying issues that need to be addressed in order to improve program effectiveness. It also provides a reality check on how successful programs are actually working, allowing organizations to make adjustments as needed. Overall, a case study monitoring and evaluation approach helps to ensure that policies and programs are achieving their objectives while providing valuable insight into how they are performing overall.
By taking a qualitative approach to data collection and analysis, case study evaluations are able to capture nuances in the context of a particular program or policy that can be overlooked when relying solely on quantitative methods. Using this approach, insights can be gleaned from looking at the individual experiences and perspectives of actors involved, providing a more detailed understanding of the impact of the program or policy than is possible with other evaluation methodologies. As such, case study monitoring evaluation is an invaluable tool in assessing the effectiveness of a particular initiative, enabling more informed decision-making as well as more effective implementation of programs and policies.
Furthermore, this approach is an effective way to uncover experiential information that can help to inform the ongoing improvement of policy and programming over time All in all, the case study monitoring evaluation approach offers an effective way to uncover experiential information necessary to inform the ongoing improvement of policy and programming. By analyzing the data gathered from this systematic approach, stakeholders can gain deeper insight into how best to make meaningful and long-term changes in their respective organizations.
Case studies come in a variety of forms, each of which can be put to a unique set of evaluation tasks. Evaluators have come to a consensus on describing six distinct sorts of case studies, which are as follows: illustrative, exploratory, critical instance, program implementation, program effects, and cumulative.
Illustrative Case Study
An illustrative case study is a type of case study that is used to provide a detailed and descriptive account of a particular event, situation, or phenomenon. It is often used in research to provide a clear understanding of a complex issue, and to illustrate the practical application of theories or concepts.
An illustrative case study typically uses qualitative data, such as interviews, surveys, or observations, to provide a detailed account of the unit being studied. The case study may also include quantitative data, such as statistics or numerical measurements, to provide additional context or to support the qualitative data.
The goal of an illustrative case study is to provide a rich and detailed description of the unit being studied, and to use this information to illustrate broader themes or concepts. For example, an illustrative case study of a successful community development project may be used to illustrate the importance of community engagement and collaboration in achieving development goals.
One of the strengths of an illustrative case study is its ability to provide a detailed and nuanced understanding of a particular issue or phenomenon. By focusing on a single case, the researcher is able to provide a detailed and in-depth analysis that may not be possible through other research methods.
However, one limitation of an illustrative case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a single unit, it may not be representative of other similar units or situations.
A well-executed case study can shed light on wider research topics or concepts through its thorough and descriptive analysis of a specific event or phenomenon.
Exploratory Case Study
An exploratory case study is a type of case study that is used to investigate a new or previously unexplored phenomenon or issue. It is often used in research when the topic is relatively unknown or when there is little existing literature on the topic.
Exploratory case studies are typically qualitative in nature and use a variety of methods to collect data, such as interviews, observations, and document analysis. The focus of the study is to gather as much information as possible about the phenomenon being studied and to identify new and emerging themes or patterns.
The goal of an exploratory case study is to provide a foundation for further research and to generate hypotheses about the phenomenon being studied. By exploring the topic in-depth, the researcher can identify new areas of research and generate new questions to guide future research.
One of the strengths of an exploratory case study is its ability to provide a rich and detailed understanding of a new or emerging phenomenon. By using a variety of data collection methods, the researcher can gather a broad range of data and perspectives to gain a more comprehensive understanding of the phenomenon being studied.
However, one limitation of an exploratory case study is that the findings may not be generalizable to other contexts or populations. Because the study is focused on a new or previously unexplored phenomenon, the findings may not be applicable to other situations or populations.
Exploratory case studies are an effective research strategy for learning about novel occurrences, developing research hypotheses, and gaining a deep familiarity with a topic of study.
Critical Instance Case Study
A critical instance case study is a type of case study that focuses on a specific event or situation that is critical to understanding a broader issue or phenomenon. The goal of a critical instance case study is to analyze the event in depth and to draw conclusions about the broader issue or phenomenon based on the analysis.
A critical instance case study typically uses qualitative data, such as interviews, observations, or document analysis, to provide a detailed and nuanced understanding of the event being studied. The data are analyzed using various methods, such as content analysis or thematic analysis, to identify patterns and themes that emerge from the data.
The critical instance case study is often used in research when a particular event or situation is critical to understanding a broader issue or phenomenon. For example, a critical instance case study of a successful disaster response effort may be used to identify key factors that contributed to the success of the response, and to draw conclusions about effective disaster response strategies more broadly.
One of the strengths of a critical instance case study is its ability to provide a detailed and in-depth analysis of a particular event or situation. By focusing on a critical instance, the researcher is able to provide a rich and nuanced understanding of the event, and to draw conclusions about broader issues or phenomena based on the analysis.
However, one limitation of a critical instance case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a specific event or situation, the findings may not be applicable to other similar events or situations.
A critical instance case study is a valuable research method that can provide a detailed and nuanced understanding of a particular event or situation and can be used to draw conclusions about broader issues or phenomena based on the analysis.
Program Implementation Program Implementation
A program implementation case study is a type of case study that focuses on the implementation of a particular program or intervention. The goal of the case study is to provide a detailed and comprehensive account of the program implementation process, and to identify factors that contributed to the success or failure of the program.
Program implementation case studies typically use qualitative data, such as interviews, observations, and document analysis, to provide a detailed and nuanced understanding of the program implementation process. The data are analyzed using various methods, such as content analysis or thematic analysis, to identify patterns and themes that emerge from the data.
The program implementation case study is often used in research to evaluate the effectiveness of a particular program or intervention, and to identify strategies for improving program implementation in the future. For example, a program implementation case study of a school-based health program may be used to identify key factors that contributed to the success or failure of the program, and to make recommendations for improving program implementation in similar settings.
One of the strengths of a program implementation case study is its ability to provide a detailed and comprehensive account of the program implementation process. By using qualitative data, the researcher is able to capture the complexity and nuance of the implementation process, and to identify factors that may not be captured by quantitative data alone.
However, one limitation of a program implementation case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a specific program or intervention, the findings may not be applicable to other programs or interventions in different settings.
An effective research tool, a case study of program implementation may illuminate the intricacies of the implementation process and point the way towards future enhancements.
Program Effects Case Study
A program effects case study is a research method that evaluates the effectiveness of a particular program or intervention by examining its outcomes or effects. The purpose of this type of case study is to provide a detailed and comprehensive account of the program’s impact on its intended participants or target population.
A program effects case study typically employs both quantitative and qualitative data collection methods, such as surveys, interviews, and observations, to evaluate the program’s impact on the target population. The data is then analyzed using statistical and thematic analysis to identify patterns and themes that emerge from the data.
The program effects case study is often used to evaluate the success of a program and identify areas for improvement. For example, a program effects case study of a community-based HIV prevention program may evaluate the program’s effectiveness in reducing HIV transmission rates among high-risk populations and identify factors that contributed to the program’s success.
One of the strengths of a program effects case study is its ability to provide a detailed and nuanced understanding of a program’s impact on its intended participants or target population. By using both quantitative and qualitative data, the researcher can capture both the objective and subjective outcomes of the program and identify factors that may have contributed to the outcomes.
However, a limitation of the program effects case study is that it may not be generalizable to other populations or contexts. Since the case study focuses on a particular program and population, the findings may not be applicable to other programs or populations in different settings.
A program effects case study is a good way to do research because it can give a detailed look at how a program affects the people it is meant for. This kind of case study can be used to figure out what needs to be changed and how to make programs that work better.
Cumulative Case Study
A cumulative case study is a type of case study that involves the collection and analysis of multiple cases to draw broader conclusions. Unlike a single-case study, which focuses on one specific case, a cumulative case study combines multiple cases to provide a more comprehensive understanding of a phenomenon.
The purpose of a cumulative case study is to build up a body of evidence through the examination of multiple cases. The cases are typically selected to represent a range of variations or perspectives on the phenomenon of interest. Data is collected from each case using a range of methods, such as interviews, surveys, and observations.
The data is then analyzed across cases to identify common themes, patterns, and trends. The analysis may involve both qualitative and quantitative methods, such as thematic analysis and statistical analysis.
The cumulative case study is often used in research to develop and test theories about a phenomenon. For example, a cumulative case study of successful community-based health programs may be used to identify common factors that contribute to program success, and to develop a theory about effective community-based health program design.
One of the strengths of the cumulative case study is its ability to draw on a range of cases to build a more comprehensive understanding of a phenomenon. By examining multiple cases, the researcher can identify patterns and trends that may not be evident in a single case study. This allows for a more nuanced understanding of the phenomenon and helps to develop more robust theories.
However, one limitation of the cumulative case study is that it can be time-consuming and resource-intensive to collect and analyze data from multiple cases. Additionally, the selection of cases may introduce bias if the cases are not representative of the population of interest.
In summary, a cumulative case study is a valuable research method that can provide a more comprehensive understanding of a phenomenon by examining multiple cases. This type of case study is particularly useful for developing and testing theories and identifying common themes and patterns across cases.
When conducting a case study evaluation approach, one of the main challenges is the need to establish a contextually relevant research design that accounts for the unique factors of the case being studied. This requires close monitoring of the case, its environment, and relevant stakeholders. In addition, the researcher must build a framework for the collection and analysis of data that is able to draw meaningful conclusions and provide valid insights into the dynamics of the case. Ultimately, an effective case study monitoring evaluation approach will allow researchers to form an accurate understanding of their research subject.
Additionally, depending on the size and scope of the case, there may be concerns regarding the availability of resources and personnel that could be allocated to data collection and analysis. To address these issues, a case study monitoring evaluation approach can be adopted, which would involve a mix of different methods such as interviews, surveys, focus groups and document reviews. Such an approach could provide valuable insights into the effectiveness and implementation of the case in question. Additionally, this type of evaluation can be tailored to the specific needs of the case study to ensure that all relevant data is collected and respected.
When dealing with a highly sensitive or confidential subject matter within a case study, researchers must take extra measures to prevent bias during data collection as well as protect participant anonymity while also collecting valid data in order to ensure reliable results
Moreover, when conducting a case study evaluation it is important to consider the potential implications of the data gathered. By taking extra measures to prevent bias and protect participant anonymity, researchers can ensure reliable results while also collecting valid data. Maintaining confidentiality and deploying ethical research practices are essential when conducting a case study to ensure an unbiased and accurate monitoring evaluation.
When planning and implementing a case study evaluation approach, it is important to ensure the guiding principles of research quality, data collection, and analysis are met. To ensure these principles are upheld, it is essential to develop a comprehensive monitoring and evaluation plan. This plan should clearly outline the steps to be taken during the data collection and analysis process. Furthermore, the plan should provide detailed descriptions of the project objectives, target population, key indicators, and timeline. It is also important to include metrics or benchmarks to monitor progress and identify any potential areas for improvement. By implementing such an approach, it will be possible to ensure that the case study evaluation approach yields valid and reliable results.
To ensure successful implementation, it is essential to establish a reliable data collection process that includes detailed information such as the scope of the study, the participants involved, and the methods used to collect data. Additionally, it is important to have a clear understanding of what will be examined through the evaluation process and how the results will be used. All in all, it is essential to establish a sound monitoring evaluation approach for a successful case study implementation. This includes creating a reliable data collection process that encompasses the scope of the study, the participants involved, and the methods used to collect data. It is also imperative to have an understanding of what will be examined and how the results will be utilized. Ultimately, effective planning is key to ensure that the evaluation process yields meaningful insights.
Benefits of Incorporating the Case Study Evaluation Approach in the Monitoring and Evaluation of Projects and Programmes
Using a case study approach in monitoring and evaluation allows for a more detailed and in-depth exploration of the project’s success, helping to identify key areas of improvement and successes that may have been overlooked through traditional evaluation. Through this case study method, specific data can be collected and analyzed to identify trends and different perspectives that can support the evaluation process. This data can allow stakeholders to gain a better understanding of the project’s successes and failures, helping them make informed decisions on how to strengthen current activities or shape future initiatives. From a monitoring and evaluation standpoint, this approach can provide an increased level of accuracy in terms of accurately assessing the effectiveness of the project.
This can provide valuable insights into what works—and what doesn’t—when it comes to implementing projects and programs, aiding decision-makers in making future plans that better meet their objectives However, monitoring and evaluation is just one approach to assessing the success of a case study. It does provide a useful insight into what initiatives may be successful, but it is important to note that there are other effective research methods, such as surveys and interviews, that can also help to further evaluate the success of a project or program.
In conclusion, a case study evaluation approach can be incredibly useful in monitoring and evaluating complex programs and policies. By exploring key themes, patterns and relationships, organizations can gain a detailed understanding of the successes, challenges and limitations of their program or policy. This understanding can then be used to inform decision-making and improve outcomes for those involved. With its ability to provide an in-depth understanding of a program or policy, the case study evaluation approach has become an invaluable tool for monitoring and evaluation professionals.
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A case study focuses on a particular unit - a person, a site, a project. It often uses a combination of quantitative and qualitative data.
Case studies can be particularly useful for understanding how different elements fit together and how different elements (implementation, context and other factors) have produced the observed impacts.
There are different types of case studies, which can be used for different purposes in evaluation. The GAO (Government Accountability Office) has described six different types of case study:
1. Illustrative : This is descriptive in character and intended to add realism and in-depth examples to other information about a program or policy. (These are often used to complement quantitative data by providing examples of the overall findings).
2. Exploratory : This is also descriptive but is aimed at generating hypotheses for later investigation rather than simply providing illustration.
3. Critical instance : This examines a single instance of unique interest, or serves as a critical test of an assertion about a program, problem or strategy.
4. Program implementation . This investigates operations, often at several sites, and often with reference to a set of norms or standards about implementation processes.
5. Program effects . This examines the causal links between the program and observed effects (outputs, outcomes or impacts, depending on the timing of the evaluation) and usually involves multisite, multimethod evaluations.
6. Cumulative . This brings together findings from many case studies to answer evaluative questions.
The following guides are particularly recommended because they distinguish between the research design (case study) and the type of data (qualitative or quantitative), and provide guidance on selecting cases, addressing causal inference, and generalizing from cases.
This guide from the US General Accounting Office outlines good practice in case study evaluation and establishes a set of principles for applying case studies to evaluations.
This paper, authored by Edith D. Balbach for the California Department of Health Services is designed to help evaluators decide whether to use a case study evaluation approach.
This guide, written by Linda G. Morra and Amy C. Friedlander for the World Bank, provides guidance and advice on the use of case studies.
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- Published: 27 November 2020
Designing process evaluations using case study to explore the context of complex interventions evaluated in trials
- Aileen Grant 1 ,
- Carol Bugge 2 &
- Mary Wells 3
Trials volume 21 , Article number: 982 ( 2020 ) Cite this article
Process evaluations are an important component of an effectiveness evaluation as they focus on understanding the relationship between interventions and context to explain how and why interventions work or fail, and whether they can be transferred to other settings and populations. However, historically, context has not been sufficiently explored and reported resulting in the poor uptake of trial results. Therefore, suitable methodologies are needed to guide the investigation of context. Case study is one appropriate methodology, but there is little guidance about what case study design can offer the study of context in trials. We address this gap in the literature by presenting a number of important considerations for process evaluation using a case study design.
In this paper, we define context, the relationship between complex interventions and context, and describe case study design methodology. A well-designed process evaluation using case study should consider the following core components: the purpose; definition of the intervention; the trial design, the case, the theories or logic models underpinning the intervention, the sampling approach and the conceptual or theoretical framework. We describe each of these in detail and highlight with examples from recently published process evaluations.
There are a number of approaches to process evaluation design in the literature; however, there is a paucity of research on what case study design can offer process evaluations. We argue that case study is one of the best research designs to underpin process evaluations, to capture the dynamic and complex relationship between intervention and context during implementation. We provide a comprehensive overview of the issues for process evaluation design to consider when using a case study design.
DQIP - ClinicalTrials.gov number, NCT01425502 - OPAL - ISRCTN57746448
Peer Review reports
Contribution to the literature
We illustrate how case study methodology can explore the complex, dynamic and uncertain relationship between context and interventions within trials.
We depict different case study designs and illustrate there is not one formula and that design needs to be tailored to the context and trial design.
Case study can support comparisons between intervention and control arms and between cases within arms to uncover and explain differences in detail.
We argue that case study can illustrate how components have evolved and been redefined through implementation.
Key issues for consideration in case study design within process evaluations are presented and illustrated with examples.
Process evaluations are an important component of an effectiveness evaluation as they focus on understanding the relationship between interventions and context to explain how and why interventions work or fail and whether they can be transferred to other settings and populations. However, historically, not all trials have had a process evaluation component, nor have they sufficiently reported aspects of context, resulting in poor uptake of trial findings [ 1 ]. Considerations of context are often absent from published process evaluations, with few studies acknowledging, taking account of or describing context during implementation, or assessing the impact of context on implementation [ 2 , 3 ]. At present, evidence from trials is not being used in a timely manner [ 4 , 5 ], and this can negatively impact on patient benefit and experience [ 6 ]. It takes on average 17 years for knowledge from research to be implemented into practice [ 7 ]. Suitable methodologies are therefore needed that allow for context to be exposed; one appropriate methodological approach is case study [ 8 , 9 ].
In 2015, the Medical Research Council (MRC) published guidance for process evaluations [ 10 ]. This was a key milestone in legitimising as well as providing tools, methods and a framework for conducting process evaluations. Nevertheless, as with all guidance, there is a need for reflection, challenge and refinement. There have been a number of critiques of the MRC guidance, including that interventions should be considered as events in systems [ 11 , 12 , 13 , 14 ]; a need for better use, critique and development of theories [ 15 , 16 , 17 ]; and a need for more guidance on integrating qualitative and quantitative data [ 18 , 19 ]. Although the MRC process evaluation guidance does consider appropriate qualitative and quantitative methods, it does not mention case study design and what it can offer the study of context in trials.
The case study methodology is ideally suited to real-world, sustainable intervention development and evaluation because it can explore and examine contemporary complex phenomena, in depth, in numerous contexts and using multiple sources of data [ 8 ]. Case study design can capture the complexity of the case, the relationship between the intervention and the context and how the intervention worked (or not) [ 8 ]. There are a number of textbooks on a case study within the social science fields [ 8 , 9 , 20 ], but there are no case study textbooks and a paucity of useful texts on how to design, conduct and report case study within the health arena. Few examples exist within the trial design and evaluation literature [ 3 , 21 ]. Therefore, guidance to enable well-designed process evaluations using case study methodology is required.
We aim to address the gap in the literature by presenting a number of important considerations for process evaluation using a case study design. First, we define the context and describe the relationship between complex health interventions and context.
What is context?
While there is growing recognition that context interacts with the intervention to impact on the intervention’s effectiveness [ 22 ], context is still poorly defined and conceptualised. There are a number of different definitions in the literature, but as Bate et al. explained ‘almost universally, we find context to be an overworked word in everyday dialogue but a massively understudied and misunderstood concept’ [ 23 ]. Ovretveit defines context as ‘everything the intervention is not’ [ 24 ]. This last definition is used by the MRC framework for process evaluations [ 25 ]; however; the problem with this definition is that it is highly dependent on how the intervention is defined. We have found Pfadenhauer et al.’s definition useful:
Context is conceptualised as a set of characteristics and circumstances that consist of active and unique factors that surround the implementation. As such it is not a backdrop for implementation but interacts, influences, modifies and facilitates or constrains the intervention and its implementation. Context is usually considered in relation to an intervention or object, with which it actively interacts. A boundary between the concepts of context and setting is discernible: setting refers to the physical, specific location in which the intervention is put into practice. Context is much more versatile, embracing not only the setting but also roles, interactions and relationships [ 22 ].
Traditionally, context has been conceptualised in terms of barriers and facilitators, but what is a barrier in one context may be a facilitator in another, so it is the relationship and dynamics between the intervention and context which are the most important [ 26 ]. There is a need for empirical research to really understand how different contextual factors relate to each other and to the intervention. At present, research studies often list common contextual factors, but without a depth of meaning and understanding, such as government or health board policies, organisational structures, professional and patient attitudes, behaviours and beliefs [ 27 ]. The case study methodology is well placed to understand the relationship between context and intervention where these boundaries may not be clearly evident. It offers a means of unpicking the contextual conditions which are pertinent to effective implementation.
The relationship between complex health interventions and context
Health interventions are generally made up of a number of different components and are considered complex due to the influence of context on their implementation and outcomes [ 3 , 28 ]. Complex interventions are often reliant on the engagement of practitioners and patients, so their attitudes, behaviours, beliefs and cultures influence whether and how an intervention is effective or not. Interventions are context-sensitive; they interact with the environment in which they are implemented. In fact, many argue that interventions are a product of their context, and indeed, outcomes are likely to be a product of the intervention and its context [ 3 , 29 ]. Within a trial, there is also the influence of the research context too—so the observed outcome could be due to the intervention alone, elements of the context within which the intervention is being delivered, elements of the research process or a combination of all three. Therefore, it can be difficult and unhelpful to separate the intervention from the context within which it was evaluated because the intervention and context are likely to have evolved together over time. As a result, the same intervention can look and behave differently in different contexts, so it is important this is known, understood and reported [ 3 ]. Finally, the intervention context is dynamic; the people, organisations and systems change over time, [ 3 ] which requires practitioners and patients to respond, and they may do this by adapting the intervention or contextual factors. So, to enable researchers to replicate successful interventions, or to explain why the intervention was not successful, it is not enough to describe the components of the intervention, they need to be described by their relationship to their context and resources [ 3 , 28 ].
What is a case study?
Case study methodology aims to provide an in-depth, holistic, balanced, detailed and complete picture of complex contemporary phenomena in its natural context [ 8 , 9 , 20 ]. In this case, the phenomena are the implementation of complex interventions in a trial. Case study methodology takes the view that the phenomena can be more than the sum of their parts and have to be understood as a whole [ 30 ]. It is differentiated from a clinical case study by its analytical focus [ 20 ].
The methodology is particularly useful when linked to trials because some of the features of the design naturally fill the gaps in knowledge generated by trials. Given the methodological focus on understanding phenomena in the round, case study methodology is typified by the use of multiple sources of data, which are more commonly qualitatively guided [ 31 ]. The case study methodology is not epistemologically specific, like realist evaluation, and can be used with different epistemologies [ 32 ], and with different theories, such as Normalisation Process Theory (which explores how staff work together to implement a new intervention) or the Consolidated Framework for Implementation Research (which provides a menu of constructs associated with effective implementation) [ 33 , 34 , 35 ]. Realist evaluation can be used to explore the relationship between context, mechanism and outcome, but case study differs from realist evaluation by its focus on a holistic and in-depth understanding of the relationship between an intervention and the contemporary context in which it was implemented [ 36 ]. Case study enables researchers to choose epistemologies and theories which suit the nature of the enquiry and their theoretical preferences.
Designing a process evaluation using case study
An important part of any study is the research design. Due to their varied philosophical positions, the seminal authors in the field of case study have different epistemic views as to how a case study should be conducted [ 8 , 9 ]. Stake takes an interpretative approach (interested in how people make sense of their world), and Yin has more positivistic leanings, arguing for objectivity, validity and generalisability [ 8 , 9 ].
Regardless of the philosophical background, a well-designed process evaluation using case study should consider the following core components: the purpose; the definition of the intervention, the trial design, the case, and the theories or logic models underpinning the intervention; the sampling approach; and the conceptual or theoretical framework [ 8 , 9 , 20 , 31 , 33 ]. We now discuss these critical components in turn, with reference to two process evaluations that used case study design, the DQIP and OPAL studies [ 21 , 37 , 38 , 39 , 40 , 41 ].
The purpose of a process evaluation is to evaluate and explain the relationship between the intervention and its components, to context and outcome. It can help inform judgements about validity (by exploring the intervention components and their relationship with one another (construct validity), the connections between intervention and outcomes (internal validity) and the relationship between intervention and context (external validity)). It can also distinguish between implementation failure (where the intervention is poorly delivered) and intervention failure (intervention design is flawed) [ 42 , 43 ]. By using a case study to explicitly understand the relationship between context and the intervention during implementation, the process evaluation can explain the intervention effects and the potential generalisability and optimisation into routine practice [ 44 ].
The DQIP process evaluation aimed to qualitatively explore how patients and GP practices responded to an intervention designed to reduce high-risk prescribing of nonsteroidal anti-inflammatory drugs (NSAIDs) and/or antiplatelet agents (see Table 1 ) and quantitatively examine how change in high-risk prescribing was associated with practice characteristics and implementation processes. The OPAL process evaluation (see Table 2 ) aimed to quantitatively understand the factors which influenced the effectiveness of a pelvic floor muscle training intervention for women with urinary incontinence and qualitatively explore the participants’ experiences of treatment and adherence.
Defining the intervention and exploring the theories or assumptions underpinning the intervention design
Process evaluations should also explore the utility of the theories or assumptions underpinning intervention design [ 49 ]. Not all theories underpinning interventions are based on a formal theory, but they based on assumptions as to how the intervention is expected to work. These can be depicted as a logic model or theory of change [ 25 ]. To capture how the intervention and context evolve requires the intervention and its expected mechanisms to be clearly defined at the outset [ 50 ]. Hawe and colleagues recommend defining interventions by function (what processes make the intervention work) rather than form (what is delivered) [ 51 ]. However, in some cases, it may be useful to know if some of the components are redundant in certain contexts or if there is a synergistic effect between all the intervention components.
The DQIP trial delivered two interventions, one intervention was delivered to professionals with high fidelity and then professionals delivered the other intervention to patients by form rather than function allowing adaptations to the local context as appropriate. The assumptions underpinning intervention delivery were prespecified in a logic model published in the process evaluation protocol [ 52 ].
Case study is well placed to challenge or reinforce the theoretical assumptions or redefine these based on the relationship between the intervention and context. Yin advocates the use of theoretical propositions; these direct attention to specific aspects of the study for investigation [ 8 ] can be based on the underlying assumptions and tested during the course of the process evaluation. In case studies, using an epistemic position more aligned with Yin can enable research questions to be designed, which seek to expose patterns of unanticipated as well as expected relationships [ 9 ]. The OPAL trial was more closely aligned with Yin, where the research team predefined some of their theoretical assumptions, based on how the intervention was expected to work. The relevant parts of the data analysis then drew on data to support or refute the theoretical propositions. This was particularly useful for the trial as the prespecified theoretical propositions linked to the mechanisms of action on which the intervention was anticipated to have an effect (or not).
Tailoring to the trial design
Process evaluations need to be tailored to the trial, the intervention and the outcomes being measured [ 45 ]. For example, in a stepped wedge design (where the intervention is delivered in a phased manner), researchers should try to ensure process data are captured at relevant time points or in a two-arm or multiple arm trial, ensure data is collected from the control group(s) as well as the intervention group(s). In the DQIP trial, a stepped wedge trial, at least one process evaluation case, was sampled per cohort. Trials often continue to measure outcomes after delivery of the intervention has ceased, so researchers should also consider capturing ‘follow-up’ data on contextual factors, which may continue to influence the outcome measure. The OPAL trial had two active treatment arms so collected process data from both arms. In addition, as the trial was interested in long-term adherence, the trial and the process evaluation collected data from participants for 2 years after the intervention was initially delivered, providing 24 months follow-up data, in line with the primary outcome for the trial.
Defining the case
Case studies can include single or multiple cases in their design. Single case studies usually sample typical or unique cases, their advantage being the depth and richness that can be achieved over a long period of time. The advantages of multiple case study design are that cases can be compared to generate a greater depth of analysis. Multiple case study sampling may be carried out in order to test for replication or contradiction [ 8 ]. Given that trials are often conducted over a number of sites, a multiple case study design is more sensible for process evaluations, as there is likely to be variation in implementation between sites. Case definition may occur at a variety of levels but is most appropriate if it reflects the trial design. For example, a case in an individual patient level trial is likely to be defined as a person/patient (e.g. a woman with urinary incontinence—OPAL trial) whereas in a cluster trial, a case is like to be a cluster, such as an organisation (e.g. a general practice—DQIP trial). Of course, the process evaluation could explore cases with less distinct boundaries, such as communities or relationships; however, the clarity with which these cases are defined is important, in order to scope the nature of the data that will be generated.
Carefully sampled cases are critical to a good case study as sampling helps inform the quality of the inferences that can be made from the data [ 53 ]. In both qualitative and quantitative research, how and how many participants to sample must be decided when planning the study. Quantitative sampling techniques generally aim to achieve a random sample. Qualitative research generally uses purposive samples to achieve data saturation, occurring when the incoming data produces little or no new information to address the research questions. The term data saturation has evolved from theoretical saturation in conventional grounded theory studies; however, its relevance to other types of studies is contentious as the term saturation seems to be widely used but poorly justified [ 54 ]. Empirical evidence suggests that for in-depth interview studies, saturation occurs at 12 interviews for thematic saturation, but typically more would be needed for a heterogenous sample higher degrees of saturation [ 55 , 56 ]. Both DQIP and OPAL case studies were huge with OPAL designed to interview each of the 40 individual cases four times and DQIP designed to interview the lead DQIP general practitioner (GP) twice (to capture change over time), another GP and the practice manager from each of the 10 organisational cases. Despite the plethora of mixed methods research textbooks, there is very little about sampling as discussions typically link to method (e.g. interviews) rather than paradigm (e.g. case study).
Purposive sampling can improve the generalisability of the process evaluation by sampling for greater contextual diversity. The typical or average case is often not the richest source of information. Outliers can often reveal more important insights, because they may reflect the implementation of the intervention using different processes. Cases can be selected from a number of criteria, which are not mutually exclusive, to enable a rich and detailed picture to be built across sites [ 53 ]. To avoid the Hawthorne effect, it is recommended that process evaluations sample from both intervention and control sites, which enables comparison and explanation. There is always a trade-off between breadth and depth in sampling, so it is important to note that often quantity does not mean quality and that carefully sampled cases can provide powerful illustrative examples of how the intervention worked in practice, the relationship between the intervention and context and how and why they evolved together. The qualitative components of both DQIP and OPAL process evaluations aimed for maximum variation sampling. Please see Table 1 for further information on how DQIP’s sampling frame was important for providing contextual information on processes influencing effective implementation of the intervention.
Conceptual and theoretical framework
A conceptual or theoretical framework helps to frame data collection and analysis [ 57 ]. Theories can also underpin propositions, which can be tested in the process evaluation. Process evaluations produce intervention-dependent knowledge, and theories help make the research findings more generalizable by providing a common language [ 16 ]. There are a number of mid-range theories which have been designed to be used with process evaluation [ 34 , 35 , 58 ]. The choice of the appropriate conceptual or theoretical framework is, however, dependent on the philosophical and professional background of the research. The two examples within this paper used our own framework for the design of process evaluations, which proposes a number of candidate processes which can be explored, for example, recruitment, delivery, response, maintenance and context [ 45 ]. This framework was published before the MRC guidance on process evaluations, and both the DQIP and OPAL process evaluations were designed before the MRC guidance was published. The DQIP process evaluation explored all candidates in the framework whereas the OPAL process evaluation selected four candidates, illustrating that process evaluations can be selective in what they explore based on the purpose, research questions and resources. Furthermore, as Kislov and colleagues argue, we also have a responsibility to critique the theoretical framework underpinning the evaluation and refine theories to advance knowledge [ 59 ].
An important consideration is what data to collect or measure and when. Case study methodology supports a range of data collection methods, both qualitative and quantitative, to best answer the research questions. As the aim of the case study is to gain an in-depth understanding of phenomena in context, methods are more commonly qualitative or mixed method in nature. Qualitative methods such as interviews, focus groups and observation offer rich descriptions of the setting, delivery of the intervention in each site and arm, how the intervention was perceived by the professionals delivering the intervention and the patients receiving the intervention. Quantitative methods can measure recruitment, fidelity and dose and establish which characteristics are associated with adoption, delivery and effectiveness. To ensure an understanding of the complexity of the relationship between the intervention and context, the case study should rely on multiple sources of data and triangulate these to confirm and corroborate the findings [ 8 ]. Process evaluations might consider using routine data collected in the trial across all sites and additional qualitative data across carefully sampled sites for a more nuanced picture within reasonable resource constraints. Mixed methods allow researchers to ask more complex questions and collect richer data than can be collected by one method alone [ 60 ]. The use of multiple sources of data allows data triangulation, which increases a study’s internal validity but also provides a more in-depth and holistic depiction of the case [ 20 ]. For example, in the DQIP process evaluation, the quantitative component used routinely collected data from all sites participating in the trial and purposively sampled cases for a more in-depth qualitative exploration [ 21 , 38 , 39 ].
The timing of data collection is crucial to study design, especially within a process evaluation where data collection can potentially influence the trial outcome. Process evaluations are generally in parallel or retrospective to the trial. The advantage of a retrospective design is that the evaluation itself is less likely to influence the trial outcome. However, the disadvantages include recall bias, lack of sensitivity to nuances and an inability to iteratively explore the relationship between intervention and outcome as it develops. To capture the dynamic relationship between intervention and context, the process evaluation needs to be parallel and longitudinal to the trial. Longitudinal methodological design is rare, but it is needed to capture the dynamic nature of implementation [ 40 ]. How the intervention is delivered is likely to change over time as it interacts with context. For example, as professionals deliver the intervention, they become more familiar with it, and it becomes more embedded into systems. The OPAL process evaluation was a longitudinal, mixed methods process evaluation where the quantitative component had been predefined and built into trial data collection systems. Data collection in both the qualitative and quantitative components mirrored the trial data collection points, which were longitudinal to capture adherence and contextual changes over time.
There is a lot of attention in the recent literature towards a systems approach to understanding interventions in context, which suggests interventions are ‘events within systems’ [ 61 , 62 ]. This framing highlights the dynamic nature of context, suggesting that interventions are an attempt to change systems dynamics. This conceptualisation would suggest that the study design should collect contextual data before and after implementation to assess the effect of the intervention on the context and vice versa.
Designing a rigorous analysis plan is particularly important for multiple case studies, where researchers must decide whether their approach to analysis is case or variable based. Case-based analysis is the most common, and analytic strategies must be clearly articulated for within and across case analysis. A multiple case study design can consist of multiple cases, where each case is analysed at the case level, or of multiple embedded cases, where data from all the cases are pulled together for analysis at some level. For example, OPAL analysis was at the case level, but all the cases for the intervention and control arms were pulled together at the arm level for more in-depth analysis and comparison. For Yin, analytical strategies rely on theoretical propositions, but for Stake, analysis works from the data to develop theory. In OPAL and DQIP, case summaries were written to summarise the cases and detail within-case analysis. Each of the studies structured these differently based on the phenomena of interest and the analytic technique. DQIP applied an approach more akin to Stake [ 9 ], with the cases summarised around inductive themes whereas OPAL applied a Yin [ 8 ] type approach using theoretical propositions around which the case summaries were structured. As the data for each case had been collected through longitudinal interviews, the case summaries were able to capture changes over time. It is beyond the scope of this paper to discuss different analytic techniques; however, to ensure the holistic examination of the intervention(s) in context, it is important to clearly articulate and demonstrate how data is integrated and synthesised [ 31 ].
There are a number of approaches to process evaluation design in the literature; however, there is a paucity of research on what case study design can offer process evaluations. We argue that case study is one of the best research designs to underpin process evaluations, to capture the dynamic and complex relationship between intervention and context during implementation [ 38 ]. Case study can enable comparisons within and across intervention and control arms and enable the evolving relationship between intervention and context to be captured holistically rather than considering processes in isolation. Utilising a longitudinal design can enable the dynamic relationship between context and intervention to be captured in real time. This information is fundamental to holistically explaining what intervention was implemented, understanding how and why the intervention worked or not and informing the transferability of the intervention into routine clinical practice.
Case study designs are not prescriptive, but process evaluations using case study should consider the purpose, trial design, the theories or assumptions underpinning the intervention, and the conceptual and theoretical frameworks informing the evaluation. We have discussed each of these considerations in turn, providing a comprehensive overview of issues for process evaluations using a case study design. There is no single or best way to conduct a process evaluation or a case study, but researchers need to make informed choices about the process evaluation design. Although this paper focuses on process evaluations, we recognise that case study design could also be useful during intervention development and feasibility trials. Elements of this paper are also applicable to other study designs involving trials.
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We would like to thank Professor Shaun Treweek for the discussions about context in trials.
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Grant, A., Bugge, C. & Wells, M. Designing process evaluations using case study to explore the context of complex interventions evaluated in trials. Trials 21 , 982 (2020). https://doi.org/10.1186/s13063-020-04880-4
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DOI : https://doi.org/10.1186/s13063-020-04880-4
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13 Case Studies
Valéry Ridde, Abdourahmane Coulibaly, and Lara Gautier
Case studies consist of an in-depth analysis of one or more cases, using a variety of methods and theoretical approaches. The choice of cases (single or multiple) studied is crucial. Case studies are particularly suitable for studying the emergence and processes involved in policy implementation and for contributing to theory-based evaluations.
Keywords: Qualitative methods, quantitative methods, mixed methods, case study, theoretical approaches, single/multiple cases, empirical triangulation, analytical generalisation
I. What does this method consist of?
Also used in anthropology, the case study approach has long been used in evaluation, where it is considered not as a method but as a research strategy (Yin 2018). By studying a policy in context and using multiple lines of evidence, the case study (single or multiple) seeks to answer ‘how’ and ‘why’ questions from a systems approach and with the support of theoretical approaches. Conducting a case study for a public policy evaluation follows a standard evaluation process: planning, drafting the protocol, preparing the field, collecting and analysing data, sharing results and making recommendations for policy improvement (Gagnon 2012). As with all evaluations, the choice of methods should follow the objectives and the evaluation question, not the other way around. A case study may thus mobilise qualitative, quantitative and different mixed methods designs.
The case study strategy is therefore appropriate when organising an evaluation of policy emergence, process, relevance or adaptation. It is often mobilised when evaluation teams have little or no control over the events and context that influence policy actions. This is often the case outside of experimental situations, which are rare in the field of public policy. It is therefore mostly recommended for understanding a contemporary, often complex, phenomenon organised in a real context.
The case study approach can be used to explain a public policy, describe it in depth or illustrate a specific situation, which can sometimes be original and enlightening for decision-making. The advantage of case studies is that they can be adapted to different situations where there are multiple variables of interest around a policy. It is also about being able to use multiple sources of data, both quantitative and qualitative, which allow for empirical triangulation. The case study strategy allows theoretical propositions and the state of scientific knowledge to guide data collection and analysis. It fits perfectly with, but is not limited to, theory-based evaluation approaches (see separate chapter on theory-based evaluation ).
There are a myriad of proposals for the types of case studies that are possible. Firstly, it is possible to use single/single case studies (involving one policy) or multiple case studies (several policies in the same organisational context or one policy in different contexts). Secondly, these cases can be studied holistically (the policy as a whole) or at different levels of analysis (the dimensions of the policy that the intervention theory will have specified or the particular regional contexts). The choice of case studies should be heuristic (to learn from the study) and strategic (to have data available within the available budget, to answer useful questions). A key criterion for case selection is to have sufficiently relevant information to understand the policy in depth and complexity. Case sampling should therefore be explicit, rigorous and transparent. The selection of case studies can thus be critical, unique, typical, revealing, instrumental, etc. This selection can also be carried out in collaboration between the research and policy teams to ensure that the choices are relevant and feasible. The selection can also be based on prior quantitative analyses to obtain the starting situation of the cases and, for example, choose cases that are very contrasting or very similar in their performance with regard to the policy being analysed.
Sometimes it can also be useful to have a diachronic approach in order to produce longitudinal case studies. For example, analysing a policy over time can reveal the influences of changes in the context or in the strategies of those implementing it, or of those benefiting from it. Starting with cases with similar initial conditions and then studying their evolution is referred to as ‘racing cases’ by Eisenhardt (Gehman et al. 2018).
When analysing the data, the case study approach requires, in addition to the usual analyses specific to the methods (content analysis, thematic analysis, descriptive or inferential statistics, etc.), to mobilise a replication logic. The idea is to compare, in a systematic and rigorous way, the empirical data and the theory, be it the theory of the policy intervention or a theoretical or conceptual framework used to understand the policy. This process is referred to by Yin as analytical generalisation. When several cases support the same theory, it is possible to suggest the presence of a replication logic ( Yin 2010).
Configurations can be heuristic tools for this analysis, whether they are organisational or rooted in critical realism (see separate chapter on realistic evaluation ). Furthermore, finding similar patterns, or situations, in different contexts strengthens the ability to generalise the results of case studies. Yin believes that analytical generalisation requires the construction of a very strong case that will be able to withstand the challenges of logical analysis. Thus, it is essential to specify this theoretical rationale at the outset of the case study, either by mobilising a theory or from the state of the art without it being entirely specific to the public policy being analysed. At the beginning of a case study, it is therefore necessary to remain at a relatively high conceptual level, at least higher than the policy under study. Secondly, the empirical results of the case study must show how they align (or not) with the theoretical argument at the outset. Finally, it will be necessary to discuss how this theoretical thinking, based on this particular policy, can also be applied to other situations and policies in the particular case study. The fact that, even at the beginning of the case study, a counter-argument (rival hypotheses) was also formulated, and that empirical evidence was sought during the data collection process (which refutes them), reinforces the validity of this process of analytical generalisation. Finally, the power of multiple case studies is that this analytical generalisation is strengthened when the results of one case are similar to those of other cases.
Some research teams even propose that case studies can lead to theory-building, especially when analysing complex objects such as public policies.
II. How is this method useful for policy evaluation?
Before deciding to embark on a case study approach, two preliminary questions should be asked which will determine the appropriateness of the approach:
Does the phenomenon I am interested in need the case(s) to be understandable? (e.g., Theory-building case studies)
Does the case(s) represent an empirical window that informs the analysis of the wider phenomenon?
Once one or the other has been answered positively, the evaluative questions can be defined:
Under what real-life conditions can public policy X, piloted in context A, be scaled up in contexts B, C, and D?
How did the controversy about public policy Y in context B emerge?
What are the success factors for the implementation of public policy X in context A?
How were public policies Y and Z implemented in context B?
Why did public policy X in context A and B fail, while it had positive effects in context C?
Why did public policy X implemented in context A fail, while public policy Y implemented in the same context A succeeded?
What is it about the characteristics of public policy Z implemented in contexts A, B, and C that informs μ theory-building case studies?
The case study can be used at any point in the evaluation process, ex ante (at the time of policy design), in itinere (during implementation), or ex post (e.g. to better understand the results produced).
III. An example of the use of this method in Burkina Faso
Simple and multiple longitudinal case studies were mobilised to study a public health financing policy in Burkina Faso (Ridde 2021).
The World Bank encouraged the government to test in a dozen districts a modality for financing health centres in addition to the state budget. The idea was to organise a performance-based payment system in which health centres and health professionals received additional funds based on the achievement of activity results. For example, for each delivery performed in the centre with a partographer, they received 3.2 euros to be shared between the structure and the staff, according to complex procedures and indicators. Verification and control processes were organised to ensure the reliability of payment claims.
To study the emergence of this new policy, we conducted a single case study (focusing on the policy) to better understand its origin, ideas, proposed solutions, people who proposed it, power issues, etc. We employed a literature review and 14 qualitative in-depth interviews with policy makers, funding agencies and experts on the subject. Using an analytical generalisation approach, we compared this emergence to understand whether what happened in Burkina Faso was also happening in Benin.
To study the implementation of the policy in Burkina Faso, we then used multiple longitudinal case studies. For reasons of time and budget, we selected three districts representing the diversity of situations in which the policy was implemented. Then, within each of these districts, we selected six cases from the primary health centres (about 30 per district) and one case that was the referral hospital (only one per district). The six cases were selected according to the three types of financing strategies that the policy wished to test, so two cases per type. We decided to select two cases with the greatest possible contrast within each of the three types: one very performant health centre and one not at all. Performance was calculated using a quantitative method (time series) on the basis of indicators of health centre attendance in the years preceding the policy. This etic analysis (from the external perspective) ranked all the health centres according to their order of performance to support case selection. The latter also benefited from the emic opinion (from the internal point of view) of local health system managers in order to take into account their own perception of the performance of the centres, beyond the quantitative approach which only gives a partial view of performance. Thus, for each of the seven cases selected per district (7×3 = 21), we used multiple sources of data to understand the challenges of policy implementation: analysis of documentation, formal qualitative interviews (between 114 and 215 per district) and informal interviews (between 26 and 168 per district), and observations of situations. A data collection grid was also used to measure the fidelity of policy implementation. In order to better understand the evolution of policy implementation, and in particular adaptations over time, three data collection moments were carried out over a 24-month period, thus following the longitudinal multiple case study approach.
Finally, these case studies have also been fruitful in studying, with a qualitative approach and a long immersion in the field, the unexpected consequences (positive or negative) of this policy. Although this dimension of the evaluation is still too little understood, its implementation in Burkina Faso has shown the relevance of this approach (Turcotte-Tremblay et al. 2017). Limiting oneself to the expected effects, which is often implied by an extreme focus on the sole theory of intervention developed by the teams that define the policy, reduces the heuristic scope of the evaluation. While successes are essential, challenges may also be necessary to improve public policies with the help of case studies.
For all these approaches, the analysis was carried out in a hybrid manner, both deductive (with respect to the intervention theory or a conceptual framework) and inductive (original empirical data). The comparison between cases, between districts and between countries allowed for an increase in abstraction in an analytical generalisation process.
IV. What are the criteria for judging the quality of the mobilisation of this method?
Judging the quality of a complex approach such as case studies requires a global vision, going beyond the specific but essential reflections of the usual methods (quantitative and qualitative). To this end, Yin (2018) proposes to study the quality of case studies in terms of four dimensions:
Construct validity (studying the expected policy and not something else): using multiple sources of evidence, describing and establishing a causal chain, involving stakeholders in the validation of the protocol and reports;
Internal validity (confidence in results): compare empirical data with each other and with theory, construct explanatory logics, account for competing and alternative hypotheses, use logical frameworks/theories of intervention;
External validity (ability to generalise results): use theories, use the logic of analytical replication;
Reliability (for the same case study, the same findings): use a policy study protocol, develop a case database.
V. What are the strengths and limitations of this method compared to others?
The main strength of the case study is its ability to ‘incorporate the unique characteristics of each case and to examine complex phenomena in their context’, i.e. in real-life conditions (Stiles 2013, 30).
The case study strategy, due to the abundance and variety of the corpus of data mobilised, and the research methods employed (qualitative, quantitative or mixed), most often allows for a rich description of the public policy(ies) being evaluated and the contexts of implementation. This is particularly true of single case studies, which allow for in-depth analysis. With regard to multiple case studies, the main advantage is that it allows for more potential variation, which increases the robustness of the explanation. The downside is that these strategies require a significant time commitment. Thus, the sheer volume of work can be problematic, especially if the deadlines set by the sponsors are short. In addition, if there are several evaluative questions, or a question that invites the linking of implementation issues to outcomes, then it may be necessary to consider combining the case study (which may focus on process analysis, for example) with another complementary research strategy, such as quasi-experimental approaches (Yin and Ridde, 2012). Finally, several biases may arise – the biased choice of case(s), low statistical power when conducting quantitative analyses. These biases may erode comparability across cases or contexts. The rich justification of the choice of cases (public policies) (Stake 1995) and the description of the context(s), as well as the process of analytical generalisation, described above, help to reduce the impact of these biases.
With regard to theory-building case studies, both advantages and disadvantages of the case study are identified (Stiles 2013). The case study strategy here consists of comparing different statements from theory with one or more observations. This can be done by describing the few cases in theoretical terms. Thus, although each detail can only be observed once, they can be very numerous and therefore useful for theory building. However, the same biases mentioned above are likely to occur (biased case selection, low statistical power). Confidence in individual statements may be eroded by these biases. On the other hand, as many statements are examined – reflecting a variety of contexts and therefore possible variations – the overall strengthening of confidence in the theory may be just as important as in a hypothesis testing study.
Some bibliographical references to go further
Gagnon, Yves-Chantal. 2012. L’étude de cas comme méthode de recherche . 2nd ed. Québec: Presses de l’Université du Québec.
Gehman, Joel. and Glaser, Vern L.. and Eisenhardt, Kathleen M.. and Gioia, Denny. and Langley, Ann. and Corley, Kevin G.. 2018. “Finding Theory–Method Fit: A Comparison of Three Qualitative Approaches to Theory Building.” Journal of Management Inquiry, 27(3): 284‑300. https://doi.org/10.1177/1056492617706029 .
Ridde, Valéry, éd. 2021. Vers une couverture sanitaire universelle en 2030? Éditions science et bien commun. Québec: Canada: Zenodo. https://doi.org/10.5281/ZENODO.5166925 .
Stake, Robert E. 1995. The Art of Case Study Research . Thousand Oaks, CA: SAGE Publications.
Stiles, William B. 2013. “Using Case Studies to Build Psychotherapeutic Theories.” Psychothérapies , 33(1): 29‑35. https://doi.org/10.3917/psys.131.0029 .
Turcotte-Tremblay, Anne-Marie. and Ali Gali-Gali, Idriss. and De Allegri, Manuela. and Ridde, Valéry. 2017. “The Unintended Consequences of Community Verifications for Performance-Based Financing in Burkina Faso.” Social Science & Medicine , 191: 226‑36. https://doi.org/10.1016/j.socscimed.2017.09.007 .
Yin, Robert K. 2010. “Analytic Generalization.” In Encyclopedia of Case Study Research , by Albert Mills, Gabrielle Durepos, and Elden Wiebe, 6. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc. https://doi.org/10.4135/9781412957397.n8 .
Yin, Robert K. 2018. Case study research and applications: design and methods . Sixth edition. Los Angeles: SAGE.
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