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Designing process evaluations using case study to explore the context of complex interventions evaluated in trials

Aileen grant.

1 School of Nursing, Midwifery and Paramedic Practice, Robert Gordon University, Garthdee Road, Aberdeen, AB10 7QB UK

Carol Bugge

2 Faculty of Health Sciences and Sport, University of Stirling, Pathfoot Building, Stirling, FK9 4LA UK

3 Department of Surgery and Cancer, Imperial College London, Charing Cross Campus, London, W6 8RP UK

Associated Data

No data and materials were used.

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.

Conclusions

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.

Trial registration

DQIP - ClinicalTrials.gov number, {"type":"clinical-trial","attrs":{"text":"NCT01425502","term_id":"NCT01425502"}} NCT01425502 - OPAL - ISRCTN57746448

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 – 14 ]; a need for better use, critique and development of theories [ 15 – 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 – 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 – 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.

Data-driven Quality Improvement in Primary Care (DQIP)

Optimising Pelvic Floor Exercises to Achieve Long-term benefits (OPAL)

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 ].

Data collection

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.

Data analysis

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.

Acknowledgements

We would like to thank Professor Shaun Treweek for the discussions about context in trials.

Abbreviations

Authors’ contributions.

AG, CB and MW conceptualised the study. AG wrote the paper. CB and MW commented on the drafts. All authors have approved the final manuscript.

No funding was received for this work.

Availability of data and materials

Ethics approval and consent to participate.

Ethics approval and consent to participate is not appropriate as no participants were included.

Consent for publication

Consent for publication is not required as no participants were included.

Competing interests

The authors declare no competing interests.

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Contributor Information

Aileen Grant, Email: [email protected] .

Carol Bugge, Email: [email protected] .

Mary Wells, Email: [email protected] .

  • Open access
  • Published: 10 November 2020

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

  • Sara Paparini   ORCID: orcid.org/0000-0002-1909-2481 1 ,
  • Judith Green 2 ,
  • Chrysanthi Papoutsi 1 ,
  • Jamie Murdoch 3 ,
  • Mark Petticrew 4 ,
  • Trish Greenhalgh 1 ,
  • Benjamin Hanckel 5 &
  • Sara Shaw 1  

BMC Medicine volume  18 , Article number:  301 ( 2020 ) Cite this article

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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.

Peer Review reports

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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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  

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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.

Conclusions

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.

Trial registration

DQIP - ClinicalTrials.gov number, NCT01425502 - OPAL - ISRCTN57746448

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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 ].

Data collection

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.

Data analysis

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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Abbreviations

Data-driven Quality Improvement in Primary Care

Medical Research Council

Nonsteroidal anti-inflammatory drugs

Optimizing Pelvic Floor Muscle Exercises to Achieve Long-term benefits

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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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  • 7.5 Writing Process: Thinking Critically About Entertainment
  • 7.6 Editing Focus: Quotations
  • 7.7 Evaluation: Effect on Audience
  • 7.8 Spotlight on … Language and Culture
  • 7.9 Portfolio: What the Arts Say About You
  • 8.1 Information and Critical Thinking
  • 8.2 Analytical Report Trailblazer: Barbara Ehrenreich
  • 8.3 Glance at Genre: Informal and Formal Analytical Reports
  • 8.4 Annotated Student Sample: "U.S. Response to COVID-19" by Trevor Garcia
  • 8.5 Writing Process: Creating an Analytical Report
  • 8.6 Editing Focus: Commas with Nonessential and Essential Information
  • 8.7 Evaluation: Reviewing the Final Draft
  • 8.8 Spotlight on … Discipline-Specific and Technical Language
  • 8.9 Portfolio: Evidence and Objectivity
  • 9.1 Breaking the Whole into Its Parts
  • 9.2 Rhetorical Analysis Trailblazer: Jamil Smith
  • 9.3 Glance at Genre: Rhetorical Strategies
  • 9.4 Annotated Student Sample: “Rhetorical Analysis: Evicted by Matthew Desmond” by Eliana Evans
  • 9.5 Writing Process: Thinking Critically about Rhetoric
  • 9.6 Editing Focus: Mixed Sentence Constructions
  • 9.7 Evaluation: Rhetorical Analysis
  • 9.8 Spotlight on … Business and Law
  • 9.9 Portfolio: How Thinking Critically about Rhetoric Affects Intellectual Growth
  • 10.1 Making a Case: Defining a Position Argument
  • 10.2 Position Argument Trailblazer: Charles Blow
  • 10.3 Glance at Genre: Thesis, Reasoning, and Evidence
  • 10.4 Annotated Sample Reading: "Remarks at the University of Michigan" by Lyndon B. Johnson
  • 10.5 Writing Process: Creating a Position Argument
  • 10.6 Editing Focus: Paragraphs and Transitions
  • 10.7 Evaluation: Varied Appeals
  • 10.8 Spotlight on … Citation
  • 10.9 Portfolio: Growth in the Development of Argument
  • 11.1 Developing Your Sense of Logic
  • 11.2 Reasoning Trailblazer: Paul D. N. Hebert
  • 11.3 Glance at Genre: Reasoning Strategies and Signal Words
  • 11.4 Annotated Sample Reading: from Book VII of The Republic by Plato
  • 11.5 Writing Process: Reasoning Supported by Evidence
  • 12.1 Introducing Research and Research Evidence
  • 12.2 Argumentative Research Trailblazer: Samin Nosrat
  • 12.3 Glance at Genre: Introducing Research as Evidence
  • 12.4 Annotated Student Sample: "Healthy Diets from Sustainable Sources Can Save the Earth" by Lily Tran
  • 12.5 Writing Process: Integrating Research
  • 12.6 Editing Focus: Integrating Sources and Quotations
  • 12.7 Evaluation: Effectiveness of Research Paper
  • 12.8 Spotlight on … Bias in Language and Research
  • 12.9 Portfolio: Why Facts Matter in Research Argumentation
  • 13.1 The Research Process: Where to Look for Existing Sources
  • 13.2 The Research Process: How to Create Sources
  • 13.3 Glance at the Research Process: Key Skills
  • 13.4 Annotated Student Sample: Research Log
  • 13.5 Research Process: Making Notes, Synthesizing Information, and Keeping a Research Log
  • 13.6 Spotlight on … Ethical Research
  • 14.1 Compiling Sources for an Annotated Bibliography
  • 14.2 Glance at Form: Citation Style, Purpose, and Formatting
  • 14.3 Annotated Student Sample: “Healthy Diets from Sustainable Sources Can Save the Earth” by Lily Tran
  • 14.4 Writing Process: Informing and Analyzing
  • 15.1 Tracing a Broad Issue in the Individual
  • 15.2 Case Study Trailblazer: Vilayanur S. Ramachandran
  • 15.3 Glance at Genre: Observation, Description, and Analysis
  • 15.4 Annotated Sample Reading: Case Study on Louis Victor "Tan" Leborgne
  • 15.5 Writing Process: Thinking Critically About How People and Language Interact
  • 15.6 Editing Focus: Words Often Confused
  • 15.8 Spotlight on … Applied Linguistics
  • 15.9 Portfolio: Your Own Uses of Language
  • 3 Unit Introduction
  • 16.1 An Author’s Choices: What Text Says and How It Says It
  • 16.2 Textual Analysis Trailblazer: bell hooks
  • 16.3 Glance at Genre: Print or Textual Analysis
  • 16.4 Annotated Student Sample: "Artists at Work" by Gwyn Garrison
  • 16.5 Writing Process: Thinking Critically About Text
  • 16.6 Editing Focus: Literary Works Live in the Present
  • 16.7 Evaluation: Self-Directed Assessment
  • 16.8 Spotlight on … Humanities
  • 16.9 Portfolio: The Academic and the Personal
  • 17.1 “Reading” Images
  • 17.2 Image Trailblazer: Sara Ludy
  • 17.3 Glance at Genre: Relationship Between Image and Rhetoric
  • 17.4 Annotated Student Sample: “Hints of the Homoerotic” by Leo Davis
  • 17.5 Writing Process: Thinking Critically and Writing Persuasively About Images
  • 17.6 Editing Focus: Descriptive Diction
  • 17.7 Evaluation: Relationship Between Analysis and Image
  • 17.8 Spotlight on … Video and Film
  • 17.9 Portfolio: Interplay Between Text and Image
  • 18.1 Mixing Genres and Modes
  • 18.2 Multimodal Trailblazer: Torika Bolatagici
  • 18.3 Glance at Genre: Genre, Audience, Purpose, Organization
  • 18.4 Annotated Sample Reading: “Celebrating a Win-Win” by Alexandra Dapolito Dunn
  • 18.5 Writing Process: Create a Multimodal Advocacy Project
  • 18.6 Evaluation: Transitions
  • 18.7 Spotlight on . . . Technology
  • 18.8 Portfolio: Multimodalism
  • 19.1 Writing, Speaking, and Activism
  • 19.2 Podcast Trailblazer: Alice Wong
  • 19.3 Glance at Genre: Language Performance and Visuals
  • 19.4 Annotated Student Sample: “Are New DOT Regulations Discriminatory?” by Zain A. Kumar
  • 19.5 Writing Process: Writing to Speak
  • 19.6 Evaluation: Bridging Writing and Speaking
  • 19.7 Spotlight on … Delivery/Public Speaking
  • 19.8 Portfolio: Everyday Rhetoric, Rhetoric Every Day
  • 20.1 Thinking Critically about Your Semester
  • 20.2 Reflection Trailblazer: Sandra Cisneros
  • 20.3 Glance at Genre: Purpose and Structure
  • 20.4 Annotated Sample Reading: “Don’t Expect Congrats” by Dale Trumbore
  • 20.5 Writing Process: Looking Back, Looking Forward
  • 20.6 Editing Focus: Pronouns
  • 20.7 Evaluation: Evaluating Self-Reflection
  • 20.8 Spotlight on … Pronouns in Context

Learning Outcomes

By the end of this section, you will be able to:

  • Revise writing to follow the genre conventions of case studies.
  • Evaluate the effectiveness and quality of a case study report.

Case studies follow a structure of background and context , methods , findings , and analysis . Body paragraphs should have main points and concrete details. In addition, case studies are written in formal language with precise wording and with a specific purpose and audience (generally other professionals in the field) in mind. Case studies also adhere to the conventions of the discipline’s formatting guide ( APA Documentation and Format in this study). Compare your case study with the following rubric as a final check.

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Access for free at https://openstax.org/books/writing-guide/pages/1-unit-introduction
  • Authors: Michelle Bachelor Robinson, Maria Jerskey, featuring Toby Fulwiler
  • Publisher/website: OpenStax
  • Book title: Writing Guide with Handbook
  • Publication date: Dec 21, 2021
  • Location: Houston, Texas
  • Book URL: https://openstax.org/books/writing-guide/pages/1-unit-introduction
  • Section URL: https://openstax.org/books/writing-guide/pages/15-7-evaluation-presentation-and-analysis-of-case-study

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Guide to case studies

What is a case study.

A case study is an in depth focussed study of a person, group, or situation that has been studied over time within its real-life context.

There are different types of case study:

  • Illustrative case studies describe an unfamiliar situation in order to help people understand it.
  • Critical instance case studies focus on a unique case, without a generalised purpose.
  • Exploratory case studies are preliminary projects to help guide a future, larger-scale project. They aim to identify research questions and possible research approaches.

We are often looking to develop patient stories as case studies and these will use qualitative methods such as interviews to find specific details and descriptions of how your subject is affected.

Patient stories are illustrative or critical instance case studies. For example, an illustrative case study might focus on a patient with an eating disorder to provide a subjective view to better help trainee nutritionists understand the illness.  A critical instance case study might focus on a patient with a very rare or uniquely complex condition or how a single patient is affected by an injury.

How do you do a case study?

1. get prepared.

  • Be very clear about the purpose of the case study, why you are doing it and what it will be used for?
  • Think about the questions you want to answer? What are your research or evaluation questions?
  • Determine what kind of case study will best suit your needs? Illustrative, Critical Instance or Exploratory?
  • Define the subject of study – is it an individual, a small group of people, or a specific situation?
  • Determine if you need ethical approval to conduct this case study – you may be asked to prove that the case study will do no harm to its participant(s).

2. Get designing!

  • Finalise your research or evaluation questions – i.e. what you want to know at the end of the study. Limit these to a manageable number – no more than 4 or 5.
  • Think about where you will find the information you need to answer your questions.  Interviewing research subjects and/ or observing will likely be the central methods of your case study, but do you need to look to additional data sources as well? For example, desk research or evidence/literature reviewing, interviewing experts, other fieldwork and so on.
  • Create a plan outlining how you will gather the information you need to answer your research or evaluation questions. Include a timeframe and be clear that you have the resources and equipment to carry out the work. Depending on the nature of the case study or the topic being studied a case study may require several meetings/interviews over a period of many months, or it might need just a one off interview. What does yours need?
  • Decide on the exact subject of the study. Is this a specific person or a small group of people? If yes, plan how you will get in touch with them and invite them to take part in the case study. How flexible can you be in terms of time and travel? Does this limit your access to potential participants?
  • Design interview questions that are open and will enable the participant to provide in-depth answers. Avoid questions that can be answered with a single yes or no and make sure the questions are flexible and allow the participant to talk openly and freely.

3. Get recruiting!

  • You may have a specific individual in mind, or specific criteria. You will need to invite people to participate and make very clear that they are able to withdraw at any point.
  • You will need consent from the participants. Make sure the purpose of the case study, why you are doing it and what it will be used, the methods and time frames are extremely clear to the potential participants. You will need written consent that demonstrates that the participant understands this. Additionally, if you intend to digitally record an interview or take notes, make sure you have permission from the participants’ first.
  • If your central method is observation, this will be open observation – the participant must be aware of your presence and agreed to it – you are not allowed to observe without the participants’ permission!

4. Get conducting!

  • Interviewing – Agree a mutually suitable time and venue for the case study interview. This may be a one off or the first of many over several months. Make sure the participant is in an environment they are comfortable and able to talk in. Equally important, however is that the environment is safe for you and is conducive to conducting a case study interview – i.e.  If it is a private space, are you safe? If it is a public space make sure it is not too noisy or likely to be affected by interruptions.
  • Decide what is the best method of recording the interview information – digital recording is less intrusive and you can engage better in the conversation, than if you attempt to just take notes. Taking notes can mean that your concentration is focused on the writing rather than the listening and you can miss vital points. It can also be off-putting for the participant if there is no eye contact because you are scribing throughout the conversation. However, some participants will not like to be digitally recorded – so it is best to discuss this with them first. If you are digitally recording always test the equipment first. Even if you are digitally recording you will still need to take notes on key points, or things that you would like to investigate further, questions that arise or points at which you don’t want to interrupt the conversation or anything that will not be captured by the recording, such as body language or other observations.
  • Depending on the total length of your case study, you might hold a one off interview, interview weekly, once every month or two, or just once or twice a year. Begin with the interview questions you prepared in the preparation and design phases, then iterate to dig deeper into the topics. Ask about experience and meaning — ask the participant what it’s like to go through the experience you’re studying and what the experience means to them. Later interviews are an opportunity to ask questions that fill gaps in your knowledge, or that are particularly relevant to the development of the case study or in answering your questions.
  • Observing – recording observation can be done manually – i.e. taking notes – or digitally via a camcorder or similar. It is important to capture detail about the subject/participant and their interactions with others and the environment, their behaviour and other context an detail that is relevant to your questions.

5. Get analysing!

  • Write up your notes or transcribe (Interviews), make notes (video) from your digital recording. Remember that if you are transcribing it is important to include pauses, laughter and other descriptive sounds and commentary on tone and intonation to better convey the story. Include the contextual information / the external environment and other observations that are important. Such as when and where the interview took place (you will not necessarily make this public) and any issues that arose such as interruptions that affected the interview or if there were multiple interviews anything of significance that happened in the periods between interviews.
  • Thematically code (look for themes) and look for key parts of the interviews that will answer your original questions. Also be very aware that the may be new or unexpected information that has come through the process that is very important or interesting.
  • Arrange the notes or transcriptions from the interviews and, or observations into a case study. It is not likely that you will be able to use the transcriptions without reorganising them, but if you are rewriting the story in your own words, be careful not to lose the meaning and language that reflects the participant.

6. Get sign off!

  • Once you have drafted your case study make sure the participant(s) have sight of it and an opportunity to say whether you have captured their story and are representing it/them as they would like.

7. Get disseminating!

  • More information about disseminating evaluations and case studies can be found on the  Evaluation Toolkit site .
  • Remember case studies are not designed for large group studies or statistical analysis and do not aim to answer a research question definitively.
  • Do background/context research where possible.
  • Establishing trust with participants is crucial and can result in less inhibited behaviour. Observing people in their home, workplaces, or other “natural” environments may be more effective than bringing them to a laboratory or office.
  • Be aware that if you are observing it is likely that because subjects know they are being studied, their behaviour will change.
  • Take notes -Extensive notes during observation will be vital.
  • Take notes even if you are digitally recoding an interview to capture your own thinking, points to follow up on or observations.
  • In some case studies, it may be appropriate to ask the participant to record experiences in a diary – especially if there are periods between your interviews or observations that you wish to capture data on.
  • Stay rigorous. A case study may feel less data-driven than a medical trial or a scientific experiment, but attention to rigor and valid methodology remains vital.
  • When reviewing your notes, discard possible conclusions that do not have detailed observation or evidence backing them up.
  • A case study might reveal new and unexpected results, and lead to research taking new directions.
  • A case study cannot be generalised to fit a whole population.
  • Since you aren’t conducting a statistical analysis, you do not need to recruit a diverse cross-section of society. You should be aware of any biases in your small sample, and make them clear in your report, but they do not invalidate your research.
  • Useful resource: ‘Case Study Research: Design and Methods’, Robert K Yin, SAGE publications 2013.

Case studies

Find inspiration for your own evaluation with these real life examples

Guidance from a range of organisations for in-depth advice

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Case Study Evaluation: Past, Present and Future Challenges: Volume 15

Table of contents, case study evaluation: past, present and future challenges, advances in program evaluation, copyright page, list of contributors, introduction, case study, methodology and educational evaluation: a personal view.

This chapter gives one version of the recent history of evaluation case study. It looks back over the emergence of case study as a sociological method, developed in the early years of the 20th Century and celebrated and elaborated by the Chicago School of urban sociology at Chicago University, starting throughout the 1920s and 1930s. Some of the basic methods, including constant comparison, were generated at that time. Only partly influenced by this methodological movement, an alliance between an Illinois-based team in the United States and a team at the University of East Anglia in the United Kingdom recast the case method as a key tool for the evaluation of social and educational programmes.

Letters from a Headmaster ☆ Originally published in Simons, H. (Ed.) (1980). Towards a Science of the Singular: Essays about Case Study in Educational Research and Evaluation. Occasional Papers No. 10. Norwich, UK: Centre for Applied Research, University of East Anglia.

Story telling and educational understanding ☆ previously published in occasional papers #12, evaluation centre, university of western michigan, 1978..

The full ‘storytelling’ paper was written in 1978 and was influential in its time. It is reprinted here, introduced by an Author's reflection on it in 2014. The chapter describes the author’s early disenchantment with traditional approaches to educational research.

He regards educational research as, at best, a misnomer, since little of it is preceded by a search . Entitled educational researchers often fancy themselves as scientists at work. But those whom they attempt to describe are often artists at work. Statistical methodologies enable educational researchers to measure something, but their measurements can neither capture nor explain splendid teaching.

Since such a tiny fraction of what is published in educational research journals influences school practitioners, professional researchers should risk trying alternative approaches to uncovering what is going on in schools.

Story telling is posited as a possible key to producing insights that inform and ultimately improve educational practice. It advocates openness to broad inquiry into the culture of the educational setting.

Case Study as Antidote to the Literal

Much programme and policy evaluation yields to the pressure to report on the productivity of programmes and is perforce compliant with the conditions of contract. Too often the view of these evaluations is limited to a literal reading of the analytical challenge. If we are evaluating X we look critically at X1, X2 and X3. There might be cause for embracing adjoining data sources such as W1 and Y1. This ignores frequent realities that an evaluation specification is only an approximate starting point for an unpredictable journey into comprehensive understanding; that the specification represents only that which is wanted by the sponsor, and not all that may be needed ; and that the contractual specification too often insists on privileging the questions and concerns of a few. Case study evaluation proves an alternative that allows for the less-than-literal in the form of analysis of contingencies – how people, phenomena and events may be related in dynamic ways, how context and action have only a blurred dividing line and how what defines the case as a case may only emerge late in the study.

Thinking about Case Studies in 3-D: Researching the NHS Clinical Commissioning Landscape in England

What is our unit of analysis and by implication what are the boundaries of our cases? This is a question we grapple with at the start of every new project. We observe that case studies are often referred to in an unreflective manner and are often conflated with geographical location. Neat units of analysis and clearly bounded cases usually do not reflect the messiness encountered during qualitative fieldwork. Others have puzzled over these questions. We briefly discuss work to problematise the use of households as units of analysis in the context of apartheid South Africa and then consider work of other anthropologists engaged in multi-site ethnography. We have found the notion of ‘following’ chains, paths and threads across sites to be particularly insightful.

We present two examples from our work studying commissioning in the English National Health Service (NHS) to illustrate our struggles with case studies. The first is a study of Practice-based Commissioning groups and the second is a study of the early workings of Clinical Commissioning Groups. In both instances we show how ideas of what constituted our unit of analysis and the boundaries of our cases became less clear as our research progressed. We also discuss pressures we experienced to add more case studies to our projects. These examples illustrate the primacy for us of understanding interactions between place, local history and rapidly developing policy initiatives. Understanding cases in this way can be challenging in a context where research funders hold different views of what constitutes a case.

The Case for Evaluating Process and Worth: Evaluation of a Programme for Carers and People with Dementia

A case study methodology was applied as a major component of a mixed-methods approach to the evaluation of a mobile dementia education and support service in the Bega Valley Shire, New South Wales, Australia. In-depth interviews with people with dementia (PWD), their carers, programme staff, family members and service providers and document analysis including analysis of client case notes and client database were used.

The strengths of the case study approach included: (i) simultaneous evaluation of programme process and worth, (ii) eliciting the theory of change and addressing the problem of attribution, (iii) demonstrating the impact of the programme on earlier steps identified along the causal pathway (iv) understanding the complexity of confounding factors, (v) eliciting the critical role of the social, cultural and political context, (vi) understanding the importance of influences contributing to differences in programme impact for different participants and (vii) providing insight into how programme participants experience the value of the programme including unintended benefits.

The broader case of the collective experience of dementia and as part of this experience, the impact of a mobile programme of support and education, in a predominately rural area grew from the investigation of the programme experience of ‘individual cases’ of carers and PWD. Investigation of living conditions, relationships, service interactions through observation and increased depth of interviews with service providers and family members would have provided valuable perspectives and thicker description of the case for increased understanding of the case and strength of the evaluation.

The Collapse of “Primary Care” in Medical Education: A Case Study of Michigan’s Community/University Health Partnerships Project

This chapter describes a case study of a social change project in medical education (primary care), in which the critical interpretive evaluation methodology I sought to use came up against the “positivist” approach preferred by senior figures in the medical school who commissioned the evaluation.

I describe the background to the study and justify the evaluation approach and methods employed in the case study – drawing on interviews, document analysis, survey research, participant observation, literature reviews, and critical incidents – one of which was the decision by the medical school hierarchy to restrict my contact with the lay community in my official evaluation duties. The use of critical ethnography also embraced wider questions about circuits of power and the social and political contexts within which the “social change” effort occurred.

Central to my analysis is John Gaventa’s theory of power as “the internalization of values that inhibit consciousness and participation while encouraging powerlessness and dependency.” Gaventa argued, essentially, that the evocation of power has as much to do with preventing decisions as with bringing them about. My chosen case illustrated all three dimensions of power that Gaventa originally uncovered in his portrait of self-interested Appalachian coal mine owners: (1) communities were largely excluded from decision making power; (2) issues were avoided or suppressed; and (3) the interests of the oppressed went largely unrecognized.

The account is auto-ethnographic, hence the study is limited by my abilities, biases, and subject positions. I reflect on these in the chapter.

The study not only illustrates the unique contribution of case study as a research methodology but also its low status in the positivist paradigm adhered to by many doctors. Indeed, the tension between the potential of case study to illuminate the complexities of community engagement through thick description and the rejection of this very method as inherently “flawed” suggests that medical education may be doomed to its neoliberal fate for some time to come.

‘Lead’ Standard Evaluation

This is a personal narrative, but I trust not a self-regarding one. For more years than I care to remember I have been working in the field of curriculum (or ‘program’) evaluation. The field by any standards is dispersed and fragmented, with variously ascribed purposes, roles, implicit values, political contexts, and social research methods. Attempts to organize this territory into an ‘evaluation theory tree’ (e.g. Alkin, M., & Christie, C. (2003). An evaluation theory tree. In M. Alkin (Ed.), Evaluation roots: Tracing theorists’ views and influences (pp. 12–65). Thousand Oaks, CA: Sage) have identified broad types or ‘branches’, but the migration of specific characteristics (like ‘case study’) or individual practitioners across the boundaries has tended to undermine the analysis at the level of detail, and there is no suggestion that it represents a cladistic taxonomy. There is, however, general agreement that the roots of evaluation practice tap into a variety of cultural sources, being grounded bureaucratically in (potentially conflicting) doctrines of accountability and methodologically in discipline-based or pragmatically eclectic formats for systematic social enquiry.

In general, this diversity is not treated as problematic. The professional evaluation community has increasingly taken the view (‘let all the flowers grow’) that evaluation models can be deemed appropriate across a wide spectrum, with their appropriateness determined by the nature of the task and its context, including in relation to hybrid studies using mixed models or displaying what Geertz (Geertz, C. (1980/1993). Blurred genres: The refiguration of social thought. The American Scholar , 49(2), 165–179) called ‘blurred genres’. However, from time to time historic tribal rivalries re-emerge as particular practitioners feel the need to defend their modus operandi (and thereby their livelihood) against paradigm shifts or governments and other sponsors of program evaluation seeking for ideological reasons to prioritize certain types of study at the expense of others. The latter possibility poses a potential threat that needs to be taken seriously by evaluators within the broad tradition showcased in this volume, interpretive qualitative case studies of educational programs that combine naturalistic description (often ‘thick’; Geertz, C. (1973). Thick description: Towards an interpretive theory of culture. In The interpretation of culture (pp. 3–30). New York, NY: Basic Books.) description with a values-orientated analysis of their implications. Such studies are more likely to seek inspiration from anthropology or critical discourse analysis than from the randomly controlled trials familiar in medical research or laboratory practice in the physical sciences, despite the impressive rigour of the latter in appropriate contexts. It is the risk of ideological allegiance that I address in this chapter.

Freedom from the Rubric

Twice-told tales how public inquiry could inform n of 1 case study research.

This chapter considers the usefulness and validity of public inquiries as a source of data and preliminary interpretation for case study research. Using two contrasting examples – the Bristol Inquiry into excess deaths in a children’s cardiac surgery unit and the Woolf Inquiry into a breakdown of governance at the London School of Economics (LSE) – I show how academics can draw fruitfully on, and develop further analysis from, the raw datasets, published summaries and formal judgements of public inquiries.

Academic analysis of public inquiries can take two broad forms, corresponding to the two main approaches to individual case study defined by Stake: instrumental (selecting the public inquiry on the basis of pre-defined theoretical features and using the material to develop and test theoretical propositions) and intrinsic (selecting the public inquiry on the basis of the particular topic addressed and using the material to explore questions about what was going on and why).

The advantages of a public inquiry as a data source for case study research typically include a clear and uncontested focus of inquiry; the breadth and richness of the dataset collected; the exceptional level of support available for the tasks of transcribing, indexing, collating, summarising and so on; and the expert interpretations and insights of the inquiry’s chair (with which the researcher may or may not agree). A significant disadvantage is that whilst the dataset collected for a public inquiry is typically ‘rich’, it has usually been collected under far from ideal research conditions. Hence, while public inquiries provide a potentially rich resource for researchers, those who seek to use public inquiry data for research must justify their choice on both ethical and scientific grounds.

Evaluation as the Co-Construction of Knowledge: Case Studies of Place-Based Leadership and Public Service Innovation

This chapter introduces the notion of the ‘Innovation Story’ as a methodological approach to public policy evaluation, which builds in greater opportunity for learning and reflexivity.

The Innovation Story is an adaptation of the case study approach and draws on participatory action research traditions. It is a structured narrative that describes a particular public policy innovation in the personalised contexts in which it is experienced by innovators. Its construction involves a discursive process through which involved actors tell their story, explain it to others, listen to their questions and co-construct knowledge of change together.

The approach was employed to elaborate five case studies of place-based leadership and public service innovation in the United Kingdom, The Netherlands and Mexico. The key findings are that spaces in which civic leaders come together from different ‘realms’ of leadership in a locality (community, business, professional managers and political leaders) can become innovation zones that foster inventive behaviour. Much depends on the quality of civic leadership, and its capacity to foster genuine dialogue and co-responsibility. This involves the evaluation seeking out influential ideas from below the level of strategic management, and documenting leadership activities of those who are skilled at ‘boundary crossing’ – for example, communicating between sectors.

The evaluator can be a key player in this process, as a convenor of safe spaces for actors to come together to discuss and deliberate before returning to practice. Our approach therefore argues for a particular awareness of the political nature of policy evaluation in terms of negotiating these spaces, and the need for politically engaged evaluators who are skilled in facilitating collective learning processes.

Evaluation Noir: The Other Side of the Experience

What are the boundaries of a case study, and what should new evaluators do when these boundaries are breached? How does a new evaluator interpret the breakdown of communication, how do new evaluators protect themselves when the evaluation fails? This chapter discusses the journey of an evaluator new to the field of qualitative evaluative inquiry. Integrating the perspective of a senior evaluator, the authors reflect on three key experiences that informed the new evaluator. The authors hope to provide a rare insight into case study practice as emotional issues turn out to be just as complex as the methodology used.

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Peer engagement in harm reduction strategies and services: a critical case study and evaluation framework from British Columbia, Canada

Affiliations.

  • 1 BC Centre for Disease Control, 655 West 12th Avenue, Vancouver, British Columbia, V5Z 4R4, Canada. [email protected].
  • 2 School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada. [email protected].
  • 3 The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom.
  • 4 BC Centre for Disease Control, 655 West 12th Avenue, Vancouver, British Columbia, V5Z 4R4, Canada.
  • 5 Society of Living Illicit Drug Users, 857 Caledonia Street, Victoria, British Columbia, V8T 1E6, Canada.
  • 6 School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada.
  • PMID: 27229314
  • PMCID: PMC4882818
  • DOI: 10.1186/s12889-016-3136-4

Background: Engaging people with drug use experience, or 'peers,' in decision-making helps to ensure harm reduction services reflect current need. There is little published on the implementation, evaluation, and effectiveness of meaningful peer engagement. This paper aims to describe and evaluate peer engagement in British Columbia from 2010-2014.

Methods: A process evaluation framework specific to peer engagement was developed and used to assess progress made, lessons learned, and future opportunities under four domains: supportive environment, equitable participation, capacity building and empowerment, and improved programming and policy. The evaluation was conducted by reviewing primary and secondary qualitative data including focus groups, formal documents, and meeting minutes.

Results: Peer engagement was an iterative process that increased and improved over time as a consequence of reflexive learning. Practical ways to develop trust, redress power imbalances, and improve relationships were crosscutting themes. Lack of support, coordination, and building on existing capacity were factors that could undermine peer engagement. Peers involved across the province reviewed and provided feedback on these results.

Conclusion: Recommendations from this evaluation can be applied to other peer engagement initiatives in decision-making settings to improve relationships between peers and professionals and to ensure programs and policies are relevant and equitable.

Keywords: Community engagement; Harm reduction; Health equity; Peer engagement; Process evaluation; Public participation; Substance use.

  • British Columbia
  • Community Health Services
  • Harm Reduction*
  • Peer Influence*
  • Program Evaluation
  • Substance-Related Disorders / prevention & control*

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Week 32: Better use of case studies in evaluation

two suitcases

Case studies are often used in evaluations – but not always in ways that use their real potential.

Recently I had an opportunity to spend some time with the evaluation unit of UNOIOS  (United Nations Office of Internal Oversight and Inspection) and some of their UN evaluation colleagues exploring ways to better use  case studies in evaluation. Here are five lessons I took away from our time together.

1.    Be clear about what you mean by a case study

Case study is a research design that involves an intensive study of one or more cases rather than an extensive study of many, and which involve multiple sources of evidence – often a combination of quantitative and qualitative data.  

Be clear about what the case is – is it a person, a site, a project, an event, a procedure, a country, or something else? And what is it a case of? A case of successful implementation - or a case that illustrates the barriers to successful implementation? A typical day? A small project, as compared to a large project?

2. Be clear about why you are doing a case study – and then choose the type of case study that matches this.

There are different types of case studies.  Choose the right one for your purpose.  This list draws on a guide " Case Studies in Evaluation " produced by the United State Government Accounting Office, which identified six  different types of case studies – and adds one more (comparative case study):

Illustrative: This is descriptive in character and intended to add realism and in-depth examples to other information about a program or policy. They are  especially useful in evaluations intended to be used by people without direct experience of a program or a situation​.  These are often used to complement quantitative data by providing examples of the overall findings.  These can range from brief narratives to  detailed, vivid, and evocative narratives that provide a vicarious experience and allow readers to understand the connections and meaning​s.

Exploratory: This is also descriptive but is aimed at generating hypotheses for later investigation rather than simply providing illustration.  This type of case study is done before planning a component of the evaluation which will involve extensive data collection (such as a survey)​.

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.

Program implementation: This investigates operations, often at several sites, and often with reference to a set of norms or standards about implementation processes.

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.  It involves detailed and strategic data collection to identify and test different theories about what has produced the observed impacts​.

Cumulative :  This brings together findings from many case studies to answer evaluative questions. 

Comparative case studies:  These are not only multiple case studies but ones which are designed to use the comparisons between the cases to build and test hypotheses.

3. Match sampling,  data collection, analysis and reporting to the type of case

In evaluation, it is very unlikely that people will be interested in the case itself without wanting to know how to use those findings to think about a larger population.  Case studies usually use some form of purposeful sampling – random sampling  is rarely appropriate (unless this is the only form of sampling that will be credible to the evaluation users).  

Carefully select the type of purposeful sampling so that appropriate inference or translation of findings can be made.   What I often see in evaluations is inappropriate choice of sampling type which then does not match the type of inference needed.  For example it would not be appropriate to sample extreme cases (such as a very successful site) and then draw conclusions as if the sample were typical cases. I often see case studies where the cases have been chosen in terms of a maximum variety sample drawn across a number of dimensions (for example, choosing countries which show a range of levels of development, region and some relevant contextual factors such as institutional arrangements in the country) -  but then the evaluation is not clear about how to use this information to say something about the larger group.

Being clear about the type of case, and the type of inference that will be made, can make it clear what sort of sample is needed.  For example, an illustrative case study might be done of a case which is identified as " typical " along some dimensions, in order to show what an average case is like.  Or outlier sampling might be used to show what the program looks like when it works particularly well or badly. Or maximum variation sampling might be used to show the range of what it looks like in different situations. 

An exploratory case study might use theory-based sampling , identifying important sub-groups according to the theory of change and sampling from each.  This could be used to develop theories of change for each case and compare them to see how they differ across different cases, or to develop an overall theory of change for the whole program or for types of projects that can be used to guide the next stage of data collection.

4. Link case studies thoughtfully to other elements of an evaluation or a monitoring and evaluation system

Think carefully about when the case studies should be done and how they can be linked. For example, exploratory case studies can be useful to do before a survey; explanatory case studies are likely to be useful after a survey.

critical case study evaluation

5. Create opportunities for iteration

If possible, don’t commit the entire evaluation budget at the beginning but set some aside to follow up emerging findings and test hypotheses by doing additional work such as:

  • More data analysis of existing data from cases
  • More data collection and analysis from existing cases
  • Adding more cases 

You can find more resources about using case studies in evaluation on the Case Study approach page on the BetterEvaluation site.

Do you have other good resources or examples to share?  Do you have questions about using case studies in evaluation?

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Computer Science > Machine Learning

Title: evaluation of predictive reliability to foster trust in artificial intelligence. a case study in multiple sclerosis.

Abstract: Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in case of prediction errors. Spotting ML failures is of paramount importance when ML predictions are used to drive clinical decisions. ML predictive reliability measures the degree of trust of a ML prediction on a new instance, thus allowing decision-makers to accept or reject it based on its reliability. To assess reliability, we propose a method that implements two principles. First, our approach evaluates whether an instance to be classified is coming from the same distribution of the training set. To do this, we leverage Autoencoders (AEs) ability to reconstruct the training set with low error. An instance is considered Out-of-Distribution (OOD) if the AE reconstructs it with a high error. Second, it is evaluated whether the ML classifier has good performances on samples similar to the newly classified instance by using a proxy model. We show that this approach is able to assess reliability both in a simulated scenario and on a model trained to predict disease progression of Multiple Sclerosis patients. We also developed a Python package, named relAI, to embed reliability measures into ML pipelines. We propose a simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not. Our method holds the promise to provide effective support to clinicians by spotting potential ML failures during deployment.

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