study case evaluation

• 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

15k Accesses

31 Citations

36 Altmetric

Metrics details

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.

Availability of data and materials

Not applicable (article based on existing available academic publications)

Abbreviations

Qualitative comparative analysis

Quasi-experimental design

Randomised controlled trial

Diez Roux AV. Complex systems thinking and current impasses in health disparities research. Am J Public Health. 2011;101(9):1627–34.

Article   Google Scholar  

Ogilvie D, Mitchell R, Mutrie N, M P, Platt S. Evaluating health effects of transport interventions: methodologic case study. Am J Prev Med 2006;31:118–126.

Walshe C. The evaluation of complex interventions in palliative care: an exploration of the potential of case study research strategies. Palliat Med. 2011;25(8):774–81.

Woolcock M. Using case studies to explore the external validity of ‘complex’ development interventions. Evaluation. 2013;19:229–48.

Cartwright N. Are RCTs the gold standard? BioSocieties. 2007;2(1):11–20.

Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med. 2018;210:2–21.

Salway S, Green J. Towards a critical complex systems approach to public health. Crit Public Health. 2017;27(5):523–4.

Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16(1):95.

Bonell C, Warren E, Fletcher A. Realist trials and the testing of context-mechanism-outcome configurations: a response to Van Belle et al. Trials. 2016;17:478.

Pallmann P, Bedding AW, Choodari-Oskooei B. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16:29.

Curran G, Bauer M, Mittman B, Pyne J, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012;50(3):217–26. https://doi.org/10.1097/MLR.0b013e3182408812 .

Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015 [cited 2020 Jun 27];350. Available from: https://www.bmj.com/content/350/bmj.h1258 .

Evans RE, Craig P, Hoddinott P, Littlecott H, Moore L, Murphy S, et al. When and how do ‘effective’ interventions need to be adapted and/or re-evaluated in new contexts? The need for guidance. J Epidemiol Community Health. 2019;73(6):481–2.

Shoveller J. A critical examination of representations of context within research on population health interventions. Crit Public Health. 2016;26(5):487–500.

Treweek S, Zwarenstein M. Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials. 2009;10(1):37.

Rosengarten M, Savransky M. A careful biomedicine? Generalization and abstraction in RCTs. Crit Public Health. 2019;29(2):181–91.

Green J, Roberts H, Petticrew M, Steinbach R, Goodman A, Jones A, et al. Integrating quasi-experimental and inductive designs in evaluation: a case study of the impact of free bus travel on public health. Evaluation. 2015;21(4):391–406.

Canguilhem G. The normal and the pathological. New York: Zone Books; 1991. (1949).

Google Scholar  

Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. Am J Community Psychol. 2009;43:267–76.

King G, Keohane RO, Verba S. Designing social inquiry: scientific inference in qualitative research: Princeton University Press; 1994.

Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629.

Yin R. Enhancing the quality of case studies in health services research. Health Serv Res. 1999;34(5 Pt 2):1209.

CAS   PubMed   PubMed Central   Google Scholar  

Raine R, Fitzpatrick R, Barratt H, Bevan G, Black N, Boaden R, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016 [cited 2020 Jun 30];4(16). Available from: https://www.journalslibrary.nihr.ac.uk/hsdr/hsdr04160#/abstract .

Craig P, Di Ruggiero E, Frohlich KL, E M, White M, Group CCGA. Taking account of context in population health intervention research: guidance for producers, users and funders of research. NIHR Evaluation, Trials and Studies Coordinating Centre; 2018.

Grant RL, Hood R. Complex systems, explanation and policy: implications of the crisis of replication for public health research. Crit Public Health. 2017;27(5):525–32.

Mahoney J. Strategies of causal inference in small-N analysis. Sociol Methods Res. 2000;4:387–424.

Turner S. Major system change: a management and organisational research perspective. In: Rosalind Raine, Ray Fitzpatrick, Helen Barratt, Gywn Bevan, Nick Black, Ruth Boaden, et al. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health. Health Serv Deliv Res. 2016;4(16) 2016. https://doi.org/10.3310/hsdr04160.

Ragin CC. Using qualitative comparative analysis to study causal complexity. Health Serv Res. 1999;34(5 Pt 2):1225.

Hanckel B, Petticrew M, Thomas J, Green J. Protocol for a systematic review of the use of qualitative comparative analysis for evaluative questions in public health research. Syst Rev. 2019;8(1):252.

Schneider CQ, Wagemann C. Set-theoretic methods for the social sciences: a guide to qualitative comparative analysis: Cambridge University Press; 2012. 369 p.

Flyvbjerg B. Five misunderstandings about case-study research. Qual Inq. 2006;12:219–45.

Tsoukas H. Craving for generality and small-N studies: a Wittgensteinian approach towards the epistemology of the particular in organization and management studies. Sage Handb Organ Res Methods. 2009:285–301.

Stake RE. The art of case study research. London: Sage Publications Ltd; 1995.

Mitchell JC. Typicality and the case study. Ethnographic research: A guide to general conduct. Vol. 238241. 1984.

Gerring J. What is a case study and what is it good for? Am Polit Sci Rev. 2004;98(2):341–54.

May C, Mort M, Williams T, F M, Gask L. Health technology assessment in its local contexts: studies of telehealthcare. Soc Sci Med 2003;57:697–710.

McGill E. Trading quality for relevance: non-health decision-makers’ use of evidence on the social determinants of health. BMJ Open. 2015;5(4):007053.

Greenhalgh T. We can’t be 100% sure face masks work – but that shouldn’t stop us wearing them | Trish Greenhalgh. The Guardian. 2020 [cited 2020 Jun 27]; Available from: https://www.theguardian.com/commentisfree/2020/jun/05/face-masks-coronavirus .

Hammersley M. So, what are case studies? In: What’s wrong with ethnography? New York: Routledge; 1992.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach. BMC Med Res Methodol. 2011;11(1):100.

Luck L, Jackson D, Usher K. Case study: a bridge across the paradigms. Nurs Inq. 2006;13(2):103–9.

Yin RK. Case study research and applications: design and methods: Sage; 2017.

Hyett N, A K, Dickson-Swift V. Methodology or method? A critical review of qualitative case study reports. Int J Qual Stud Health Well-Being. 2014;9:23606.

Carolan CM, Forbat L, Smith A. Developing the DESCARTE model: the design of case study research in health care. Qual Health Res. 2016;26(5):626–39.

Greenhalgh T, Annandale E, Ashcroft R, Barlow J, Black N, Bleakley A, et al. An open letter to the BMJ editors on qualitative research. Bmj. 2016;352.

Thomas G. A typology for the case study in social science following a review of definition, discourse, and structure. Qual Inq. 2011;17(6):511–21.

Lincoln YS, Guba EG. Judging the quality of case study reports. Int J Qual Stud Educ. 1990;3(1):53–9.

Riley DS, Barber MS, Kienle GS, Aronson JK, Schoen-Angerer T, Tugwell P, et al. CARE guidelines for case reports: explanation and elaboration document. J Clin Epidemiol. 2017;89:218–35.

Download references

Acknowledgements

Not applicable

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).

Author information

Authors and affiliations.

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

Sara Paparini, Chrysanthi Papoutsi, Trish Greenhalgh & Sara Shaw

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

Judith Green

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

Jamie Murdoch

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

Mark Petticrew

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

Benjamin Hanckel

You can also search for this author in PubMed   Google Scholar

Contributions

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

Corresponding author

Correspondence to Sara Paparini .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Cite this article.

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

Download citation

Received : 03 July 2020

Accepted : 07 September 2020

Published : 10 November 2020

DOI : https://doi.org/10.1186/s12916-020-01777-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative
• Case studies
• Mixed-method
• Public health
• Health services research
• Interventions

BMC Medicine

ISSN: 1741-7015

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 27 November 2020

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

• Aileen Grant 1 ,
• Carol Bugge 2 &
• Mary Wells 3  

Trials volume  21 , Article number:  982 ( 2020 ) Cite this article

9613 Accesses

6 Citations

5 Altmetric

Metrics details

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

Peer Review reports

Contribution to the literature

We illustrate how case study methodology can explore the complex, dynamic and uncertain relationship between context and interventions within trials.

We depict different case study designs and illustrate there is not one formula and that design needs to be tailored to the context and trial design.

Case study can support comparisons between intervention and control arms and between cases within arms to uncover and explain differences in detail.

We argue that case study can illustrate how components have evolved and been redefined through implementation.

Key issues for consideration in case study design within process evaluations are presented and illustrated with examples.

Process evaluations are an important component of an effectiveness evaluation as they focus on understanding the relationship between interventions and context to explain how and why interventions work or fail and whether they can be transferred to other settings and populations. However, historically, not all trials have had a process evaluation component, nor have they sufficiently reported aspects of context, resulting in poor uptake of trial findings [ 1 ]. Considerations of context are often absent from published process evaluations, with few studies acknowledging, taking account of or describing context during implementation, or assessing the impact of context on implementation [ 2 , 3 ]. At present, evidence from trials is not being used in a timely manner [ 4 , 5 ], and this can negatively impact on patient benefit and experience [ 6 ]. It takes on average 17 years for knowledge from research to be implemented into practice [ 7 ]. Suitable methodologies are therefore needed that allow for context to be exposed; one appropriate methodological approach is case study [ 8 , 9 ].

In 2015, the Medical Research Council (MRC) published guidance for process evaluations [ 10 ]. This was a key milestone in legitimising as well as providing tools, methods and a framework for conducting process evaluations. Nevertheless, as with all guidance, there is a need for reflection, challenge and refinement. There have been a number of critiques of the MRC guidance, including that interventions should be considered as events in systems [ 11 , 12 , 13 , 14 ]; a need for better use, critique and development of theories [ 15 , 16 , 17 ]; and a need for more guidance on integrating qualitative and quantitative data [ 18 , 19 ]. Although the MRC process evaluation guidance does consider appropriate qualitative and quantitative methods, it does not mention case study design and what it can offer the study of context in trials.

The case study methodology is ideally suited to real-world, sustainable intervention development and evaluation because it can explore and examine contemporary complex phenomena, in depth, in numerous contexts and using multiple sources of data [ 8 ]. Case study design can capture the complexity of the case, the relationship between the intervention and the context and how the intervention worked (or not) [ 8 ]. There are a number of textbooks on a case study within the social science fields [ 8 , 9 , 20 ], but there are no case study textbooks and a paucity of useful texts on how to design, conduct and report case study within the health arena. Few examples exist within the trial design and evaluation literature [ 3 , 21 ]. Therefore, guidance to enable well-designed process evaluations using case study methodology is required.

We aim to address the gap in the literature by presenting a number of important considerations for process evaluation using a case study design. First, we define the context and describe the relationship between complex health interventions and context.

What is context?

While there is growing recognition that context interacts with the intervention to impact on the intervention’s effectiveness [ 22 ], context is still poorly defined and conceptualised. There are a number of different definitions in the literature, but as Bate et al. explained ‘almost universally, we find context to be an overworked word in everyday dialogue but a massively understudied and misunderstood concept’ [ 23 ]. Ovretveit defines context as ‘everything the intervention is not’ [ 24 ]. This last definition is used by the MRC framework for process evaluations [ 25 ]; however; the problem with this definition is that it is highly dependent on how the intervention is defined. We have found Pfadenhauer et al.’s definition useful:

Context is conceptualised as a set of characteristics and circumstances that consist of active and unique factors that surround the implementation. As such it is not a backdrop for implementation but interacts, influences, modifies and facilitates or constrains the intervention and its implementation. Context is usually considered in relation to an intervention or object, with which it actively interacts. A boundary between the concepts of context and setting is discernible: setting refers to the physical, specific location in which the intervention is put into practice. Context is much more versatile, embracing not only the setting but also roles, interactions and relationships [ 22 ].

Traditionally, context has been conceptualised in terms of barriers and facilitators, but what is a barrier in one context may be a facilitator in another, so it is the relationship and dynamics between the intervention and context which are the most important [ 26 ]. There is a need for empirical research to really understand how different contextual factors relate to each other and to the intervention. At present, research studies often list common contextual factors, but without a depth of meaning and understanding, such as government or health board policies, organisational structures, professional and patient attitudes, behaviours and beliefs [ 27 ]. The case study methodology is well placed to understand the relationship between context and intervention where these boundaries may not be clearly evident. It offers a means of unpicking the contextual conditions which are pertinent to effective implementation.

The relationship between complex health interventions and context

Health interventions are generally made up of a number of different components and are considered complex due to the influence of context on their implementation and outcomes [ 3 , 28 ]. Complex interventions are often reliant on the engagement of practitioners and patients, so their attitudes, behaviours, beliefs and cultures influence whether and how an intervention is effective or not. Interventions are context-sensitive; they interact with the environment in which they are implemented. In fact, many argue that interventions are a product of their context, and indeed, outcomes are likely to be a product of the intervention and its context [ 3 , 29 ]. Within a trial, there is also the influence of the research context too—so the observed outcome could be due to the intervention alone, elements of the context within which the intervention is being delivered, elements of the research process or a combination of all three. Therefore, it can be difficult and unhelpful to separate the intervention from the context within which it was evaluated because the intervention and context are likely to have evolved together over time. As a result, the same intervention can look and behave differently in different contexts, so it is important this is known, understood and reported [ 3 ]. Finally, the intervention context is dynamic; the people, organisations and systems change over time, [ 3 ] which requires practitioners and patients to respond, and they may do this by adapting the intervention or contextual factors. So, to enable researchers to replicate successful interventions, or to explain why the intervention was not successful, it is not enough to describe the components of the intervention, they need to be described by their relationship to their context and resources [ 3 , 28 ].

What is a case study?

Case study methodology aims to provide an in-depth, holistic, balanced, detailed and complete picture of complex contemporary phenomena in its natural context [ 8 , 9 , 20 ]. In this case, the phenomena are the implementation of complex interventions in a trial. Case study methodology takes the view that the phenomena can be more than the sum of their parts and have to be understood as a whole [ 30 ]. It is differentiated from a clinical case study by its analytical focus [ 20 ].

The methodology is particularly useful when linked to trials because some of the features of the design naturally fill the gaps in knowledge generated by trials. Given the methodological focus on understanding phenomena in the round, case study methodology is typified by the use of multiple sources of data, which are more commonly qualitatively guided [ 31 ]. The case study methodology is not epistemologically specific, like realist evaluation, and can be used with different epistemologies [ 32 ], and with different theories, such as Normalisation Process Theory (which explores how staff work together to implement a new intervention) or the Consolidated Framework for Implementation Research (which provides a menu of constructs associated with effective implementation) [ 33 , 34 , 35 ]. Realist evaluation can be used to explore the relationship between context, mechanism and outcome, but case study differs from realist evaluation by its focus on a holistic and in-depth understanding of the relationship between an intervention and the contemporary context in which it was implemented [ 36 ]. Case study enables researchers to choose epistemologies and theories which suit the nature of the enquiry and their theoretical preferences.

Designing a process evaluation using case study

An important part of any study is the research design. Due to their varied philosophical positions, the seminal authors in the field of case study have different epistemic views as to how a case study should be conducted [ 8 , 9 ]. Stake takes an interpretative approach (interested in how people make sense of their world), and Yin has more positivistic leanings, arguing for objectivity, validity and generalisability [ 8 , 9 ].

Regardless of the philosophical background, a well-designed process evaluation using case study should consider the following core components: the purpose; the definition of the intervention, the trial design, the case, and the theories or logic models underpinning the intervention; the sampling approach; and the conceptual or theoretical framework [ 8 , 9 , 20 , 31 , 33 ]. We now discuss these critical components in turn, with reference to two process evaluations that used case study design, the DQIP and OPAL studies [ 21 , 37 , 38 , 39 , 40 , 41 ].

The purpose of a process evaluation is to evaluate and explain the relationship between the intervention and its components, to context and outcome. It can help inform judgements about validity (by exploring the intervention components and their relationship with one another (construct validity), the connections between intervention and outcomes (internal validity) and the relationship between intervention and context (external validity)). It can also distinguish between implementation failure (where the intervention is poorly delivered) and intervention failure (intervention design is flawed) [ 42 , 43 ]. By using a case study to explicitly understand the relationship between context and the intervention during implementation, the process evaluation can explain the intervention effects and the potential generalisability and optimisation into routine practice [ 44 ].

The DQIP process evaluation aimed to qualitatively explore how patients and GP practices responded to an intervention designed to reduce high-risk prescribing of nonsteroidal anti-inflammatory drugs (NSAIDs) and/or antiplatelet agents (see Table  1 ) and quantitatively examine how change in high-risk prescribing was associated with practice characteristics and implementation processes. The OPAL process evaluation (see Table  2 ) aimed to quantitatively understand the factors which influenced the effectiveness of a pelvic floor muscle training intervention for women with urinary incontinence and qualitatively explore the participants’ experiences of treatment and adherence.

Defining the intervention and exploring the theories or assumptions underpinning the intervention design

Process evaluations should also explore the utility of the theories or assumptions underpinning intervention design [ 49 ]. Not all theories underpinning interventions are based on a formal theory, but they based on assumptions as to how the intervention is expected to work. These can be depicted as a logic model or theory of change [ 25 ]. To capture how the intervention and context evolve requires the intervention and its expected mechanisms to be clearly defined at the outset [ 50 ]. Hawe and colleagues recommend defining interventions by function (what processes make the intervention work) rather than form (what is delivered) [ 51 ]. However, in some cases, it may be useful to know if some of the components are redundant in certain contexts or if there is a synergistic effect between all the intervention components.

The DQIP trial delivered two interventions, one intervention was delivered to professionals with high fidelity and then professionals delivered the other intervention to patients by form rather than function allowing adaptations to the local context as appropriate. The assumptions underpinning intervention delivery were prespecified in a logic model published in the process evaluation protocol [ 52 ].

Case study is well placed to challenge or reinforce the theoretical assumptions or redefine these based on the relationship between the intervention and context. Yin advocates the use of theoretical propositions; these direct attention to specific aspects of the study for investigation [ 8 ] can be based on the underlying assumptions and tested during the course of the process evaluation. In case studies, using an epistemic position more aligned with Yin can enable research questions to be designed, which seek to expose patterns of unanticipated as well as expected relationships [ 9 ]. The OPAL trial was more closely aligned with Yin, where the research team predefined some of their theoretical assumptions, based on how the intervention was expected to work. The relevant parts of the data analysis then drew on data to support or refute the theoretical propositions. This was particularly useful for the trial as the prespecified theoretical propositions linked to the mechanisms of action on which the intervention was anticipated to have an effect (or not).

Tailoring to the trial design

Process evaluations need to be tailored to the trial, the intervention and the outcomes being measured [ 45 ]. For example, in a stepped wedge design (where the intervention is delivered in a phased manner), researchers should try to ensure process data are captured at relevant time points or in a two-arm or multiple arm trial, ensure data is collected from the control group(s) as well as the intervention group(s). In the DQIP trial, a stepped wedge trial, at least one process evaluation case, was sampled per cohort. Trials often continue to measure outcomes after delivery of the intervention has ceased, so researchers should also consider capturing ‘follow-up’ data on contextual factors, which may continue to influence the outcome measure. The OPAL trial had two active treatment arms so collected process data from both arms. In addition, as the trial was interested in long-term adherence, the trial and the process evaluation collected data from participants for 2 years after the intervention was initially delivered, providing 24 months follow-up data, in line with the primary outcome for the trial.

Defining the case

Case studies can include single or multiple cases in their design. Single case studies usually sample typical or unique cases, their advantage being the depth and richness that can be achieved over a long period of time. The advantages of multiple case study design are that cases can be compared to generate a greater depth of analysis. Multiple case study sampling may be carried out in order to test for replication or contradiction [ 8 ]. Given that trials are often conducted over a number of sites, a multiple case study design is more sensible for process evaluations, as there is likely to be variation in implementation between sites. Case definition may occur at a variety of levels but is most appropriate if it reflects the trial design. For example, a case in an individual patient level trial is likely to be defined as a person/patient (e.g. a woman with urinary incontinence—OPAL trial) whereas in a cluster trial, a case is like to be a cluster, such as an organisation (e.g. a general practice—DQIP trial). Of course, the process evaluation could explore cases with less distinct boundaries, such as communities or relationships; however, the clarity with which these cases are defined is important, in order to scope the nature of the data that will be generated.

Carefully sampled cases are critical to a good case study as sampling helps inform the quality of the inferences that can be made from the data [ 53 ]. In both qualitative and quantitative research, how and how many participants to sample must be decided when planning the study. Quantitative sampling techniques generally aim to achieve a random sample. Qualitative research generally uses purposive samples to achieve data saturation, occurring when the incoming data produces little or no new information to address the research questions. The term data saturation has evolved from theoretical saturation in conventional grounded theory studies; however, its relevance to other types of studies is contentious as the term saturation seems to be widely used but poorly justified [ 54 ]. Empirical evidence suggests that for in-depth interview studies, saturation occurs at 12 interviews for thematic saturation, but typically more would be needed for a heterogenous sample higher degrees of saturation [ 55 , 56 ]. Both DQIP and OPAL case studies were huge with OPAL designed to interview each of the 40 individual cases four times and DQIP designed to interview the lead DQIP general practitioner (GP) twice (to capture change over time), another GP and the practice manager from each of the 10 organisational cases. Despite the plethora of mixed methods research textbooks, there is very little about sampling as discussions typically link to method (e.g. interviews) rather than paradigm (e.g. case study).

Purposive sampling can improve the generalisability of the process evaluation by sampling for greater contextual diversity. The typical or average case is often not the richest source of information. Outliers can often reveal more important insights, because they may reflect the implementation of the intervention using different processes. Cases can be selected from a number of criteria, which are not mutually exclusive, to enable a rich and detailed picture to be built across sites [ 53 ]. To avoid the Hawthorne effect, it is recommended that process evaluations sample from both intervention and control sites, which enables comparison and explanation. There is always a trade-off between breadth and depth in sampling, so it is important to note that often quantity does not mean quality and that carefully sampled cases can provide powerful illustrative examples of how the intervention worked in practice, the relationship between the intervention and context and how and why they evolved together. The qualitative components of both DQIP and OPAL process evaluations aimed for maximum variation sampling. Please see Table  1 for further information on how DQIP’s sampling frame was important for providing contextual information on processes influencing effective implementation of the intervention.

Conceptual and theoretical framework

A conceptual or theoretical framework helps to frame data collection and analysis [ 57 ]. Theories can also underpin propositions, which can be tested in the process evaluation. Process evaluations produce intervention-dependent knowledge, and theories help make the research findings more generalizable by providing a common language [ 16 ]. There are a number of mid-range theories which have been designed to be used with process evaluation [ 34 , 35 , 58 ]. The choice of the appropriate conceptual or theoretical framework is, however, dependent on the philosophical and professional background of the research. The two examples within this paper used our own framework for the design of process evaluations, which proposes a number of candidate processes which can be explored, for example, recruitment, delivery, response, maintenance and context [ 45 ]. This framework was published before the MRC guidance on process evaluations, and both the DQIP and OPAL process evaluations were designed before the MRC guidance was published. The DQIP process evaluation explored all candidates in the framework whereas the OPAL process evaluation selected four candidates, illustrating that process evaluations can be selective in what they explore based on the purpose, research questions and resources. Furthermore, as Kislov and colleagues argue, we also have a responsibility to critique the theoretical framework underpinning the evaluation and refine theories to advance knowledge [ 59 ].

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.

Availability of data and materials

No data and materials were used.

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

Blencowe NB. Systematic review of intervention design and delivery in pragmatic and explanatory surgical randomized clinical trials. Br J Surg. 2015;102:1037–47.

Article   CAS   PubMed   Google Scholar  

Dixon-Woods M. The problem of context in quality improvement. In: Foundation TH, editor. Perspectives on context: The Health Foundation; 2014.

Wells M, Williams B, Treweek S, Coyle J, Taylor J. Intervention description is not enough: evidence from an in-depth multiple case study on the untold role and impact of context in randomised controlled trials of seven complex interventions. Trials. 2012;13(1):95.

Article   PubMed   PubMed Central   Google Scholar  

Grant A, Sullivan F, Dowell J. An ethnographic exploration of influences on prescribing in general practice: why is there variation in prescribing practices? Implement Sci. 2013;8(1):72.

Lang ES, Wyer PC, Haynes RB. Knowledge translation: closing the evidence-to-practice gap. Ann Emerg Med. 2007;49(3):355–63.

Article   PubMed   Google Scholar  

Ward V, House AF, Hamer S. Developing a framework for transferring knowledge into action: a thematic analysis of the literature. J Health Serv Res Policy. 2009;14(3):156–64.

Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510–20.

Yin R. Case study research and applications: design and methods. Los Angeles: Sage Publications Inc; 2018.

Google Scholar  

Stake R. The art of case study research. Thousand Oaks, California: Sage Publications Ltd; 1995.

Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, Moore L, O’Cathain A, Tinati T, Wight D, et al. Process evaluation of complex interventions: Medical Research Council guidance. Br Med J. 2015;350.

Hawe P. Minimal, negligible and negligent interventions. Soc Sci Med. 2015;138:265–8.

Moore GF, Evans RE, Hawkins J, Littlecott H, Melendez-Torres GJ, Bonell C, Murphy S. From complex social interventions to interventions in complex social systems: future directions and unresolved questions for intervention development and evaluation. Evaluation. 2018;25(1):23–45.

Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. BMC Med. 2018;16(1):95.

Rutter H, Savona N, Glonti K, Bibby J, Cummins S, Finegood DT, Greaves F, Harper L, Hawe P, Moore L, et al. The need for a complex systems model of evidence for public health. Lancet. 2017;390(10112):2602–4.

Moore G, Cambon L, Michie S, Arwidson P, Ninot G, Ferron C, Potvin L, Kellou N, Charlesworth J, Alla F, et al. Population health intervention research: the place of theories. Trials. 2019;20(1):285.

Kislov R. Engaging with theory: from theoretically informed to theoretically informative improvement research. BMJ Qual Saf. 2019;28(3):177–9.

Boulton R, Sandall J, Sevdalis N. The cultural politics of ‘Implementation Science’. J Med Human. 2020;41(3):379-94. h https://doi.org/10.1007/s10912-020-09607-9 .

Cheng KKF, Metcalfe A. Qualitative methods and process evaluation in clinical trials context: where to head to? Int J Qual Methods. 2018;17(1):1609406918774212.

Article   Google Scholar  

Richards DA, Bazeley P, Borglin G, Craig P, Emsley R, Frost J, Hill J, Horwood J, Hutchings HA, Jinks C, et al. Integrating quantitative and qualitative data and findings when undertaking randomised controlled trials. BMJ Open. 2019;9(11):e032081.

Thomas G. How to do your case study, 2nd edition edn. London: Sage Publications Ltd; 2016.

Grant A, Dreischulte T, Guthrie B. Process evaluation of the Data-driven Quality Improvement in Primary Care (DQIP) trial: case study evaluation of adoption and maintenance of a complex intervention to reduce high-risk primary care prescribing. BMJ Open. 2017;7(3).

Pfadenhauer L, Rohwer A, Burns J, Booth A, Lysdahl KB, Hofmann B, Gerhardus A, Mozygemba K, Tummers M, Wahlster P, et al. Guidance for the assessment of context and implementation in health technology assessments (HTA) and systematic reviews of complex interventions: the Context and Implementation of Complex Interventions (CICI) framework: Integrate-HTA; 2016.

Bate P, Robert G, Fulop N, Ovretveit J, Dixon-Woods M. Perspectives on context. London: The Health Foundation; 2014.

Ovretveit J. Understanding the conditions for improvement: research to discover which context influences affect improvement success. BMJ Qual Saf. 2011;20.

Medical Research Council: Process evaluation of complex interventions: UK Medical Research Council (MRC) guidance. 2015.

May CR, Johnson M, Finch T. Implementation, context and complexity. Implement Sci. 2016;11(1):141.

Bate P. Context is everything. In: Perpesctives on Context. The Health Foundation 2014.

Horton TJ, Illingworth JH, Warburton WHP. Overcoming challenges in codifying and replicating complex health care interventions. Health Aff. 2018;37(2):191–7.

O'Connor AM, Tugwell P, Wells GA, Elmslie T, Jolly E, Hollingworth G, McPherson R, Bunn H, Graham I, Drake E. A decision aid for women considering hormone therapy after menopause: decision support framework and evaluation. Patient Educ Couns. 1998;33:267–79.

Creswell J, Poth C. Qualiative inquiry and research design, fourth edition edn. Thousan Oaks, California: Sage Publications; 2018.

Carolan CM, Forbat L, Smith A. Developing the DESCARTE model: the design of case study research in health care. Qual Health Res. 2016;26(5):626–39.

Takahashi ARW, Araujo L. Case study research: opening up research opportunities. RAUSP Manage J. 2020;55(1):100–11.

Tight M. Understanding case study research, small-scale research with meaning. London: Sage Publications; 2017.

May C, Finch T. Implementing, embedding, and integrating practices: an outline of normalisation process theory. Sociology. 2009;43:535.

Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice. A consolidated framework for advancing implementation science. Implement Sci. 2009;4.

Pawson R, Tilley N. Realist evaluation. London: Sage; 1997.

Dreischulte T, Donnan P, Grant A, Hapca A, McCowan C, Guthrie B. Safer prescribing - a trial of education, informatics & financial incentives. N Engl J Med. 2016;374:1053–64.

Grant A, Dreischulte T, Guthrie B. Process evaluation of the Data-driven Quality Improvement in Primary Care (DQIP) trial: active and less active ingredients of a multi-component complex intervention to reduce high-risk primary care prescribing. Implement Sci. 2017;12(1):4.

Dreischulte T, Grant A, Hapca A, Guthrie B. Process evaluation of the Data-driven Quality Improvement in Primary Care (DQIP) trial: quantitative examination of variation between practices in recruitment, implementation and effectiveness. BMJ Open. 2018;8(1):e017133.

Grant A, Dean S, Hay-Smith J, Hagen S, McClurg D, Taylor A, Kovandzic M, Bugge C. Effectiveness and cost-effectiveness randomised controlled trial of basic versus biofeedback-mediated intensive pelvic floor muscle training for female stress or mixed urinary incontinence: protocol for the OPAL (Optimising Pelvic Floor Exercises to Achieve Long-term benefits) trial mixed methods longitudinal qualitative case study and process evaluation. BMJ Open. 2019;9(2):e024152.

Hagen S, McClurg D, Bugge C, Hay-Smith J, Dean SG, Elders A, Glazener C, Abdel-fattah M, Agur WI, Booth J, et al. Effectiveness and cost-effectiveness of basic versus biofeedback-mediated intensive pelvic floor muscle training for female stress or mixed urinary incontinence: protocol for the OPAL randomised trial. BMJ Open. 2019;9(2):e024153.

Steckler A, Linnan L. Process evaluation for public health interventions and research; 2002.

Durlak JA. Why programme implementation is so important. J Prev Intervent Commun. 1998;17(2):5–18.

Bonell C, Oakley A, Hargreaves J, VS, Rees R. Assessment of generalisability in trials of health interventions: suggested framework and systematic review. Br Med J. 2006;333(7563):346–9.

Article   CAS   Google Scholar  

Grant A, Treweek S, Dreischulte T, Foy R, Guthrie B. Process evaluations for cluster-randomised trials of complex interventions: a proposed framework for design and reporting. Trials. 2013;14(1):15.

Yin R. Case study research: design and methods. London: Sage Publications; 2003.

Bugge C, Hay-Smith J, Grant A, Taylor A, Hagen S, McClurg D, Dean S: A 24 month longitudinal qualitative study of women’s experience of electromyography biofeedback pelvic floor muscle training (PFMT) and PFMT alone for urinary incontinence: adherence, outcome and context. ICS Gothenburg 2019 2019. https://www.ics.org/2019/abstract/473 . Access 10.9.2020.

Suzanne Hagen, Andrew Elders, Susan Stratton, Nicole Sergenson, Carol Bugge, Sarah Dean, Jean Hay-Smith, Mary Kilonzo, Maria Dimitrova, Mohamed Abdel-Fattah, Wael Agur, Jo Booth, Cathryn Glazener, Karen Guerrero, Alison McDonald, John Norrie, Louise R Williams, Doreen McClurg. Effectiveness of pelvic floor muscle training with and without electromyographic biofeedback for urinary incontinence in women: multicentre randomised controlled trial BMJ 2020;371. https://doi.org/10.1136/bmj.m3719 .

Cook TD. Emergent principles for the design, implementation, and analysis of cluster-based experiments in social science. Ann Am Acad Pol Soc Sci. 2005;599(1):176–98.

Hoffmann T, Glasziou P, Boutron I, Milne R, Perera R, Moher D. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. Br Med J. 2014;348.

Hawe P, Shiell A, Riley T. Complex interventions: how “out of control” can a randomised controlled trial be? Br Med J. 2004;328(7455):1561–3.

Grant A, Dreischulte T, Treweek S, Guthrie B. Study protocol of a mixed-methods evaluation of a cluster randomised trial to improve the safety of NSAID and antiplatelet prescribing: Data-driven Quality Improvement in Primary Care. Trials. 2012;13:154.

Flyvbjerg B. Five misunderstandings about case-study research. Qual Inq. 2006;12(2):219–45.

Thorne S. The great saturation debate: what the “S word” means and doesn’t mean in qualitative research reporting. Can J Nurs Res. 2020;52(1):3–5.

Guest G, Bunce A, Johnson L. How many interviews are enough?: an experiment with data saturation and variability. Field Methods. 2006;18(1):59–82.

Guest G, Namey E, Chen M. A simple method to assess and report thematic saturation in qualitative research. PLoS One. 2020;15(5):e0232076.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Davidoff F, Dixon-Woods M, Leviton L, Michie S. Demystifying theory and its use in improvement. BMJ Qual Saf. 2015;24(3):228–38.

Rycroft-Malone J. The PARIHS framework: a framework for guiding the implementation of evidence-based practice. J Nurs Care Qual. 2004;4:297-304.

Kislov R, Pope C, Martin GP, Wilson PM. Harnessing the power of theorising in implementation science. Implement Sci. 2019;14(1):103.

Cresswell JW, Plano Clark VL. Designing and conducting mixed methods research. Thousand Oaks: Sage Publications Ltd; 2007.

Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. Am J Community Psychol. 2009;43:267–76.

Craig P, Ruggiero E, Frohlich KL, Mykhalovskiy E, White M. Taking account of context in population health intervention research: guidance for producers, users and funders of research: National Institute for Health Research; 2018. https://www.ncbi.nlm.nih.gov/books/NBK498645/pdf/Bookshelf_NBK498645.pdf .

Download references

Acknowledgements

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

No funding was received for this work.

Author information

Authors and affiliations.

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

Aileen Grant

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

Carol Bugge

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

You can also search for this author in PubMed   Google Scholar

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.

Corresponding author

Correspondence to Aileen Grant .

Ethics declarations

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.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Cite this article.

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

Download citation

Received : 09 April 2020

Accepted : 06 November 2020

Published : 27 November 2020

DOI : https://doi.org/10.1186/s13063-020-04880-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Process evaluation
• Case study design

ISSN: 1745-6215

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• 15.7 Evaluation: Presentation and Analysis of Case Study
• 1 Unit Introduction
• Introduction
• 1.1 "Reading" to Understand and Respond
• 1.2 Social Media Trailblazer: Selena Gomez
• 1.3 Glance at Critical Response: Rhetoric and Critical Thinking
• 1.4 Annotated Student Sample: Social Media Post and Responses on Voter Suppression
• 1.5 Writing Process: Thinking Critically About a “Text”
• 1.6 Evaluation: Intention vs. Execution
• 1.7 Spotlight on … Academia
• 1.8 Portfolio: Tracing Writing Development
• Further Reading
• Works Cited
• 2.1 Seeds of Self
• 2.2 Identity Trailblazer: Cathy Park Hong
• 2.3 Glance at the Issues: Oppression and Reclamation
• 2.4 Annotated Sample Reading from The Souls of Black Folk by W. E. B. Du Bois
• 2.5 Writing Process: Thinking Critically about How Identity Is Constructed Through Writing
• 2.6 Evaluation: Antiracism and Inclusivity
• 2.7 Spotlight on … Variations of English
• 2.8 Portfolio: Decolonizing Self
• 3.1 Identity and Expression
• 3.2 Literacy Narrative Trailblazer: Tara Westover
• 3.3 Glance at Genre: The Literacy Narrative
• 3.4 Annotated Sample Reading: from Narrative of the Life of Frederick Douglass by Frederick Douglass
• 3.5 Writing Process: Tracing the Beginnings of Literacy
• 3.6 Editing Focus: Sentence Structure
• 3.7 Evaluation: Self-Evaluating
• 3.8 Spotlight on … The Digital Archive of Literacy Narratives (DALN)
• 3.9 Portfolio: A Literacy Artifact
• Works Consulted
• 2 Unit Introduction
• 4.1 Exploring the Past to Understand the Present
• 4.2 Memoir Trailblazer: Ta-Nehisi Coates
• 4.3 Glance at Genre: Conflict, Detail, and Revelation
• 4.4 Annotated Sample Reading: from Life on the Mississippi by Mark Twain
• 4.5 Writing Process: Making the Personal Public
• 4.6 Editing Focus: More on Characterization and Point of View
• 4.7 Evaluation: Structure and Organization
• 4.8 Spotlight on … Multilingual Writers
• 4.9 Portfolio: Filtered Memories
• 5.1 Profiles as Inspiration
• 5.2 Profile Trailblazer: Veronica Chambers
• 5.3 Glance at Genre: Subject, Angle, Background, and Description
• 5.4 Annotated Sample Reading: “Remembering John Lewis” by Carla D. Hayden
• 5.5 Writing Process: Focusing on the Angle of Your Subject
• 5.6 Editing Focus: Verb Tense Consistency
• 5.7 Evaluation: Text as Personal Introduction
• 5.8 Spotlight on … Profiling a Cultural Artifact
• 5.9 Portfolio: Subject as a Reflection of Self
• 6.1 Proposing Change: Thinking Critically About Problems and Solutions
• 6.2 Proposal Trailblazer: Atul Gawande
• 6.3 Glance at Genre: Features of Proposals
• 6.4 Annotated Student Sample: “Slowing Climate Change” by Shawn Krukowski
• 6.5 Writing Process: Creating a Proposal
• 6.6 Editing Focus: Subject-Verb Agreement
• 6.7 Evaluation: Conventions, Clarity, and Coherence
• 6.8 Spotlight on … Technical Writing as a Career
• 6.9 Portfolio: Reflecting on Problems and Solutions
• 7.1 Thumbs Up or Down?
• 7.2 Review Trailblazer: Michiko Kakutani
• 7.3 Glance at Genre: Criteria, Evidence, Evaluation
• 7.4 Annotated Student Sample: "Black Representation in Film" by Caelia Marshall
• 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.

As an Amazon Associate we earn from qualifying purchases.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

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

© Apr 5, 2023 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Do Your Students Know How to Analyze a Case—Really?

Explore more.

• Case Teaching
• Student Engagement

J ust as actors, athletes, and musicians spend thousands of hours practicing their craft, business students benefit from practicing their critical-thinking and decision-making skills. Students, however, often have limited exposure to real-world problem-solving scenarios; they need more opportunities to practice tackling tough business problems and deciding on—and executing—the best solutions.

To ensure students have ample opportunity to develop these critical-thinking and decision-making skills, we believe business faculty should shift from teaching mostly principles and ideas to mostly applications and practices. And in doing so, they should emphasize the case method, which simulates real-world management challenges and opportunities for students.

To help educators facilitate this shift and help students get the most out of case-based learning, we have developed a framework for analyzing cases. We call it PACADI (Problem, Alternatives, Criteria, Analysis, Decision, Implementation); it can improve learning outcomes by helping students better solve and analyze business problems, make decisions, and develop and implement strategy. Here, we’ll explain why we developed this framework, how it works, and what makes it an effective learning tool.

The Case for Cases: Helping Students Think Critically

Business students must develop critical-thinking and analytical skills, which are essential to their ability to make good decisions in functional areas such as marketing, finance, operations, and information technology, as well as to understand the relationships among these functions. For example, the decisions a marketing manager must make include strategic planning (segments, products, and channels); execution (digital messaging, media, branding, budgets, and pricing); and operations (integrated communications and technologies), as well as how to implement decisions across functional areas.

Faculty can use many types of cases to help students develop these skills. These include the prototypical “paper cases”; live cases , which feature guest lecturers such as entrepreneurs or corporate leaders and on-site visits; and multimedia cases , which immerse students into real situations. Most cases feature an explicit or implicit decision that a protagonist—whether it is an individual, a group, or an organization—must make.

For students new to learning by the case method—and even for those with case experience—some common issues can emerge; these issues can sometimes be a barrier for educators looking to ensure the best possible outcomes in their case classrooms. Unsure of how to dig into case analysis on their own, students may turn to the internet or rely on former students for “answers” to assigned cases. Or, when assigned to provide answers to assignment questions in teams, students might take a divide-and-conquer approach but not take the time to regroup and provide answers that are consistent with one other.

To help address these issues, which we commonly experienced in our classes, we wanted to provide our students with a more structured approach for how they analyze cases—and to really think about making decisions from the protagonists’ point of view. We developed the PACADI framework to address this need.

PACADI: A Six-Step Decision-Making Approach

The PACADI framework is a six-step decision-making approach that can be used in lieu of traditional end-of-case questions. It offers a structured, integrated, and iterative process that requires students to analyze case information, apply business concepts to derive valuable insights, and develop recommendations based on these insights.

Prior to beginning a PACADI assessment, which we’ll outline here, students should first prepare a two-paragraph summary—a situation analysis—that highlights the key case facts. Then, we task students with providing a five-page PACADI case analysis (excluding appendices) based on the following six steps.

Step 1: Problem definition. What is the major challenge, problem, opportunity, or decision that has to be made? If there is more than one problem, choose the most important one. Often when solving the key problem, other issues will surface and be addressed. The problem statement may be framed as a question; for example, How can brand X improve market share among millennials in Canada? Usually the problem statement has to be re-written several times during the analysis of a case as students peel back the layers of symptoms or causation.

Step 2: Alternatives. Identify in detail the strategic alternatives to address the problem; three to five options generally work best. Alternatives should be mutually exclusive, realistic, creative, and feasible given the constraints of the situation. Doing nothing or delaying the decision to a later date are not considered acceptable alternatives.

Step 3: Criteria. What are the key decision criteria that will guide decision-making? In a marketing course, for example, these may include relevant marketing criteria such as segmentation, positioning, advertising and sales, distribution, and pricing. Financial criteria useful in evaluating the alternatives should be included—for example, income statement variables, customer lifetime value, payback, etc. Students must discuss their rationale for selecting the decision criteria and the weights and importance for each factor.

Step 4: Analysis. Provide an in-depth analysis of each alternative based on the criteria chosen in step three. Decision tables using criteria as columns and alternatives as rows can be helpful. The pros and cons of the various choices as well as the short- and long-term implications of each may be evaluated. Best, worst, and most likely scenarios can also be insightful.

Step 5: Decision. Students propose their solution to the problem. This decision is justified based on an in-depth analysis. Explain why the recommendation made is the best fit for the criteria.

Step 6: Implementation plan. Sound business decisions may fail due to poor execution. To enhance the likeliness of a successful project outcome, students describe the key steps (activities) to implement the recommendation, timetable, projected costs, expected competitive reaction, success metrics, and risks in the plan.

“Students note that using the PACADI framework yields ‘aha moments’—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.”

PACADI’s Benefits: Meaningfully and Thoughtfully Applying Business Concepts

The PACADI framework covers all of the major elements of business decision-making, including implementation, which is often overlooked. By stepping through the whole framework, students apply relevant business concepts and solve management problems via a systematic, comprehensive approach; they’re far less likely to surface piecemeal responses.

As students explore each part of the framework, they may realize that they need to make changes to a previous step. For instance, when working on implementation, students may realize that the alternative they selected cannot be executed or will not be profitable, and thus need to rethink their decision. Or, they may discover that the criteria need to be revised since the list of decision factors they identified is incomplete (for example, the factors may explain key marketing concerns but fail to address relevant financial considerations) or is unrealistic (for example, they suggest a 25 percent increase in revenues without proposing an increased promotional budget).

In addition, the PACADI framework can be used alongside quantitative assignments, in-class exercises, and business and management simulations. The structured, multi-step decision framework encourages careful and sequential analysis to solve business problems. Incorporating PACADI as an overarching decision-making method across different projects will ultimately help students achieve desired learning outcomes. As a practical “beyond-the-classroom” tool, the PACADI framework is not a contrived course assignment; it reflects the decision-making approach that managers, executives, and entrepreneurs exercise daily. Case analysis introduces students to the real-world process of making business decisions quickly and correctly, often with limited information. This framework supplies an organized and disciplined process that students can readily defend in writing and in class discussions.

PACADI in Action: An Example

Here’s an example of how students used the PACADI framework for a recent case analysis on CVS, a large North American drugstore chain.

The CVS Prescription for Customer Value*

PACADI Stage

Summary Response

How should CVS Health evolve from the “drugstore of your neighborhood” to the “drugstore of your future”?

Alternatives

A1. Kaizen (continuous improvement)

A2. Product development

A3. Market development

A4. Personalization (micro-targeting)

Criteria (include weights)

C1. Customer value: service, quality, image, and price (40%)

C2. Customer obsession (20%)

C3. Growth through related businesses (20%)

C4. Customer retention and customer lifetime value (20%)

Each alternative was analyzed by each criterion using a Customer Value Assessment Tool

Alternative 4 (A4): Personalization was selected. This is operationalized via: segmentation—move toward segment-of-1 marketing; geodemographics and lifestyle emphasis; predictive data analysis; relationship marketing; people, principles, and supply chain management; and exceptional customer service.

Implementation

Partner with leading medical school

Curbside pick-up

Pet pharmacy

E-newsletter for customers and employees

Employee incentive program

CVS beauty days

Expand to Latin America and Caribbean

Healthier/happier corner

Holiday toy drives/community outreach

*Source: A. Weinstein, Y. Rodriguez, K. Sims, R. Vergara, “The CVS Prescription for Superior Customer Value—A Case Study,” Back to the Future: Revisiting the Foundations of Marketing from Society for Marketing Advances, West Palm Beach, FL (November 2, 2018).

Results of Using the PACADI Framework

When faculty members at our respective institutions at Nova Southeastern University (NSU) and the University of North Carolina Wilmington have used the PACADI framework, our classes have been more structured and engaging. Students vigorously debate each element of their decision and note that this framework yields an “aha moment”—they learned something surprising in the case that led them to think differently about the problem and their proposed solution.

These lively discussions enhance individual and collective learning. As one external metric of this improvement, we have observed a 2.5 percent increase in student case grade performance at NSU since this framework was introduced.

Tips to Get Started

The PACADI approach works well in in-person, online, and hybrid courses. This is particularly important as more universities have moved to remote learning options. Because students have varied educational and cultural backgrounds, work experience, and familiarity with case analysis, we recommend that faculty members have students work on their first case using this new framework in small teams (two or three students). Additional analyses should then be solo efforts.

To use PACADI effectively in your classroom, we suggest the following:

Advise your students that your course will stress critical thinking and decision-making skills, not just course concepts and theory.

Use a varied mix of case studies. As marketing professors, we often address consumer and business markets; goods, services, and digital commerce; domestic and global business; and small and large companies in a single MBA course.

As a starting point, provide a short explanation (about 20 to 30 minutes) of the PACADI framework with a focus on the conceptual elements. You can deliver this face to face or through videoconferencing.

Give students an opportunity to practice the case analysis methodology via an ungraded sample case study. Designate groups of five to seven students to discuss the case and the six steps in breakout sessions (in class or via Zoom).

Ensure case analyses are weighted heavily as a grading component. We suggest 30–50 percent of the overall course grade.

Once cases are graded, debrief with the class on what they did right and areas needing improvement (30- to 40-minute in-person or Zoom session).

Encourage faculty teams that teach common courses to build appropriate instructional materials, grading rubrics, videos, sample cases, and teaching notes.

When selecting case studies, we have found that the best ones for PACADI analyses are about 15 pages long and revolve around a focal management decision. This length provides adequate depth yet is not protracted. Some of our tested and favorite marketing cases include Brand W , Hubspot , Kraft Foods Canada , TRSB(A) , and Whiskey & Cheddar .

Art Weinstein , Ph.D., is a professor of marketing at Nova Southeastern University, Fort Lauderdale, Florida. He has published more than 80 scholarly articles and papers and eight books on customer-focused marketing strategy. His latest book is Superior Customer Value—Finding and Keeping Customers in the Now Economy . Dr. Weinstein has consulted for many leading technology and service companies.

Herbert V. Brotspies , D.B.A., is an adjunct professor of marketing at Nova Southeastern University. He has over 30 years’ experience as a vice president in marketing, strategic planning, and acquisitions for Fortune 50 consumer products companies working in the United States and internationally. His research interests include return on marketing investment, consumer behavior, business-to-business strategy, and strategic planning.

John T. Gironda , Ph.D., is an assistant professor of marketing at the University of North Carolina Wilmington. His research has been published in Industrial Marketing Management, Psychology & Marketing , and Journal of Marketing Management . He has also presented at major marketing conferences including the American Marketing Association, Academy of Marketing Science, and Society for Marketing Advances.

Related Articles

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

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

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

Published on May 8, 2019 by Shona McCombes . Revised on June 22, 2023.

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

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

Table of contents

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

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

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

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

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

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

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

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

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

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

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

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

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

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

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

Prevent plagiarism. Run a free check.

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

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

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

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

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

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

Research bias

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved November 20, 2023, from https://www.scribbr.com/methodology/case-study/

Is this article helpful?

Shona McCombes

Other students also liked, primary vs. secondary sources | difference & examples, what is a theoretical framework | guide to organizing, what is action research | definition & examples, what is your plagiarism score.

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.

About the Editors

About the authors.

• Jill Russell
• Trisha Greenhalgh
• Saville Kushner

We’re listening — tell us what you think

Something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Join us after half-term for A-Level Strong Foundations workshops. Coming to Birmingham, Leeds, London and Manchester Learn more →

Reference Library

Collections

• See what's new
• All Resources
• Student Resources
• Assessment Resources
• Teaching Resources
• CPD Courses
• Livestreams

Study notes, videos, interactive activities and more!

Psychology news, insights and enrichment

Currated collections of free resources

Browse resources by topic

• All Psychology Resources

Resource Selections

Currated lists of resources

Study Notes

Case Studies

Last updated 22 Mar 2021

• Share on Facebook
• Share on Twitter
• Share by Email

Case studies are very detailed investigations of an individual or small group of people, usually regarding an unusual phenomenon or biographical event of interest to a research field. Due to a small sample, the case study can conduct an in-depth analysis of the individual/group.

Evaluation of case studies:

- Case studies create opportunities for a rich yield of data, and the depth of analysis can in turn bring high levels of validity (i.e. providing an accurate and exhaustive measure of what the study is hoping to measure).

- Studying abnormal psychology can give insight into how something works when it is functioning correctly, such as brain damage on memory (e.g. the case study of patient KF, whose short-term memory was impaired following a motorcycle accident but left his long-term memory intact, suggesting there might be separate physical stores in the brain for short and long-term memory).

- The detail collected on a single case may lead to interesting findings that conflict with current theories, and stimulate new paths for research.

- There is little control over a number of variables involved in a case study, so it is difficult to confidently establish any causal relationships between variables.

- Case studies are unusual by nature, so will have poor reliability as replicating them exactly will be unlikely.

- Due to the small sample size, it is unlikely that findings from a case study alone can be generalised to a whole population.

- The case study’s researcher may become so involved with the study that they exhibit bias in their interpretation and presentation of the data, making it challenging to distinguish what is truly objective/factual.

• Case Studies

You might also like

A level psychology topic quiz - research methods.

Quizzes & Activities

Case Studies: Example Answer Video for A Level SAM 3, Paper 1, Q4 (5 Marks)

Topic Videos

Research Methods: MCQ Revision Test 1 for AQA A Level Psychology

Example answers for research methods: a level psychology, paper 2, june 2018 (aqa).

Exam Support

Our subjects

• › Criminology
• › Economics
• › Geography
• › Health & Social Care
• › Psychology
• › Sociology
• › Teaching & learning resources
• › Student revision workshops
• › Online student courses
• › CPD for teachers
• › Livestreams
• › Teaching jobs

Boston House, 214 High Street, Boston Spa, West Yorkshire, LS23 6AD Tel: 01937 848885

• › Contact us
• › Terms of use
• › Privacy & cookies

© 2002-2023 Tutor2u Limited. Company Reg no: 04489574. VAT reg no 816865400.

Organizing Your Social Sciences Research Assignments

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

Definition and Introduction

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

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

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

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

How to Approach Writing a Case Analysis Paper

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

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

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

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

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

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

Structure and Writing Style

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

Introduction

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

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

Background Analysis

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

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

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

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

Identification of Problems

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

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

Alternative Solutions

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

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

Recommended Courses of Action

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

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

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

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

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

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

Problems to Avoid

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

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

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

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

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

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

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

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

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

• << Previous: Reviewing Collected Works
• Next: Writing a Case Study >>
• Last Updated: Sep 15, 2023 9:37 AM
• URL: https://libguides.usc.edu/writingguide/assignments

This website may not work correctly because your browser is out of date. Please update your browser .

Week 32: Better use of case studies in evaluation

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.

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?

Related content

'week 32: better use of case studies in evaluation' is referenced in:.

• Impact evaluation

Back to top

© 2022 BetterEvaluation. All right reserved.

• Case Study Evaluation Approach
• Career Center

A case study evaluation approach can be an incredibly powerful tool for monitoring and evaluating complex programs and policies. By identifying common themes and patterns, this approach allows us to better understand the successes and challenges faced by the program. In this article, we’ll explore the benefits of using a case study evaluation approach in the monitoring and evaluation of projects, programs, and public policies.

Table of Contents

Introduction to Case Study Evaluation Approach

The advantages of a case study evaluation approach, types of case studies, potential challenges with a case study evaluation approach, guiding principles for successful implementation of a case study evaluation approach.

• Benefits of Incorporating the Case Study Evaluation Approach in the Monitoring and Evaluation of Projects and Programs

A case study evaluation approach is a great way to gain an in-depth understanding of a particular issue or situation. This type of approach allows the researcher to observe, analyze, and assess the effects of a particular situation on individuals or groups.

An individual, a location, or a project may serve as the focal point of a case study’s attention. Quantitative and qualitative data are frequently used in conjunction with one another.

It also allows the researcher to gain insights into how people react to external influences. By using a case study evaluation approach, researchers can gain insights into how certain factors such as policy change or a new technology have impacted individuals and communities. The data gathered through this approach can be used to formulate effective strategies for responding to changes and challenges. Ultimately, this monitoring and evaluation approach helps organizations make better decision about the implementation of their plans.

This approach can be used to assess the effectiveness of a policy, program, or initiative by considering specific elements such as implementation processes, outcomes, and impact. A case study evaluation approach can provide an in-depth understanding of the effectiveness of a program by closely examining the processes involved in its implementation. This includes understanding the context, stakeholders, and resources to gain insight into how well a program is functioning or has been executed. By evaluating these elements, it can help to identify areas for improvement and suggest potential solutions. The findings from this approach can then be used to inform decisions about policies, programs, and initiatives for improved outcomes.

It is also useful for determining if other policies, programs, or initiatives could be applied to similar situations in order to achieve similar results or improved outcomes. All in all, the case study monitoring evaluation approach is an effective method for determining the effectiveness of specific policies, programs, or initiatives. By researching and analyzing the successes of previous cases, this approach can be used to identify similar approaches that could be applied to similar situations in order to achieve similar results or improved outcomes.

A case study evaluation approach offers the advantage of providing in-depth insight into a particular program or policy. This can be accomplished by analyzing data and observations collected from a range of stakeholders such as program participants, service providers, and community members. The monitoring and evaluation approach is used to assess the impact of programs and inform the decision-making process to ensure successful implementation. The case study monitoring and evaluation approach can help identify any underlying issues that need to be addressed in order to improve program effectiveness. It also provides a reality check on how successful programs are actually working, allowing organizations to make adjustments as needed. Overall, a case study monitoring and evaluation approach helps to ensure that policies and programs are achieving their objectives while providing valuable insight into how they are performing overall.

By taking a qualitative approach to data collection and analysis, case study evaluations are able to capture nuances in the context of a particular program or policy that can be overlooked when relying solely on quantitative methods. Using this approach, insights can be gleaned from looking at the individual experiences and perspectives of actors involved, providing a more detailed understanding of the impact of the program or policy than is possible with other evaluation methodologies. As such, case study monitoring evaluation is an invaluable tool in assessing the effectiveness of a particular initiative, enabling more informed decision-making as well as more effective implementation of programs and policies.

Furthermore, this approach is an effective way to uncover experiential information that can help to inform the ongoing improvement of policy and programming over time All in all, the case study monitoring evaluation approach offers an effective way to uncover experiential information necessary to inform the ongoing improvement of policy and programming. By analyzing the data gathered from this systematic approach, stakeholders can gain deeper insight into how best to make meaningful and long-term changes in their respective organizations.

Case studies come in a variety of forms, each of which can be put to a unique set of evaluation tasks. Evaluators have come to a consensus on describing six distinct sorts of case studies, which are as follows: illustrative, exploratory, critical instance, program implementation, program effects, and cumulative.

Illustrative Case Study

An illustrative case study is a type of case study that is used to provide a detailed and descriptive account of a particular event, situation, or phenomenon. It is often used in research to provide a clear understanding of a complex issue, and to illustrate the practical application of theories or concepts.

An illustrative case study typically uses qualitative data, such as interviews, surveys, or observations, to provide a detailed account of the unit being studied. The case study may also include quantitative data, such as statistics or numerical measurements, to provide additional context or to support the qualitative data.

The goal of an illustrative case study is to provide a rich and detailed description of the unit being studied, and to use this information to illustrate broader themes or concepts. For example, an illustrative case study of a successful community development project may be used to illustrate the importance of community engagement and collaboration in achieving development goals.

One of the strengths of an illustrative case study is its ability to provide a detailed and nuanced understanding of a particular issue or phenomenon. By focusing on a single case, the researcher is able to provide a detailed and in-depth analysis that may not be possible through other research methods.

However, one limitation of an illustrative case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a single unit, it may not be representative of other similar units or situations.

A well-executed case study can shed light on wider research topics or concepts through its thorough and descriptive analysis of a specific event or phenomenon.

Exploratory Case Study

An exploratory case study is a type of case study that is used to investigate a new or previously unexplored phenomenon or issue. It is often used in research when the topic is relatively unknown or when there is little existing literature on the topic.

Exploratory case studies are typically qualitative in nature and use a variety of methods to collect data, such as interviews, observations, and document analysis. The focus of the study is to gather as much information as possible about the phenomenon being studied and to identify new and emerging themes or patterns.

The goal of an exploratory case study is to provide a foundation for further research and to generate hypotheses about the phenomenon being studied. By exploring the topic in-depth, the researcher can identify new areas of research and generate new questions to guide future research.

One of the strengths of an exploratory case study is its ability to provide a rich and detailed understanding of a new or emerging phenomenon. By using a variety of data collection methods, the researcher can gather a broad range of data and perspectives to gain a more comprehensive understanding of the phenomenon being studied.

However, one limitation of an exploratory case study is that the findings may not be generalizable to other contexts or populations. Because the study is focused on a new or previously unexplored phenomenon, the findings may not be applicable to other situations or populations.

Exploratory case studies are an effective research strategy for learning about novel occurrences, developing research hypotheses, and gaining a deep familiarity with a topic of study.

Critical Instance Case Study

A critical instance case study is a type of case study that focuses on a specific event or situation that is critical to understanding a broader issue or phenomenon. The goal of a critical instance case study is to analyze the event in depth and to draw conclusions about the broader issue or phenomenon based on the analysis.

A critical instance case study typically uses qualitative data, such as interviews, observations, or document analysis, to provide a detailed and nuanced understanding of the event being studied. The data are analyzed using various methods, such as content analysis or thematic analysis, to identify patterns and themes that emerge from the data.

The critical instance case study is often used in research when a particular event or situation is critical to understanding a broader issue or phenomenon. For example, a critical instance case study of a successful disaster response effort may be used to identify key factors that contributed to the success of the response, and to draw conclusions about effective disaster response strategies more broadly.

One of the strengths of a critical instance case study is its ability to provide a detailed and in-depth analysis of a particular event or situation. By focusing on a critical instance, the researcher is able to provide a rich and nuanced understanding of the event, and to draw conclusions about broader issues or phenomena based on the analysis.

However, one limitation of a critical instance case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a specific event or situation, the findings may not be applicable to other similar events or situations.

A critical instance case study is a valuable research method that can provide a detailed and nuanced understanding of a particular event or situation and can be used to draw conclusions about broader issues or phenomena based on the analysis.

Program Implementation Program Implementation

A program implementation case study is a type of case study that focuses on the implementation of a particular program or intervention. The goal of the case study is to provide a detailed and comprehensive account of the program implementation process, and to identify factors that contributed to the success or failure of the program.

Program implementation case studies typically use qualitative data, such as interviews, observations, and document analysis, to provide a detailed and nuanced understanding of the program implementation process. The data are analyzed using various methods, such as content analysis or thematic analysis, to identify patterns and themes that emerge from the data.

The program implementation case study is often used in research to evaluate the effectiveness of a particular program or intervention, and to identify strategies for improving program implementation in the future. For example, a program implementation case study of a school-based health program may be used to identify key factors that contributed to the success or failure of the program, and to make recommendations for improving program implementation in similar settings.

One of the strengths of a program implementation case study is its ability to provide a detailed and comprehensive account of the program implementation process. By using qualitative data, the researcher is able to capture the complexity and nuance of the implementation process, and to identify factors that may not be captured by quantitative data alone.

However, one limitation of a program implementation case study is that the findings may not be generalizable to other contexts or populations. Because the case study focuses on a specific program or intervention, the findings may not be applicable to other programs or interventions in different settings.

An effective research tool, a case study of program implementation may illuminate the intricacies of the implementation process and point the way towards future enhancements.

Program Effects Case Study

A program effects case study is a research method that evaluates the effectiveness of a particular program or intervention by examining its outcomes or effects. The purpose of this type of case study is to provide a detailed and comprehensive account of the program’s impact on its intended participants or target population.

A program effects case study typically employs both quantitative and qualitative data collection methods, such as surveys, interviews, and observations, to evaluate the program’s impact on the target population. The data is then analyzed using statistical and thematic analysis to identify patterns and themes that emerge from the data.

The program effects case study is often used to evaluate the success of a program and identify areas for improvement. For example, a program effects case study of a community-based HIV prevention program may evaluate the program’s effectiveness in reducing HIV transmission rates among high-risk populations and identify factors that contributed to the program’s success.

One of the strengths of a program effects case study is its ability to provide a detailed and nuanced understanding of a program’s impact on its intended participants or target population. By using both quantitative and qualitative data, the researcher can capture both the objective and subjective outcomes of the program and identify factors that may have contributed to the outcomes.

However, a limitation of the program effects case study is that it may not be generalizable to other populations or contexts. Since the case study focuses on a particular program and population, the findings may not be applicable to other programs or populations in different settings.

A program effects case study is a good way to do research because it can give a detailed look at how a program affects the people it is meant for. This kind of case study can be used to figure out what needs to be changed and how to make programs that work better.

Cumulative Case Study

A cumulative case study is a type of case study that involves the collection and analysis of multiple cases to draw broader conclusions. Unlike a single-case study, which focuses on one specific case, a cumulative case study combines multiple cases to provide a more comprehensive understanding of a phenomenon.

The purpose of a cumulative case study is to build up a body of evidence through the examination of multiple cases. The cases are typically selected to represent a range of variations or perspectives on the phenomenon of interest. Data is collected from each case using a range of methods, such as interviews, surveys, and observations.

The data is then analyzed across cases to identify common themes, patterns, and trends. The analysis may involve both qualitative and quantitative methods, such as thematic analysis and statistical analysis.

The cumulative case study is often used in research to develop and test theories about a phenomenon. For example, a cumulative case study of successful community-based health programs may be used to identify common factors that contribute to program success, and to develop a theory about effective community-based health program design.

One of the strengths of the cumulative case study is its ability to draw on a range of cases to build a more comprehensive understanding of a phenomenon. By examining multiple cases, the researcher can identify patterns and trends that may not be evident in a single case study. This allows for a more nuanced understanding of the phenomenon and helps to develop more robust theories.

However, one limitation of the cumulative case study is that it can be time-consuming and resource-intensive to collect and analyze data from multiple cases. Additionally, the selection of cases may introduce bias if the cases are not representative of the population of interest.

In summary, a cumulative case study is a valuable research method that can provide a more comprehensive understanding of a phenomenon by examining multiple cases. This type of case study is particularly useful for developing and testing theories and identifying common themes and patterns across cases.

When conducting a case study evaluation approach, one of the main challenges is the need to establish a contextually relevant research design that accounts for the unique factors of the case being studied. This requires close monitoring of the case, its environment, and relevant stakeholders. In addition, the researcher must build a framework for the collection and analysis of data that is able to draw meaningful conclusions and provide valid insights into the dynamics of the case. Ultimately, an effective case study monitoring evaluation approach will allow researchers to form an accurate understanding of their research subject.

Additionally, depending on the size and scope of the case, there may be concerns regarding the availability of resources and personnel that could be allocated to data collection and analysis. To address these issues, a case study monitoring evaluation approach can be adopted, which would involve a mix of different methods such as interviews, surveys, focus groups and document reviews. Such an approach could provide valuable insights into the effectiveness and implementation of the case in question. Additionally, this type of evaluation can be tailored to the specific needs of the case study to ensure that all relevant data is collected and respected.

When dealing with a highly sensitive or confidential subject matter within a case study, researchers must take extra measures to prevent bias during data collection as well as protect participant anonymity while also collecting valid data in order to ensure reliable results

Moreover, when conducting a case study evaluation it is important to consider the potential implications of the data gathered. By taking extra measures to prevent bias and protect participant anonymity, researchers can ensure reliable results while also collecting valid data. Maintaining confidentiality and deploying ethical research practices are essential when conducting a case study to ensure an unbiased and accurate monitoring evaluation.

When planning and implementing a case study evaluation approach, it is important to ensure the guiding principles of research quality, data collection, and analysis are met. To ensure these principles are upheld, it is essential to develop a comprehensive monitoring and evaluation plan. This plan should clearly outline the steps to be taken during the data collection and analysis process. Furthermore, the plan should provide detailed descriptions of the project objectives, target population, key indicators, and timeline. It is also important to include metrics or benchmarks to monitor progress and identify any potential areas for improvement. By implementing such an approach, it will be possible to ensure that the case study evaluation approach yields valid and reliable results.

To ensure successful implementation, it is essential to establish a reliable data collection process that includes detailed information such as the scope of the study, the participants involved, and the methods used to collect data. Additionally, it is important to have a clear understanding of what will be examined through the evaluation process and how the results will be used. All in all, it is essential to establish a sound monitoring evaluation approach for a successful case study implementation. This includes creating a reliable data collection process that encompasses the scope of the study, the participants involved, and the methods used to collect data. It is also imperative to have an understanding of what will be examined and how the results will be utilized. Ultimately, effective planning is key to ensure that the evaluation process yields meaningful insights.

Benefits of Incorporating the Case Study Evaluation Approach in the Monitoring and Evaluation of Projects and Programmes

Using a case study approach in monitoring and evaluation allows for a more detailed and in-depth exploration of the project’s success, helping to identify key areas of improvement and successes that may have been overlooked through traditional evaluation. Through this case study method, specific data can be collected and analyzed to identify trends and different perspectives that can support the evaluation process. This data can allow stakeholders to gain a better understanding of the project’s successes and failures, helping them make informed decisions on how to strengthen current activities or shape future initiatives. From a monitoring and evaluation standpoint, this approach can provide an increased level of accuracy in terms of accurately assessing the effectiveness of the project.

This can provide valuable insights into what works—and what doesn’t—when it comes to implementing projects and programs, aiding decision-makers in making future plans that better meet their objectives However, monitoring and evaluation is just one approach to assessing the success of a case study. It does provide a useful insight into what initiatives may be successful, but it is important to note that there are other effective research methods, such as surveys and interviews, that can also help to further evaluate the success of a project or program.

In conclusion, a case study evaluation approach can be incredibly useful in monitoring and evaluating complex programs and policies. By exploring key themes, patterns and relationships, organizations can gain a detailed understanding of the successes, challenges and limitations of their program or policy. This understanding can then be used to inform decision-making and improve outcomes for those involved. With its ability to provide an in-depth understanding of a program or policy, the case study evaluation approach has become an invaluable tool for monitoring and evaluation professionals.

Leave a Comment Cancel Reply

Your email address will not be published.

Featured Jobs

Senior business development manager.

• Vienna, VA 22180, USA (Remote)

Program Analyst- Human Resources Specialist

• United States

ACRC International Projects Manager

• Manchester, UK
• University of Manchester

MERL Advisor

• Washington, DC, USA
• Plan International USA

Mental Health and Resilience Advisor – USAID Ukraine (Mid-Level)

Sr. monitoring, evaluation and learning specialist pqlc · washington, dc (hybrid).

• Solidarity Center

Deputy Director, Institutional Support and Program Implementation (ISPI)

• Virginia, USA

Chief of Party – Ukraine TPM

Deputy chief of party – ukraine tpm, chief of party – sudan tpm, how strong is my resume.

Only 2% of resumes land interviews.

Optimize your resume

Expert recommendations to help you create the best resume!

READY TO LAND M&E JOB YOU LOVE?

Get our FREE walkthrough guide to landing a job in International Development

We will never spam or sell your data! You can unsubscribe at any time.

Services you might be interested in

Useful guides ....

Masters, PhD and Certificate in M&E

What is Evaluation?

What is the difference between Monitoring and Evaluation?

Types of Evaluation

Monitoring, Evaluation, Accountability, and Learning (MEAL)

Deals & Coupons ...

LAND A JOB REFERRAL IN 2 WEEKS (NO ONLINE APPS!)

Sign Up & To Get My Free Referral Toolkit Now:

Writing A Case Study

A Complete Case Study Writing Guide With Examples

Published on: Jun 14, 2019

Last updated on: Nov 16, 2023

People also read

Simple Case Study Format for Students to Follow

Understand the Types of Case Study Here

Brilliant Case Study Examples and Templates For Your Help

Share this article

Many writers find themselves grappling with the challenge of crafting persuasive and engaging case studies. 

The process can be overwhelming, leaving them unsure where to begin or how to structure their study effectively. And, without a clear plan, it's tough to show the value and impact in a convincing way.

But don’t worry!

In this blog, we'll guide you through a systematic process, offering step-by-step instructions on crafting a compelling case study. 

Along the way, we'll share valuable tips and illustrative examples to enhance your understanding. So, let’s get started.

On This Page On This Page

What is a Case Study? 

A case study is a detailed analysis and examination of a particular subject, situation, or phenomenon. It involves comprehensive research to gain a deep understanding of the context and variables involved. 

Typically used in academic, business, and marketing settings, case studies aim to explore real-life scenarios, providing insights into challenges, solutions, and outcomes. They serve as valuable tools for learning, decision-making, and showcasing success stories.

Tough Essay Due? Hire Tough Writers!

Types of Case Studies

Case studies come in various forms, each tailored to address specific objectives and areas of interest. Here are some of the main types of case studies :

• Illustrative Case Studies: These focus on describing a particular situation or event, providing a detailed account to enhance understanding.
• Exploratory Case Studies: Aimed at investigating an issue and generating initial insights, these studies are particularly useful when exploring new or complex topics.
• Explanatory Case Studies: These delve into the cause-and-effect relationships within a given scenario, aiming to explain why certain outcomes occurred.
• Intrinsic Case Studies: Concentrating on a specific case that holds intrinsic value, these studies explore the unique qualities of the subject itself.
• Instrumental Case Studies: These are conducted to understand a broader issue and use the specific case as a means to gain insights into the larger context.
• Collective Case Studies: Involving the study of multiple cases, this type allows for comparisons and contrasts, offering a more comprehensive view of a phenomenon or problem.

How To Write a Case Study - 9 Steps

Crafting an effective case study involves a structured approach to ensure clarity, engagement, and relevance. 

Here's a step-by-step guide on how to write a compelling case study:

Step 1: Define Your Objective

Before diving into the writing process, clearly define the purpose of your case study. Identify the key questions you want to answer and the specific goals you aim to achieve. 

Whether it's to showcase a successful project, analyze a problem, or demonstrate the effectiveness of a solution, a well-defined objective sets the foundation for a focused and impactful case study.

Step 2: Conduct Thorough Research

Gather all relevant information and data related to your chosen case. This may include interviews, surveys, documentation, and statistical data. 

Ensure that your research is comprehensive, covering all aspects of the case to provide a well-rounded and accurate portrayal. 

The more thorough your research, the stronger your case study's foundation will be.

Step 3: Introduction: Set the Stage

Begin your case study with a compelling introduction that grabs the reader's attention. Clearly state the subject and the primary issue or challenge faced. 

Engage your audience by setting the stage for the narrative, creating intrigue, and highlighting the significance of the case.

Step 4: Present the Background Information

Provide context by presenting the background information of the case. Explore relevant history, industry trends, and any other factors that contribute to a deeper understanding of the situation. 

This section sets the stage for readers, allowing them to comprehend the broader context before delving into the specifics of the case.

Step 5: Outline the Challenges Faced

Identify and articulate the challenges or problems encountered in the case. Clearly define the obstacles that needed to be overcome, emphasizing their significance. 

This section sets the stakes for your audience and prepares them for the subsequent exploration of solutions.

Step 6: Detail the Solutions Implemented

Describe the strategies, actions, or solutions applied to address the challenges outlined. Be specific about the decision-making process, the rationale behind the chosen solutions, and any alternatives considered. 

This part of the case study demonstrates problem-solving skills and showcases the effectiveness of the implemented measures.

Paper Due? Why Suffer? That's our Job!

Step 7: Showcase Measurable Results

Present tangible outcomes and results achieved as a direct consequence of the implemented solutions. Use data, metrics, and success stories to quantify the impact. 

Whether it's increased revenue, improved efficiency, or positive customer feedback, measurable results add credibility and validation to your case study.

Step 8: Include Engaging Visuals

Enhance the readability and visual appeal of your case study by incorporating relevant visuals such as charts, graphs, images, and infographics. 

Visual elements not only break up the text but also provide a clearer representation of data and key points, making your case study more engaging and accessible.

Step 9: Provide a Compelling Conclusion

Wrap up your case study with a strong and conclusive summary. Revisit the initial objectives, recap key findings, and emphasize the overall success or significance of the case. 

This section should leave a lasting impression on your readers, reinforcing the value of the presented information.

Case Study Methods

The methods employed in case study writing are diverse and flexible, catering to the unique characteristics of each case. Here are common methods used in case study writing:

Conducting one-on-one or group interviews with individuals involved in the case to gather firsthand information, perspectives, and insights.

• Observation

Directly observing the subject or situation to collect data on behaviors, interactions, and contextual details.

• Document Analysis

Examining existing documents, records, reports, and other written materials relevant to the case to gather information and insights.

• Surveys and Questionnaires

Distributing structured surveys or questionnaires to relevant stakeholders to collect quantitative data on specific aspects of the case.

• Participant Observation

Combining direct observation with active participation in the activities or events related to the case to gain an insider's perspective.

• Triangulation

Using multiple methods (e.g., interviews, observation, and document analysis) to cross-verify and validate the findings, enhancing the study's reliability.

• Ethnography

Immersing the researcher in the subject's environment over an extended period, focusing on understanding the cultural context and social dynamics.

Case Study Format

Effectively presenting your case study is as crucial as the content itself. Follow these formatting guidelines to ensure clarity and engagement:

• Opt for fonts that are easy to read, such as Arial, Calibri, or Times New Roman.
• Maintain a consistent font size, typically 12 points for the body text.
• Aim for double-line spacing to maintain clarity and prevent overwhelming the reader with too much text.
• Utilize bullet points to present information in a concise and easily scannable format.
• Use numbered lists when presenting a sequence of steps or a chronological order of events.
• Bold or italicize key phrases or important terms to draw attention to critical points.
• Use underline sparingly, as it can sometimes be distracting in digital formats.
• Choose the left alignment style.
• Use hierarchy to distinguish between different levels of headings, making it easy for readers to navigate.

If you're still having trouble organizing your case study, check out this blog on case study format for helpful insights.

Case Study Examples

If you want to understand how to write a case study, examples are a fantastic way to learn. That's why we've gathered a collection of intriguing case study examples for you to review before you begin writing.

Case Study Research Example

Case Study Template

Case Study Introduction Example

Amazon Case Study Example

Business Case Study Example

APA Format Case Study Example

Psychology Case Study Example

Medical Case Study Example

UX Case Study Example

Looking for more examples? Check out our blog on case study examples for your inspiration!

Benefits and Limitations of Case Studies

Case studies are a versatile and in-depth research method, providing a nuanced understanding of complex phenomena. 

However, like any research approach, case studies come with their set of benefits and limitations. Some of them are given below:

Tips for Writing an Effective Case Study

Here are some important tips for writing a good case study:

• Clearly articulate specific, measurable research questions aligned with your objectives.
• Identify whether your case study is exploratory, explanatory, intrinsic, or instrumental.
• Choose a case that aligns with your research questions, whether it involves an individual case or a group of people through multiple case studies.
• Explore the option of conducting multiple case studies to enhance the breadth and depth of your findings.
• Present a structured format with clear sections, ensuring readability and alignment with the type of research.
• Clearly define the significance of the problem or challenge addressed in your case study, tying it back to your research questions.
• Collect and include quantitative and qualitative data to support your analysis and address the identified research questions.
• Provide sufficient detail without overwhelming your audience, ensuring a comprehensive yet concise presentation.
• Emphasize how your findings can be practically applied to real-world situations, linking back to your research objectives.
• Acknowledge and transparently address any limitations in your study, ensuring a comprehensive and unbiased approach.

To sum it up, creating a good case study involves careful thinking to share valuable insights and keep your audience interested. 

Stick to basics like having clear questions and understanding your research type. Choose the right case and keep things organized and balanced.

Remember, your case study should tackle a problem, use relevant data, and show how it can be applied in real life. Be honest about any limitations, and finish with a clear call-to-action to encourage further exploration.

However, if you are having issues understanding how to write a case study, it is best to hire the professionals.  Hiring a paper writing service online will ensure that you will get best grades on your essay without any stress of a deadline. 

So be sure to check out case study writing service online and stay up to the mark with your grades. 

Frequently Asked Questions

What is the purpose of a case study.

The objective of a case study is to do intensive research on a specific matter, such as individuals or communities. It's often used for academic purposes where you want the reader to know all factors involved in your subject while also understanding the processes at play.

What are the sources of a case study?

Some common sources of a case study include:

• Archival records
• Direct observations and encounters
• Participant observation
• Facts and statistics
• Physical artifacts

What is the sample size of a case study?

A normally acceptable size of a case study is 30-50. However, the final number depends on the scope of your study and the on-ground demographic realities.

Barbara P (Literature, Marketing)

Dr. Barbara is a highly experienced writer and author who holds a Ph.D. degree in public health from an Ivy League school. She has worked in the medical field for many years, conducting extensive research on various health topics. Her writing has been featured in several top-tier publications.

Paper Due? Why Suffer? That’s our Job!

Keep reading

We value your privacy

We use cookies to improve your experience and give you personalized content. Do you agree to our cookie policy?

Website Data Collection

We use data collected by cookies and JavaScript libraries.

Are you sure you want to cancel?

Your preferences have not been saved.

• Open access
• Published: 14 November 2023

Six-month cost-effectiveness of adding motivational interviewing or a stratified vocational advice intervention to usual case management for workers with musculoskeletal disorders: the MI-NAV economic evaluation

• Alexander Tingulstad 1 ,
• Esther T. Maas 2 ,
• Tarjei Rysstad 1 ,
• Britt Elin Øiestad 1 ,
• Fiona Aanesen 3 ,
• Are Hugo Pripp 1 ,
• Maurits W. Van Tulder 4 &
• Margreth Grotle 1 , 5  

Journal of Occupational Medicine and Toxicology volume  18 , Article number:  25 ( 2023 ) Cite this article

64 Accesses

Metrics details

This study evaluates the six-month cost-effectiveness and cost-benefits of motivational interviewing (MI) or a stratified vocational advice intervention (SVAI) added to usual case management (UC) for workers on sick leave due to musculoskeletal disorders.

This study was conducted alongside a three-arm RCT including 514 employed workers on sick leave for at least 50% for ≥ 7 weeks. All participants received UC. The UC + MI group received two MI sessions, and the UC + SVAI group received 1–4 SVAI sessions. Sickness absence days, quality-adjusted life-years (QALYs), and societal costs were measured between baseline and six months.

Adding MI to UC, resulted in incremental cost-reduction of -2580EUR (95%CI -5687;612), and a reduction in QALYs of -0.001 (95%CI -0.02;0.01). Secondly, adding MI to UC resulted in an incremental cost-reduction of -538EUR (95%CI -1358;352), and reduction of 5.08 (95%CI -3.3;13.5) sickness-absence days. Financial return estimates were positive, but not statistically significant. Adding SVAI to UC, resulted in an incremental cost-reduction of -2899 EUR (95% CI -5840;18), and a reduction in QALYs of 0.002 (95% CI -0.02;0.01). Secondly, adding SVAI to UC resulted in an statistically significant incremental cost-reduction of -695 EUR (95% CI -1459;-3), and a reduction of 7.9 (95% CI -0.04;15.9) sickness absence days. Financial return estimates were positive and statistically significant. The probabilities of cost-effectiveness for QALYs were high for adding MI or SVAI (ceiling ratio 0.90).

Conclusions

In comparison to UC only, adding MI to UC tends to be cost-effective. Adding SVAI to UC is cost-effective for workers on sick leave due to musculoskeletal disorders.

Trial registration

ClinicalTrials.gov (identifier: NCT03871712).

Introduction

Musculoskeletal disorders (MSDs) are the main reason for disability worldwide [ 1 ]. In Europe, MSDs are also the most frequent cause of reduced work productivity and sickness absence [ 2 , 3 ]. Sickness absence is associated with significant costs for individuals and society [ 2 , 4 ]. Furthermore, staying at work or returning to work after sickness absence is essential for a persons’ identity, social role and status in society [ 5 ]. To address sickness absence and the large economic burden related to this, effective and cost-effective interventions targeting barriers to return to work (RTW) are needed [ 6 ].

Finding effective RTW interventions is difficult due to the complexity of long-term sick leave. Interactions between individual, workplace, healthcare, compensation system and societal factors may hamper RTW [ 7 ]. Two potential interventions that have shown to be promising to reduce sick leave duration are Motivational Interviewing (MI) and Stratified Vocational Advice Intervention (SVAI). MI is a person-centred counselling style aimed at increasing motivation for change [ 8 ]. The SVAI intervention was based on the principles of case management to help participant overcome obstacles to RTW [ 9 ].

A recent trial (MI-NAV) [ 10 , 11 ] studied the additional effectiveness of either MI or SVAI to usual case management (UC) to reduce sickness absence days over a six-month period. This study focused on workers with MSDs who had been on sick leave for more than seven consecutive weeks. Adding MI or SVAI to UC reduced sickness absence by an average of seven workdays over six months. Although seven days seems a relevant difference, it was not statistically significant and the wide confidence intervals (CIs) indicated imprecise estimates [ 11 ].

Considering the limited resources for RTW interventions, stakeholders are interested in the effectiveness as well as if it is worth their money before implementation [ 12 ]. Economic evaluations provide such information by estimating the difference in effects and costs between two or more interventions, and relate those to each other [ 13 ].

Therefore, this study aimed to evaluate the six-month cost-effectiveness, cost-utility, and cost–benefit of adding MI to UC, and adding SVAI to UC for workers who have been on sick leave for at least seven weeks due to MSDs.

The methods have been previously reported in the study protocol [ 10 ], and the publication of clinical effectiveness [ 11 ]. We followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Cost-Effectiveness Analysis Randomised Clinical Trial taskforce recommendations [ 14 ].

Study design

An economic evaluation was conducted alongside a three-arm, pragmatic RCT [ 10 ]. The trial included participants between April 2019 and October 2020. As well as the economic evaluation, the trial had a follow-up at six months. The trial was conducted in cooperation with the Norwegian Labour and Welfare Administration (NAV). The project was approved by the Norwegian Centre for Research Data (project nm. 861249), and the trial was conducted according to the Helsinki declaration and the General Data Protection Regulation.

Participants, randomisation, and stratification

Eligible participants were workers aged 18–67 years, employed either full or part-time, and on sick leave due to MSDs for at least 50% of their contracted work hours for more than seven consecutive weeks. All participants were diagnosed with MSDs listed in the 2 nd edition of the International Classification of Primary Care (ICPC-2) [ 15 ]. Excluded were those with serious somatic or mental health disorders affecting their work ability and in need of specialised treatment (e.g., cancer, psychotic disorders), pregnant women, unemployed, freelancers and self-employed workers, and those lacking sufficient proficiency in either Norwegian or English to answer questionnaires or communicate by telephone.

Candidates who agreed to participate received an electronic link to written information about the trial, an electronic informed consent form and a baseline questionnaire.

The Örebro Musculoskeletal Pain Screening Questionnaire Short Form (ÖMPSQ-SF) [ 16 ], and the Keele STarT MSK Tool [ 17 , 18 ] were used to stratify the participants into two risk groups of long-term sick leave. Participants with ≥ 9 on the Keele STarT MSK Tool and ≥ 60 on the ÖMPSQ-SF were stratified to a ‘high-risk group’, all others were stratified to a ‘medium/low-risk group’. After stratification, participants were randomised using a 1:1:1 ratio. Allocation was concealed for the recruitment staff. A blinded statistician prepared a computer-generated allocation sequence for each risk-group, only available for the person in charge of group allocation.

Interventions

A detailed description of the rationale, development and content of the intervention can be found elsewhere [ 10 , 11 ]. A fidelity assessment of the MI intervention [ 19 ], and a process evaluation of the SVAI have been published previously [ 20 ]. All participants received UC for sick leave, consistent with Norway’ standard practice, which provides full wage replacement benefits for up to 12 months. The usual case management has the following timeline: within the first 4 weeks of sick leave, an RTW plan is made by the employer and employee; within 7 weeks, a dialogue meeting between the employee, employer, and other relevant stakeholders such as general practitioner (GP), is arranged by the employer. Within week 26 of the sick leave period, NAV arranges a second dialogue meeting between the employee, the employer and in some cases the GP who issued the sick leave.

In addition to UC, participants randomised to the UC + MI arm were offered two face-to-face sessions of MI from a NAV caseworker; the first as soon as possible after random allocation, and the second two weeks later. The NAV caseworkers were educated in MI [ 10 ].

The participants in the UC + SVAI arm were offered UC and vocational advice and case management from physiotherapists. In the UC + SVAI group, those stratified to the low/medium-risk group were offered 1–2 telephone sessions, while participants in the high-risk group were offered 3–4 sessions. The first session was conducted as soon as possible after inclusion, and the intervention ended when the participant reached six months of consecutive sick leave or had RTW for four consecutive weeks. Eight physiotherapists were trained over a five-day course to provide SVAI.

Effect measures

The primary effect measure was the number of sickness absence days over a six-month period, defined as lost workdays. To accurately represent time away from work, we accounted the participants’ contracted work hours and amount of sick leave. This was then summed up and converted to lost workdays, assuming a five-day working week. Data was obtained from national registries, including information on sick leave benefits, sick leave certificates, disability pensions, and contracted work hours. In Norway, people may work alongside part-time disability pensions, so any increase in disability pensions from baseline was counted as sick leave.

The secondary effect measure was health-related quality of life expressed in terms of quality adjusted life years (QALYs). First, the participants’ health states were measured by the EuroQol-5 Dimensions-5 Levels (EQ-5D-5L) [ 21 ]. Then, the UK tariff was used to convert these health states into utility scores, anchored at 0 “death” and 1 “perfect health”, with negative values representing health states worse than death. We used the UK tariff, as a Norwegian tariff is not available. QALYs were calculated using the “area under the curve” approach. The willingness-to-pay threshold for this outcome was based on the Norwegian governmental report No. 34 to the parliament with a value of NOK 275,000 (Euro (€) 27,500/USD 35,628) per QALY [ 22 ].

Cost measures

Since this study adopted a societal perspective, we included both direct and indirect costs. Direct costs included costs of the intervention, primary healthcare use (e.g., general practitioner, physiotherapist, manual therapist, or other therapists), and secondary healthcare use (e.g., hospitalisation or rehabilitation). To calculate intervention costs, we employed a micro-costing approach and included training and mentoring costs. Intervention costs were provided per hour by NAV. Information on other health care use and costs was retrieved from national registers: The Norwegian Health Economics Administration and the Norwegian Patient Registry. Indirect costs consisted of work absenteeism and productivity losses due to paid and unpaid work. We obtained absenteeism data from national registries and valued it using estimates from official statistics on average income stratified by gender. Productivity losses due to unpaid work were measured using the Institute for Medical Technology Assessment Productivity Cost Questionnaire (iPCQ) [ 23 ]. The iPCQ has been translated and culturally adapted to Norwegian and found to have good measurement properties when used among patients with long-term MSDs [ 24 ]. These costs were valued using a recommended Norwegian shadow price (€150). All costs were converted to 2021 Euros, the last year of data collection, using exchange rates from the European Central Bank. Since the follow-up period of the intervention was less than one year, there was no need to discount the costs and effects.

Statistical analyses

Analyses were performed in accordance with the published statistical analysis plan [ 10 ]. All analyses were performed according to the intention to treat principle. Unless stated otherwise, data were analysed using Stata (version 16, Stata Corp, College station, TX).

Missing data

We anticipated few missing values for the primary outcome and the work-related secondary outcomes, as information was obtained from the Norwegian national social security system registry. In this registry, all individuals who received any form of benefits are registered by their social security number. We assumed that missing data from the EQ-5D-5L were missing at random and imputed missing values with a multiple imputation model. Missing data was imputed using Multivariate Imputation by Chained Equations (MICE) with Predictive Mean Matching [ 25 ]. The imputation model included duration of sick leave at baseline, risk groups from the Keele STarT MSK and ÖMPSQ-SF, work satisfaction, and self-rated health. Ten complete datasets were imputed. Analyses were performed per imputed dataset separately, and the results were then pooled using Rubin’s rules [ 25 ]. MICE was performed using SPSS statistics 25 (IBM).

Cost-effectiveness analysis & cost-utility analysis

In the cost-effectiveness analyses, the outcome measure was sickness absence days, and productivity costs were excluded to prevent double counting. In the cost-utility analyses, productivity costs were included. We used linear regression models, both adjusted and unadjusted for confounders (sex, age, BMI, smoking, education level and physical activity) to analyse disaggregate cost differences. Differences in total costs and effects between treatment groups were obtained from a system of seemingly unrelated regressions that accounted for the potential correlation between costs and effects [ 26 ]. These total cost and effect differences were adjusted for baseline and confounders. In both analyses, the incremental cost-effectiveness ratio (ICER) was calculated by dividing the corrected differences in costs by those in effects. To assess uncertainty, we used a bootstrap method with 10,000 replicated datasets. To illustrate the statistical uncertainty surrounding the ICERs, bootstrapped cost and effect pairs were plotted on a cost-effectiveness plane (CE plane) with incremental costs on the y-axis and incremental effects on the x-axis, and on cost-effectiveness acceptability curves (CEACs).

Cost–benefit analysis

The cost–benefit analysis (CBA) was performed from NAV’s perspective. Costs were defined as intervention costs, and benefits as the difference in total monetized outcome measures between the intervention groups and control group. Positive benefits indicate reduced spending of the intervention groups compared with the control group. Two cost–benefit metrics were calculated: (1) net benefits (NBs), and (2) benefit cost ratio (BCR).

NB = Benefits – Costs

BCR = Benefits / Costs

To quantify precision, 95% bootstrapped confidence intervals (CIs) were estimated, using 10,000 replications. Financial returns are positive if NB > 0 and BCR > 1.

Sensitivity analyses

The following sensitivity analyses were carried out: 1) Complete-case analysis (including participants with complete data only). 2) Uncertainty of the ICER (incremental cost-effectiveness ratio) will be tested by bootstrapping with 5,000 repetitions.

Participants

A total of 514 workers participated in the trial, while five participants withdrew, leaving 509 (99%) participants for analyses. No adverse events were reported. A detailed flow chart is shown elsewhere [ 11 ]. Table 1 shows that baseline characteristics were similar across the three groups. The median age of participants was 49 years (range 24–66 years) and 57% were women. Totally, 341 participants (66%) worked in full-time positions, and 315 (62%) were on full sick leave at baseline. The mean quality adjusted life years (SD) was 0.58 (0.21). The self-reported level of musculoskeletal health, according to the Musculoskeletal Health Questionnaire (MSK-HQ), was low to moderate with an average score of 27 (on a scale from 0 to 56, where a higher score indicates better health status).

Cost differences

Mean costs within each study group are presented in Table 2 . Total costs were highest in the UC group (€25345 (Standard Error of the Mean (SEM):1226)), followed by the UC + MI ((€22524 (SEM1229)) and the UC + SVAI (€21716 (SEM 1103)). In all three groups, over 90% of costs were due to costs related to absenteeism and productivity losses. The cost of the interventions (MI or SVAI) was less than 0.5% of the total costs.

Intervention costs were higher in the UC + MI and the UC + SVAI groups compared to the UC group (Table 2 ). All other costs were lower for both intervention groups compared to the UC group. Comparing UC + MI to UC, the UC + MI group had lower total societal costs in the adjusted analysis (-2594 (95% CI -5733 to 497)). Comparing UC + SVAI to UC, total societal costs were in favour of UC + SVAI (-2858 (95%CI -5701 to 55)). Absenteeism was the biggest cost driver.

Effect differences

Comparing UC + MI to UC, the difference in QALYs was -0.001 (95% CI -0.15 to 0.01) and the reduction in sickness absence days was 5.1 (95% CI -3.3 to 13.5).

Comparing UC + SVAI to UC, the difference in QALYs was -0.002 (95% CI -0.02 to 0.01)) and the reduction in sickness absence days was 7.9 (95% CI -0.04 to 15.9).

Cost-effectiveness & cost-utility

Comparing UC + MI to UC, we found an ICER of 1,756,221 for QALYs, indicating that 1,756,221 EUR would, on average, be saved in the intervention group compared to the control group per QALY gained. Similarly, for the UC + MI group, we found an ICER of 106, indicating a saved average of 106 EUR for each day of sickness absence compared to the UC group. Figure  1 and Table 3 show that most incremental cost-effectiveness (CE) pairs were located on the southern (for QALYs) and southeast (for sickness absence) quadrant(s) of the CE-plane, indicating that the intervention was on average less costly for improving QALYs, and less costly and more effective for reducing sickness absence.

Cost-Utility plane & Cost-Utility acceptability curve for different ceiling ratios (NOK) for qualityadjusted life-years indicating the probability of cost-effectiveness of Motivational Interviewing versus control for workers on ( a ) QALYs or ( b ) sickness absence due to a musculoskeletal disorder

Comparing UC + SVAI to UC, we found an ICER of 1,553,061, indicating that 1,553,061 EUR would, on average, be saved in the intervention group compared to the control group per 1 QALY gained. Similarly, for the UC + SVAI to UC, we found an ICER of 88, indicating a saved average of that 88 EUR per day reduction in sickness absence compared to the UC group. Figure  1 and Table 3 show that most incremental CE pairs were located on the southeast quadrant of the CE plane, indicating that the intervention was on average less costly and more effective.

Both CEACs show that the probability of UC + MI and UC + SVAI being cost-effective compared with UC only was higher than 90% for all willingness-to-pay thresholds (Figs.  1 and  2 ).

Cost-Utility plane & Cost-Utility acceptability curve for different ceiling ratios (Norwegian Kroner) for quality-adjusted life-years indicating the probability of cost-effectiveness of Stratified Vocational Advice Interventions (SVAI) versus control for workers on ( a ) QALYs or ( b ) sickness absence due to a musculoskeletal disorder

The results of CBA were in favour of the MI + UC group (Table 4 ). The total benefit was 2874 EUR (95% CI -563 to 6299) in the MI + UC group compared with UC group. The mean net benefit (subtracting intervention cost from total benefit) was 2821 EUR (95% CI -617 to 6246) per worker. The BCR (i.e., amount of money returned per Euro invested) was 54 (95% CI-10 to 124). The estimated maximal probability of return was 94.5%, indicating 94.5% probability for NAV to expect a positive return on investment from the intervention.

The results of CBA were in favour of the SVAI + UC group (Table 4 ). The total benefit was 3706 EUR (95% CI -548 to 7049) in the SVAI + UC group compared with UC group. The mean net benefit (subtracting intervention cost from total benefit) was 3628 (95% CI 388 to 6911) per worker. The BCR (i.e., amount of money returned per Euro invested) was 48 (95% CI 6 to 91). The estimated maximal probability of return as 98.5%, indicating 98.5% probability for NAV to expect a positive return on investment from the intervention.

Sensitivity analysis

When re-running the analysis on complete cases only, and using 5,000 bootstraps, we observed similar results to those of the main analysis (Appendix 1 ).

Main findings

This health economic evaluation assessed adding MI or a SVAI to usual UC for workers on sick leave due to MSDs. In comparison with UC, adding MI had an overall high willingness-to-pay (0.9) and maximum probability of return (94.5%). Financial return estimates were likely to be positive. Adding SVAI to UC showed a high willingness-to-pay (0.9) and maximum probability of return (98.5%). Financial return estimates were highly positive.

Comparison with other studies

To our knowledge, no previous studies has evaluated the cost-effectiveness of MI for this group of workers on sick leave with MSDs. The effectiveness [ 11 ], and cost-effectiveness results showed a similar pattern in which MI on average reduced sickness absence days among people with MSDs [ 27 , 28 ]. As presented in the effectiveness study [ 11 ], a seven-day difference may be considered an important effect. However, the trial was not powered to detect this difference as statistically significant. Large variability in the data may also have reduced the statistical power of the trial, for example participants were heterogenous with respect to diagnosis and previous sick leaves.

The cost-effectiveness results of adding a SVAI to UC supports the findings of a previous trial indicating that vocational advice could lead to reduced absence and cost savings for society [ 9 ].

Since 90% of costs in this study were due to sickness absence and productivity losses, this emphasises the importance of RTW interventions. Both MI and SVAI had a high likelihood of being cost-effective and had a positive return on investment. These results are in line with a systematic review on effectiveness and cost-effectiveness studies, showing that for individuals on long-term sick leave due to back pain, interventions including interaction between employees, care personnel and employers appear to be more efficient and cost-effective than other workplace-linked interventions [ 29 ].

Strengths and limitations

This study has several strengths in the implementation of the intervention, as well as analytical strengths. The multi-arm RCT design made it possible to compare two additional interventions with a single UC group, optimising the use of limited research resources. Secondly, detailed national registry data included data for 99% of the trial participants. The analyses were performed based on the pre-registered statistical analysis plan. We used non-parametric bootstrapping methods to determine the imprecision around the estimates, which are recommended to handle skewed cost data. All these attributes support the validity of the findings observed in this study.

The main limitation of this study is the variability of the MI and SVAI intervention, both of which are context- and provider-dependent. Fidelity assessment of the MI intervention showed that the caseworkers had variable proficiency throughout the trial [ 19 ]. The process evaluation of the SVAI showed that the intervention was delivered in accordance with the intervention protocol and conversation guide [ 20 ]. These findings come from a pragmatic trial, providing results that are valid within the study's specific context. However, these findings need to be replicated in other settings for broader validation. Another limitation of this study was a relatively small sample that could have resulted in non-statistically significant results, because the study was not powered to detect these differences. However, for the main outcomes of the economic evaluation, the cost-effectiveness planes and CEACs, 10,000 bootstraps were used which provide information on the accuracy of the estimate. Thirdly, this study did not include the use of medication in the economic evaluation. However, it is unlikely that this will have had any effect on this study since the interventions were not aimed at medication use, and medication use will most likely only have contributed to a small proportion of the costs [ 30 , 31 ]. Another limitation was the missing data for QALYs due to non-response and drop-out/lost-to-follow up. To address this issue, we employed multivariate imputation methods, which is recommended practice for dealing with missing values in economic evaluation research [ 25 ]. Results of the complete-case analysis, where no imputation was applied, yielded similar findings to the main analysis. Therefore, the degree of dropping out of participants during the follow-up time did not influence the main results.

Implications for practice and research

Despite higher intervention costs, the two interventions could potentially reduce costs of sick leave if implemented widely. One could also argue that the high intervention costs would decrease over time due to less need for initial training of intervention providers.

Future research could assess the cost-effectiveness of MI and SVAI in other jurisdictions, because the usual care setting as well as costs related to sick leave are highly context dependent. Furthermore, further studies could focus on other diagnoses such as common mental disorders, or workers with MSD and a comorbid serious mental health disorder, which are also frequent reasons for sick leave and interesting groups for stakeholders. In a recent systematic review by Dewa et al. [ 32 ] on RTW interventions for mental health related sick leave, they emphasised the importance of conducting more economic evaluations in various disability and health systems.

Overall, we found tendencies for adding MI to UC to be cost-effective and cost-beneficial compared with UC for workers on sick leave due to MSDs. Similarly, incorporating a SVAI to UC for the same group of workers is also cost-effective and cost-beneficial. Despite the higher intervention costs, implementation of these interventions has the potential to reduce the societal costs related to sick leave.

Availability of data and materials

Requests to access data should be addressed to [email protected] anonymised individual participant data (including data dictionary) will be available on request, to researchers who provide a methodologically sound scientific proposal that has been approved by an ethics committee and by the scientific board of the MI-NAV study.

Abbreviations

Benefit cost ratio

Cost-effectiveness

Cost-effectiveness plane

Cost-effectiveness acceptability curves

Confidence interval

EuroQol-5 Dimensions-5 Levels

Incremental cost-effectiveness ratio

2 Nd edition of the International Classification of Primary Care

Institute for Medical Technology Assessment Productivity Cost Questionnaire

Inter quartile range

International Society for Pharmacoeconomics and Outcomes Research

Motivational interviewing

Multivariate Imputation by Chained Equations

Musculoskeletal disorders

Musculoskeletal Health Questionnaire

The Norwegian Labour and Welfare Administration

Net benefit

Northeast-Quadrant

Northwest-Quadrant

Örebro Musculoskeletal Pain Screening Questionnaire Short Form

Quality-adjusted life-years

• Return to work

Standard deviation

Southeast-Quadrant

Standard Error of the Mean

Stratified vocational advice intervention

Southwest-Quadrant

Usual case management

Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. The Lancet. 2020;396(10258):1204–22.

Article   Google Scholar  

Kinge JM, Sælensminde K, Dieleman J, Vollset SE, Norheim OF. Economic losses and burden of disease by medical conditions in Norway. Health Policy. 2017;121(6):691–8.

Article   PubMed   Google Scholar  

Bevan S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract Res Clin Rheumatol. 2015;29(3):356–73.

De Kok J, Vroonhof P, Snijders J, et al. (2019). Work-related musculoskeletal disorders: prevalence, costs and demographics in the EU. European agency for safety and health at work. 2019.

Waddell G, Burton AK. Is work good for your health and well-being? London: TSO; 2006.

Google Scholar  

Dol M, Varatharajan S, Neiterman E, et al. Systematic Review of the Impact on Return to Work of Return-to-Work Coordinators. J Occup Rehabil. 2021. https://doi.org/10.1007/s10926-021-09975-6 .

Aasdahl L, Fimland MS, Bjørnelv GM, et al. Economic evaluation of inpatient multimodal occupational rehabilitation vs. outpatient acceptance and commitment therapy for sick-listed workers with musculoskeletal-or common mental Disorders. J Occup rehab. 2023;23:1–10.

Miller WR, Rollnick S. Motivational interviewing: Helping people change. Guilford press; 2012.

Wynne-Jones G, Artus M, Bishop A, et al. Effectiveness and costs of a vocational advice service to improve work outcomes in patients with musculoskeletal pain in primary care: a cluster randomised trial (SWAP trial ISRCTN 52269669). Pain. 2018;159(1):128–38.

Øiestad BE, Aanesen F, Løchting I, et al. Study protocol for a randomized controlled trial of the effectiveness of adding motivational interviewing or stratified vocational advice intervention to usual case management on return to work for people with musculoskeletal disorders The MI-NAV study. BMC Musculoskeletal Dis. 2020;21(1):1–10.

Aanesen F, Grotle M, Rysstad TL, et al. Effectiveness of adding motivational interviewing or a stratified vocational advice intervention to usual case management on return to work for people with musculoskeletal disorders: the MI-NAV randomised controlled trial. Occup Environ Med. 2023;80(1):42–50.

Burdorf A. Economic evaluation in occupational health—its goals, challenges, and opportunities. Scandinavian J Work Environ Health. 2007;1:161–4.

van Dongen JM, van Wier MF, Tompa E, et al. Trial-based economic evaluations in occupational health: principles, methods, and recommendations. J Occup Environ Med. 2014;56(6):563.

Article   PubMed   PubMed Central   Google Scholar  

Ramsey SD, Willke RJ, Glick H, et al. Cost-effectiveness analysis alongside clinical trials II—an ISPOR good research practices task force report. Value in Health. 2015;18(2):161–72.

Wonca international classification committee. International classification of primary care 2nd edition. Wonca. Accessed February 11, 2022. https://www.globalfamilydoctor.com/groups/WorkingParties/wicc.aspx .

Linton SJ, Nicholas M, Macdonald S. Development of a short form of the orebro musculoskeletal pain screening questionnaire. Spine (Phila Pa 1976). 2011;36(22):1891–5. https://doi.org/10.1097/BRS.0b013e3181f8f775 .

Dunn KM, Campbell P, Lewis M, et al. Refinement and validation of a tool for stratifying patients with musculoskeletal pain. Eur J Pain. 2021. https://doi.org/10.1002/ejp.1821 .

Rysstad T, Grotle M, Aasdahl L, et al. Stratifying workers on sick leave due to musculoskeletal pain: translation, cross-cultural adaptation and construct validity of the Norwegian Keele STarT MSK tool. Scand J Pain. 2022;2:325–35.

Løchting I, Hagen R, Monsen CK, et al. Fidelity of a motivational interviewing intervention for improving return to work for people with musculoskeletal disorders. Int J Environ Res Pub Health. 2021;18(19):10324. https://doi.org/10.3390/ijerph181910324 .

Aanesen F, Øiestad BE, Grotle M, et al. Implementing a stratified vocational advice intervention for people on sick leave with musculoskeletal disorders: a multimethod process evaluation. J Occup Rehab. 2021;1:1–3. https://doi.org/10.1007/s10926-021-10007-6 .

Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727–36.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Norwegian Ministry of Health and Care Services. Principles for priority setting in health care. Oslo: Norwegian Ministraty of Health and Care Services; 2016.

Bouwmans C, Krol M, Severens H, Koopmanschap M, Brouwer W, Hakkaart-van RL. The iMTA productivity cost questionnaire: a standardized instrument for measuring and valuing health-related productivity losses. Value in health. 2015;18(6):753–8.

Munk R, Storheim K, Småstuen MC, Grotle M. Measuring productivity costs in patients with musculoskeletal disorders: measurement properties of the Institute for medical technology assessment productivity cost questionnaire. Value in Health. 2019;22(12):1410–6.

White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99.

Willan AR, Briggs AH, Hoch JS. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ. 2004;13(5):461–75.

Gross DP, Park J, Rayani F, Norris CM, Esmail S. Motivational interviewing improves sustainable return to work in injured workers after rehabilitation: a cluster randomized controlled trial. Arch Phys Med Rehabil. 2017;98(12):2355–63.

Park J, Esmail S, Rayani F, Norris CM, Gross DP. Motivational interviewing for workers with disabling musculoskeletal disorders: results of a cluster randomized control trial. J Occup Rehabil. 2018;28(2):252–64.

Carroll C, Rick J, Pilgrim H, Cameron J, Hillage J. Workplace involvement improves return to work rates among employees with back pain on long-term sick leave: a systematic review of the effectiveness and cost-effectiveness of interventions. Disabil Rehabil. 2010;32(8):607–21.

Jensen C, Nielsen CV, Jensen OK, Petersen KD. Cost-effectiveness and cost-benefit analyses of a multidisciplinary intervention compared with a brief intervention to facilitate return to work in sick-listed patients with low back pain. LWW. 2013;38:1059–67.

Meijer EM, Sluiter JK, Heyma A, Sadiraj K, Frings-Dresen MHW. Cost-effectiveness of multidisciplinary treatment in sick-listed patients with upper extremity musculoskeletal disorders: a randomized, controlled trial with one-year follow-up. Int Arch Occup Environ Health. 2006;79:654–64.

Dewa CS, Hoch JS, Loong D, Trojanowski L, Bonato S. Evidence for the cost-effectiveness of return-to-work interventions for mental illness related sickness absences: a systematic literature review. J Occup Rehabil. 2021;31:26–40.

Download references

Acknowledgements

We are very grateful to all who have contributed to the study: the study participants, the SVAI physiotherapists, NAV caseworkers, and the patient engagement panel at Oslo University hospital. We would like to thank user representative Astrid Torgersen Lunestad, the NAV directorate by Bjørn Are Hultman, Kari Paulsen, Ann Kristin Johnson, Solgunn Måløy, Jørgen Grøttan and Ola Thune, our collaborators at NTNU, especially Lene Aasdahl for advice on organising the registry data, MI supervisor for the NAV caseworkers Christine K. Monsen, research assistant Rune Solli responsible for recruitment, collaborator in the MI and SVAI evaluation Hedda Eik and all other collaborators in the MI-NAV study.

Funding was granted by governmental organisations. The research Council of Norway was the main funder of the trial (grant no. 280431) and had no role in the design, data collection, analysis, reporting or dissemination of the trial. The Norwegian Labour and Welfare Administration (NAV) and Oslo Metropolitan University contributed with personnel, infrastructure, and coordination of the trial.

Author information

Authors and affiliations.

Department of Rehabilitation and Health Technology, Centre for Intelligent Musculoskeletal Health, Oslo Metropolitan University, St.Olavs Plass, P.O. Box 4, Oslo, 0130, Norway

Alexander Tingulstad, Tarjei Rysstad, Britt Elin Øiestad, Are Hugo Pripp & Margreth Grotle

Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, and the Amsterdam Movement Sciences Research Institute, de Boelelaan 1085, Amsterdam, 1081 HV, The Netherlands

Esther T. Maas

National Institute of Occupational Health, Majorstuen, P.O. Box 5330, Oslo, 0304, Norway

Fiona Aanesen

Faculty Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van Der Boechorststraat 7, Amsterdam, 1081 BT, The Netherlands

Maurits W. Van Tulder

Department of Research, Innovation and Education, Division of Clinical Neuroscience, Research and Communication Unit for Musculoskeletal Health (FORMI), Oslo University Hospital, Ullevål, Building 37B, P.O. Box 4956, Oslo, Nydalen, 0424, Norway

Margreth Grotle

You can also search for this author in PubMed   Google Scholar

Contributions

All authors critically revised the manuscript for important intellectual content. ETM conducted the analyses, and wrote the first draft with AT. AT and AHP were also involved in cleaning and preparing the registry data. All authors critically revised and commented on the manuscript drafts. AT, TLR, and FA contributed to recruitment of participants and conducting the trial. BEØ, MvT, and MG were involved in planning the study. BEØ was the coordinating investigator, and MG was the project manager.

Corresponding author

Correspondence to Alexander Tingulstad .

Ethics declarations

Ethics approval and consent to participate.

The Regional Committee for Medical and Health Research Ethics assessed the trial according to the ACT 2008–06-20 no.44: Act on medical and health research and decided that it did not need approval from the Committee (2018/1326/REK sør-øst A). The Norwegian Center for Research Data has approved the project (identifier: 861249). The trial was registered at ClinicalTrials.gov on the 12 th of March 2019 (identifier: NCT03871712).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:.

Appendix 1. Cost-effectiveness analysis results (Sensitivity analyses).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Cite this article.

Tingulstad, A., Maas, E.T., Rysstad, T. et al. Six-month cost-effectiveness of adding motivational interviewing or a stratified vocational advice intervention to usual case management for workers with musculoskeletal disorders: the MI-NAV economic evaluation. J Occup Med Toxicol 18 , 25 (2023). https://doi.org/10.1186/s12995-023-00394-2

Download citation

Received : 13 September 2023

Accepted : 08 November 2023

Published : 14 November 2023

DOI : https://doi.org/10.1186/s12995-023-00394-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Vocational rehabilitation
• Economic evaluation
• Randomized controlled trial

Journal of Occupational Medicine and Toxicology

ISSN: 1745-6673

• Submission enquiries: [email protected]
• General enquiries: [email protected]

What is Case Evaluation?

When two parties have gotten wrapped up in a dispute and one party would like to resolve the situation without litigation, but the other is resistant, it may be beneficial to try using case evaluation. Case evaluation will often reevaluate the stubborn party’s position and help wrap up the case in a way that can make both parties happy.

Understanding the case evaluation can help determine whether a case is a good candidate for the process. While it is not a guarantee, it is an alternative dispute resolution process. It can resolve the dispute outside of a trial and help the parties move forward. Suppose one is trapped in a dispute or litigation that is not moving forward. In that case, case evaluation may be an excellent way to push the case toward settlement rather than unnecessary litigation.

Defining Case Evaluation :

Case evaluation is an intervention process for cases either before they are filed or shortly after. The process may be completed based on an agreement of the parties or by the order of a judge. In a case evaluation, a neutral party not involved in the case looks over the case shortly after it is filed and evaluates it for each party’s strengths, weaknesses, and the possibility of success.

The neutral is usually an attorney that understands the subject matter and can make an unbiased evaluation of the case for the parties, balancing their interests and the court’s interests to settle and clear up backlogged cases. Depending on the state and parties’ wishes, the evaluation may happen either by written submissions or by oral presentation to the neutral.

After reviewing the case, the neutral will evaluate each side’s strengths while also highlighting and discussing the weaknesses the case has. This presentation aims to help the parties decide the likelihood of success should they continue.

Common characteristics of the case evaluation include:

• Confidential :   The evaluation of the case between the neutral and the parties will usually be confidential. The neutral will not share the discussions that take place within the evaluation with the judge or anyone else apart from the parties. However, if necessary, the parties should discuss this between themselves and the neutral.  
• Neutrality:  The process will be neutral. The evaluator will approach the parties’ dispute without bias. Neither party should have sway over the neutral, and they should not take sides but merely present the strengths and weaknesses of each case with their prediction for the outcome of the case. This helps to ensure that the parties’ positions are treated and considered fairly.
• Opinion:  The neutral will present an opinion to the parties. This will happen after the parties have presented their evidence and the neutral has had a chance to review everything. This opinion can help guide the parties in their decisions moving forward. 
• Discussion:  The parties and the neutral will usually meet to discuss the case or the opinion presented by the neutral. This discussion will usually include the neutral sharing their thoughts on the merits of the case, and the parties consider the case as well as potential settlement agreements .  
• Non-Binding:  The evaluator cannot decide the case or force the parties to agree or follow the evaluator’s decision. The process exists only to provide the parties with a chance to hear about the potential outcome of their case, reevaluate their positions, or discuss a settlement. The opinion created by the evaluator is not binding . It cannot influence the case’s outcome, which is why it should not be shared with anyone involved.  
• Collaborative: Once the neutral provides their evaluation of the case, the parties can collaborate and decide whether a settlement may be acceptable. The parties may be more willing to work together after they have a better grasp on their case. This is not always the case after a neutral present their opinion. Still, it can be a good starting point for further discussions.  

This is a brief overview of the more formal process that many courts have implemented to help clear backlogged calendars. In addition, there are a variety of ways that parties may choose to participate in case evaluation. 

It is becoming more popular for a single party to meet with an evaluator and present their case and examine the strengths and weaknesses of their case, along with the other party’s defenses. While this is not as thorough of a process, it can help a party determine if the litigation is worth the risk. 

The Role of the Neutral:

To understand the process, even more, it can be helpful to examine the role of the neutral. Within a case evaluation , the neutral, or panel of neutrals, will take all of the evidence and arguments presented and determine the strengths and weaknesses of each party’s position while predicting the outcome of a trial on the case.

The neutral is not there to help the parties decide about a settlement or encourage them to do so. Instead, the neutral is there merely to evaluate the case and show the parties the path the case may take moving forward.

Reasons to Use Case Evaluation:

The reasons that the parties may choose case evaluation will differ with each case. Still, some common themes run throughout the cases that end up in front of an evaluator. If any of these reasons ring true for a case, the parties may benefit from the neutral evaluation and the insight it brings. Some common reasons to send a case to evaluation include:  

• They think the case could settle. Courts and the legal system are often incredibly overwhelmed by the number of cases that need to be tried at any given time. This is why many judges will encourage settlement. Suppose a case is filed and a judge sees the potential for settlement early. In that case, they may recommend to neutral evaluation to encourage the parties to move towards a settlement. Additionally, the parties may see the benefit in having an evolution for settlement purposes.  
• The parties need an unbiased and accurate opinion of the case.  Parties to a legal dispute often see their case in a very optimistic way. This is normal and often necessary for parties to recover what they are entitled to. However, optimism can occasionally drive the parties to have unrealistic expectations for the case, and it may stop any fruitful conversation or accurate work. The evaluator may be able to reign in the parties’ expectations and paint an accurate picture of the possible results of the trial may be. This could lead to settlement discussions or a trial with fewer disputes than initially stated.  
• There is a stubborn party.  Evaluators will often see one party with a clear path forward and another party that believes they are entitled to more than they are, slowing resolution. In a similar fashion to the realistic picture above, The neutral may help the stubborn party come to terms with the potential outcome and reach an agreement that seemed impossible.  
• The parties contracted for it.  Contracting parties may agree to submit any disputes that arise to a neutral before a case is filed. This clause can be included in a contract and enforced when a contractual dispute arises. This requires that the parties submit a good faith and accurate representation to the neutral so that the neutral has the most accurate picture of the dispute before they present their evaluation. This can stop disputes from reaching litigation in the first place.  
• The parties agree to submit it post-dispute.  Parties may also agree to submit the case to early neutral evaluation after a dispute arises. All parties must agree to submit their dispute to the evaluator, meaning that it cannot be forced unilaterally. Again, the parties must present a good-faith representation of their case so the neutral can make an accurate evaluation. 

If a case needs a set of outside eyes looking at the evidence to help the parties fully understand the outcomes and possible weaknesses a case may have, it is always a good idea to have that third-party evaluator look at it.

Particular Cases:

While most civil cases would benefit from some form of neutral evaluation, certain cases may be particularly successful after undergoing case evaluation:

• Child Custody :  Parties seeking to establish or modify child custody orders may benefit from case evaluation because an unbiased evaluation of the positions can assist this process. The parties are often strongly fueled by emotion, and a neutral’s opinion can help provide an accurate portrayal of the case.
• Divorce :  Similar to child custody cases, case evaluation, can often help the parties see an accurate picture of what the court would award. And may help them to protect their interests rather than relying on their emotion and anger to drive them forward.  
• Contracts:  Contracts can include an agreement to submit a dispute to early neutral evaluation, as mentioned above. Contract cases often have a step-by-step evaluation of case, so having a neutral evaluate the case early on can save time for the case and provide clarity about the strength of the claim.  

While these types of cases are often particularly suited for evaluation as a whole, that does not mean that every child support or family law case will undergo evaluation. 

Additionally, it is not an exclusive list. Many cases will benefit from an evaluation, even if they do not fit in one of these categories .

Advantages of Case Evaluation:  

There are several important advantages of case evaluation. These include:

• Expertise:  The neutral can be appointed or chosen based on their knowledge about the subject matter of the dispute . This can both ensure the parties are confident that the neutral is competent and that the evaluation is fair and understanding of any unique challenges.  
• Focus:  There are often many aspects of a case that can distract the parties from the most critical parts of the case. Emotions often drive the parties to focus on more minor points of lesser importance. An evaluation can help the parties adjust their focus to the most critical aspects of their case, which can provide new insights and a more straightforward path to success in the case.
• Confidential:  Because the entire process is confidential, it allows the parties to speak freely, knowing that nothing will be shared publicly or in court. This can help encourage settlement and help the parties feel like their entire story was presented.
• Neutral:  The neutral evaluator’s opinion can help the parties feel that their case has real value. It also helps them understand the value of the other party’s case.  
• Voluntary:  Because the parties are choosing to participate in the evaluation and choosing to settle or continue in the case after, the parties are often more forthcoming in the process and can see value in the chance to learn more about their case.
• Chance to Settle Early:  Case evaluation gives the parties a chance to come together and hear the strengths and weaknesses of both cases. Then have the opportunity to discuss a settlement before the parties spend too much on attorneys and court fees.  

Case evaluation can help the parties gain a deeper understanding of their position and the likely outcome of the case. Doing so can bring clarity to the parties, even if an agreement is not reached .

Disadvantages of Case Evaluation:

There are also disadvantages to case evaluation that can impact the accessibility or necessity of the process and must be considered. These include:

• Cost: Because the process is voluntary, there is no guarantee that the case will be resolved after the evaluation . The evaluation may add costs on top of other litigation costs .
• Stress: When the process does not provide a settlement or clarification for the parties, it can feel like wasted time and stress for something that was not fruitful.
• Refusal: Sometimes, a party will hear that their case is not strong but still refuse to settle it. This can drag out the process of resolving the case and keep the parties in a state of limbo where they know what will likely happen but cannot act on it.

Case evaluation can be beneficial in many cases to clarify and understand the outcome and help encourage settlement between the parties. For many, case evaluation is not an initial idea when alternative dispute resolution is necessary. Still, it may help encourage the parties to find an outcome that benefits them and moves the case forward.

Must-read Articles:

• Recent Posts

• The Art of Decision Making: Improving Decision-Making Skills - November 17, 2023
• EEOC Mediation: Understanding the Process - November 15, 2023
• The Implications of the Contract Clause: Protecting the Right to Private Contracts - November 8, 2023

Chatbots May ‘Hallucinate’ More Often Than Many Realize

When summarizing facts, ChatGPT technology makes things up about 3 percent of the time, according to research from a new start-up. A Google system’s rate was 27 percent.

Amr Awadallah, the chief executive of Vectara, warns that its chatbot software doesn’t always tell the truth. Credit... Cayce Clifford for The New York Times

Supported by

• Share full article

By Cade Metz

Cade Metz has been watching chatbots hallucinate since 2017.

• Published Nov. 6, 2023 Updated Nov. 16, 2023

When the San Francisco start-up OpenAI unveiled its ChatGPT online chatbot late last year , millions were wowed by the humanlike way it answered questions, wrote poetry and discussed almost any topic. But most people were slow to realize that this new kind of chatbot often makes things up .

Listen to This Article

Open this article in the New York Times Audio app on iOS.

When Google introduced a similar chatbot several weeks later, it spewed nonsense about the James Webb telescope . The next day, Microsoft’s new Bing chatbot offered up all sorts of bogus information about the Gap, Mexican nightlife and the singer Billie Eilish. Then, in March, ChatGPT cited a half dozen fake court cases while writing a 10-page legal brief that a lawyer submitted to a federal judge in Manhattan.

Now a new start-up called Vectara, founded by former Google employees, is trying to figure out how often chatbots veer from the truth. The company’s research estimates that even in situations designed to prevent it from happening, chatbots invent information at least 3 percent of the time — and as high as 27 percent.

Experts call this chatbot behavior “hallucination.” It may not be a problem for people tinkering with chatbots on their personal computers, but it is a serious issue for anyone using this technology with court documents, medical information or sensitive business data.

Because these chatbots can respond to almost any request in an unlimited number of ways, there is no way of definitively determining how often they hallucinate. “You would have to look at all of the world’s information,” said Simon Hughes, the Vectara researcher who led the project.

Dr. Hughes and his team asked these systems to perform a single, straightforward task that is readily verified: Summarize news articles. Even then, the chatbots persistently invented information.

“We gave the system 10 to 20 facts and asked for a summary of those facts,” said Amr Awadallah, the chief executive of Vectara and a former Google executive. “That the system can still introduce errors is a fundamental problem.”

The researchers argue that when these chatbots perform other tasks — beyond mere summarization — hallucination rates may be higher.

Their research also showed that hallucination rates vary widely among the leading A.I. companies. OpenAI’s technologies had the lowest rate, around 3 percent. Systems from Meta, which owns Facebook and Instagram, hovered around 5 percent. The Claude 2 system offered by Anthropic, an OpenAI rival also based in San Francisco, topped 8 percent. A Google system, Palm chat, had the highest rate at 27 percent.

An Anthropic spokeswoman, Sally Aldous, said, “Making our systems helpful, honest and harmless, which includes avoiding hallucinations, is one of our core goals as a company.”

Google declined to comment, and OpenAI and Meta did not immediately respond to requests for comment.

With this research, Dr. Hughes and Mr. Awadallah want to show people that they must be wary of information that comes from chatbots and even the service that Vectara sells to businesses. Many companies are now offering this kind of technology for business use.

Based in Palo Alto, Calif., Vectara is a 30-person start-up backed by $28.5 million in seed funding. One of its founders, Amin Ahmad, a former Google artificial intelligence researcher, has been working with this kind of technology since 2017, when it was incubated inside Google and a handful of other companies.

Much as Microsoft’s Bing search chatbot can retrieve information from the open internet, Vectara’s service can retrieve information from a company’s private collection of emails, documents and other files.

The researchers also hope that their methods — which they are sharing publicly and will continue to update — will help spur efforts across the industry to reduce hallucinations. OpenAI, Google and others are working to minimize the issue through a variety of techniques, though it is not clear whether they can eliminate the problem.

“A good analogy is a self-driving car,” said Philippe Laban, a researcher at Salesforce who has long explored this kind of technology. “You cannot keep a self-driving car from crashing. But you can try to make sure it is safer than a human driver.”

Chatbots like ChatGPT are driven by a technology called a large language model , or L.L.M., which learns its skills by analyzing enormous amounts of digital text, including books, Wikipedia articles and online chat logs. By pinpointing patterns in all that data, an L.L.M. learns to do one thing in particular: guess the next word in a sequence of words .

Because the internet is filled with untruthful information, these systems repeat the same untruths. They also rely on probabilities: What is the mathematical chance that the next word is “playwright”? From time to time, they guess incorrectly.

The new research from Vectara shows how this can happen. In summarizing news articles, chatbots do not repeat untruths from other parts of the internet. They just get the summarization wrong.

For example, the researchers asked Google’s large language model, Palm chat, to summarize this short passage from a news article:

The plants were found during the search of a warehouse near Ashbourne on Saturday morning. Police said they were in “an elaborate grow house.” A man in his late 40s was arrested at the scene.

It gave this summary, completely inventing a value for the plants the man was growing and assuming — perhaps incorrectly — that they were cannabis plants:

Police have arrested a man in his late 40s after cannabis plants worth an estimated £100,000 were found in a warehouse near Ashbourne.

This phenomenon also shows why a tool like Microsoft’s Bing chatbot can get things wrong as it retrieves information from the internet. If you ask the chatbot a question, it can call Microsoft’s Bing search engine and run an internet search. But it has no way of pinpointing the right answer. It grabs the results of that internet search and summarizes them for you.

Sometimes, this summary is very flawed. Some bots will cite internet addresses that are entirely made up.

Companies like OpenAI, Google and Microsoft have developed ways to improve the accuracy of their technologies. OpenAI, for example, tries to refine its technology with feedback from human testers, who rate the chatbot’s responses, separating useful and truthful answers from those that are not. Then, using a technique called reinforcement learning, the system spends weeks analyzing the ratings to better understand what it is fact and what is fiction.

But researchers warn that chatbot hallucination is not an easy problem to solve. Because chatbots learn from patterns in data and operate according to probabilities, they behave in unwanted ways at least some of the time.

To determine how often the chatbots hallucinated when summarizing news articles, Vectara’s researchers used another large language model to check the accuracy of each summary. That was the only way of efficiently checking such a huge number of summaries.

But James Zou, a Stanford computer science professor, said this method came with a caveat. The language model doing the checking can also make mistakes.

“The hallucination detector could be fooled — or hallucinate itself,” he said.

Audio produced by Kate Winslett .

Cade Metz is a technology reporter and the author of “Genius Makers: The Mavericks Who Brought A.I. to Google, Facebook, and The World.” He covers artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas. More about Cade Metz

Advertisement