single site case study

The Advantages and Limitations of Single Case Study Analysis

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).

Limitations

Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.

References 

Bennett, A. and Checkel, J. T. (2012) ‘Process Tracing: From Philosophical Roots to Best Practice’, Simons Papers in Security and Development, No. 21/2012, School for International Studies, Simon Fraser University: Vancouver.

Bennett, A. and Elman, C. (2006a) ‘Qualitative Research: Recent Developments in Case Study Methods’, Annual Review of Political Science , 9, 455-476.

Bennett, A. and Elman, C. (2006b) ‘Complex Causal Relations and Case Study Methods: The Example of Path Dependence’, Political Analysis , 14, 3, 250-267.

Bennett, A. and Elman, C. (2007) ‘Case Study Methods in the International Relations Subfield’, Comparative Political Studies , 40, 2, 170-195.

Bennett, A. and Elman, C. (2010) Case Study Methods. In C. Reus-Smit and D. Snidal (eds) The Oxford Handbook of International Relations . Oxford University Press: Oxford. Ch. 29.

Berg, B. and Lune, H. (2012) Qualitative Research Methods for the Social Sciences . Pearson: London.

Bryman, A. (2012) Social Research Methods . Oxford University Press: Oxford.

David, M. and Sutton, C. D. (2011) Social Research: An Introduction . SAGE Publications Ltd: London.

Diamond, J. (1992) ‘Economic development and democracy reconsidered’, American Behavioral Scientist , 35, 4/5, 450-499.

Eckstein, H. (1975) Case Study and Theory in Political Science. In R. Gomm, M. Hammersley, and P. Foster (eds) Case Study Method . SAGE Publications Ltd: London.

Flyvbjerg, B. (2006) ‘Five Misunderstandings About Case-Study Research’, Qualitative Inquiry , 12, 2, 219-245.

Geertz, C. (1973) The Interpretation of Cultures: Selected Essays by Clifford Geertz . Basic Books Inc: New York.

Gerring, J. (2004) ‘What is a Case Study and What Is It Good for?’, American Political Science Review , 98, 2, 341-354.

Gerring, J. (2006a) Case Study Research: Principles and Practices . Cambridge University Press: Cambridge.

Gerring, J. (2006b) ‘Single-Outcome Studies: A Methodological Primer’, International Sociology , 21, 5, 707-734.

Gerring, J. (2007) ‘Is There a (Viable) Crucial-Case Method?’, Comparative Political Studies , 40, 3, 231-253.

King, G., Keohane, R. O. and Verba, S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton University Press: Chichester.

Layne, C. (1994) ‘Kant or Cant: The Myth of the Democratic Peace’, International Security , 19, 2, 5-49.

Levy, J. S. (2008) ‘Case Studies: Types, Designs, and Logics of Inference’, Conflict Management and Peace Science , 25, 1-18.

Lipset, S. M. (1959) ‘Some Social Requisites of Democracy: Economic Development and Political Legitimacy’, The American Political Science Review , 53, 1, 69-105.

Lyotard, J-F. (1984) The Postmodern Condition: A Report on Knowledge . University of Minnesota Press: Minneapolis.

MacMillan, A. (2008) ‘Deviant Democratization in India’, Democratization , 15, 4, 733-749.

Maoz, Z. (2002) Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey and M. Brecher (eds) Evaluating Methodology in International Studies . University of Michigan Press: Ann Arbor.

May, T. (2011) Social Research: Issues, Methods and Process . Open University Press: Maidenhead.

Owen, J. M. (1994) ‘How Liberalism Produces Democratic Peace’, International Security , 19, 2, 87-125.

Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

Van Evera, S. (1997) Guide to Methods for Students of Political Science . Cornell University Press: Ithaca.

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

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A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research

Michael g. kahn.

* Department of Pediatrics, Colorado Clinical and Translational Sciences Institute, University of Colorado, Aurora

† Anschutz Medical Center, Colorado Clinical and Translational Sciences Institute, University of Colorado, Aurora

Marsha A. Raebel

‡ Institute for Health Research, Kaiser Permanente Colorado, Denver

§ School of Pharmacy, University of Colorado

Jason M. Glanz

‖ Department of Epidemiology, Colorado School of Public Health, Aurora, CO

Karen Riedlinger

¶ Northwest Kaiser Center for Health Research, Portland

John F. Steiner

Introduction.

Answers to clinical and public health research questions increasingly require aggregated data from multiple sites. Data from electronic health records and other clinical sources are useful for such studies, but require stringent quality assessment. Data quality assessment is particularly important in multisite studies to distinguish true variations in care from data quality problems.

We propose a “fit-for-use” conceptual model for data quality assessment and a process model for planning and conducting single-site and multisite data quality assessments. These approaches are illustrated using examples from prior multisite studies.

Critical components of multisite data quality assessment include: thoughtful prioritization of variables and data quality dimensions for assessment; development and use of standardized approaches to data quality assessment that can improve data utility over time; iterative cycles of assessment within and between sites; targeting assessment toward data domains known to be vulnerable to quality problems; and detailed documentation of the rationale and outcomes of data quality assessments to inform data users. The assessment process requires constant communication between site-level data providers, data coordinating centers, and principal investigators.

A conceptually based and systematically executed approach to data quality assessment is essential to achieve the potential of the electronic revolution in health care. High-quality data allow “learning health care organizations” to analyze and act on their own information, to compare their outcomes to peers, and to address critical scientific questions from the population perspective.

Answers to important clinical research questions increasingly require data that are aggregated across multiple sites. Some research studies require large sample sizes to detect small but important treatment benefits or harms. 1 Comparative effectiveness research (CER) studies attempt to associate naturally occurring clinical practice variations with differences in clinical outcomes. In both cases, findings arising from a multisite study are more likely to be gen-eralizable than findings from a single site. In multisite research studies, site-level differences in disease incidence, predictive variables, and health outcomes can either represent true “small area” variation in practice patterns and outcomes, 2 , 3 or variability in data collection methods across sites. Distinguishing between true and artifactual variation that arises from problems with data quality is an essential first step in a multisite comparative effectiveness study.

Assessing data variability across sites is particularly important in studies that use data from the electronic health records (EHRs). EHR data are gathered during routine practice by individuals with a wide range of backgrounds and with different levels of commitment to data quality. As a result, EHR data are rarely subjected to the stringent data quality assessment that is routinely applied to prospective observational or interventional research. 4 If data quality issues with EHR data across sites can be identified and ad-dressed 5 – 7 using a standardized approach, this rich data source can increase the efficiency and reduce the costs of observational CER studies and pragmatic clinical trials.

Data quality assessments (DQAs) for multisite studies are typically performed in 2 stages. In stage 1, source datasets are created at each site and evaluated using a “fit-for-use” perspective. A comprehensive approach to the initial stage of data quality assessment in an EHR-based, multisite study requires both within-site and across-site data quality assessment using consensus standards, as multisite assessment often identifies data quality issues not evident from single-site assessment alone. These assessments typically include comparisons of simple cross-tabulations and descriptive statistics such as means, medians, and histograms across sites. Once agreed-upon standards are met, smaller analytic datasets designed to test specific hypotheses are created. Data quality assessment stage 2 is then conducted on these analytic datasets, typically focusing on the independent and dependent variables directly related to the research question. When necessary, medical record review is used in stage 2 to validate outcomes, exposures, and/or covariates.

Individual researchers tend to focus only on “data cleaning” necessary to test a hypothesis of interest within an already-assembled dataset—in other words, on stage 2. Although some investigators, data analysts, and data coordinating centers develop procedures for assessing data quality during stage 1, a comprehensive conceptual framework for this initial stage is lacking. In this paper, we present a pragmatic conceptual model for stage 1 characterization of data quality using a “fit-for-use” perspective. 8 , 9 We then describe an approach for single-site and multisite data quality assessment in large observational studies that employ EHR data. Examples from actual multisite studies illustrate data quality issues and highlight approaches to detecting and resolving these problems.

Conceptual Model for Clinical Data Quality Assessment

Many frameworks have been proposed in the information sciences literature to conceptualize the multiple dimensions of data quality. 10 – 18 All approaches acknowledge that data quality is a multidimensional process that can require trade-offs between dimensions because of organizational and data-use priorities and resource constraints. None of these approaches have been directly applied to clinical or health services research studies, however.

Recent publications in information sciences have focused on achieving data quality standards that makes the data “fit-for-use by data consumers.” 19 Unfortunately, competing descriptive models have resulted in the lack of a clear unified framework for measuring data quality or procedures to improve data quality. 12 In a comprehensive review, Wand and Wang listed 26 data quality dimensions. Five of these dimensions (accuracy, reliability, timeliness, relevance, and completeness) appear in most published data quality frameworks. 20 Wang and Strong proposed a comprehensive “fit-for-use” data quality assessment model that encompassed both a data-element and a data-system perspective. 19 In its original form, the model categorized 118 data quality features into 4 high-level categories: intrinsic features, contextual features, representational features, and accessibility. These 4 categories were used to define 15 data quality dimensions. Given the absence of conceptual frameworks for data quality in clinical or health services research, we have modified and simplified this model in Table 1 , and have also provided clinical examples.

Other categories of data quality (representation and accessibility) defined by Wang and Strong are not included here but address important additional system-level considerations for data users.

Modified from Wang and Strong. 19

Clinical data quality assessment must always be considered within the design and implementation of the particular study. No standardized definitions apply across all data contexts. Yet, several uniform elements should always be considered when evaluating fitness for use. These include (a) the intended data application; (b) the quality characteristics of highest importance within the application; (c) the user's expectations of useful information; and (d) the resources available. The weighting of each element varies from situation to situation.

A Process for Single-Site and Multisite Data Quality Assessment

Multisite research projects are comprised of data elements collected, extracted, examined, and formatted at individual sites. Combining site-specific data into a multisite dataset is rarely a straightforward process. Within each research study, specific data quality assessment routines are selected based on local knowledge of potential data problems, previous data quality concerns, or the requirements of a central data coordinating center. The data quality assessment process then proceeds through multiple iterations of within-site and cross-site assessment, as illustrated in Figure 1 . Data problems identified within a single site may result in data reextraction and quality reassessment. Problems with data accuracy and with the programming used to extract and transform the data manifest at this stage. When data are aggregated across sites, additional data quality assessments may identify new anomalies, necessitating additional data quality assessment cycles at the original sites. After correction or explanation of data anomalies at each site, the data quality assessment cycle continues until data-sets exceed a preestablished quality threshold ( Fig. 1 ).

Detailed process model for multisite data quality assessment. Multiple data quality assessment cycles occur at both the single-site and multisite level. Each decision point (diamond) requires an examination of ≥ 1 data quality measures against predefined acceptability criteria. Many actions require continued engagement with sites to distinguish expected from unexpected site-specific data variability. HER indicates electronic health record; N, no; Y, yes.

Table 2 illustrates the results of applying the stage 1 data quality assessment process shown in Figure 1 to an EHR-based study that originally included 4 sites. In the example, the exposure was a drug dispensing within a specified timeframe and the outcome required an ICD9-CM coded diagnosis. One site had far fewer patients with the drug exposure than would be expected given its population size. Data quality assessment revealed that the site was not capturing claims for all dispensed prescriptions. When exclusion criteria were applied, this site also lost 65% of the initial cohort, in comparison with 19%–27% at the other 3 sites. Finally, no patients at this fourth site appeared to have experienced the study outcome, whereas the other three sites had between 75 and 157 outcome events. Evaluation of this discrepancy revealed that diagnostic claims were incomplete at the outlier site. The site ultimately was dropped from the study.

Approaches for Data Quality Assessment

Single-site data quality assessment.

The conceptual model in Table 1 ensures that relevant data quality dimensions are considered, but does not specify approaches that researchers can use to determine data quality. Specific assessment methods must be selected and executed to determine how well a given dataset meets quality expectations. Unlike the complex statistical models used to test specific study hypotheses, data quality assessment typically relies on simple distributions, cross-tabulations, and graphical visualizations to aid in inspecting data. In Table 3 we present a comprehensive set of data quality rules and quality assessment methods, adapted from Maydanchik. 18 In this approach, 5 categories of rules—attribute domain constraints, relational integrity, historical integrity, state-dependent rules, and attribute dependency rules—are operationalized through multiple assessment methods.

IM indicates intramuscularly; IV, intravenously; PO, orally; SSN, social security number.

Modified from Maydanchik. 18

Attribute domain constraints focus on individual variables, looking for anomalies in data values, distributions, units, and missingness. For example, a date of birth in the year 1390 would be identified as an invalid value (perhaps a digit transposition for the year 1930). A comparison of simple descriptive statistics for prescription claims in Table 2 identified a missing data problem at 1 site.

Relational integrity rules look for inconsistencies across multiple-related variables, seeking to detect data quality issues in comparing elements from 1 data table to related elements in another data table. These rules are often called “double-checks” and “triple-checks.” As an example, a count of 8 prescriptions in a summary table should correspond to 8 filled prescriptions in the original pharmacy table.

Historical data rules assess temporal relationships. The large number of assessment methods in this category emphasizes the complex temporal in most datasets. Historical rules examine sequences, gaps, patterns and dependencies across multiple data values, and variables. For example, an individual who died in 2008 cannot have a hospitalization in 2009.

State-dependent objects rules extend the analysis of temporal data to include logical consistency, where the sequence of temporal events conforms to knowledge about the expected or allowed evolution of a process or set of states over time. For example, a series of prenatal visits would be expected to culminate in an outcome (such as a birth), and then be followed by a postpartum checkup. Similarly, assessing the impact of changes in coding schemes or allowed values over time would be included in this class of data quality assessments.

Attribute dependency rules are the most complex because they combine real-world knowledge about how objects and processes are measured and represented in a dataset. These rules examine conditional dependencies and expected correlations across subsets of data and aggregates. For example, a postpartum checkup would be unlikely to occur 18 months after the delivery date.

Rote application of all data quality assessment methods in Table 3 to a dataset would result in thousands of actual data quality measures. The resources in a research study are never sufficient to assess all data elements against all data quality dimensions. Thus, prioritization is critical. For a given study, the set of critical data quality assessment methods will vary on the basis of the features of the variables that drive the key scientific questions. However, in general, the DQA methods listed under the “attribute domain constraints” and “relational integrity rules” in Table 3 can be applied broadly across all data elements in a dataset or database.

As an example, the Observational Source Characteristics Analysis Report (OSCAR) tool developed by the Observational Medical Outcomes Partnership (OMOP) program generates summary statistics for all categorical and continuous variables, resulting in thousands of assessments without any prioritization. 21 The related Generalized Review of OSCAR Uniform Checking (GROUCH) tool extracts only those OSCAR measures that fail to meet prespecified data quality specifications. 22 These thresholds can be set for all categorical or continuous variables or for individual variables in the dataset. The OSCAR tool is comprehensive but nonselective; the GROUCH tool provides prioritization and specificity.

Multisite Data Quality Assessment

Local data entry processes, data quality validation checks, data storage models, and data extraction routines affect the data structure at a single site. The term syntactic variability expresses data variability caused by differences in the representation of data elements. For example, weights may be recorded and stored in different locations within an EHR, and in different formats or units. Failure to extract data from all locations and to transform into a common format would result in incomplete data. Syntactic issues tend to be detectable and resolvable using single-site data.

When data are combined from multiple sites, data that are syntactically identical (same format, same units) can show important differences if data elements that supposedly represent the same concept actually represent different concepts at each site. The term semantic variability expresses data variability caused by differences in the meaning of data elements. Differences in data collection, abstraction and extraction methods, or measurement protocols can result in semantic variability. For example, failure to distinguish between fasting and random blood glucose, finger-stick or venipuncture sampling, or serum or plasma measurements would result in glucose values that do not represent the same concept. Semantic variability is difficult to detect using single-site data alone because data semantics tend to be consistent within an institution. Only when data are combined from multiple sites can such semantic differences be detected.

The assessment methods in Table 3 were developed to assess single-site data quality. Few standardized approaches have been proposed to extend such single-site data quality assessment methods to multisite data. Two standardized approaches in clinical and health services research are: (1) The OMOP GROUCH data quality analysis tool described previously; and (2) The HMO Research Network (HMORN) Virtual Data Warehouse (VDW). The OMOP GROUCH tool implements 35 data quality rules. Eleven rules explicitly compare OSCAR-generated data quality measures from 1 site against all other sites. 22 The HMORN VDW research resource maintains a standardized quality assessment battery of SAS programs that enables all HMORN sites to evaluate their VDW data tables in comparison with a standardized set of “pass/fail” metrics common across all HMORN sites and to evaluate within-site attribute domain constraints, relational integrity, and historical integrity. 23

In multisite data comparisons, calculation of simple descriptive statistics such as expected event rates, frequency distributions, and time trends allows detection of typical semantic anomalies such as: wide variation in counts or event rates, differences in distributions (eg, histograms) and temporal trends, including sudden deviation from previous trends, and the degree of missing data (as exemplified in Table 2 ). Such differences may be quite pronounced. In Figure 2 , we provide a comparison of fasting serum glucose tests completed per 1000 patients at 7 different sites over a 6-year period. The data in this figure starkly demonstrate 4 different quality anomalies.

Variability over time and across sites in number of serum fasting glucose tests performed per 1000 patients.

An observed site-level deviation in stage 1 does not necessarily indicate a data quality issue. True differences in populations, measurement processes, clinical workflows, or treatment strategies can result in significant differences across data sources. Such naturally occurring variation in clinical practice has been a source of important research for many years. 3 , 24 , 25 The data quality assessment process in Figure 1 will detect these differences, but cannot always explain them. As highlighted in Figures 1 and ​ and2, 2 , observed anomalies need to be interpreted in collaboration with the local site to determine if the differences can be explained by factors other than data quality. Site data owners have intimate knowledge about local workflows, data collection conventions, and changes in technologies (new systems, new updates, expanded implementations) that usually are not known to multisite collaborators.

In dynamic datasets that are regularly updated, information gleaned from previous data quality assessments within and between sites can identify areas that need extra evaluation during subsequent data quality assessments. For example, the HMORN VDW conducts across-site quality assessment yearly, whereas within-site assessment of previously identified data quality concerns occurs during the intervening months. By guiding resources and attention to known data quality concerns, data efficiency and consistency from each primary site improve over time.

Detailed documentation of the rationale for conducting data quality assessment and the outcomes of those assessments is essential, because every data quality assessment plan is a compromise between limited time and resources and the desire for the highest possible data quality. Investigators must be aware that, in many cases, data quality decisions are made by database managers and programmers who provide support to multiple studies that rely on the same data sources. Investigators may not be aware of crucial data quality decisions unless they are recorded and accessible.

Implications for Clinical Research Data Networks

A more standardized and comprehensive approach to stage 1 data quality assessment is likely to improve the validity of multisite, EHR-based comparative effectiveness research. When data variability across multiple sites reflects true differences in clinical practice, researchers gain the opportunity to measure medication comparative safety and effectiveness, procedures and tests utilization in an observational setting. Once variability is identified and data quality problems are eliminated, traditional stage 2 data quality approaches such as manual chart review (ideally using the EHR, but at times requiring manual record review) can be used to assure the validity of critical exposure and outcome variables. Numerous studies using manual record review as the “gold standard” have demonstrated that automated clinical data from managed-care organizations can inaccurately measure disease incidence, with positive predictive values as low as 20%. 26 – 30 These inaccuracies can be independent or dependent of treatment status, thus leading to differential or nondifferential misclassification bias that can result in both false-positive and false-negative study findings. 31 Although manual medical record review can provide a “gold standard” for validating automated clinical data, it is expensive and time consuming, and thus cannot be used routinely in stage 1 data quality assessments. Developing automated stage 1 methods to identify and resolve data anomalies across multiple sites is therefore essential to both the efficiency and the validity of CER studies that rely on cross-site comparisons.

In recent years, decentralized data models for multisite research (distributed research networks, or DRNs) have been developed that bypass the need to transfer data containing protected health information to a central data ware-house. 32 – 40 In a DRN, a centralized data coordinating center may still be responsible for data quality assessment. But as the data are not pooled, the data coordinating center must devote more attention to planning and conducting quality assessment. In a DRN, the data coordinating center distributes programming code to all participating sites. A programmer then reviews and runs the code on local data, and sends the results back to the data coordinating center for evaluation. Such a process can potentially be less efficient than a traditional centralized model initially. However, the preparatory work required in DRN assessment may lead to progressive improvements in data quality over time, as programmers have to anticipate data issues and write their code to reduce inefficiencies.

In 2010, the President's Council of Advisors on Science and Technology (PCAST) report recommended the use of mandatory “metadata” tags attached to every data ele-ment. 41 These tags provide additional information about the data element, such as where the data was created (data provenance) and privacy permissions and restrictions. These metadata tags could be expanded to include the data quality measures as listed in Table 3 , either as a single summary data quality statistic or as individual values for specific data quality measures that were applied to the data before release. An example of data quality metadata tags for continuous variables would be tags that include the attribute's mean, median, SD, interquartile range, and percentage missing across the original dataset. The simple distribution measures created by the OMOP OSCAR tool could be a useful initial set of data quality metadata tags consistent with the PCAST recommendation.

Data quality metadata tags are analogous to other metadata documentation that is required to support effective data sharing across institutions. Because data sharing is a National Institutes of Health priority, 42 appropriate resources should be included in the study budget directed explicitly to metadata documentation, including documenting data quality assessment results. In the future, new informatics tools could be developed that could perform data quality and automatically attach the appropriate DQA metadata tags and assessment results directly to the dataset.

Few publications have described the use of data quality models in health care data. 43 – 46 One case study in a large national intensive care unit registry examined the causes of error that influenced data accuracy and completeness at local sites and at a central coordinating center. 44 This paper categorized data errors into 3 categories (setup and organization, data collection, and quality improvement), described error-promoting processes at data collection sites and at the central coordinating center, and proposed a comprehensive framework for improving data quality. This paper did not address most elements of the conceptual model in Table 2 , and did not describe in detail the approaches used to identify and correct those data quality issues.

Because the literature is sparse, and because systematic approaches to stage 1 data quality assessment have not been proposed for both single-site and multisite projects, a conceptual framework is useful for addressing data variability in a logical and comprehensive manner. This framework can be translated into a rigorously applied strategy for data quality assessment, which defines data quality dimensions and assessment methods to assess the variability in a multisite dataset. To execute this strategy in a multisite study, a strong collaborative relationship between members of the data coordinating center and the individual sites is essential because identifying and correcting data errors is of necessity iterative. If well-designed, this strategy assures that data quality issues discovered at individual sites can be used to improve data quality for subsequent studies.

If the variability in populations, treatment exposures, and clinical outcomes is due to true “small area variations” in clinical practice or health plan benefit design, CER studies can provide critical information about medical treatment effectiveness and safety or innovations in care delivery across broad populations. However, apparent data variability because of unrecognized data quality problems has the potential to invalidate study findings. Careful differentiation between real and spurious data variability at both the single-site and multisite level is essential if the promise of CER is to be achieved.

Acknowledgments

Supported by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable Partnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute).

The authors declare no conflict of interest.

Introducing single case study research design: an overview

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• 1 Napier University, Edinburgh.
• PMID: 27712405
• DOI: 10.7748/nr.5.4.15.s3

An overview of case study research The use of case study design in research has a long and distinguished history in many disciplines ( 1 ). It is used in law, education, history, medicine, psychology and business ( 2 ). Yin ( 3 ) stated that case studies are essential for social science and that they are used extensively in practice-oriented professions. Examples of studies include those of Freud, from which he derived his theories of personality ( 4 ), and Whyte's study of neighbourhood gangs in Chicago ( 5 ), which was found by other researchers ( 6 ) to be a typical case and again, as in the example of Freud, led to the development of theory. The term 'case study' refers to both a process of inquiry - that is, studying a case or designing and executing a case study - and its end product, the case study or 'case report' ( 7 ).

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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• Published: 05 April 2024

Single-case experimental designs: the importance of randomization and replication

• René Tanious   ORCID: orcid.org/0000-0002-5466-1002 1 ,
• Rumen Manolov   ORCID: orcid.org/0000-0002-9387-1926 2 ,
• Patrick Onghena 3 &
• Johan W. S. Vlaeyen   ORCID: orcid.org/0000-0003-0437-6665 1  

Nature Reviews Methods Primers volume  4 , Article number:  27 ( 2024 ) Cite this article

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Single-case experimental designs are rapidly growing in popularity. This popularity needs to be accompanied by transparent and well-justified methodological and statistical decisions. Appropriate experimental design including randomization, proper data handling and adequate reporting are needed to ensure reproducibility and internal validity. The degree of generalizability can be assessed through replication.

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Acknowledgements

R.T. and J.W.S.V. disclose support for the research of this work from the Dutch Research Council and the Dutch Ministry of Education, Culture and Science (NWO gravitation grant number 024.004.016) within the research project ‘New Science of Mental Disorders’ ( www.nsmd.eu ). R.M. discloses support from the Generalitat de Catalunya’s Agència de Gestió d’Ajusts Universitaris i de Recerca (grant number 2021SGR00366).

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Rumen Manolov

Methodology of Educational Sciences Research Group, Faculty of Psychology and Educational Science, KU Leuven, Leuven, Belgium

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Tanious, R., Manolov, R., Onghena, P. et al. Single-case experimental designs: the importance of randomization and replication. Nat Rev Methods Primers 4 , 27 (2024). https://doi.org/10.1038/s43586-024-00312-8

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Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

A collaborative campus community policing model: a case study in fostering collective efficacy among students, faculty and staff.

Hassan Ramzah , University of Nebraska-Lincoln Follow

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Spring 2024

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Educational Administration

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Elizabeth Niehaus

This qualitative, single case study explored whether and how a campus police agency’s community policing model fostered collective efficacy among students, faculty, staff members and campus police. Using Bandura’s (2000) collective efficacy theory, this study was guided by the following questions: (1) How does a campus police department use a community policing model to build partnerships with students, faculty, and staff? (2) How do police officers, students, faculty, and staff perceive these partnership-building efforts, particularly as it relates to fostering collective efficacy within the campus community? (3) Through what processes have campus police partnerships with students, faculty, and staff contributed to participation in the co-production of campus safety? (4) What do the campus police relationships with students, faculty, and staff reveal about building trust and establishing legitimacy? Participants included four members of the campus police department, as well as three students and three campus staff members. The case site was a mid-sized, public higher education institution in the Midwest. Data collection involved document analysis, observations of campus police activities, and interviews with students, staff members and campus police officers. The findings of this study suggest community policing in a campus environment influenced mutual trust beliefs among students, staff members and campus police in their capability to succeed and work collaboratively to solve problems. These shared beliefs contributed to student and staff members’ willingness to intervene to promote campus safety. This study offers implications for research and recommendations for practice based on these findings.

Copyright 2024, Hassan Ramzah. Used by permission

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Ramzah, Hassan, "A Collaborative Campus Community Policing Model: A Case Study in Fostering Collective Efficacy Among Students, Faculty and Staff" (2024). Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023– . 101. https://digitalcommons.unl.edu/dissunl/101

Since May 25, 2024

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Bricks Builder  is a visual site builder that allows users to create custom, responsive websites  directly within the WordPress environment. It is designed to enhance user experience as it integrates seamlessly with WordPress, enabling beginners and experienced developers  to construct websites visually through a drag-and-drop interface.

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Bricks Builder stands out for its performance and flexibility. It is a newer yet robust alternative to other builders, offering:

• Speed  — Bricks is incredibly fast and lightweight.
• Ease of use  — The interface is intuitive, making it easy for beginners and professionals.
• Customization  — Bricks offers extensive customizability through custom CSS and JavaScript and is compatible with a wide range of WordPress plugins.

Getting started with Bricks

Bricks is a premium theme that requires a paid subscription. To use it, you need to purchase one of their plans. Once you’ve made your payment, you can download a zip file on your dashboard that can be uploaded to your WordPress site, along with a license key that activates Bricks within your WordPress installation .

After installing the Bricks theme, a new Bricks  menu item appears in your WordPress dashboard .

To activate your license, go to the dashboard, click Activate license , enter your license key from your Bricks account, and click Activate . This will enable full functionality of the Bricks theme.

If you like to try Bricks before committing to a purchase, you can set up a trial account at try.bricksbuilder.io . This platform provides access to a pre-configured WordPress site with Bricks installed, allowing you to explore its features and capabilities at no initial cost.

How to launch the Bricks page builder

To design a new page with the Bricks page builder, navigate to Pages  > Add New  or click an existing page. You can launch the Bricks editor by clicking on Edit with Bricks .

You can now drag elements from the Elements panel  to design your page.

Understanding the Bricks interface

The Bricks interface offers a seamless integration with the WordPress environment.

Here’s a breakdown of its core interface:

• Builder workspace  — The main area of Bricks is the builder workspace, which is a visual representation of your website. Here, you can interact directly with your pages, dragging elements into place, resizing, and rearranging components.
• Toolbar  — At the top of the workspace, the toolbar provides quick access to essential functionalities such as saving your work, previewing your site, and accessing the settings menu and responsive controls.
• Elements panel  — On the left side of the interface, the elements panel is your toolbox. It contains all the building blocks you can use on your site, such as text boxes, images, buttons, and more. You can drag these elements from the panel directly onto your page.
• Structure view  — Often located beside or as a toggle within the elements panel, the structure view provides a hierarchical outline of your page. This view is particularly useful for understanding the layout and depth of your page elements, allowing you to select and edit nested items easily.

Using custom fonts in Bricks

Bricks offer the exciting feature of allowing you to upload custom fonts  to your WordPress site. To do this, navigate to Bricks  > Custom Fonts .

This section lets you upload your font files in various formats, including WOFF2, WOFF, or TTF. You can also upload different font variations, such as thin, semi-bold, normal, and bold.

To add a custom font, click on Add New . Then, enter a name for the font in the Add title  field. Next, select the font variant you wish to upload, choose the font style, and click Edit .

A dropdown will appear, providing fields to upload the font file. Select your font file and upload it. You can repeat this process by clicking Add a font variant  for as many variants as possible. Finally, click Publish .

To apply your newly uploaded custom font to text on your WordPress site, follow these steps:

• Go to the page where you want to use the new font and click Edit with Bricks .
• Click on the text you want to change.
• Information about the selected text will be displayed in the left panel. Click on STYLE .
• Expand the TYPOGRAPHY  dropdown to view more options.
• In the Font family  field, browse the available fonts, including Google fonts and your custom uploads.
• Select your custom font from the list to update your text immediately.

Now, your text will display using your custom font, giving your page a personalized touch.

Using templates in Bricks

Templates are a fundamental feature in Bricks and come in various types, including headers, footers, and blog post layouts. These templates can range from a single section, like a website header or a hero section, to full-page content, such as blog post layouts, archive pages, search results, and error pages.

Creating a template

To create a template:

• Navigate to Bricks  > Templates  in your dashboard.
• Choose to either add a new template or import an existing one.

Applying templates

To apply a template to a page:

• Navigate to the page where you want to use the template.

• Here, you can access both the templates you created and the community templates .

Remote templates

Bricks’ templates offer unique capabilities not found in other site builders. Remote templates mean templates created and used in other sites you want to use in your site.

To allow other sites to use your Bricks templates, go to Bricks  > Settings  > Templates  in your WordPress dashboard and enable the My Templates Access  checkbox.

Use Whitelist URLs  and Password Protection  to restrict template access to people who know the correct password.

To use templates from other sites, go to Bricks  > Settings  > Templates , scroll to the Remote templates  section, and paste the URL of the Bricks site you want to retrieve templates from into the Remote template URL  field.

If you set a password, you must make sure that you write it under the Remote template Password . Then click Save Settings .

To use these templates, navigate to the page builder and click Templates  like before; a new section of Remote templates  will appear with all the templates from the remote site.

Using dynamic data in Bricks

Bricks allows you to incorporate dynamic data  into your templates and pages, making it a versatile tool for building dynamic websites.

You can include various dynamic elements such as featured images, post titles, publication dates, author names, categories, tags, site titles, and all your custom fields.

Dynamic data is particularly useful when creating specific templates in Bricks, like your blog post template or templates for custom post types, such as a single property listing.

To add dynamic data, start typing { on the canvas or click the “bolt” icon next to most fields in the settings panel.

Here’s how to create a single post template using dynamic data with Bricks:

• Navigate to Bricks  > Template  to create a new template. Assign a title to your template, set its type to Single , and then either Publish  it or Save as draft .
• Click on Edit with Bricks  to start configuring your template to use dynamic data.
• Style the page as desired. Typically, you will want elements like the post title, featured image, publication date, author’s name, and the post content—all set to display dynamically.

To set this template as the default for all posts:

• Click the settings icon in the toolbar, go to Template Settings  > Conditions , and then click the + ADD CONDITION  button.

To simplify the styling of this template, add a default post to use as a reference for the dynamic elements:

• Click the settings icon in the toolbar, and go to Template Settings  > POPULATE CONTENT .

Additionally, Bricks supports using content from Advanced Custom Fields (ACF), enhancing your ability to create detailed, custom-tailored pages.

Handling form submissions with Bricks

The form submission feature was introduced in Bricks 1.9.2 to allow you to capture form submissions. This allows you to create a custom table named bricks_form_submissions in your database (plus your WP database prefix).

You can enable the new Save form submissions in database  setting under Bricks  > Settings  > General  > Miscellaneous .

Then click Save Settings . A Form Submissions tab will appear ( Bricks  > Form Submissions ), holding your various form submission tables.

Collecting form submissions

Collecting form submissions in Bricks does not require an extra plugin. You just have to use the Form  element to create a form and set the fields correctly.

In the settings panel, click Actions , then select the Save Submission  action.

Next, click Save Submission  settings to give the form a descriptive name. This name will be used to access data from the form.

Now, when you save and open the page. You can complete the form and then navigate back to Bricks  > Form submission  to see the form data.

You can also download a CSV file of the various form submissions. In the Bricks  > Settings  > General  > Miscellaneous  section where you configured this feature, you can reset or delete the database table.

Using WooCommerce with Bricks

WooCommerce  is a free plugin that enhances your WordPress site with e-commerce capabilities. It’s globally recognized as a leading open-source  solution for setting up and managing an online store.

Bricks integrate seamlessly with WooCommerce, providing a robust in-theme builder for crafting your entire store, from the main shop page to individual product pages, product archives, carts, checkouts, and account pages.

Bricks offers specialized elements and templates specifically for WooCommerce, allowing you to design your store’s layout.

To start using the WooCommerce Builder in Bricks, install and activate the WooCommerce plugin. Once activated, you can use Bricks to visually design and customize various WooCommerce templates, similar to how you would manage single post layouts.

To create a simple Shop page:

• Navigate to Pages  and select the Shop  page.

Using these elements, you can arrange and style your page as desired.

Like creating a single post template, you can create a single product template and set its conditions to work with all products.

Query loops in Bricks

The Query Loop builder lets you query your database according to your query parameters and renders the query results you want to show inside the loop. It can be used in layout elements like container, Accordion, and Slider.

You can query post types, taxonomy terms, and users. For example, you can query a list of blog authors, community members, and team members.

To use the Query loop, add a Container element to the canvas. Enable the Use Query Loop  setting to turn your container into a loop item.

Once you’ve enabled the Use Query Loop  setting, you’ll see a Query control (infinity icon). Click the icon to open the query control and set the query parameters for retrieving the content from your database.

The container now serves as your repeater item. All elements inside this container are repeated as often as there are query results. This academy guide explains how it works.

Bricks pricing

Bricks do not have a free plan. They only create an account  to let you try their theme, but you can’t use it for your site unless you pay.

Bricks offer basic plans and a lifetime plan . The lifetime plan offers unlimited privileges, but you decide what you want.

They offer the convenience of having almost everything you need within the platform, contributing to a seamless and efficient workflow and making it a worthwhile investment for those seeking a robust and versatile web design solution.

Bricks review: Is it worth a try?

One potential drawback is that Bricks does not offer a free version, unlike Elementor. However, Bricks Builder has been gaining attention in the industry with its blend of simplicity and powerful features.

A 2024 survey  of 1,144 WordPress professionals carried out by The Admin Bar says that “Bricks was the only page builder to increase its numbers over last year, racking in 32% of the total” with the following table:

This shows how many WordPress professionals are embracing it even though it is still a relatively new entrant in the market.

Bricks operate as a complete theme. This means you cannot mix and match features from other themes with its page builder. Also, in terms of speed, as a newer page builder designed for high-performing websites, Bricks Builder is reputed to be faster than Elementor Pro.

Using a page builder designed for speed alone can not guarantee the best performance. Regardless of the type of website you are building, your hosting provider plays a crucial role in its performance.

At Kinsta, we are known for providing premium WordPress Hosting  with dozens of premium features. We boast in this!

We operate on a fully containerized architecture powered exclusively by the Google Cloud Platform  on Google’s Premium Tier network. This allows us to provide you with a large selection of the fastest data servers, incredible performance, server-level caching, dedicated resources, and enhanced security.

Check out what our customers say , or contact us  to learn more about our managed hosting solution and how it excels.

This article has explored the Bricks Builder, its workings, some of its features, and how to use them. We’ve examined its comprehensive theme-based approach, customization capabilities, and performance benefits.

While Bricks lacks a free version like Elementor, its integrated design and frequent updates may provide superior efficiency for optimizing their website’s performance.

Do you have any questions about how to use the WordPress Bricks theme builder? Let us know in the comments section below!

Joel is a Frontend developer working at Kinsta as a Technical Editor. He is a passionate teacher with love for open source and has written over 200 technical articles majorly around JavaScript and it's frameworks.

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Multiple Case Research Design

• First Online: 10 November 2021

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This chapter addresses the peculiarities, characteristics, and major fallacies of multiple case research designs. The major advantage of multiple case research lies in cross-case analysis. A multiple case research design shifts the focus from understanding a single case to the differences and similarities between cases. Thus, it is not just conducting more (second, third, etc.) case studies. Rather, it is the next step in developing a theory about factors driving differences and similarities. Also, researchers find relevant information on how to write a multiple case research design paper and learn about typical methodologies used for this research design. The chapter closes with referring to overlapping and adjacent research designs.

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Hunziker, S., Blankenagel, M. (2021). Multiple Case Research Design. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_9

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