The Advantages and Limitations of Single Case Study Analysis

single case study qualitative research

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.

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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.

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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.

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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.

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

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[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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  • Published: 22 November 2022

Single case studies are a powerful tool for developing, testing and extending theories

  • Lyndsey Nickels   ORCID: orcid.org/0000-0002-0311-3524 1 , 2 ,
  • Simon Fischer-Baum   ORCID: orcid.org/0000-0002-6067-0538 3 &
  • Wendy Best   ORCID: orcid.org/0000-0001-8375-5916 4  

Nature Reviews Psychology volume  1 ,  pages 733–747 ( 2022 ) Cite this article

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Psychology embraces a diverse range of methodologies. However, most rely on averaging group data to draw conclusions. In this Perspective, we argue that single case methodology is a valuable tool for developing and extending psychological theories. We stress the importance of single case and case series research, drawing on classic and contemporary cases in which cognitive and perceptual deficits provide insights into typical cognitive processes in domains such as memory, delusions, reading and face perception. We unpack the key features of single case methodology, describe its strengths, its value in adjudicating between theories, and outline its benefits for a better understanding of deficits and hence more appropriate interventions. The unique insights that single case studies have provided illustrate the value of in-depth investigation within an individual. Single case methodology has an important place in the psychologist’s toolkit and it should be valued as a primary research tool.

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The authors thank all of those pioneers of and advocates for single case study research who have mentored, inspired and encouraged us over the years, and the many other colleagues with whom we have discussed these issues.

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Nickels, L., Fischer-Baum, S. & Best, W. Single case studies are a powerful tool for developing, testing and extending theories. Nat Rev Psychol 1 , 733–747 (2022). https://doi.org/10.1038/s44159-022-00127-y

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Single-Case Design, Analysis, and Quality Assessment for Intervention Research

Michele a. lobo.

1 Biomechanics & Movement Science Program, Department of Physical Therapy, University of Delaware, Newark, DE, USA

Mariola Moeyaert

2 Division of Educational Psychology & Methodology, State University of New York at Albany, Albany, NY, USA

Andrea Baraldi Cunha

Iryna babik, background and purpose.

The purpose of this article is to describe single-case studies, and contrast them with case studies and randomized clinical trials. We will highlight current research designs, analysis techniques, and quality appraisal tools relevant for single-case rehabilitation research.

Summary of Key Points

Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single case studies involve repeated measures, and manipulation of and independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, and external validity for generalizability of results, particularly when the study designs incorporate replication, randomization, and multiple participants. Single case studies should not be confused with case studies/series (ie, case reports), which are reports of clinical management of one patient or a small series of patients.

Recommendations for Clinical Practice

When rigorously designed, single-case studies can be particularly useful experimental designs in a variety of situations, even when researcher resources are limited, studied conditions have low incidences, or when examining effects of novel or expensive interventions. Readers will be directed to examples from the published literature in which these techniques have been discussed, evaluated for quality, and implemented.

Introduction

The purpose of this article is to present current tools and techniques relevant for single-case rehabilitation research. Single-case (SC) studies have been identified by a variety of names, including “n of 1 studies” and “single-subject” studies. The term “single-case study” is preferred over the previously mentioned terms because previous terms suggest these studies include only one participant. In fact, as will be discussed below, for purposes of replication and improved generalizability, the strongest SC studies commonly include more than one participant.

A SC study should not be confused with a “case study/series “ (also called “case report”. In a typical case study/series, a single patient or small series of patients is involved, but there is not a purposeful manipulation of an independent variable, nor are there necessarily repeated measures. Most case studies/series are reported in a narrative way while results of SC studies are presented numerically or graphically. 1 , 2 This article defines SC studies, contrasts them with randomized clinical trials, discusses how they can be used to scientifically test hypotheses, and highlights current research designs, analysis techniques, and quality appraisal tools that may be useful for rehabilitation researchers.

In SC studies, measurements of outcome (dependent variables) are recorded repeatedly for individual participants across time and varying levels of an intervention (independent variables). 1 – 5 These varying levels of intervention are referred to as “phases” with one phase serving as a baseline or comparison, so each participant serves as his/her own control. 2 In contrast to case studies and case series in which participants are observed across time without experimental manipulation of the independent variable, SC studies employ systematic manipulation of the independent variable to allow for hypothesis testing. 1 , 6 As a result, SC studies allow for rigorous experimental evaluation of intervention effects and provide a strong basis for establishing causal inferences. Advances in design and analysis techniques for SC studies observed in recent decades have made SC studies increasingly popular in educational and psychological research. Yet, the authors believe SC studies have been undervalued in rehabilitation research, where randomized clinical trials (RCTs) are typically recommended as the optimal research design to answer questions related to interventions. 7 In reality, there are advantages and disadvantages to both SC studies and RCTs that should be carefully considered in order to select the best design to answer individual research questions. While there are a variety of other research designs that could be utilized in rehabilitation research, only SC studies and RCTs are discussed here because SC studies are the focus of this article and RCTs are the most highly recommended design for intervention studies. 7

When designed and conducted properly, RCTs offer strong evidence that changes in outcomes may be related to provision of an intervention. However, RCTs require monetary, time, and personnel resources that many researchers, especially those in clinical settings, may not have available. 8 RCTs also require access to large numbers of consenting participants that meet strict inclusion and exclusion criteria that can limit variability of the sample and generalizability of results. 9 The requirement for large participant numbers may make RCTs difficult to perform in many settings, such as rural and suburban settings, and for many populations, such as those with diagnoses marked by lower prevalence. 8 To rely exclusively on RCTs has the potential to result in bodies of research that are skewed to address the needs of some individuals while neglecting the needs of others. RCTs aim to include a large number of participants and to use random group assignment to create study groups that are similar to one another in terms of all potential confounding variables, but it is challenging to identify all confounding variables. Finally, the results of RCTs are typically presented in terms of group means and standard deviations that may not represent true performance of any one participant. 10 This can present as a challenge for clinicians aiming to translate and implement these group findings at the level of the individual.

SC studies can provide a scientifically rigorous alternative to RCTs for experimentally determining the effectiveness of interventions. 1 , 2 SC studies can assess a variety of research questions, settings, cases, independent variables, and outcomes. 11 There are many benefits to SC studies that make them appealing for intervention research. SC studies may require fewer resources than RCTs and can be performed in settings and with populations that do not allow for large numbers of participants. 1 , 2 In SC studies, each participant serves as his/her own comparison, thus controlling for many confounding variables that can impact outcome in rehabilitation research, such as gender, age, socioeconomic level, cognition, home environment, and concurrent interventions. 2 , 11 Results can be analyzed and presented to determine whether interventions resulted in changes at the level of the individual, the level at which rehabilitation professionals intervene. 2 , 12 When properly designed and executed, SC studies can demonstrate strong internal validity to determine the likelihood of a causal relationship between the intervention and outcomes and external validity to generalize the findings to broader settings and populations. 2 , 12 , 13

Single Case Research Designs for Intervention Research

There are a variety of SC designs that can be used to study the effectiveness of interventions. Here we discuss: 1) AB designs, 2) reversal designs, 3) multiple baseline designs, and 4) alternating treatment designs, as well as ways replication and randomization techniques can be used to improve internal validity of all of these designs. 1 – 3 , 12 – 14

The simplest of these designs is the AB Design 15 ( Figure 1 ). This design involves repeated measurement of outcome variables throughout a baseline control/comparison phase (A ) and then throughout an intervention phase (B). When possible, it is recommended that a stable level and/or rate of change in performance be observed within the baseline phase before transitioning into the intervention phase. 2 As with all SC designs, it is also recommended that there be a minimum of five data points in each phase. 1 , 2 There is no randomization or replication of the baseline or intervention phases in the basic AB design. 2 Therefore, AB designs have problems with internal validity and generalizability of results. 12 They are weak in establishing causality because changes in outcome variables could be related to a variety of other factors, including maturation, experience, learning, and practice effects. 2 , 12 Sample data from a single case AB study performed to assess the impact of Floor Play intervention on social interaction and communication skills for a child with autism 15 are shown in Figure 1 .

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An example of results from a single-case AB study conducted on one participant with autism; two weeks of observation (baseline phase A) were followed by seven weeks of Floor Time Play (intervention phase B). The outcome measure Circles of Communications (reciprocal communication with two participants responding to each other verbally or nonverbally) served as a behavioral indicator of the child’s social interaction and communication skills (higher scores indicating better performance). A statistically significant improvement in Circles of Communication was found during the intervention phase as compared to the baseline. Note that although a stable baseline is recommended for SC studies, it is not always possible to satisfy this requirement, as you will see in Figures 1 – 4 . Data were extracted from Dionne and Martini (2011) 15 utilizing Rohatgi’s WebPlotDigitizer software. 78

If an intervention does not have carry-over effects, it is recommended to use a Reversal Design . 2 For example, a reversal A 1 BA 2 design 16 ( Figure 2 ) includes alternation of the baseline and intervention phases, whereas a reversal A 1 B 1 A 2 B 2 design 17 ( Figure 3 ) consists of alternation of two baseline (A 1 , A 2 ) and two intervention (B 1 , B 2 ) phases. Incorporating at least four phases in the reversal design (i.e., A 1 B 1 A 2 B 2 or A 1 B 1 A 2 B 2 A 3 B 3 …) allows for a stronger determination of a causal relationship between the intervention and outcome variables, because the relationship can be demonstrated across at least three different points in time – change in outcome from A 1 to B 1 , from B 1 to A 2 , and from A 2 to B 2 . 18 Before using this design, however, researchers must determine that it is safe and ethical to withdraw the intervention, especially in cases where the intervention is effective and necessary. 12

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An example of results from a single-case A 1 BA 2 study conducted on eight participants with stable multiple sclerosis (data on three participants were used for this example). Four weeks of observation (baseline phase A 1 ) were followed by eight weeks of core stability training (intervention phase B), then another four weeks of observation (baseline phase A 2 ). Forward functional reach test (the maximal distance the participant can reach forward or lateral beyond arm’s length, maintaining a fixed base of support in the standing position; higher scores indicating better performance) significantly improved during intervention for Participants 1 and 3 without further improvement observed following withdrawal of the intervention (during baseline phase A 2 ). Data were extracted from Freeman et al. (2010) 16 utilizing Rohatgi’s WebPlotDigitizer software. 78

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An example of results from a single-case A 1 B 1 A 2 B 2 study conducted on two participants with severe unilateral neglect after a right-hemisphere stroke. Two weeks of conventional treatment (baseline phases A 1, A 2 ) alternated with two weeks of visuo-spatio-motor cueing (intervention phases B 1 , B 2 ). Performance was assessed in two tests of lateral neglect, the Bells Cancellation Test (Figure A; lower scores indicating better performance) and the Line Bisection Test (Figure B; higher scores indicating better performance). There was a statistically significant intervention-related improvement in participants’ performance on the Line Bisection Test, but not on the Bells Test. Data were extracted from Samuel at al. (2000) 17 utilizing Rohatgi’s WebPlotDigitizer software. 78

A recent study used an ABA reversal SC study to determine the effectiveness of core stability training in 8 participants with multiple sclerosis. 16 During the first four weekly data collections, the researchers ensured a stable baseline, which was followed by eight weekly intervention data points, and concluded with four weekly withdrawal data points. Intervention significantly improved participants’ walking and reaching performance ( Figure 2 ). 16 This A 1 BA 2 design could have been strengthened by the addition of a second intervention phase for replication (A 1 B 1 A 2 B 2 ). For instance, a single-case A 1 B 1 A 2 B 2 withdrawal design aimed to assess the efficacy of rehabilitation using visuo-spatio-motor cueing for two participants with severe unilateral neglect after a severe right-hemisphere stroke. 17 Each phase included 8 data points. Statistically significant intervention-related improvement was observed, suggesting that visuo-spatio-motor cueing might be promising for treating individuals with very severe neglect ( Figure 3 ). 17

The reversal design can also incorporate a cross over design where each participant experiences more than one type of intervention. For instance, a B 1 C 1 B 2 C 2 design could be used to study the effects of two different interventions (B and C) on outcome measures. Challenges with including more than one intervention involve potential carry-over effects from earlier interventions and order effects that may impact the measured effectiveness of the interventions. 2 , 12 Including multiple participants and randomizing the order of intervention phase presentations are tools to help control for these types of effects. 19

When an intervention permanently changes an individual’s ability, a return to baseline performance is not feasible and reversal designs are not appropriate. Multiple Baseline Designs (MBDs) are useful in these situations ( Figure 4 ). 20 MBDs feature staggered introduction of the intervention across time: each participant is randomly assigned to one of at least 3 experimental conditions characterized by the length of the baseline phase. 21 These studies involve more than one participant, thus functioning as SC studies with replication across participants. Staggered introduction of the intervention allows for separation of intervention effects from those of maturation, experience, learning, and practice. For example, a multiple baseline SC study was used to investigate the effect of an anti-spasticity baclofen medication on stiffness in five adult males with spinal cord injury. 20 The subjects were randomly assigned to receive 5–9 baseline data points with a placebo treatment prior to the initiation of the intervention phase with the medication. Both participants and assessors were blind to the experimental condition. The results suggested that baclofen might not be a universal treatment choice for all individuals with spasticity resulting from a traumatic spinal cord injury ( Figure 4 ). 20

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An example of results from a single-case multiple baseline study conducted on five participants with spasticity due to traumatic spinal cord injury. Total duration of data collection was nine weeks. The first participant was switched from placebo treatment (baseline) to baclofen treatment (intervention) after five data collection sessions, whereas each consecutive participant was switched to baclofen intervention at the subsequent sessions through the ninth session. There was no statistically significant effect of baclofen on viscous stiffness at the ankle joint. Data were extracted from Hinderer at al. (1990) 20 utilizing Rohatgi’s WebPlotDigitizer software. 78

The impact of two or more interventions can also be assessed via Alternating Treatment Designs (ATDs) . In ATDs, after establishing the baseline, the experimenter exposes subjects to different intervention conditions administered in close proximity for equal intervals ( Figure 5 ). 22 ATDs are prone to “carry-over effects” when the effects of one intervention influence the observed outcomes of another intervention. 1 As a result, such designs introduce unique challenges when attempting to determine the effects of any one intervention and have been less commonly utilized in rehabilitation. An ATD was used to monitor disruptive behaviors in the school setting throughout a baseline followed by an alternating treatment phase with randomized presentation of a control condition or an exercise condition. 23 Results showed that 30 minutes of moderate to intense physical activity decreased behavioral disruptions through 90 minutes after the intervention. 23 An ATD was also used to compare the effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks in four participants with autism. 22 Results showed that participants independently performed more steps with the custom-made video prompts ( Figure 5 ). 22

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An example of results from a single case alternating treatment study conducted on four participants with autism (data on two participants were used for this example). After the observation phase (baseline), effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks were identified (treatment phase), after which only the best treatment was used (best treatment phase). Custom-made video prompts were most effective for improving participants’ performance of multi-step cooking tasks. Data were extracted from Mechling at al. (2013) 22 utilizing Rohatgi’s WebPlotDigitizer software. 78

Regardless of the SC study design, replication and randomization should be incorporated when possible to improve internal and external validity. 11 The reversal design is an example of replication across study phases. The minimum number of phase replications needed to meet quality standards is three (A 1 B 1 A 2 B 2 ), but having four or more replications is highly recommended (A 1 B 1 A 2 B 2 A 3 …). 11 , 14 In cases when interventions aim to produce lasting changes in participants’ abilities, replication of findings may be demonstrated by replicating intervention effects across multiple participants (as in multiple-participant AB designs), or across multiple settings, tasks, or service providers. When the results of an intervention are replicated across multiple reversals, participants, and/or contexts, there is an increased likelihood a causal relationship exists between the intervention and the outcome. 2 , 12

Randomization should be incorporated in SC studies to improve internal validity and the ability to assess for causal relationships among interventions and outcomes. 11 In contrast to traditional group designs, SC studies often do not have multiple participants or units that can be randomly assigned to different intervention conditions. Instead, in randomized phase-order designs , the sequence of phases is randomized. Simple or block randomization is possible. For example, with simple randomization for an A 1 B 1 A 2 B 2 design, the A and B conditions are treated as separate units and are randomly assigned to be administered for each of the pre-defined data collection points. As a result, any combination of A-B sequences is possible without restrictions on the number of times each condition is administered or regard for repetitions of conditions (e.g., A 1 B 1 B 2 A 2 B 3 B 4 B 5 A 3 B 6 A 4 A 5 A 6 ). With block randomization for an A 1 B 1 A 2 B 2 design, two conditions (e.g., A and B) would be blocked into a single unit (AB or BA), randomization of which to different time periods would ensure that each condition appears in the resulting sequence more than two times (e.g., A 1 B 1 B 2 A 2 A 3 B 3 A 4 B 4 ). Note that AB and reversal designs require that the baseline (A) always precedes the first intervention (B), which should be accounted for in the randomization scheme. 2 , 11

In randomized phase start-point designs , the lengths of the A and B phases can be randomized. 2 , 11 , 24 – 26 For example, for an AB design, researchers could specify the number of time points at which outcome data will be collected, (e.g., 20), define the minimum number of data points desired in each phase (e.g., 4 for A, 3 for B), and then randomize the initiation of the intervention so that it occurs anywhere between the remaining time points (points 5 and 17 in the current example). 27 , 28 For multiple-baseline designs, a dual-randomization, or “regulated randomization” procedure has been recommended. 29 If multiple-baseline randomization depends solely on chance, it could be the case that all units are assigned to begin intervention at points not really separated in time. 30 Such randomly selected initiation of the intervention would result in the drastic reduction of the discriminant and internal validity of the study. 29 To eliminate this issue, investigators should first specify appropriate intervals between the start points for different units, then randomly select from those intervals, and finally randomly assign each unit to a start point. 29

Single Case Analysis Techniques for Intervention Research

The What Works Clearinghouse (WWC) single-case design technical documentation provides an excellent overview of appropriate SC study analysis techniques to evaluate the effectiveness of intervention effects. 1 , 18 First, visual analyses are recommended to determine whether there is a functional relation between the intervention and the outcome. Second, if evidence for a functional effect is present, the visual analysis is supplemented with quantitative analysis methods evaluating the magnitude of the intervention effect. Third, effect sizes are combined across cases to estimate overall average intervention effects which contributes to evidence-based practice, theory, and future applications. 2 , 18

Visual Analysis

Traditionally, SC study data are presented graphically. When more than one participant engages in a study, a spaghetti plot showing all of their data in the same figure can be helpful for visualization. Visual analysis of graphed data has been the traditional method for evaluating treatment effects in SC research. 1 , 12 , 31 , 32 The visual analysis involves evaluating level, trend, and stability of the data within each phase (i.e., within-phase data examination) followed by examination of the immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases (i.e., between-phase comparisons). When the changes (and/or variability) in level are in the desired direction, are immediate, readily discernible, and maintained over time, it is concluded that the changes in behavior across phases result from the implemented treatment and are indicative of improvement. 33 Three demonstrations of an intervention effect are necessary for establishing a functional relation. 1

Within-phase examination

Level, trend, and stability of the data within each phase are evaluated. Mean and/or median can be used to report the level, and trend can be evaluated by determining whether the data points are monotonically increasing or decreasing. Within-phase stability can be evaluated by calculating the percentage of data points within 15% of the phase median (or mean). The stability criterion is satisfied if about 85% (80% – 90%) of the data in a phase fall within a 15% range of the median (or average) of all data points for that phase. 34

Between-phase examination

Immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases are evaluated next. For this, several nonoverlap indices have been proposed that all quantify the proportion of measurements in the intervention phase not overlapping with the baseline measurements. 35 Nonoverlap statistics are typically scaled as percent from 0 to 100, or as a proportion from 0 to 1. Here, we briefly discuss the Nonoverlap of All Pairs ( NAP ), 36 the Extended Celeration Line ( ECL ), the Improvement Rate Difference ( IRD) , 37 and the TauU and the TauU-adjusted, TauU adj , 35 as these are the most recent and complete techniques. We also examine the Percentage of Nonoverlapping Data ( PND ) 38 and the Two Standard Deviations Band Method, as these are frequently used techniques. In addition, we include the Percentage of Nonoverlapping Corrected Data ( PNCD ) – an index applying to the PND after controlling for baseline trend. 39

Nonoverlap of all pairs (NAP)

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., N = n A * n B ). Count the number of overlapping pairs, n o , counting all ties as 0.5. Then define the percent of the pairs that show no overlap. Alternatively, one can count the number of positive (P), negative (N), and tied (T) pairs 2 , 36 :

Extended Celeration Line (ECL)

ECL or split middle line allows control for a positive Phase A trend. Nonoverlap is defined as the proportion of Phase B ( n b ) data that are above the median trend plotted from Phase A data ( n B< sub > Above Median trend A </ sub > ), but then extended into Phase B: ECL = n B Above Median trend A n b ∗ 100

As a consequence, this method depends on a straight line and makes an assumption of linearity in the baseline. 2 , 12

Improvement rate difference (IRD)

This analysis is conceptualized as the difference in improvement rates (IR) between baseline ( IR B ) and intervention phases ( IR T ). 38 The IR for each phase is defined as the number of “improved data points” divided by the total data points in that phase. IRD, commonly employed in medical group research under the name of “risk reduction” or “risk difference” attempts to provide an intuitive interpretation for nonoverlap and to make use of an established, respected effect size, IR B - IR B , or the difference between two proportions. 37

TauU and TauU adj

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Count the number of positive (P), negative (N), and tied (T) pairs, and use the following formula: TauU = P - N P + N + τ

The TauU adj is an adjustment of TauU for monotonic trend in baseline. Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Each baseline observation can be paired with all later baseline observations (n A *(n A -1)/2). 2 , 35 Then the baseline trend can be computed: TauU adf = P - N - S trend P + N + τ ; S trend = P A – NA

Online calculators might assist researchers in obtaining the TauU and TauU adjusted coefficients ( http://www.singlecaseresearch.org/calculators/tau-u ).

Percentage of nonoverlapping data (PND)

If anticipating an increase in the outcome, locate the highest data point in the baseline phase and then calculate the percent of the intervention phase data points that exceed it. If anticipating a decrease in the outcome, find the lowest data point in the baseline phase and then calculate the percent of the treatment phase data points that are below it: PND = n B Overlap A n b ∗ 100 . A PND < 50 would mark no observed effect, PND = 50–70 signifies a questionable effect, and PND > 70 suggests the intervention was effective. 40 The percentage of nonoverlapping (PNDC) corrected was proposed in 2009 as an extension of the PND. 39 Prior to applying the PND, a data correction procedure is applied eliminating pre-existing baseline trend. 38

Two Standard Deviation Band Method

When the stability criterion described above is met within phases, it is possible to apply the two standard deviation band method. 12 , 41 First, the mean of the data for a specific condition is calculated and represented with a solid line. In the next step, the standard deviation of the same data is computed and two dashed lines are represented: one located two standard deviations above the mean and the other – two standard deviations below. For normally distributed data, few points (less than 5%) are expected to be outside the two standard deviation bands if there is no change in the outcome score due to the intervention. However, this method is not considered a formal statistical procedure, as the data cannot typically be assumed to be normal, continuous, or independent. 41

Statistical Analysis

If the visual analysis indicates a functional relationship (i.e., three demonstrations of the effectiveness of the intervention effect), it is recommended to proceed with the quantitative analyses, reflecting the magnitude of the intervention effect. First, effect sizes are calculated for each participant (individual-level analysis). Moreover, if the research interest lies in the generalizability of the effect size across participants, effect sizes can be combined across cases to achieve an overall average effect size estimate (across-case effect size).

Note that quantitative analysis methods are still being developed in the domain of SC research 1 and statistical challenges of producing an acceptable measure of treatment effect remain. 14 , 42 , 43 Therefore, the WWC standards strongly recommend conducting sensitivity analysis and reporting multiple effect size estimators. If consistency across different effect size estimators is identified, there is stronger evidence for the effectiveness of the treatment. 1 , 18

Individual-level effect size analysis

The most common effect sizes recommended for SC analysis are: 1) standardized mean difference Cohen’s d ; 2) standardized mean difference with correction for small sample sizes Hedges’ g ; and 3) the regression-based approach which has the most potential and is strongly recommended by the WWC standards. 1 , 44 , 45 Cohen’s d can be calculated using following formula: d = X A ¯ - X B ¯ s p , with X A ¯ being the baseline mean, X B ¯ being the treatment mean, and s p indicating the pooled within-case standard deviation. Hedges’ g is an extension of Cohen’s d , recommended in the context of SC studies as it corrects for small sample sizes. The piecewise regression-based approach does not only reflect the immediate intervention effect, but also the intervention effect across time:

i stands for the measurement occasion ( i = 0, 1,… I ). The dependent variable is regressed on a time indicator, T , which is centered around the first observation of the intervention phase, D , a dummy variable for the intervention phase, and an interaction term of these variables. The equation shows that the expected score, Ŷ i , equals β 0 + β 1 T i in the baseline phase, and ( β 0 + β 2 ) + ( β 1 + β 3 ) T i in the intervention phase. β 0 , therefore, indicates the expected baseline level at the start of the intervention phase (when T = 0), whereas β 1 marks the linear time trend in the baseline scores. The coefficient β 2 can then be interpreted as an immediate effect of the intervention on the outcome, whereas β 3 signifies the effect of the intervention across time. The e i ’s are residuals assumed to be normally distributed around a mean of zero with a variance of σ e 2 . The assumption of independence of errors is usually not met in the context of SC studies because repeated measures are obtained within a person. As a consequence, it can be the case that the residuals are autocorrelated, meaning that errors closer in time are more related to each other compared to errors further away in time. 46 – 48 As a consequence, a lag-1 autocorrelation is appropriate (taking into account the correlation between two consecutive errors: e i and e i –1 ; for more details see Verbeke & Molenberghs, (2000). 49 In Equation 1 , ρ indicates the autocorrelation parameter. If ρ is positive, the errors closer in time are more similar; if ρ is negative, the errors closer in time are more different, and if ρ equals zero, there is no correlation between the errors.

Across-case effect sizes

Two-level modeling to estimate the intervention effects across cases can be used to evaluate across-case effect sizes. 44 , 45 , 50 Multilevel modeling is recommended by the WWC standards because it takes the hierarchical nature of SC studies into account: measurements are nested within cases and cases, in turn, are nested within studies. By conducting a multilevel analysis, important research questions can be addressed (which cannot be answered by single-level analysis of SC study data), such as: 1) What is the magnitude of the average treatment effect across cases? 2) What is the magnitude and direction of the case-specific intervention effect? 3) How much does the treatment effect vary within cases and across cases? 4) Does a case and/or study level predictor influence the treatment’s effect? The two-level model has been validated in previous research using extensive simulation studies. 45 , 46 , 51 The two-level model appears to have sufficient power (> .80) to detect large treatment effects in at least six participants with six measurements. 21

Furthermore, to estimate the across-case effect sizes, the HPS (Hedges, Pustejovsky, and Shadish) , or single-case educational design ( SCEdD)-specific mean difference, index can be calculated. 52 This is a standardized mean difference index specifically designed for SCEdD data, with the aim of making it comparable to Cohen’s d of group-comparison designs. The standard deviation takes into account both within-participant and between-participant variability, and is typically used to get an across-case estimator for a standardized change in level. The advantage of using the HPS across-case effect size estimator is that it is directly comparable with Cohen’s d for group comparison research, thus enabling the use of Cohen’s (1988) benchmarks. 53

Valuable recommendations on SC data analyses have recently been provided. 54 , 55 They suggest that a specific SC study data analytic technique can be chosen based on: (1) the study aims and the desired quantification (e.g., overall quantification, between-phase quantifications, randomization, etc.), (2) the data characteristics as assessed by visual inspection and the assumptions one is willing to make about the data, and (3) the knowledge and computational resources. 54 , 55 Table 1 lists recommended readings and some commonly used resources related to the design and analysis of single-case studies.

Recommend readings and resources related to the design and analysis of single-case studies.

Quality Appraisal Tools for Single-Case Design Research

Quality appraisal tools are important to guide researchers in designing strong experiments and conducting high-quality systematic reviews of the literature. Unfortunately, quality assessment tools for SC studies are relatively novel, ratings across tools demonstrate variability, and there is currently no “gold standard” tool. 56 Table 2 lists important SC study quality appraisal criteria compiled from the most common scales; when planning studies or reviewing the literature, we recommend readers consider these criteria. Table 3 lists some commonly used SC quality assessment and reporting tools and references to resources where the tools can be located.

Summary of important single-case study quality appraisal criteria.

Quality assessment and reporting tools related to single-case studies.

When an established tool is required for systematic review, we recommend use of the What Works Clearinghouse (WWC) Tool because it has well-defined criteria and is developed and supported by leading experts in the SC research field in association with the Institute of Education Sciences. 18 The WWC documentation provides clear standards and procedures to evaluate the quality of SC research; it assesses the internal validity of SC studies, classifying them as “Meeting Standards”, “Meeting Standards with Reservations”, or “Not Meeting Standards”. 1 , 18 Only studies classified in the first two categories are recommended for further visual analysis. Also, WWC evaluates the evidence of effect, classifying studies into “Strong Evidence of a Causal Relation”, “Moderate Evidence of a Causal Relation”, or “No Evidence of a Causal Relation”. Effect size should only be calculated for studies providing strong or moderate evidence of a causal relation.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 is another useful SC research tool developed recently to improve the quality of single-case designs. 57 SCRIBE consists of a 26-item checklist that researchers need to address while reporting the results of SC studies. This practical checklist allows for critical evaluation of SC studies during study planning, manuscript preparation, and review.

Single-case studies can be designed and analyzed in a rigorous manner that allows researchers strength in assessing causal relationships among interventions and outcomes, and in generalizing their results. 2 , 12 These studies can be strengthened via incorporating replication of findings across multiple study phases, participants, settings, or contexts, and by using randomization of conditions or phase lengths. 11 There are a variety of tools that can allow researchers to objectively analyze findings from SC studies. 56 While a variety of quality assessment tools exist for SC studies, they can be difficult to locate and utilize without experience, and different tools can provide variable results. The WWC quality assessment tool is recommended for those aiming to systematically review SC studies. 1 , 18

SC studies, like all types of study designs, have a variety of limitations. First, it can be challenging to collect at least five data points in a given study phase. This may be especially true when traveling for data collection is difficult for participants, or during the baseline phase when delaying intervention may not be safe or ethical. Power in SC studies is related to the number of data points gathered for each participant so it is important to avoid having a limited number of data points. 12 , 58 Second, SC studies are not always designed in a rigorous manner and, thus, may have poor internal validity. This limitation can be overcome by addressing key characteristics that strengthen SC designs ( Table 2 ). 1 , 14 , 18 Third, SC studies may have poor generalizability. This limitation can be overcome by including a greater number of participants, or units. Fourth, SC studies may require consultation from expert methodologists and statisticians to ensure proper study design and data analysis, especially to manage issues like autocorrelation and variability of data. 2 Fifth, while it is recommended to achieve a stable level and rate of performance throughout the baseline, human performance is quite variable and can make this requirement challenging. Finally, the most important validity threat to SC studies is maturation. This challenge must be considered during the design process in order to strengthen SC studies. 1 , 2 , 12 , 58

SC studies can be particularly useful for rehabilitation research. They allow researchers to closely track and report change at the level of the individual. They may require fewer resources and, thus, can allow for high-quality experimental research, even in clinical settings. Furthermore, they provide a tool for assessing causal relationships in populations and settings where large numbers of participants are not accessible. For all of these reasons, SC studies can serve as an effective method for assessing the impact of interventions.

Acknowledgments

This research was supported by the National Institute of Health, Eunice Kennedy Shriver National Institute of Child Health & Human Development (1R21HD076092-01A1, Lobo PI) and the Delaware Economic Development Office (Grant #109).

Some of the information in this manuscript was presented at the IV Step Meeting in Columbus, OH, June 2016.

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Research Method

Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

Case Study Research

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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Single Case Research Design

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This chapter addresses single-case research designs’ peculiarities, characteristics, and significant fallacies. A single case research design is a collective term for an in-depth analysis of a small non-random sample. The focus of this design is in-depth. This characteristic distinguishes the case study research from other research designs that understand the individual case as a relatively insignificant and interchangeable aspect of a population or sample. Also, researchers find relevant information on writing a single case research design paper and learn about typical methods used for this research design. The chapter closes by referring to overlapping and adjacent research designs.

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Integrating qualitative research within a clinical trials unit: developing strategies and understanding their implementation in contexts

  • Jeremy Segrott   ORCID: orcid.org/0000-0001-6215-0870 1 ,
  • Sue Channon 2 ,
  • Amy Lloyd 4 ,
  • Eleni Glarou 2 , 3 ,
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  • Jacqueline Hughes 2 ,
  • Nina Jacob 2 ,
  • Sarah Milosevic 2 ,
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  • Bethan Pell 6 ,
  • Mike Robling 2 ,
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Background/aims

The value of using qualitative methods within clinical trials is widely recognised. How qualitative research is integrated within trials units to achieve this is less clear. This paper describes the process through which qualitative research has been integrated within Cardiff University’s Centre for Trials Research (CTR) in Wales, UK. We highlight facilitators of, and challenges to, integration.

We held group discussions on the work of the Qualitative Research Group (QRG) within CTR. The content of these discussions, materials for a presentation in CTR, and documents relating to the development of the QRG were interpreted at a workshop attended by group members. Normalisation Process Theory (NPT) was used to structure analysis. A writing group prepared a document for input from members of CTR, forming the basis of this paper.

Actions to integrate qualitative research comprised: its inclusion in Centre strategies; formation of a QRG with dedicated funding/roles; embedding of qualitative research within operating systems; capacity building/training; monitoring opportunities to include qualitative methods in studies; maximising the quality of qualitative research and developing methodological innovation. Facilitators of these actions included: the influence of the broader methodological landscape within trial/study design and its promotion of the value of qualitative research; and close physical proximity of CTR qualitative staff/students allowing sharing of methodological approaches. Introduction of innovative qualitative methods generated interest among other staff groups. Challenges included: pressure to under-resource qualitative components of research, preference for a statistical stance historically in some research areas and funding structures, and difficulties faced by qualitative researchers carving out individual academic profiles when working across trials/studies.

Conclusions

Given that CTUs are pivotal to the design and conduct of RCTs and related study types across multiple disciplines, integrating qualitative research into trials units is crucial if its contribution is to be fully realised. We have made explicit one trials unit’s experience of embedding qualitative research and present this to open dialogue on ways to operationalise and optimise qualitative research in trials. NPT provides a valuable framework with which to theorise these processes, including the importance of sense-making and legitimisation when introducing new practices within organisations.

Peer Review reports

The value of using qualitative methods within randomised control trials (RCTs) is widely recognised [ 1 , 2 , 3 ]. Qualitative research generates important evidence on factors affecting trial recruitment/retention [ 4 ] and implementation, aiding interpretation of quantitative data [ 5 ]. Though RCTs have traditionally been viewed as sitting within a positivist paradigm, recent methodological innovations have developed new trial designs that draw explicitly on both quantitative and qualitative methods. For instance, in the field of complex public health interventions, realist RCTs seek to understand the mechanisms through which interventions generate hypothesised impacts, and how interactions across different implementation contexts form part of these mechanisms. Proponents of realist RCTs—which integrate experimental and realist paradigms—highlight the importance of using quantitative and qualitative methods to fully realise these aims and to generate an understanding of intervention mechanisms and how context shapes them [ 6 ].

A need for guidance on how to conduct good quality qualitative research is being addressed, particularly in relation to feasibility studies for RCTs [ 7 ] and process evaluations embedded within trials of complex interventions [ 5 ]. There is also guidance on the conduct of qualitative research within trials at different points in the research cycle, including development, conduct and reporting [ 8 , 9 ].

A high proportion of trials are based within or involve clinical trials units (CTUs). In the UK the UKCRC Registered CTU Network describes them as:

… specialist units which have been set up with a specific remit to design, conduct, analyse and publish clinical trials and other well-designed studies. They have the capability to provide specialist expert statistical, epidemiological, and other methodological advice and coordination to undertake successful clinical trials. In addition, most CTUs will have expertise in the coordination of trials involving investigational medicinal products which must be conducted in compliance with the UK Regulations governing the conduct of clinical trials resulting from the EU Directive for Clinical Trials.

Thus, CTUs provide the specialist methodological expertise needed for the conduct of trials, and in the case of trials of investigational medicinal products, their involvement may be mandated to ensure compliance with relevant regulations. As the definition above suggests, CTUs also conduct and support other types of study apart from RCTs, providing a range of methodological and subject-based expertise.

However, despite their central role in the conduct and design of trials, (and other evaluation designs) little has been written about how CTUs have integrated qualitative work within their organisation at a time when such methods are, as stated above, now recognised as an important aspect of RCTs and evaluation studies more generally. This is a significant gap, since integration at the organisational level arguably shapes how qualitative research is integrated within individual studies, and thus it is valuable to understand how CTUs have approached the task. There are different ways of involving qualitative work in trials units, such as partnering with other departments (e.g. social science) or employing qualitative researchers directly. Qualitative research can be imagined and configured in different ways—as a method that generates data to inform future trial and intervention design, as an embedded component within an RCT or other evaluation type, or as a parallel strand of research focusing on lived experiences of illness, for instance. Understanding how trials units have integrated qualitative research is valuable, as it can shed light on which strategies show promise, and in which contexts, and how qualitative research is positioned within the field of trials research, foregrounding the value of qualitative research. However, although much has been written about its use within trials, few accounts exist of how trials units have integrated qualitative research within their systems and structures.

This paper discusses the process of embedding qualitative research within the work of one CTU—Cardiff University’s Centre for Trials Research (CTR). It highlights facilitators of this process and identifies challenges to integration. We use the Normalisation Process Theory (NPT) as a framework to structure our experience and approach. The key gap addressed by this paper is the implementation of strategies to integrate qualitative research (a relatively newly adopted set of practices and processes) within CTU systems and structures. We acknowledge from the outset that there are multiple ways of approaching this task. What follows therefore is not a set of recommendations for a preferred or best way to integrate qualitative research, as this will comprise diverse actions according to specific contexts. Rather, we examine the processes through which integration occurred in our own setting and highlight the potential value of these insights for others engaged in the work of promoting qualitative research within trials units.

Background to the integration of qualitative research within CTR

The CTR was formed in 2015 [ 10 ]. It brought together three existing trials units at Cardiff University: the South East Wales Trials Unit, the Wales Cancer Trials Unit, and the Haematology Clinical Trials Unit. From its inception, the CTR had a stated aim of developing a programme of qualitative research and integrating it within trials and other studies. In the sections below, we map these approaches onto the framework offered by Normalisation Process Theory to understand the processes through which they helped achieve embedding and integration of qualitative research.

CTR’s aims (including those relating to the development of qualitative research) were included within its strategy documents and communicated to others through infrastructure funding applications, annual reports and its website. A Qualitative Research Group (QRG), which had previously existed within the South East Wales Trials Unit, with dedicated funding for methodological specialists and group lead academics, was a key mechanism through which the development of a qualitative portfolio was put into action. Integration of qualitative research within Centre systems and processes occurred through the inclusion of qualitative research in study adoption processes and representation on committees. The CTR’s study portfolio provided a basis to track qualitative methods in new and existing studies, identify opportunities to embed qualitative methods within recently adopted studies (at the funding application stage) and to manage staff resources. Capacity building and training were an important focus of the QRG’s work, including training courses, mentoring, creation of an academic network open to university staff and practitioners working in the field of healthcare, presentations at CTR staff meetings and securing of PhD studentships. Standard operating procedures and methodological guidance on the design and conduct of qualitative research (e.g. templates for developing analysis plans) aimed to create a shared understanding of how to undertake high-quality research, and a means to monitor the implementation of rigorous approaches. As the QRG expanded its expertise it sought to develop innovative approaches, including the use of visual [ 11 ] and ethnographic methods [ 12 ].

Understanding implementation—Normalisation Process Theory (NPT)

Normalisation Process Theory (NPT) provides a model with which to understand the implementation of new sets of practices and their normalisation within organisational settings. The term ‘normalisation’ refers to how new practices become routinised (part of the everyday work of an organisation) through embedding and integration [ 13 , 14 ]. NPT defines implementation as ‘the social organisation of work’ and is concerned with the social processes that take place as new practices are introduced. Embedding involves ‘making practices routine elements of everyday life’ within an organisation. Integration takes the form of ‘sustaining embedded practices in social contexts’, and how these processes lead to the practices becoming (or not becoming) ‘normal and routine’ [ 14 ]. NPT is concerned with the factors which promote or ‘inhibit’ attempts to embed and integrate the operationalisation of new practices [ 13 , 14 , 15 ].

Embedding new practices is therefore achieved through implementation—which takes the form of interactions in specific contexts. Implementation is operationalised through four ‘generative mechanisms’— coherence , cognitive participation , collective action and reflexive monitoring [ 14 ]. Each mechanism is characterised by components comprising immediate and organisational work, with actions of individuals and organisations (or groups of individuals) interdependent. The mechanisms operate partly through forms of investment (i.e. meaning, commitment, effort, and comprehension) [ 14 ].

Coherence refers to how individuals/groups make sense of, and give meaning to, new practices. Sense-making concerns the coherence of a practice—whether it ‘holds together’, and its differentiation from existing activities [ 15 ]. Communal and individual specification involve understanding new practices and their potential benefits for oneself or an organisation. Individuals consider what new practices mean for them in terms of tasks and responsibilities ( internalisation ) [ 14 ].

NPT frames the second mechanism, cognitive participation , as the building of a ‘community of practice’. For a new practice to be initiated, individuals and groups within an organisation must commit to it [ 14 , 15 ]. Cognitive participation occurs through enrolment —how people relate to the new practice; legitimation —the belief that it is right for them to be involved; and activation —defining which actions are necessary to sustain the practice and their involvement [ 14 ]. Making the new practices work may require changes to roles (new responsibilities, altered procedures) and reconfiguring how colleagues work together (changed relationships).

Third, Collective Action refers to ‘the operational work that people do to enact a set of practices’ [ 14 ]. Individuals engage with the new practices ( interactional workability ) reshaping how members of an organisation interact with each other, through creation of new roles and expectations ( relational interaction ) [ 15 ]. Skill set workability concerns how the work of implementing a new set of practices is distributed and the necessary roles and skillsets defined [ 14 ]. Contextual integration draws attention to the incorporation of a practice within social contexts, and the potential for aspects of these contexts, such as systems and procedures, to be modified as a result [ 15 ].

Reflexive monitoring is the final implementation mechanism. Collective and individual appraisal evaluate the value of a set of practices, which depends on the collection of information—formally and informally ( systematisation ). Appraisal may lead to reconfiguration in which procedures of the practice are redefined or reshaped [ 14 , 15 ].

We sought to map the following: (1) the strategies used to embed qualitative research within the Centre, (2) key facilitators, and (3) barriers to their implementation. Through focused group discussions during the monthly meetings of the CTR QRG and in discussion with the CTR senior management team throughout 2019–2020 we identified nine types of documents (22 individual documents in total) produced within the CTR which had relevant information about the integration of qualitative research within its work (Table  1 ). The QRG had an ‘open door’ policy to membership and welcomed all staff/students with an interest in qualitative research. It included researchers who were employed specifically to undertake qualitative research and other staff with a range of study roles, including trial managers, statisticians, and data managers. There was also diversity in terms of career stage, including PhD students, mid-career researchers and members of the Centre’s Executive team. Membership was therefore largely self-selected, and comprised of individuals with a role related to, or an interest in, embedding qualitative research within trials. However, the group brought together diverse methodological perspectives and was not solely comprised of methodological ‘champions’ whose job it was to promote the development of qualitative research within the centre. Thus whilst the group (and by extension, the authors of this paper) had a shared appreciation of the value of qualitative research within a trials centre, they also brought varied methodological perspectives and ways of engaging with it.

All members of the QRG ( n  = 26) were invited to take part in a face-to-face, day-long workshop in February 2019 on ‘How to optimise and operationalise qualitative research in trials: reflections on CTR structure’. The workshop was attended by 12 members of staff and PhD students, including members of the QRG and the CTR’s senior management team. Recruitment to the workshop was therefore inclusive, and to some extent opportunistic, but all members of the QRG were able to contribute to discussions during regular monthly group meetings and the drafting of the current paper.

The aim of the workshop was to bring together information from the documents in Table  1 to generate discussion around the key strategies (and their component activities) that had been adopted to integrate qualitative research into CTR, as well as barriers to, and facilitators of, their implementation. The agenda for the workshop involved four key areas: development and history of the CTR model; mapping the current model within CTR; discussing the structure of other CTUs; and exploring the advantages and disadvantages of the CTR model.

During the workshop, we discussed the use of NPT to conceptualise how qualitative research had been embedded within CTR’s systems and practices. The group produced spider diagrams to map strategies and actions on to the four key domains (or ‘generative mechanisms’ of NPT) summarised above, to aid the understanding of how they had functioned, and the utility of NPT as a framework. This is summarised in Table  2 .

Detailed notes were made during the workshop. A core writing group then used these notes and the documents in Table  1 to develop a draft of the current paper. This was circulated to all members of the CTR QRG ( n  = 26) and stored within a central repository accessible to them to allow involvement and incorporate the views of those who were not able to attend the workshop. This draft was again presented for comments in the monthly CTR QRG meeting in February 2021 attended by n  = 10. The Standards for QUality Improvement Reporting Excellence 2.0 (SQUIRE) guidelines were used to inform the structure and content of the paper (see supplementary material) [ 16 ].

In the following sections, we describe the strategies CTR adopted to integrate qualitative research. These are mapped against NPT’s four generative mechanisms to explore the processes through which the strategies promoted integration, and facilitators of and barriers to their implementation. A summary of the strategies and their functioning in terms of the generative mechanisms is provided in Table  2 .

Coherence—making sense of qualitative research

In CTR, many of the actions taken to build a portfolio of qualitative research were aimed at enabling colleagues, and external actors, to make sense of this set of methodologies. Centre-level strategies and grant applications for infrastructure funding highlighted the value of qualitative research, the added benefits it would bring, and positioned it as a legitimate set of practices alongside existing methods. For example, a 2014 application for renewal of trials unit infrastructure funding stated:

We are currently in the process of undertaking […] restructuring for our qualitative research team and are planning similar for trial management next year. The aim of this restructuring is to establish greater hierarchical management and opportunities for staff development and also provide a structure that can accommodate continuing growth.

Within the CTR, various forms of communication on the development of qualitative research were designed to enable staff and students to make sense of it, and to think through its potential value for them, and ways in which they might engage with it. These included presentations at staff meetings, informal meetings between project teams and the qualitative group lead, and the visibility of qualitative research on the public-facing Centre website and Centre committees and systems. For instance, qualitative methods were included (and framed as a distinct set of practices) within study adoption forms and committee agendas. Information for colleagues described how qualitative methods could be incorporated within funding applications for RCTs and other evaluation studies to generate new insights into questions research teams were already keen to answer, such as influences on intervention implementation fidelity. Where externally based chief investigators approached the Centre to be involved in new grant applications, the existence of the qualitative team and group lead enabled the inclusion of qualitative research to be actively promoted at an early stage, and such opportunities were highlighted in the Centre’s brochure for new collaborators. Monthly qualitative research network meetings—advertised across CTR and to external research collaborators, were also designed to create a shared understanding of qualitative research methods and their utility within trials and other study types (e.g. intervention development, feasibility studies, and observational studies). Training events (discussed in more detail below) also aided sense-making.

Several factors facilitated the promotion of qualitative research as a distinctive and valuable entity. Among these was the influence of the broader methodological landscape within trial design which was promoting the value of qualitative research, such as guidance on the evaluation of complex interventions by the Medical Research Council [ 17 ], and the growing emphasis placed on process evaluations within trials (with qualitative methods important in understanding participant experience and influences on implementation) [ 5 ]. The attention given to lived experience (both through process evaluations and the move to embed public involvement in trials) helped to frame qualitative research within the Centre as something that was appropriate, legitimate, and of value. Recognition by research funders of the value of qualitative research within studies was also helpful in normalising and legitimising its adoption within grant applications.

The inclusion of qualitative methods within influential methodological guidance helped CTR researchers to develop a ‘shared language’ around these methods, and a way that a common understanding of the role of qualitative research could be generated. One barrier to such sense-making work was the varying extent to which staff and teams had existing knowledge or experience of qualitative research. This varied across methodological and subject groups within the Centre and reflected the history of the individual trials units which had merged to form the Centre.

Cognitive participation—legitimising qualitative research

Senior CTR leaders promoted the value and legitimacy of qualitative research. Its inclusion in centre strategies, infrastructure funding applications, and in public-facing materials (e.g. website, investigator brochures), signalled that it was appropriate for individuals to conduct qualitative research within their roles, or to support others in doing so. Legitimisation also took place through informal channels, such as senior leadership support for qualitative research methods in staff meetings and participation in QRG seminars. Continued development of the QRG (with dedicated infrastructure funding) provided a visible identity and equivalence with other methodological groups (e.g. trial managers, statisticians).

Staff were asked to engage with qualitative research in two main ways. First, there was an expansion in the number of staff for whom qualitative research formed part of their formal role and responsibilities. One of the three trials units that merged to form CTR brought with it a qualitative team comprising methodological specialists and a group lead. CTR continued the expansion of this group with the creation of new roles and an enlarged nucleus of researchers for whom qualitative research was the sole focus of their work. In part, this was linked to the successful award of projects that included a large qualitative component, and that were coordinated by CTR (see Table  3 which describes the PUMA study).

Members of the QRG were encouraged to develop their own research ideas and to gain experience as principal investigators, and group seminars were used to explore new ideas and provide peer support. This was communicated through line management, appraisal, and informal peer interaction. Boundaries were not strictly demarcated (i.e. staff located outside the qualitative team were already using qualitative methods), but the new team became a central focus for developing a growing programme of work.

Second, individuals and studies were called upon to engage in new ways with qualitative research, and with the qualitative team. A key goal for the Centre was that groups developing new research ideas should give more consideration in general to the potential value and inclusion of qualitative research within their funding applications. Specifically, they were asked to do this by thinking about qualitative research at an early point in their application’s development (rather than ‘bolting it on’ after other elements had been designed) and to draw upon the expertise and input of the qualitative team. An example was the inclusion of questions on qualitative methods within the Centre’s study adoption form and representation from the qualitative team at the committee which reviewed new adoption requests. Where adoption requests indicated the inclusion of qualitative methods, colleagues were encouraged to liaise with the qualitative team, facilitating the integration of its expertise from an early stage. Qualitative seminars offered an informal and supportive space in which researchers could share initial ideas and refine their methodological approach. The benefits of this included the provision of sufficient time for methodological specialists to be involved in the design of the proposed qualitative component and ensuring adequate costings had been drawn up. At study adoption group meetings, scrutiny of new proposals included consideration of whether new research proposals might be strengthened through the use of qualitative methods where these had not initially been included. Meetings of the QRG—which reviewed the Centre’s portfolio of new studies and gathered intelligence on new ideas—also helped to identify, early on, opportunities to integrate qualitative methods. Communication across teams was useful in identifying new research ideas and embedding qualitative researchers within emerging study development groups.

Actions to promote greater use of qualitative methods in funding applications fed through into a growing number of studies with a qualitative component. This helped to increase the visibility and legitimacy of qualitative methods within the Centre. For example, the PUMA study [ 12 ], which brought together a large multidisciplinary team to develop and evaluate a Paediatric early warning system, drew heavily on qualitative methods, with the qualitative research located within the QRG. The project introduced an extensive network of collaborators and clinical colleagues to qualitative methods and how they could be used during intervention development and the generation of case studies. Further information about the PUMA study is provided in Table  3 .

Increasing the legitimacy of qualitative work across an extensive network of staff, students and collaborators was a complex process. Set within the continuing dominance of quantitative methods with clinical trials, there were variations in the extent to which clinicians and other collaborators embraced the value of qualitative methods. Research funding schemes, which often continued to emphasise the quantitative element of randomised controlled trials, inevitably fed through into the focus of new research proposals. Staff and external collaborators were sometimes uncertain about the added value that qualitative methods would bring to their trials. Across the CTR there were variations in the speed at which qualitative research methods gained legitimacy, partly based on disciplinary traditions and their influences. For instance, population health trials, often located within non-health settings such as schools or community settings, frequently involved collaboration with social scientists who brought with them experience in qualitative methods. Methodological guidance in this field, such as MRC guidance on process evaluations, highlighted the value of qualitative methods and alternatives to the positivist paradigm, such as the value of realist RCTs. In other, more clinical areas, positivist paradigms had greater dominance. Established practices and methodological traditions across different funders also influenced the ease of obtaining funding to include qualitative research within studies. For drugs trials (CTIMPs), the influence of regulatory frameworks on study design, data collection and the allocation of staff resources may have played a role. Over time, teams gained repeated experience of embedding qualitative research (and researchers) within their work and took this learning with them to subsequent studies. For example, the senior clinician quoted within the PUMA case study (Table  3 below) described how they had gained an appreciation of the rigour of qualitative research and an understanding of its language. Through these repeated interactions, embedding of qualitative research within studies started to become the norm rather than the exception.

Collective action—operationalising qualitative research

Collective action concerns the operationalisation of new practices within organisations—the allocation and management of the work, how individuals interact with each other, and the work itself. In CTR the formation of a Qualitative Research Group helped to allocate and organise the work of building a portfolio of studies. Researchers across the Centre were called upon to interact with qualitative research in new ways. Presentations at staff meetings and the inclusion of qualitative research methods in portfolio study adoption forms were examples of this ( interactive workability ). It was operationalised by encouraging study teams to liaise with the qualitative research lead. Development of standard operating procedures, templates for costing qualitative research and methodological guidance (e.g. on analysis plans) also helped encourage researchers to interact with these methods in new ways. For some qualitative researchers who had been trained in the social sciences, working within a trials unit meant that they needed to interact in new and sometimes unfamiliar ways with standard operating procedures, risk assessments, and other trial-based systems. Thus, training needs and capacity-building efforts were multidirectional.

Whereas there had been a tendency for qualitative research to be ‘bolted on’ to proposals for RCTs, the systems described above were designed to embed thinking about the value and design of the qualitative component from the outset. They were also intended to integrate members of the qualitative team with trial teams from an early stage to promote effective integration of qualitative methods within larger trials and build relationships over time.

Standard Operating Procedures (SOPs), formal and informal training, and interaction between the qualitative team and other researchers increased the relational workability of qualitative methods within the Centre—the confidence individuals felt in including these methods within their studies, and their accountability for doing so. For instance, study adoption forms prompted researchers to interact routinely with the qualitative team at an early stage, whilst guidance on costing grants provided clear expectations about the resources needed to deliver a proposed set of qualitative data collection.

Formation of the Qualitative Research Group—comprised of methodological specialists, created new roles and skillsets ( skill set workability ). Research teams were encouraged to draw on these when writing funding applications for projects that included a qualitative component. Capacity-building initiatives were used to increase the number of researchers with the skills needed to undertake qualitative research, and for these individuals to develop their expertise over time. This was achieved through formal training courses, academic seminars, mentoring from experienced colleagues, and informal knowledge exchange. Links with external collaborators and centres engaged in building qualitative research supported these efforts. Within the Centre, the co-location of qualitative researchers with other methodological and trial teams facilitated knowledge exchange and building of collaborative relationships, whilst grouping of the qualitative team within a dedicated office space supported a collective identity and opportunities for informal peer support.

Some aspects of the context in which qualitative research was being developed created challenges to operationalisation. Dependence on project grants to fund qualitative methodologists meant that there was a continuing need to write further grant applications whilst limiting the amount of time available to do so. Similarly, researchers within the team whose role was funded largely by specific research projects could sometimes find it hard to create sufficient time to develop their personal methodological interests. However, the cultivation of a methodologically varied portfolio of work enabled members of the team to build significant expertise in different approaches (e.g. ethnography, discourse analysis) that connected individual studies.

Reflexive monitoring—evaluating the impact of qualitative research

Inclusion of questions/fields relating to qualitative research within the Centre’s study portfolio database was a key way in which information was collected ( systematisation ). It captured numbers of funding applications and funded studies, research design, and income generation. Alongside this database, a qualitative resource planner spreadsheet was used to link individual members of the qualitative team with projects and facilitate resource planning, further reinforcing the core responsibilities and roles of qualitative researchers within CTR. As with all staff in the Centre, members of the qualitative team were placed on ongoing rather than fixed-term contracts, reflecting their core role within CTR. Planning and strategy meetings used the database and resource planner to assess the integration of qualitative research within Centre research, identify opportunities for increasing involvement, and manage staff recruitment and sustainability of researcher posts. Academic meetings and day-to-day interaction fulfilled informal appraisal of the development of the group, and its position within the Centre. Individual appraisal was also important, with members of the qualitative team given opportunities to shape their role, reflect on progress, identify training needs, and further develop their skillset, particularly through line management systems.

These forms of systematisation and appraisal were used to reconfigure the development of qualitative research and its integration within the Centre. For example, group strategies considered how to achieve long-term integration of qualitative research from its initial embedding through further promoting the belief that it formed a core part of the Centre’s business. The visibility and legitimacy of qualitative research were promoted through initiatives such as greater prominence on the Centre’s website. Ongoing review of the qualitative portfolio and discussion at academic meetings enabled the identification of areas where increased capacity would be helpful, both for qualitative staff, and more broadly within the Centre. This prompted the qualitative group to develop an introductory course to qualitative methods open to all Centre staff and PhD students, aimed at increasing understanding and awareness. As the qualitative team built its expertise and experience it also sought to develop new and innovative approaches to conducting qualitative research. This included the use of visual and diary-based methods [ 11 ] and the adoption of ethnography to evaluate system-level clinical interventions [ 12 ]. Restrictions on conventional face-to-face qualitative data collection due to the COVID-19 pandemic prompted rapid adoption of virtual/online methods for interviews, observation, and use of new internet platforms such as Padlet—a form of digital note board.

In this paper, we have described the work undertaken by one CTU to integrate qualitative research within its studies and organisational culture. The parallel efforts of many trials units to achieve these goals arguably come at an opportune time. The traditional designs of RCTs have been challenged and re-imagined by the increasing influence of realist evaluation [ 6 , 18 ] and the widespread acceptance that trials need to understand implementation and intervention theory as well as assess outcomes [ 17 ]. Hence the widespread adoption of embedded mixed methods process evaluations within RCTs. These broad shifts in methodological orthodoxies, the production of high-profile methodological guidance, and the expectations of research funders all create fertile ground for the continued expansion of qualitative methods within trials units. However, whilst much has been written about the importance of developing qualitative research and the possible approaches to integrating qualitative and quantitative methods within studies, much less has been published on how to operationalise this within trials units. Filling this lacuna is important. Our paper highlights how the integration of a new set of practices within an organisation can become embedded as part of its ‘normal’ everyday work whilst also shaping the practices being integrated. In the case of CTR, it could be argued that the integration of qualitative research helped shape how this work was done (e.g. systems to assess progress and innovation).

In our trials unit, the presence of a dedicated research group of methodological specialists was a key action that helped realise the development of a portfolio of qualitative research and was perhaps the most visible evidence of a commitment to do so. However, our experience demonstrates that to fully realise the goal of developing qualitative research, much work focuses on the interaction between this ‘new’ set of methods and the organisation into which it is introduced. Whilst the team of methodological specialists was tasked with, and ‘able’ to do the work, the ‘work’ itself needed to be integrated and embedded within the existing system. Thus, alongside the creation of a team and methodological capacity, promoting the legitimacy of qualitative research was important to communicate to others that it was both a distinctive and different entity, yet similar and equivalent to more established groups and practices (e.g. trial management, statistics, data management). The framing of qualitative research within strategies, the messages given out by senior leaders (formally and informally) and the general visibility of qualitative research within the system all helped to achieve this.

Normalisation Process Theory draws our attention to the concepts of embedding (making a new practice routine, normal within an organisation) and integration —the long-term sustaining of these processes. An important process through which embedding took place in our centre concerned the creation of messages and systems that called upon individuals and research teams to interact with qualitative research. Research teams were encouraged to think about qualitative research and consider its potential value for their studies. Critically, they were asked to do so at specific points, and in particular ways. Early consideration of qualitative methods to maximise and optimise their inclusion within studies was emphasised, with timely input from the qualitative team. Study adoption systems, centre-level processes for managing financial and human resources, creation of a qualitative resource planner, and awareness raising among staff, helped to reinforce this. These processes of embedding and integration were complex and they varied in intensity and speed across different areas of the Centre’s work. In part this depended on existing research traditions, the extent of prior experience of working with qualitative researchers and methods, and the priorities of subject areas and funders. Centre-wide systems, sometimes linked to CTR’s operation as a CTU, also helped to legitimise and embed qualitative research, lending it equivalence with other research activity. For example, like all CTUs, CTR was required to conform with the principles of Good Clinical Practice, necessitating the creation of a quality management system, operationalised through standard operating procedures for all areas of its work. Qualitative research was included, and became embedded, within these systems, with SOPs produced to guide activities such as qualitative analysis.

NPT provides a helpful way of understanding how trials units might integrate qualitative research within their work. It highlights how new practices interact with existing organisational systems and the work needed to promote effective interaction. That is, alongside the creation of a team or programme of qualitative research, much of the work concerns how members of an organisation understand it, engage with it, and create systems to sustain it. Embedding a new set of practices may be just as important as the quality or characteristics of the practices themselves. High-quality qualitative research is of little value if it is not recognised and drawn upon within new studies for instance. NPT also offers a helpful lens with which to understand how integration and embedding occur, and the mechanisms through which they operate. For example, promoting the legitimacy of a new set of practices, or creating systems that embed it, can help sustain these practices by creating an organisational ambition and encouraging (or requiring) individuals to interact with them in certain ways, redefining their roles accordingly. NPT highlights the ways in which integration of new practices involves bi-directional exchanges with the organisation’s existing practices, with each having the potential to re-shape the other as interaction takes place. For instance, in CTR, qualitative researchers needed to integrate and apply their methods within the quality management and other systems of a CTU, such as the formalisation of key processes within standard operating procedures, something less likely to occur outside trials units. Equally, project teams (including those led by externally based chief investigators) increased the integration of qualitative methods within their overall study design, providing opportunities for new insights on intervention theory, implementation and the experiences of practitioners and participants.

We note two aspects of the normalisation processes within CTR that are slightly less well conceptualised by NPT. The first concerns the emphasis within coherence on identifying the distinctiveness of new practices, and how they differ from existing activities. Whilst differentiation was an important aspect of the integration of qualitative research in CTR, such integration could be seen as operating partly through processes of de-differentiation, or at least equivalence. That is, part of the integration of qualitative research was to see it as similar in terms of rigour, coherence, and importance to other forms of research within the Centre. To be viewed as similar, or at least comparable to existing practices, was to be legitimised.

Second, whilst NPT focuses mainly on the interaction between a new set of practices and the organisational context into which it is introduced, our own experience of introducing qualitative research into a trials unit was shaped by broader organisational and methodological contexts. For example, the increasing emphasis placed upon understanding implementation processes and the experiences of research participants in the field of clinical trials (e.g. by funders), created an environment conducive to the development of qualitative research methods within our Centre. Attempts to integrate qualitative research within studies were also cross-organisational, given that many of the studies managed within the CTR drew together multi-institutional teams. This provided important opportunities to integrate qualitative research within a portfolio of studies that extended beyond CTR and build a network of collaborators who increasingly included qualitative methods within their funding proposals. The work of growing and integrating qualitative research within a trials unit is an ongoing one in which ever-shifting macro-level influences can help or hinder, and where the organisations within which we work are never static in terms of barriers and facilitators.

The importance of utilising qualitative methods within RCTs is now widely recognised. Increased emphasis on the evaluation of complex interventions, the influence of realist methods directing greater attention to complexity and the widespread adoption of mixed methods process evaluations are key drivers of this shift. The inclusion of qualitative methods within individual trials is important and previous research has explored approaches to their incorporation and some of the challenges encountered. Our paper highlights that the integration of qualitative methods at the organisational level of the CTU can shape how they are taken up by individual trials. Within CTR, it can be argued that qualitative research achieved high levels of integration, as conceptualised by Normalisation Process Theory. Thus, qualitative research became recognised as a coherent and valuable set of practices, secured legitimisation as an appropriate focus of individual and organisational activity and benefitted from forms of collective action which operationalised these organisational processes. Crucially, the routinisation of qualitative research appeared to be sustained, something which NPT suggests helps define integration (as opposed to initial embedding). However, our analysis suggested that the degree of integration varied by trial area. This variation reflected a complex mix of factors including disciplinary traditions, methodological guidance, existing (un)familiarity with qualitative research, and the influence of regulatory frameworks for certain clinical trials.

NPT provides a valuable framework with which to understand how these processes of embedding and integration occur. Our use of NPT draws attention to the importance of sense-making and legitimisation as important steps in introducing a new set of practices within the work of an organisation. Integration also depends, across each mechanism of NPT, on the building of effective relationships, which allow individuals and teams to work together in new ways. By reflecting on our experiences and the decisions taken within CTR we have made explicit one such process for embedding qualitative research within a trials unit, whilst acknowledging that approaches may differ across trials units. Mindful of this fact, and the focus of the current paper on one trials unit’s experience, we do not propose a set of recommendations for others who are working to achieve similar goals. Rather, we offer three overarching reflections (framed by NPT) which may act as a useful starting point for trials units (and other infrastructures) seeking to promote the adoption of qualitative research.

First, whilst research organisations such as trials units are highly heterogenous, processes of embedding and integration, which we have foregrounded in this paper, are likely to be important across different contexts in sustaining the use of qualitative research. Second, developing a plan for the integration of qualitative research will benefit from mapping out the characteristics of the extant system. For example, it is valuable to know how familiar staff are with qualitative research and any variations across teams within an organisation. Thirdly, NPT frames integration as a process of implementation which operates through key generative mechanisms— coherence , cognitive participation , collective action and reflexive monitoring . These mechanisms can help guide understanding of which actions help achieve embedding and integration. Importantly, they span multiple aspects of how organisations, and the individuals within them, work. The ways in which people make sense of a new set of practices ( coherence ), their commitment towards it ( cognitive participation ), how it is operationalised ( collective action ) and the evaluation of its introduction ( reflexive monitoring ) are all important. Thus, for example, qualitative research, even when well organised and operationalised within an organisation, is unlikely to be sustained if appreciation of its value is limited, or people are not committed to it.

We present our experience of engaging with the processes described above to open dialogue with other trials units on ways to operationalise and optimise qualitative research in trials. Understanding how best to integrate qualitative research within these settings may help to fully realise the significant contribution which it makes the design and conduct of trials.

Availability of data and materials

Some documents cited in this paper are either freely available from the Centre for Trials Research website or can be requested from the author for correspondence.

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Acknowledgements

Members of the Centre for Trials Research (CTR) Qualitative Research Group were collaborating authors: C Drew (Senior Research Fellow—Senior Trial Manager, Brain Health and Mental Wellbeing Division), D Gillespie (Director, Infection, Inflammation and Immunity Trials, Principal Research Fellow), R Hale (now Research Associate, School of Social Sciences, Cardiff University), J Latchem-Hastings (now Lecturer and Postdoctoral Fellow, School of Healthcare Sciences, Cardiff University), R Milton (Research Associate—Trial Manager), B Pell (now PhD student, DECIPHer Centre, Cardiff University), H Prout (Research Associate—Qualitative), V Shepherd (Senior Research Fellow), K Smallman (Research Associate), H Stanton (Research Associate—Senior Data Manager). Thanks are due to Kerry Hood and Aimee Grant for their involvement in developing processes and systems for qualitative research within CTR.

No specific grant was received to support the writing of this paper.

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JS contributed to the design of the work and interpretation of data and was responsible for leading the drafting and revision of the paper. SC contributed to the design of the work, the acquisition of data and the drafting and revision of the paper. AL contributed to the design of the work, the acquisition of data and the drafting and revision of the paper. EG contributed to a critical review of the manuscript and provided additional relevant references. JH provided feedback on initial drafts of the paper and contributed to subsequent revisions. JHu provided feedback on initial drafts of the paper and contributed to subsequent revisions. NG provided feedback on initial drafts of the paper and contributed to subsequent revisions. SM was involved in the acquisition and analysis of data and provided a critical review of the manuscript. YM was involved in the acquisition and analysis of data and provided a critical review of the manuscript. MR was involved in the interpretation of data and critical review and revision of the paper. HS contributed to the conception and design of the work, the acquisition and analysis of data, and the revision of the manuscript. JT provided feedback on initial drafts of the paper and contributed to subsequent revisions. LB-H made a substantial contribution to the design and conception of the work, led the acquisition and analysis of data, and contributed to the drafting and revision of the paper.

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Segrott, J., Channon, S., Lloyd, A. et al. Integrating qualitative research within a clinical trials unit: developing strategies and understanding their implementation in contexts. Trials 25 , 323 (2024). https://doi.org/10.1186/s13063-024-08124-7

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DOI : https://doi.org/10.1186/s13063-024-08124-7

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Virtual coaching, self-directed learning, and the implementation of evidence-based practices: a single qualitative case study.

Elisabeth Myers , Liberty University Follow

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Doctor of Philosophy in Education (PhD)

Christine Saba

virtual coaching, self-directed learning, evidence-based practices, self-determination theory, sustained implementation

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Education | Educational Leadership

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Myers, Elisabeth, "Virtual Coaching, Self-Directed Learning, and the Implementation of Evidence-Based Practices: A Single Qualitative Case Study" (2024). Doctoral Dissertations and Projects . 5543. https://digitalcommons.liberty.edu/doctoral/5543

The purpose of this single instrumental case study was to understand how a virtual coaching program provides opportunities for self-directed learning during the implementation of evidence-based practices for adults at Navigator Coaching. The theory guiding this study was Deci and Ryan’s self-determination theory as conceptualizations of self-directed learning described in the literature mirror descriptions of self-determination. The central research question was: How does a virtual coaching program provide opportunities for self-directed learning during the implementation of evidence-based practices? As a single instrumental case, the setting for this study was one virtual life-coaching program in North America. The sample of participants included 12 adults who were currently enrolled in the program for a minimum of 6 months and participated in weekly program activities. Multiple data collection methods were employed to describe and understand the case: observations, audiovisual materials, and individual interviews. Interpretational analysis and a multistep data analysis process including direct interpretation, categorical aggregation, correspondence tables, and interpretive commentaries were utilized to develop the themes and overall synthesis of the case. Opportunities for self-directed learning were provided in weekly live sessions, modules in the program library, and in the Facebook group. Program members utilized instructional opportunities to satisfy their need for autonomy, thus becoming students of self. Participation in a purposeful community that was focused on solutions provided opportunities for program members to satisfy competence and relatedness needs. Program members implemented evidence-based practices and developed skills to create weekly learning plans, which assisted them in becoming agents of their highest selves.

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    The purpose of this single instrumental case study was to understand how a virtual coaching program provides opportunities for self-directed learning during the implementation of evidence-based practices for adults at Navigator Coaching. The theory guiding this study was Deci and Ryan's self-determination theory as conceptualizations of self-directed learning described in the literature ...

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