Strategies to account for time and process in Qualitative Comparative Analysis (QCA)

Qualitative comparative institutional analysis of environmental governance: Implications from research on payments for ecosystem services

Many evaluations of infrastructure projects rely on methods that ignore the complexity of the projects. Although case studies are attentive to project complexity, it is difficult to identify general patterns that would apply to a larger sample of projects. Qualitative comparative analysis is a method that preserves the complexity of projects and generates insights across cases. In this contribution, we discuss our experiences with using qualitative comparative analysis for the evaluation of the planning and implementation of complex infrastructure projects. We will provide a short introduction into the main properties of the method (complex causality, systematic comparison) as well as describe some of the main operations (calibration, truth table analysis, interpretation). This will serve to demonstrate why qualitative comparative analysis is a fitting evaluation method in project development and implementation. Next, we will show how we used the method in a research project that aimed to find out under what conditions unplanned events in the implementation of infrastructure projects were dealt with satisfactorily, that is, what it took to respond to these events in an apt manner. Based on our experiences, we will summarize main lessons learned for conducting qualitative comparative analysis proper and provide suggestions for further reading.

Qualitative Comparative Analysis (QCA) was launched in the late 1980s by Charles Ragin, as a research approach bridging case-oriented and variable-oriented perspectives. It conceives cases as complex combinations of attributes (i.e. configurations), is designed to process multiple cases, and enables one to identify, through minimization algorithms, the core equifinal combinations of conditions leading to an outcome of interest. It systematizes the analysis in terms of necessity and sufficiency, models social reality in terms of set-theoretic relations, and provides powerful logical tools for complexity reduction. It initially came along with one technique, crisp-set QCA (csQCA), requiring dichotomized coding of data. As it has expanded, the QCA field has been enriched by new techniques such as multi-value QCA (mvQCA) and especially fuzzy-set QCA (fsQCA), both of which enable finer-grained calibration. It has also developed further with diverse extensions and more advanced designs, including mixed- and multimethod designs in which QCA is sequenced with focused case studies or with statistical analyses. QCA’s emphasis on causal complexity makes it very fit to address various types of objects and research questions touching upon political decision making—and indeed QCA has been applied in multiple related social scientific fields. While QCA can be exploited in different ways, it is most frequently used for theory evaluation purposes, with a streamlined protocol including a sequence of core operations and good practices. Several reliable software options are also available to implement the core of the QCA procedure. However, given QCA’s case-based foundation, much researcher input is still required at different stages. As it has further developed, QCA has been subject to fierce criticism, especially from a mainstream statistical perspective. This has stimulated further innovations and refinements, in particular in terms of parameters of fit and robustness tests which also correspond to the growth of QCA applications in larger-n designs. Altogether the field has diversified and broadened, and different users may exploit QCA in various ways, from smaller-n case-oriented uses to larger-n more analytic uses, and following different epistemological positions regarding causal claims. This broader field can therefore be labeled as that of both “Configurational Comparative Methods” (CCMs) and “Set-Theoretic Methods” (STMs).

Uncertainty undermines causal claims; however, the nature of causal claims decides what counts as relevant uncertainty. Empirical robustness is imperative in regularity theories of causality. Regularity theory features strongly in QCA, making its case sensitivity a weakness. Following qualitative comparative analysis (QCA) founder Charles Ragin’s emphasis on ontological realism, this article suggests causality as a power and thus breaks with the ontological determinism of regularity theories. Exercising causal powers makes it possible for human agents to achieve an outcome but does not determine that they will. The article explains how QCA’s truth table analysis “models” possibilistic uncertainty and how crisp sets do this better than fuzzy sets. Causal power is at the heart of critical realist philosophy of science. Like Ragin, critical realism suggests empirical analysis as merely describing underlying causal relationships. Empirical statements must be substantively interpreted into causal claims. The article is critical of “empiricist” QCA that infers causality from the robustness of set relationships.

In a simulation-based analysis of Qualitative Comparative Analysis (QCA), Krogslund et al. (2015) conclude that its performance is suboptimal in several settings. I review their simulation setups and discuss three errors that were made in their analysis. First, the simulations involving inclusion thresholds are overpowered based on a misunderstanding of their role in truth table analyses. Second, the fact that a truth table analysis could exhibit model ambiguity and yield more than one model is ignored. If multiple models are derived from a truth table and they are combined into one, one overestimates the complexity of the models and underestimates their number, making it impossible to retrieve the target model of the simulation. Third, the simulations on the consequences of including irrelevant conditions intermingle sensitivity to overfitting with sensitivity to varying the inclusion thresholds. A reconsideration of KCP’s simulations correcting for the errors confirms some of their findings, but also reveals that some of those errors lead to an underestimation of QCA’s robustness. On a broader level, the review underscores that simulations are useful for the evaluation of QCA, but that simulation designs need to match QCA’s mechanics and principles to produce valid conclusions about its performance.

Qualitative Comparative Analysis (QCA) is increasingly establishing itself as a method in social research. QCA is a set-theoretic, truth-table-based method that identifies complex combinations of conditions (configurations) that are necessary and/or sufficient for an outcome. An advantage of QCA is that it models the complexity of social phenomena by accounting for conjunctural, asymmetric, and equifinal patterns. Accordingly, the method does not assume isolated net effects of single variables but recognizes that the effect of a single condition (that is, an explanatory factor) often unfolds only in combination with other conditions. Moreover, QCA acknowledges that the occurrence of a phenomenon can have a different explanation from its non-occurrence. Finally, QCA allows for different, mutually non-exclusive explanations of the same phenomenon. QCA is not only a technique; there is a diversity of approaches to how it can be implemented before, during and after the “technical moment,” depending on the analytic goals related to contributing to theory, engaging with cases, and the approach to explanation. Particularly since 2012, an increasing number of scholars have turned to using QCA to investigate public administrations. Even though the boundaries of Public Administration (PA) as an academic discipline are difficult to determine, it can be defined as an intellectual forum for those who want to understand both public administrations as organizations and their relationships to political, economic, and societal actors—especially in the adoption and implementation of public policies. Owing to its fragmented nature, there has been a long-lasting debate about the methodological sophistication and appropriateness of different comparative methods. In particular, the high complexity and strong context dependencies of causal patterns challenge theory-building and empirical analysis in Public Administration. Moreover, administrative settings are often characterized by relatively low numbers of cases for comparison, as well as strongly multilevel empirical settings. QCA as a technique allows for context-sensitive analyses that take into account this complexity. Against this background, it is not surprising that applications of QCA have become more widespread among scholars of Public Administration. A systematic review of articles using QCA published in the major Public Administration journals shows that the use of QCA started in mid-2000s and then grew exponentially. The review shows that, especially in two thematic areas, QCA has high analytical value and may (alongside traditional methodological approaches) help improve theories and methods of PA. The first area is the study of organizational decision-making and the role of bureaucrats during the adoption and implementation of public policies and service delivery. The second area where QCA has great merits is in explaining different features of public organizations. Especially in evaluation research where the aim is to investigate performance of various kinds (especially effectiveness in terms of both policy and management), QCA is a useful analytical tool to model these highly context-dependent relationships. The QCA method is constantly evolving. The development of good practices for different QCA approaches as well as several methodological innovations and software improvements increases its potential benefits for the future of Public Administration research.

Qualitative Comparative Analysis (QCA) is a method, developed by the American social scientist Charles C. Ragin since the 1980s, which has had since then great and ever-increasing success in research applications in various political science subdisciplines and teaching programs. It counts as a broadly recognized addition to the methodological spectrum of political science. QCA is based on set theory. Set theory models “if … then” hypotheses in a way that they can be interpreted as sufficient or necessary conditions. QCA differentiates between crisp sets in which cases can only be full members or not, while fuzzy sets allow for degrees of membership. With fuzzy sets it is, for example, possible to distinguish highly developed democracies from less developed democracies that, nevertheless, are rather democracies than not. This means that fuzzy sets account for differences in degree without giving up the differences in kind. In the end, QCA produces configurational statements that acknowledge that conditions usually appear in conjunction and that there can be more than one conjunction that implies an outcome (equifinality). There is a strong emphasis on a case-oriented perspective. QCA is usually (but not exclusively) applied in y-centered research designs. A standardized algorithm has been developed and implemented in various software packages that takes into account the complexity of the social world surrounding us, also acknowledging the fact that not every theoretically possible variation of explanatory factors also exists empirically. Parameters of fit, such as consistency and coverage, help to evaluate how well the chosen explanatory factors account for the outcome to be explained. There is also a range of graphical tools that help to illustrate the results of a QCA. Set theory goes well beyond an application in QCA, but QCA is certainly its most prominent variant. There is a very lively QCA community that currently deals with the following aspects: the establishment of a code of standards for QCA applications; QCA as part of mixed-methods designs, such as combinations of QCA and statistical analyses, or a sequence of QCA and (comparative) case studies (via, e.g., process tracing); the inclusion of time aspects into QCA; Coincidence Analysis (CNA, where an a priori decision on which is the explanatory factor and which the condition is not taken) as an alternative to the use of the Quine-McCluskey algorithm; the stability of results; the software development; and the more general question whether QCA development activities should rather target research design or technical issues. From this, a methodological agenda can be derived that asks for the relationship between QCA and quantitative techniques, case study methods, and interpretive methods, but also for increased efforts in reaching a shared understanding of the mission of QCA.

During the last decade, qualitative comparative analysis (QCA) has become an increasingly popular research approach in the management and business literature. As an approach, QCA consists of both a set of analytical techniques and a conceptual perspective, and the origins of QCA as an analytical technique lie outside the management and business literature. In the 1980s, Charles Ragin, a sociologist and political scientist, developed a systematic, comparative methodology as an alternative to qualitative, case-oriented approaches and to quantitative, variable-oriented approaches. Whereas the analytical technique of QCA was developed outside the management literature, the conceptual perspective underlying QCA has a long history in the management literature, in particular in the form of contingency and configurational theory that have played an important role in management theories since the late 1960s. Until the 2000s, management researchers only sporadically used QCA as an analytical technique. Between 2007 and 2008, a series of seminal articles in leading management journals laid the conceptual, methodological, and empirical foundations for QCA as a promising research approach in business and management. These articles led to a “first” wave of QCA research in management. During the first wave—occurring between approximately 2008 and 2014—researchers successfully published QCA-based studies in leading management journals and triggered important methodological debates, ultimately leading to a revival of the configurational perspective in the management literature. Following the first wave, a “second” wave—between 2014 and 2018—saw a rapid increase in QCA publications across several subfields in management research, the development of methodological applications of QCA, and an expansion of scholarly debates around the nature, opportunities, and future of QCA as a research approach. The second wave of QCA research in business and management concluded with researchers’ taking stock of the plethora of empirical studies using QCA for identifying best practice guidelines and advocating for the rise of a “neo-configurational” perspective, a perspective drawing on set-theoretic logic, causal complexity, and counterfactual analysis. Nowadays, QCA is an established approach in some research areas (e.g., organization theory, strategic management) and is diffusing into several adjacent areas (e.g., entrepreneurship, marketing, and accounting), a situation that promises new opportunities for advancing the analytical technique of QCA as well as configurational thinking and theorizing in the business and management literature. To advance the analytical foundations of QCA, researchers may, for example, advance robustness tests for QCA or focus on issues of endogeneity and omitted variables in QCA. To advance the conceptual foundations of QCA, researchers may, for example, clarify the links between configurational theory and related theoretical perspectives, such as systems theory or complexity theory, or develop theories on the temporal dynamics of configurations and configurational change. Ultimately, after a decade of growing use and interest in QCA and given the unique strengths of this approach for addressing questions relevant to management research, QCA will continue to influence research in business and management.

In Qualitative Comparative Analysis (QCA), empirical researchers use the consistency value as one, if not sole, criterion to decide whether an association between a term and an outcome is consistent with a set-relational claim. Braumoeller (2015) points out that the consistency value is unsuitable for this purpose. We need to know the probability of obtaining it under the null hypothesis of no systematic relation. He introduces permutation testing for estimating the $p$ value of a consistency score as a safeguard against false positives. In this paper, I introduce permutation-based power estimation as a safeguard against false-negative conclusions. Low power might lead to the false exclusion of truth table rows from the minimization procedure and the generation and interpretation of invalid solutions. For a variety of constellations between an alternative and null hypothesis and numbers of cases, simulations demonstrate that power estimates can range from 1 to 0. Ex post power analysis for 63 truth table analyses shows that even under the most favorable constellation of parameters, about half of them can be considered low-powered. This points to the value of estimating power and calculating the required number of cases before the truth table analysis.

BackgroundQualitative Comparative Analysis (QCA) is a method for examining configurational causality by identifying pathways that lead to an outcome of interest. There is a growing body of literature that uses QCA to measure child well-being due to its ability to generate evidence of causality for complex social phenomena. This scoping review examines how QCA studies are being employed to investigate child well-being and assesses the potential of QCA as a method to produce intervention-focused evidence and to contribute to future methodological development to address the complexity of child well-being.MethodWe systematically searched Embase, PsyINFO, MEDLINE, Social Policy and Practice, Global Health, Econlit, Scopus and Web of Science for peer-reviewed studies that had used QCA methods in child well-being studies. We searched studies published in English up until 2023. Systematic reviews and meta-analyses using QCA were excluded due to insufficient methodological detail for inclusion in our analysis. We followed the PRISMA-ScR flowchart and guidelines for study screening to ensure a systematic selection process. Data extraction was undertaken to capture information of most relevance to QCA best practice. Data were analysed using a basic qualitative content analysis approach.ResultsThe search identified 626 papers, of which 28 met our inclusion criteria. Dimensions of well-being included: psychological/mental health (n = 9); physical health (n = 2); language development under education (n = 1); socio-emotional health (n = 7); physical and psychological/mental health (n = 3), psychological/mental health and education (n = 1); and multi-dimensional health (n = 3). Two studies stated explicitly that they used well-being concepts—subjective well-being and psychological well-being. Most studies (n = 23) were predominantly in high income countries (HIC). Commonly reported strengths of QCA were the capacity to a) describe various pathways or combinations of pathways to the same outcome (equifinality); and b) examine conjunctural causation (combination of absent/present conditions), known as ‘causal complexity’. Weaknesses related to a) generalisability of the data; and b) the number of causal conditions that can be included in the analysis. Our findings suggest that QCA can be effectively used alongside traditional analyses to provide a more nuanced understanding.ConclusionQCA is a promising method with potential to address complexity when assessing the different dimensions of child well-being. More comprehensive guidelines are now available that offer good practices to enhance the quality of the QCA research. To build greater confidence using this method, scholars are recommended to adhere to these good practices to establish the highest levels of transparency of the analysis.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-23821-x.

In educational policy research, linking specific practices to specific outcomes is an important (though not the only) goal, which can bias researchers (and funders) toward employing purely quantitative methods. Given the context-specific nature of policy implementation in education, however, we argue that understanding how specific practices lead to specific outcomes in specific conditions or contexts is critical to improving education. Qualitative comparative analysis is a method of qualitative research that we argue can help to answer these kinds of questions in studies of educational policies and reforms. Qualitative comparative analysis is a case-oriented research method designed to identify causal relationships between variables and a particular outcome. Distinct from quantitative causal methods, qualitative comparative analysis requires qualitative data to identify conditions (and combinations of conditions) that lead to a particular result; it is context driven, just as many educational reforms must necessarily be. We contend that qualitative comparative analysis has the potential to be of use to educational researchers in investigating complex problems of cause and effect using qualitative data. As such, our aim here is to provide a general overview of the characteristics, processes, and outcomes of qualitative comparative analysis. In so doing, we hope to offer guidance to educational researchers around how and when to use qualitative comparative analysis, as well as recommendations for current educational issues that could be investigated with qualitative comparative analysis.

Based on the INUS theory of causality, the search target of qualitative comparative analysis (QCA) is to find all the minimally sufficient conditions for the outcome’s occurrence in a data set, where the condition’s sufficiency, the necessity of the condition’s components, and the completeness of the solution are three core requirements. However, QCA’s current top-down approach, which relies on a truth table and Boolean minimization, cannot meet the main objective of QCA. Conditions generated by the top-down approach can be insufficient for the outcome or contain unnecessary components that can be removed. We found evidence supporting our arguments by examining the correctness of top-down QCA in Study 1. Then, we show that QCA can also proceed with a “bottom-up” search strategy in sufficiency analysis, similar to coincidence analysis (CNA). We contrast solutions of the top-down and bottom-up QCA approaches by analyzing a simulated crisp-set data set in Study 2 and a real-world fuzzy-set data set in Study 3. Both results show that only the bottom-up approach can produce all the minimally sufficient conditions. We contribute to the ongoing debate pertain QCA solution types and QCA algorithms by critically evaluating the limitations of QCA’s top-down approach and introducing a bottom-up approach for QCA.

Latent Condition Qualitative Comparative Analysis: A Mixed Method for Qualitative Analysis

Qualitative comparative analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. To explore the strengths and weaknesses of using QCA for HSR. Using data from semistructured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. Ninety administrative and clinical key informants interviewed across 10 NYC area safety net hospitals. Transcribed interviews were coded by 3 investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods.

Research background: Alongside with the development of configurative comparative analysis aiming at identification of necessary and sufficient conditions, various formal methods used for this purpose have been formulated during the last decades. One of them is qualitative comparative analysis (QCA), one of approaches used for causal explanation of phenomena of cases performed in the field of international economics and global affairs. Purpose of the article: The main purpose of the article is to provide a detailed overview of the QCA method in global context, to define its methodologic foundations and consequently introduce the key concepts of the method. The article also provides a comparison of QCA to typical tools of qualitative and quantitative approaches. On the basis of this part, both pros and cons of QCA are derived. Methods: Basically, the methods of analysis, deduction and comparison are used to fulfil the purpose of the article. The existing and available papers and books coping with the topic of QCA and its position among other research methods are reviewed to provide an overview on the selected method. Findings & Value added: The QCA is a method based on analysing stated relations. It bridges the quantitative and qualitative research and reveals certain patterns based on causal complexity principles, however, it is done regarding heterogeneity and diversity of individual researched cases. It is a method applicable to the middle number of cases, it means too few cases for statistical methods on the other hand too many cases for typical qualitative approaches.