Sociology Qualitative Comparative Analysis (QCA)
by
Axel Marx
  • LAST MODIFIED: 28 November 2016
  • DOI: 10.1093/obo/9780199756384-0188

Introduction

The social sciences use a wide range of research methods and techniques ranging from experiments to techniques which analyze observational data such as statistical techniques, qualitative text analytic techniques, ethnographies, and many others. In the 1980s a new technique emerged, named Qualitative Comparative Analysis (QCA), which aimed to provide a formalized way to systematically compare a small number (5<N<75) of case studies. John Gerring in the 2001 version of his introduction to social sciences identified QCA as one of the only genuine methodological innovations of the last few decades. In recent years, QCA has also been applied to large-N studies (Glaesser 2015, cited under Applications of QCA; Ragin 2008, cited under The Essential Features of QCA) and the application of QCA to perform large-N analysis is in full development. This annotated bibliography aims to provide an overview of the main contributions of QCA as a research technique as well as an introduction to some specific issues as well as QCA applications. The contribution starts with sketching the emergence of QCA and situating the method in the debate between “qualitative” and “quantitative” methods. This contextualization is important to understand and appreciate that QCA in essence is a qualitative case-based research technique and not a quantitative variable-oriented technique. Next, the article discusses some key features of QCA and identifies some of the main books and handbooks on QCA as well as some of the criticism. In a third section, the overview focuses attention on the importance of cases and case selection in QCA. The fourth section introduces the way in which QCA builds explanatory models and presents the key contributions on the selection of explanatory factors, model specification, and testing. The fifth section canvasses the applications of QCA in the social sciences and identifies some interesting examples. Finally, since QCA is a formalized data-analytic technique based on algorithms, the overview ends with an overview of the main software package which can assist in the application of QCA.

Qualitative Case-Based Research in the Social Sciences

This section grounds Qualitative Comparative Analysis (QCA) in the tradition of qualitative case-based methods. As a research approach QCA mainly focuses on the systematic comparison of cases in order to find patterns of difference and similarity between cases. The initial intention of Ragin 1987 (cited under The Essential Features of QCA) was to develop an original “synthetic strategy” as a middle way between the case-oriented (or “qualitative”) and the variable-oriented (or “quantitative”) approaches, which would “integrate the best features of the case-oriented approach with the best features of the variable-oriented approach” (Ragin 1987, p. 84). However, instead of grounding qualitative research on the premises of quantitative research such as King, et al. 1994 did, Ragin aimed to develop a method which is firmly rooted on a case-based qualitative approach (Ragin and Becker 1992; Ragin 1997 for a systematic discussion of the differences between QCA and the approach advocated by King, et al. 1994). In recent years the fundamental differences between case-based and variable-oriented approaches have been further elaborated in terms of selection of units of observation or cases, approaches to explanation, causal analysis, measurement of concepts, and external validity (scope and generalization). Many researchers including Charles Ragin, Andrew Bennett (George and Bennett 2005), John Gerring (Gerring 2007, Gerring 2012), David Collier (Brady and Collier 2004) and James Mahoney (Mahoney and Rueschemeyer 2003) have contributed significantly to identifying the key ontological, epistemological, and logical differences between the two approaches. Goertz and Mahoney 2012 brings this literature together and shows the distinct differences between quantitative and qualitative research. The authors refer to two “cultures” of conducting social-scientific research. In this distinction QCA falls firmly in the “camp” of qualitative research. The overview below identifies some key texts which discuss these differences more in depth.

  • Brady, H., and D. Collier, eds. 2004. Rethinking social inquiry: Diverse tools, shared standards. Lanham, MD: Rowman and Littlefield.

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    This edited volume goes into a detailed discussion with King, et al. 1994 and shows the distinctive strengths of different approaches with a strong emphasis on the distinctive strengths of qualitative case-based methods. Book also introduces the idea of process-tracing for within-case analysis. Reprint 2010.

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    • George, A., and A. Bennett. 2005. Case research and theory development. Cambridge, MA: MIT.

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      Very extensive treatment of how case-based research focusing on longitudinal analysis and process-tracing can contribute to both theory development and theory testing. Discusses many examples from empirical political science research.

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      • Gerring, J. 2007. Case study research: Principles and practice. Cambridge, UK: Cambridge Univ. Press.

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        Very good introduction into what a case study is and what analytic and descriptive purposes it serves in social science research.

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        • Gerring, J. 2012. Social science methodology: A unified framework. Cambridge, UK: Cambridge Univ. Press.

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          An update of the 2001 volume which provides a concise introduction to different research approaches and techniques in the social sciences. Clearly shows the added value of different approaches and aims to overcome “the one versus the other” approaches.

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          • Goertz, G., and J. Mahoney. 2012. A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton, NJ: Princeton Univ. Press.

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            Book elaborates the differences between qualitative and quantitative research. They elaborate these differences in terms of (1) approaches to explanation, (2) conceptions of causation, (3) approaches toward multivariate explanations, (4) equifinality, (5) scope and causal generalization, (6) case selection, (7) weighting observations, (8) substantively important cases, (9) lack of fit, and (10) concepts and measurement.

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            • King, G., R. Keohane, and S. Verba. 1994. Designing social enquiry: Scientific inference in qualitative research. Princeton, NJ: Princeton Univ. Press.

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              A much-quoted and highly influential book on research design for the social sciences. This book aimed to discuss and assess qualitative research and argued that qualitative research should be benchmarked against standards used in quantitative research such as never select cases on the dependent variables, making sure one has always more observations than variables, maximize variation, and so on.

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              • Mahoney, J., and D. Rueschemeyer, eds. 2003. Comparative historical analysis in the social sciences. Cambridge, UK: Cambridge Univ. Press.

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                This is a very impressive volume with chapters written by the best researchers in macro-sociological research and comparative politics. It shows the key strengths of comparative historical research for explaining key social phenomena such as revolutions, social provisions, and democracy. In addition it combines masterfully substantive discussions with methodological implications and challenges and in this way shows how case-based research contributes fundamentally to understanding social change.

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                • Poteete, A., M. Janssen, and E. Ostrom. 2010. Working together: Collective action, the commons and multiple methods in practice. Princeton, NJ: Princeton Univ. Press.

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                  The study of Common Pool Resources (CPRs) has been one of the most theoretically advanced subjects in social sciences. This excellent book introduces different research designs to analyze questions related to the governance of CPRs and situates QCA nicely in the universe of different research designs and strategies.

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                  • Ragin, C. C. 1997. Turning the tables: How case-oriented methods challenge variable-oriented methods. Comparative Social Research 16:27–42.

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                    Engages directly with the work of King, et al. 1994 and fundamentally disagrees with its authors Ragin argues that qualitative case-based research is based on different standards and that this type of research should be assessed on the basis of these standards.

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                    • Ragin, C. C., and H. Becker. 1992. What is a case? Exploring the foundations of social inquiry. Cambridge, UK: Cambridge Univ. Press.

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                      Brings together leading researchers to discuss the deceptively easy question “what is a case?” and shows the many different approaches toward case-study research. One red line going through the contributions is the emphasis on thinking hard about the question “what is my case a case of?” in theoretical terms.

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                      The Emergence of QCA

                      This section introduces contributions which form the foundations and origin of Qualitative Comparative Analysis (QCA). QCA is a research design and analytic technique developed by Charles C. Ragin from the mid-1980s onward (Marx, et al. 2014). QCA builds on the work of leading sociologists who conducted macro-comparative research on understanding the emergence of political systems. Especially influencing in this context was Barrington Moore’s contributions (Moore 1966) on the social origins of dictatorship and democracy. Moore 1966 provided an extensive in-depth comparative analysis of a limited number of cases, focusing on the combinations of antecedent conditions linked to specific, large-scale historical transformations. The analytic foundation of the work centered on a series of pair-wise case comparisons. Early work of Ragin (Ragin, et al. 1984) focused on formalizing a technique that would enable researchers to systematically compare more cases pair-wise and integrate within-case and cross-case analysis. An important concern was to remain true to the nature of the qualitative argumentation with its key focus on the question of “how things happen” and how combinations of explanatory conditions explain an outcome (Chirot and Ragin 1975, Delacroix and Ragin 1978). Building on the work of Mill 1967, Ragin developed the idea of chemical causation (see also Ragin 1987, p. 25, cited under The Essential Features of QCA), arguing that causal conditions often must combine in order to generate qualitative change in social scientific phenomena such as the start of a revolution or the emergence of democracies. Ragin was especially interested in formalizing ways in which to capture how three or more factors combined to generate a qualitative change.

                      • Chirot, D., and C. Ragin. 1975. The market, tradition and peasant rebellion: The case of Romania. American Sociological Review 40.4: 428–444.

                        DOI: 10.2307/2094430Save Citation »Export Citation »E-mail Citation »

                        Paper uses historical census data relevant to explaining the Romanian peasant rebellion of 1907. The paper uses multiple regression techniques on county level data and identifies the importance of interaction effects to explain an outcome. This finding sets of a reflection on the importance of the combination of conditions to explain outcomes in the social sciences.

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                        • Delacroix, J., and C. Ragin. 1978. Modernizing institutions, mobilization, and Third World development: A cross-national study. American Journal of Sociology 84:123–150.

                          DOI: 10.1086/226743Save Citation »Export Citation »E-mail Citation »

                          Discusses in depth the analysis of interactions between variables (i.e., interaction effects) as an explanatory strategy in social scientific research. The paper started a five-year journey looking systematically into interaction models and led to a novel approach based on Boolean algebra which would lead to QCA.

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                          • Marx, A., B. Rihoux, and C. Ragin. 2014. The origins, development and application of qualitative comparative analysis (QCA): The first 25 years. European Political Science Review 6.1: 115–142.

                            DOI: 10.1017/S1755773912000318Save Citation »Export Citation »E-mail Citation »

                            This paper describes in detail the origins of QCA and the early developments in the method leading up to the publication of The Comparative Method. Next, the main features of the method, as presented in the early work, are discussed also in relation to the first criticisms. The paper proceeds with discussing the early applications and innovations.

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                            • Mill, J. S. 1967. A system of logic: Ratiocinative and inductive. Toronto: Univ. of Toronto Press.

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                              Originally published 1843. Classic treatment of the idea of combinatorial causation and the use of systematic comparisons based on semi-experiments and pair-wise comparisons.

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                              • Moore, B. 1966. Social origins of dictatorship and democracy: Lord and peasant in the making of the modern world. Boston: Beacon Press.

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                                World-class example of in-depth, case-based historical sociological research. Uses a wealth of data to carefully construct a middle-range theory on the emergence of democracy.

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                                • Ragin, C., S. Mayer, and K. Drass. 1984. Assessing discrimination: A Boolean approach. American Sociological Review 49:221–234.

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                                  The paper focuses on previous statistical analysis of discrimination and discusses the limitations of these analytic techniques with a specific focus on the difficulties of analyzing interaction effects between multiple variables. To overcome these difficulties Boolean approaches are introduced which allow for a more “holistic,” that is, combinations of factors leading to an outcome, comparison of cases (in this case individuals).

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                                  The Essential Features of QCA

                                  This section zooms further in on Qualitative Comparative Analysis (QCA) and includes some of the foundational works and handbooks on QCA. QCA has some distinct features which make it stand out as a methodological tool. First, the technique allows researchers to identify multiple conjunctural causation (Ragin 1987, Rihoux and Ragin 2009, Fiss 2011). This implies that (a) most often, it is a combination of explanatory variables (conditions) that produces (significant) variation in the outcome); (b) different combinations of conditions may produce the same outcome; and (c) depending on the interaction with other conditions, a given condition may very well have a different impact on the outcome (i.e., in some cases a condition produces the presence of an outcome, while in other cases—in interaction with different conditions—the same condition may lead to the absence of the outcome. A second feature of QCA is that it uses set logic (Ragin 2000, Schneider and Wagemann 2012). A set-theoretic approach starts from the idea that cases are members of a “set,” which are theoretical constructs. A set assesses whether, or to what degree, a case is a member of a set. Set-theoretic analysis allows for the analysis of necessary and sufficient (combination) of conditions since it allows for the analysis of subset relations. In QCA, one often makes the distinction between crisp sets (dichotomous) or fuzzy sets (gradual measurement). A third feature of QCA is that it uses Boolean algebra and truth tables to compare cases (Ragin 1987, Ragin 2008). QCA starts with creating a data matrix. Next QCA pools the information of the data-matrix together in a truth table which lists all theoretical possible combinations (2k where K = number of conditions) of configurations. In case of five conditions and one outcome, a truth table consists of 32 rows (i.e., 2 to the power of 5). Each case is placed in one row. A row of a truth table can contain several cases or none. This truth table is a first step in the synthesis of the data. The next step is Boolean minimization—that is, reducing the many different Boolean expressions (i.e., rows of a truth table), to the shortest possible expression that unveils the regularities in the data. A key characteristic of truth table analysis is that it allows for the analysis of limited diversity, that is, the fact that rows of a truth table do not contain any empirical observations. These are so-called logical remainders. There are now several good introductions to QCA available in English (Schneider and Wagemann 2010, Schneider and Wagemann 2012), French (De Meur and Rihoux 2002) and German (Schneider and Wagemann 2007).

                                  • De Meur, G., and B. Rihoux. 2002. L’analyse quali-quantitative comparée (AQQC-QCA): Approche, techniques et applications en sciences humaines. Louvain-la-Neuve, Belgium: Academia-Bruylant.

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                                    Well-structured and easy accessible French introduction to crisp-set QCA.

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                                    • Fiss, P. 2011. Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal 54:393–420.

                                      DOI: 10.5465/AMJ.2011.60263120Save Citation »Export Citation »E-mail Citation »

                                      Focuses on the importance of typologies in analyzing complex cause-effect relationships in organizational sciences and introduces QCA as the most suitable technique available to analyze and build typologies.

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                                      • Ragin, C. 1987. The comparative method moving beyond qualitative and quantitative strategies. Berkeley and London: Univ. of California Press.

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                                        The Comparative Method is a prize-winning and one of the most cited methodological books in sociology and political science which has also been translated in many languages. It lays the foundations of QCA and introduces the idea of crisp-sets, Boolean algebra, truth tables, and logical minimization. It remains a very interesting read and accessible introduction to the key ideas behind QCA as a technique, but more importantly QCA as a research approach and way of design research questions and projects.

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                                        • Ragin, C. C. 2000. Fuzzy-set social science. Chicago: Chicago Univ. Press.

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                                          This volume situates QCA between qualitative case-based research which generates complex results and quantitative variable-oriented research which strives for parsimony. Specific emphasis is paid to the issue of the construction of a research population and the selection of cases. In a second part, the volume introduces at length the idea of fuzzy-sets as an extension of crisp-sets (in order to deal with the criticism of rough measurement of crisp-sets) and discusses the use of set-theory to analyze necessity and sufficiency of (combinations of) conditions.

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                                          • Ragin, C. C. 2008. Redesigning social inquiry: Fuzzy sets and beyond. Chicago: Chicago Univ. Press.

                                            DOI: 10.7208/chicago/9780226702797.001.0001Save Citation »Export Citation »E-mail Citation »

                                            This book contrasts QCA against standard quantitative methods on four dimensions and highlights its distinctive strengths. First it contrasts set-theoretic relations with correlational connections and associations. Second, it makes a distinction between calibrating sets and measuring variables. Third, it shows how configurational thinking differs from thinking in terms of additive “independent” variables. Finally, it shows how the analysis of causal complexity in QCA differs from an analysis of net effects in regression analysis.

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                                            • Rihoux, B., and C. Ragin, eds. 2009. Configurational comparative methods: Qualitative Comparative Analysis (CSQCA) and related techniques. Thousand Oaks, CA: SAGE.

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                                              This nice little volume provides a hands-on introduction to both fuzzy-set and multi-value (TOSMANA) QCA and how to apply it. Authors explain best practices in terms of case-selection, condition selection, reduction of complexity in the truth table. In addition, it provides an introduction to the technical aspects of QCA as a data-analytic technical as well as QCA as a research approach and design.

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                                              • Schneider, Carsten Q., and Claudius Wagemann. 2007. QCA und fsQCA: Ein einführendes Lehrbuch für Anwender und jene, die es werden wollen. Opladen, Germany: Barbara-Budrich-Verlag.

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                                                Elaborate German introduction to and handbook on crisp-set and fuzzy-set QCA.

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                                                • Schneider, C. Q., and C. Wagemann. 2010. Standards of good practice in Qualitative Comparative Analysis (QCA) and fuzzy-sets. Comparative Sociology 9.3: 397–418.

                                                  DOI: 10.1163/156913210X12493538729793Save Citation »Export Citation »E-mail Citation »

                                                  Nice shorthand on some do’s and don’ts when performing a QCA analysis. The paper presents twenty-six proposals on what constitutes an appropriate QCA analysis divided into three components. Some recommendations focus on the initial research steps before the QCA analysis is performed. A second set of recommendations focuses on the analysis of data and a third set of recommendations focus on handling data after the analytical moment of data analysis.

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                                                  • Schneider, C., and C. Wagemann. 2012. Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge, UK: Cambridge Univ. Press.

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                                                    The book introduces QCA and explains in depth all its main features. This is illustrated with examples and often presented in a step-by-step way in order to facilitate applications. The book starts off with an extensive discussion of set theory, sets and how to measure and calibrate them, how to analyze set relations, and the notion of truth tables. Next they go into the analysis of real data and the different aspects related to applications of QCA. Highly recommended.

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                                                    • Wagemann, C., and C. Q. Schneider. 2010. Qualitative Comparative Analysis (QCA) and fuzzy-sets: Agenda for a research approach and a data analysis technique. Comparative Sociology 9.3: 376–396.

                                                      DOI: 10.1163/156913210X12493538729838Save Citation »Export Citation »E-mail Citation »

                                                      Paper introduces the epistemology of QCA and discusses its applicability to social science research questions with a specific focus on analyzing necessity and sufficiency of explanatory conditions.

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                                                      Comparisons with Other Techniques

                                                      This section compares different Qualitative Comparative Analysis (QCA) approaches as well as compares QCA to some other techniques. Concerning different QCA approaches a main difference emerges between set-theoretic approaches and multi-value approaches. The multi-value approach was developed by Lasse Cronqvist (Cronqvist and Berg-Schlosser 2009) following criticism on using crisp-sets (dichotomous) as a rather crude measurement tool to measure conditions and outcome. Multi-value QCA introduced the possibility of using three- or four-level scales (ordinal or nominal). However, it is not rooted in set-theory. Hence, it was criticized for not being able to provide an analysis of sufficiency and necessity. These criticisms were discussed in depth in the work of Alrik Thiem (Thiem 2013, Thiem 2014), who developed a new approach which integrates features of multi-value and fuzzy-set QCA named generalized-set Qualitative Comparative Analysis (gsQCA). In terms of comparison to other techniques, most attention has been paid to comparing QCA to statistical-based regression techniques. Grofman and Schneider 2009 compared QCA to logistical regression, which also uses dichotomous variables. Vis 2012 compared fuzzy-set analysis with regression analysis. Cooper and Glaesser 2011 compare fsQCA and cluster analysis. It should be noted that some authors (Thiem, et al. 2015) remain skeptical of these comparisons since QCA and statistical-based techniques are grounded on very different assumptions making comparisons difficult.

                                                      • Cooper, B., and J. Glaesser. 2011. Using case-based approaches to analyse large datasets: A comparison of Ragin’s fsQCA and fuzzy cluster analysis. International Journal of Social Research Methodology 14.1: 31–48.

                                                        DOI: 10.1080/13645579.2010.483079Save Citation »Export Citation »E-mail Citation »

                                                        This paper compares fsQCA with cluster analysis based on data from the National Child Development Study. The aim is to analyze how different techniques produce different classifications and how they perform in terms of predictive power. The paper finds that the two methods produce similar results.

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                                                        • Cronqvist, L., and D. Berg-Schlosser. 2009. Multi-value QCA (mvQCA). In Configurational comparative methods: Qualitative Comparative Analysis (QCA) and related techniques. Edited by B. Rihoux and C. Ragin, 69–87. Thousand Oaks, CA: SAGE.

                                                          DOI: 10.4135/9781452226569.n4Save Citation »Export Citation »E-mail Citation »

                                                          This chapter reflects on the limitations of using dichotomized conditions and on the advantages of using multi-values QCA based on nominal and ordinal scales. It also introduces a new software tool, TOSMANA, which was developed for the use of multi-value conditions and outcomes.

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                                                          • Grofman, B., and C. Q. Schneider. 2009. An introduction to crisp set QCA, with a comparison to binary logistic regression. Political Research Quarterly 62:662–672.

                                                            DOI: 10.1177/1065912909338464Save Citation »Export Citation »E-mail Citation »

                                                            This paper is interesting since it compares the strengths and weaknesses of crisp-set QCA vis-á-vis logistic regression. First the paper introduces the QCA methodology, applies it to a dataset, and then analyzes the same data set using logistic regression analysis to highlight the key-differences between the different methods.

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                                                            • Thiem, A. 2013. Clearly crisp, and not fuzzy: A reassessment of the (putative) pitfalls of multi-value QCA. Field Methods 25.2: 197–207.

                                                              DOI: 10.1177/1525822X13478135Save Citation »Export Citation »E-mail Citation »

                                                              Multi-value QCA (mvQCA) has received far less attention by researchers due to some assumed weaknesses. This papers addresses these assumptions and shows the usefulness and added value of mvQCA.

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                                                              • Thiem, A. 2014. Unifying configurational comparative methods: Generalized-set Qualitative Comparative Analysis. Sociological Methods & Research 43.2: 313–337.

                                                                DOI: 10.1177/0049124113500481Save Citation »Export Citation »E-mail Citation »

                                                                Paper advances the idea that multi-value QCA can also be set-theoretic and then develops a generalized-set Qualitative Comparative Analysis (gsQCA), an approach that integrates features of multi-value QCA and fuzzy-set QCA into a single framework based on truth table construction and minimization procedures.

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                                                                • Thiem, A., M. Baumgartner, and D. Bol. 2015. Still lost in translation! A correction of three misunderstandings between configurational comparativists and regressional analysts. Comparative Political Studies 49.6: 742–774.

                                                                  DOI: 10.1177/0010414014565892Save Citation »Export Citation »E-mail Citation »

                                                                  Paper compares configurational comparative methods with regressional analytic methods and argues that they fundamentally differ in terms of their logic of inference, which hypotheses they can test, and their conception of causal complexity especially in relation to the nature of combinatioral effects (interaction effects in regression versus conjunctions in configurational methods).

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                                                                  • Vis, B. 2012. The comparative advantages of fsQCA and regression analysis for moderately large-N analyses. Sociological Methods and Research 41.1: 168–198.

                                                                    DOI: 10.1177/0049124112442142Save Citation »Export Citation »E-mail Citation »

                                                                    Vis assesses the strengths and weaknesses of regression analysis and fuzzy-set qualitative comparative analysis (fsQCA) for studies with a moderately large-n (between approximately 50 and 100) with an application to a study of fifty-three governments in Western democracies. This comparison demonstrates that while each approach has merits and demerits, fsQCA leads to a fuller understanding of the conditions under which the outcome occurs and is better able to capture the different interaction effects.

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                                                                    Criticisms of QCA

                                                                    This section introduces some of the main criticisms of Qualitative Comparative Analysis (QCA). QCA has also been the subject of several critiques. Some criticisms have been formulated by researchers who in essence assess QCA on the basis of a different logic and dismiss the technique and approach on these grounds (Collier 2014). These critics have claimed that QCA is static and deterministic (Goldthorpe 1997), is over-reliant on one single case (Lieberson 1991), is unable to distinguish real from random data (Lieberson 2004), is sensitive to data manipulation and measurement error (Hug 2013), has built-in confirmation bias (Lucas and Szatrowski 2014), and is based on assumptions which are not tested (Seawright 2005). Also researchers using the QCA logic have formulated several criticisms mainly with the purpose of improving the method and approach. Baumgartner has criticized QCA for not being able to identify causal structures that involve causal chains. In order to address this issue he has developed a new method called “coincidence analysis” (CNA). A second point of contention has focused on how to treat limited diversity in QCA and what to do with logical remainders. Researchers can make several decisions on what to do with logical remainders. These decisions will influence the level of parsimony one can achieve with a QCA analysis. Schneider and Wagemann 2012 (cited under The Essential Features of QCA) introduced an Enhanced Standard Analysis which does not rest on “untenable” assumptions with regard to logical remainders. This approach has been criticized by Cooper and Glaesser 2016.

                                                                    • Baumgartner, M. 2013. Detecting causal chains in small-N data. Field Methods 25:3–24.

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                                                                      Paper shows that QCA does not correctly analyze data generated by causal chains due to the Quine-McCluskey algorithm on which it is based. The paper introduces an alternative approach called Coincidence Analysis which is able to detect causal chains.

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                                                                      • Collier, D., ed. 2014. Symposium: The set-theoretic comparative method: Critical assessment and the search for alternatives. Qualitative & Multi-Method Research: Newsletter of the American Political Science Association Organized Section for Qualitative and Multi-Method Research 12.1 (Spring).

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                                                                        Supports the goals of QCA but critically engages with its technical underpinnings and used algorithms. Collection argues QCA should leave the algorithms behind.

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                                                                        • Cooper, B., and J. Glaesser. 2016. Qualitative Comparative Analysis, necessary conditions and limited diversity: Some problematic consequences of Schneider and Wagemann’s Enhanced Standard Analysis. Field Methods 28.3.

                                                                          DOI: 10.1177/1525822X15598974Save Citation »Export Citation »E-mail Citation »

                                                                          The way in which limited diversity is dealt with in QCA, through the inclusion or exclusion of logical reminders, is of central importance. Ragin allowed for several ways how to deal with this issue generating three types of results: complex, intermediate, and parsimonious. Schneider and Wagemann develop an Enhanced Standard Analysis (ESA) to deal with this issue. This paper shows that the ESA procedure is problematic.

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                                                                          • Goldthorpe, J. H. 1997. Current issues in comparative macrosociology: A debate on methodological issues. Comparative Social Research 16:1–26.

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                                                                            Engages in the debate on “variable” versus “case”-oriented research and highlights some limitations to case-based methods such as QCA including the claim that QCA is deterministic, it entails relationships that are invariant, and is often confronted with the dilemma of too many conditions and too few cases which makes it difficult to exclude rivalry explanations.

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                                                                            • Hug, S. 2013. Qualitative Comparative Analysis: How inductive use and measurement error lead to problematic inference. Political Analysis 21.2: 252–265.

                                                                              DOI: 10.1093/pan/mps061Save Citation »Export Citation »E-mail Citation »

                                                                              Hug argues that QCA is suited to test deterministic hypotheses under the assumption of error-free measures of the explanatory conditions and outcome. He argues that only in a few research areas theories are sufficiently advanced to yield deterministic hypotheses. Moreover, the assumption of error-free measures makes the inductive use of QCA problematic since this type of application might miss measurement errors.

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                                                                              • Lucas, S., and A. Szatrowski. 2014. Qualitative Comparative Analysis in critical perspective. Sociological Methodology 44.1 (August): 1–79.

                                                                                DOI: 10.1177/0081175014532763Save Citation »Export Citation »E-mail Citation »

                                                                                Highly critical paper which uses simulations to test whether QCA can identify causal processes which are known to be true. Paper shows that QCA only identifies these causal processes in a limited number of cases and hence questions the ontological foundations of QCA. Paper generated a special issue in which several authors, including Charles Ragin, were invited to comment and reanalyze the data. Some of these contributions in turn put the analysis of Lucas and Szatrowsiki into question.

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                                                                                • Lieberson, S. 1991. Small N’s and big conclusions: An examination of the reasoning in comparative studies based on a small number of cases. Social Forces 70:307–320.

                                                                                  DOI: 10.1093/sf/70.2.307Save Citation »Export Citation »E-mail Citation »

                                                                                  Discusses at length some of the assumptions on which QCA is based including its deterministic nature and the idea that there is no room for errors in measurement.

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                                                                                  • Lieberson, S. 2004. Comments on the use and utility of QCA. Qualitative Methods: Newsletter of the American Political Science Association Organized Section on Qualitative Methods 2.2: 13–14.

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                                                                                    Argues that a QCA analysis is not able to distinguish between real and random data and that a QCA analysis of purely random data will generate “valid” results and possible causal paths to an outcome.

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                                                                                    • Seawright, J. 2005. Qualitative Comparative Analysis vis-à-vis regression. Studies in Comparative International Development 40.1: 3–26.

                                                                                      DOI: 10.1007/BF02686284Save Citation »Export Citation »E-mail Citation »

                                                                                      Critically discusses some assumptions on which QCA is based, most notably the assumption that QCA assumes that there are no omitted variables in the explanatory model, an assumption which is hard in a context in which social phenomena are typically influenced by many explanatory conditions.

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                                                                                      • Thiem, A. 2014. Navigating the complexities of Qualitative Comparative Analysis: Case numbers, necessity relations and model ambiguities. Evaluation Review: A Journal of Applied Social Research 38.6: 487–513.

                                                                                        DOI: 10.1177/0193841X14550863Save Citation »Export Citation »E-mail Citation »

                                                                                        Critical paper which identifies several pitfalls related to the application QCA with a specific focus on the number of cases in an analysis, the analysis of necessity and model specification. Paper makes the strong argument that many results presented in past QCA research are sensitive to the identified pitfalls.

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                                                                                        • Thiem, A., and M. Baumgartner. 2016. Modeling causal irrelevance in evaluations of configurational comparative methods. Sociological Methodology 44.1.

                                                                                          DOI: 10.1177/0081175016654736Save Citation »Export Citation »E-mail Citation »

                                                                                          This paper provides a rebuttal to Lucas and Szatrowski and argues that correlation-based evaluation designs as used by Lucas and Szatrowski are not appropriate and shows, on the basis of alternative tests, that QCA does not suffer from confirmation bias.

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                                                                                          • Thiem, A., R. Spöhel, and A. Dusa. 2016. Enhancing sensitivity diagnostics for Qualitative Comparative Analysis. Political Analysis 24.1: 104–120.

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                                                                                            Paper engages with sensitivity evaluations as performed by Lucas and Szatrowski 2014 and Hug 2013 and introduces a new approach which they label combinatorial computation. The paper shows that the simulations performed by Lucas and Szatrowski 2014 and Hug 2013 are based on specific assumptions which are not made explicit.

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                                                                                            Case Selection and Combining Cross-Case and Within-Case Analysis

                                                                                            As mentioned in the first section, Qualitative Comparative Analysis (QCA) is foremost a case-based approach which implies that each individual case is considered as a complex entity (a whole—a configuration of conditions/ variables) which needs to be comprehended and which should not be forgotten in the course of the analysis. Researchers which use QCA often get themselves first familiarized with some cases. Different parts of each case are understood in relation to one another and in terms of the total picture that they form together as a case. Cases in QCA are regarded as configurations of conditions/variables. After a QCA analysis, each result (i.e., each causal path to an outcome) can be traced back to one or more cases which will allow for further within-case analysis. Given the case-based nature of QCA significant attention is paid to case-selection. Several case selection strategies are possible (Gerring 2007) In QCA applications significant attention is paid to case selection which follows a MSDO design, Most-Similar Different Outcomes design (Przeworski and Teune 1970). This research design aims to identify very similar cases, which do exhibit variation on the outcome and explanatory conditions/variable which are of interest to the researcher. Given its reliance on cases some commentators have argued that QCA is too sensitive to individual cases, since the inclusion or exclusion of a single case can modify the results of an analysis (Goldthorpe 1997, cited under Criticisms of QCA). This criticism has been nuanced (Marx, et al. 2013) by arguing that the inclusion of new cases is not problematic (if one assumes that the number of conditions stays equal). Two situations can occur. First, a case is added to a row in a truth table which already contains other empirical cases. This will result in the fact that this casual path explains more cases but does not alter the results. Second, the inclusion of new cases can also result in the discovery of new causal path to an outcome. The exclusion of cases is potentially more problematic since the exclusion of a case can result in the disappearance of contradictions which indicates that the explanatory model does not explain all the cases under investigation. If the exclusion of cases is not conducted transparently and is not supported by theoretical or methodological arguments it might influence the results and hence is troublesome. This section identifies contributions which deal with case-selection and scoping (clearly defining the boundaries of the research population) in QCA, comparative case designs and designs which link QCA analysis to within-case analysis.

                                                                                            • Berg-Schlosser, D., and G. De Meur. 2009. Comparative research design: Case and variable selection. In Configurational comparative methods. Qualitative Comparative Analysis (CSQCA) and related techniques. Edited by B. Rihoux and C. Ragin, 19–32. Thousand Oaks, CA: SAGE.

                                                                                              DOI: 10.4135/9781452226569.n2Save Citation »Export Citation »E-mail Citation »

                                                                                              Gives an overview of different case-selection techniques with pros and cons and provides illustrative examples of different strategies which are especially suited for a QCA analysis.

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                                                                                              • Gerring, J. 2007. Case study research: Principles and practice. Cambridge, UK: Cambridge Univ. Press.

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                                                                                                Chapter 5 (co-written with Jason Seawright) extensively discusses several techniques for choosing case studies, including comparative case designs which is especially relevant for QCA.

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                                                                                                • Marx, A., and G. Van Hootegem. 2007. A comparative configurational case analysis of ergonomic injuries. Journal of Business Research 60:522–530.

                                                                                                  DOI: 10.1016/j.jbusres.2007.01.012Save Citation »Export Citation »E-mail Citation »

                                                                                                  Paper applies QCA to a specific type of job in organizational studies and elaborates nicely what a MSDO design implies for a QCA application. It also shows the limited external validity of QCA as a consequence of stringently defining research populations.

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                                                                                                  • Marx, A., B. Cambre, and B. Rihoux. 2013. Crisp-set Qualitative Comparative Analysis in organizational studies. In Configurational theory and methods in organizational research. Edited by P. Fiss, B. Cambré, and A. Marx, 23–47. Research in the Sociology of Organizations. Bingley, UK: Emerald.

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                                                                                                    Discusses case-selection for QCA in the area of organizations studies and also engages and discusses in depth the criticism that QCA is too sensitive to single cases.

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                                                                                                    • Przeworski, A., and H. Teune. 1970. The logic of comparative social inquiry. New York: Wiley.

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                                                                                                      Early but excellent book which introduces the basics of macro (country-level) comparative research in the social sciences. Also develops the approach of comparing very similar cases which generate very different outcomes.

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                                                                                                      • Ragin, C. C., and H. Becker. 1992. What is a case? Exploring the foundations of social inquiry. Cambridge, UK: Cambridge Univ. Press.

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                                                                                                        Goes to the essence of the question of what a case study is and brings together leading researchers to explain why they use case studies and how they identify them.

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                                                                                                        • Rihoux, B., and B. Lobe. 2009. The case for Qualitative Comparative Analysis (QCA): Adding leverage for thick cross-case comparison. In The Sage handbook of case-based methods. Edited by D. Byrne and C. Ragin, 222–243. London: SAGE.

                                                                                                          DOI: 10.4135/9781446249413.n13Save Citation »Export Citation »E-mail Citation »

                                                                                                          Very good paper to get acquainted with QCA which guides the reader through the different steps of QCA as an approach as well as QCA as a technique. Paper shows how the technical aspects of QCA (truth tables, logical minimization, etc.) fit within QCA as an approach which also implies a need to go back to the cases to complement cross-case data observations with within-case data on causal processes and mechanisms which link explanatory conditions to an outcome.

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                                                                                                          • Rohlfing, I., and C. Q. Schneider. 2013. Improving research on necessary conditions: Formalized case selection for process tracing after QCA. Political Research Quarterly 66.1: 220–235.

                                                                                                            DOI: 10.1177/1065912912468269iSave Citation »Export Citation »E-mail Citation »

                                                                                                            Paper shows how process tracing based on purposefully selected cases can complement findings on cross-case patterns identified with QCA and contributes to developing mixed-method research strategies which uses QCA.

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                                                                                                            • Schneider, C., and I. Rohlfing. 2013. Combining QCA and process tracing in set-theoretic multi-method research. Sociological Methods and Research 42.4: 559–597.

                                                                                                              DOI: 10.1177/0049124113481341Save Citation »Export Citation »E-mail Citation »

                                                                                                              Paper advances principles on how to integrate within-case analysis (case study research proper) in QCA with a specific focus on relevant cases for further analysis.

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                                                                                                              Explanatory Models in QCA and Condition Selection and Operationalization

                                                                                                              Given its set-theoretic nature QCA allows researchers to assess the necessity and sufficiency of combinations of conditions. Analyzing relationships between conditions and outcomes in QCA in essence is about analyzing set-relations. A key feature of set-relations is that one set can be a subset or superset of another set which implies that one can analyze whether one set is necessary and/or sufficient for another set. Hence, QCA allow researchers to identify necessary (i.e., a condition must be present for a certain outcome to occur) and sufficient (i.e., a condition can by itself produce a certain outcome) conditions (Ragin 1987, pp. 99–101, cited under The Essential Features of QCA). Especially the identification of necessary conditions has received increasing attention in the social sciences (Goertz and Starr 2003). It also enables researchers to identify explanatory conditions which are so-called INUS conditions. James Mackie called INUS conditions, in The Cement of the Universe, conditions that are insufficient but Necessary part of a combination of conditions which is itself Unnecessary but Sufficient for the occurrence of the outcome. In order to analyze these set-theoretic relations researchers need to select explanatory conditions and measure them. There are several ways in which researchers can select conditions in the context of QCA (Amenta and Poulsen 1994). This can range from deductive approaches which are grounded in theory toward inductive approaches which are based on detailed case-based empirical research. Also, there are different ways in which conditions and outcomes can be operationalized and measured. In QCA one can use dichotomous conditions (crisp-sets) or more fine-grained measures (fuzzy-sets). This choice is not so much dependent on the available data but on the purpose of the research. Collier and Adcock 1999 demonstrates that gradual measures of measuring democracy, an often used explanatory condition in QCA, are not necessarily better than binary approaches toward measuring democracy. Also the way in which set-membership is assigned to a case (calibration) in fsQCA can influence the interpretation of results (Glaesser and Cooper 2014). Finally, the choice of measurement also depends on the complexity of the concept. The latter is nicely illustrated by Vis, et al. 2013 in their approach toward measuring economic performance.

                                                                                                              • Amenta, E., and J. Poulsen. 1994. Where to begin: A survey of five approaches to selecting independent variables for qualitative comparative analysis. Sociological Methods and Research 23.1: 22–53.

                                                                                                                DOI: 10.1177/0049124194023001002Save Citation »Export Citation »E-mail Citation »

                                                                                                                Provides a discussion of selecting conditions to be included in a QCA analysis. They identify five case selection strategies but spend most time in advocating one, the conjunctural theories approach. They apply this to theories of the welfare state.

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                                                                                                                • Collier, D., and R. Adcock. 1999. Democracy and dichotomies: A pragmatic approach to choices about concepts. Annual Review of Political Science 2:537–565.

                                                                                                                  DOI: 10.1146/annurev.polisci.2.1.537Save Citation »Export Citation »E-mail Citation »

                                                                                                                  Paper does not engage directly with QCA and the discussion of fuzzy-sets versus crisp-sets but is very instructive in showing that gradualism in measuring concepts should not be pursued in all cases. They apply to the concept of democracy and show democracy for some analytic purposes is best conceptualized as a dichotomy.

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                                                                                                                  • Glaesser, J., and B. Cooper. 2014. Exploring the consequences of a recalibration of causal conditions when assessing sufficiency with fuzzy set QCA. International Journal of Social Research Methodology 17.4: 387–401.

                                                                                                                    DOI: 10.1080/13645579.2013.769782Save Citation »Export Citation »E-mail Citation »

                                                                                                                    Measuring set membership in a fuzzy-set (calibration) can be done in several ways and is a central component of a QCA analysis. This paper shows how two alternative calibrations of a condition affect the assessment of consistency (with sufficiency) and provides guidance on how to interpret and assess alternative calibrations.

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                                                                                                                    • Goertz, G., and H. Starr. 2003. Necessary conditions: Theory, methodology, and applications. New York: Rowman and Littlefield.

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                                                                                                                      QCA as a set-theoretic method allows for the identification of necessary and sufficient conditions. This book shows the significant importance of necessary conditions for social-scientific research.

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                                                                                                                      • Vis, B., J. Woldendorp, and H. Keman. 2013. Examining variation in economic performance using fuzzy-sets. Quality and Quantity 47:1971–1989.

                                                                                                                        DOI: 10.1007/s11135-011-9637-4Save Citation »Export Citation »E-mail Citation »

                                                                                                                        Very thoughtful application of fsQCA to explain economic performance of nineteen OECD (Organisation for Economic Co-operation and Development) countries over different time periods. Especially interesting for the operationalization and measurement of economic performance as a fuzzy-set combining different dimensions of economic performance and integrating different data-sources.

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                                                                                                                        Model Specification and Parameters of Fit

                                                                                                                        Besides its set-theoretic nature, QCA has a distinct way of generating explanatory models which is best described as an iterative way of developing an explanatory model which relies on a dialogue between theory and evidence. The key mechanism for developing an explanatory model in QCA is the presence of contradictions (Ragin 1987, pp. 113–118, cited under The Essential Features of QCA, Rihoux and De Meur 2009, pp. 48–50). Contradictions occur in QCA when an identical configuration of conditions are linked to both the presence and absence of an outcome. Contradictions are revealed through the transformation of a data matrix into a truth table. If a truth table reveals contradictions, they should be resolved, primarily by identifying omitted causal conditions (Ragin 1987, p. 113; see also Rihoux and De Meur 2009, pp. 48–49, for complementary strategies). Hence, the development of an explanatory model in QCA goes hand in hand with resolving contradictions. This back and forth process of including and excluding theoretically and empirically relevant conditions in a model until a model has been identified with no or only a few contradictions is the key mechanism for developing an explanatory model for analytic purposes. As Ragin 2005 (p. 34) argues, “the resolution of contradictions [. . .] deepens knowledge and understanding of cases and also may expand and elaborate theory.” In later years, the issue of contradictions was taken on board in a more formal measure to assess the validity which was labeled “consistency” (Ragin 2006). Consistency evaluates the degree to which a combination of conditions constitutes a perfect subset of an outcome. If consistency is 1 then there are no contradictions. A second measure was developed to evaluate a consistent combination’s empirical importance by assessing the proportion of cases with the outcome that the combination covers. This measure was labeled “coverage” (Ragin 2006). These measures enable researchers to assess the degree to which a model explains the outcomes observed in the cases and also the relative weight of each causal combination. In sum, QCA aims to explain an outcome on the basis of a number of explanatory conditions (i.e., model) which combine in different ways to generate the presence of the outcome or the absence of the outcome. Different combinations of conditions can lead to the presence of the outcome or the absence of the outcome. Hence, the selection of conditions is important as well as the construction of the explanatory model which is used to analyze the data. This will also affect the occurrence and significance of limited diversity and the way this is addressed through counterfactual reasoning (Cooper and Glaesser 2011). This section includes contributions which focus on the importance of number of cases for model specification (Cooper and Glaesser 2016); selection of conditions, the operationalization of conditions and outcome, the building of explanatory models in QCA (Marx 2010, Marx and Dusa 2011) and the assessment of the validity of these models (Skaaning 2011, Thiem 2015).

                                                                                                                        • Cooper, B., and J. Glaesser. 2011. Paradoxes and pitfalls in using fuzzy set QCA: Illustrations from a critical review of a study of educational inequality. Sociological Research Online 16.3: 8.

                                                                                                                          DOI: 10.5153/sro.2444Save Citation »Export Citation »E-mail Citation »

                                                                                                                          This paper introduces some problems which can arise when fsQCA, and especially the software, is applied in a non-reflexive way with a specific focus on two issues. First the paper focuses on limited diversity and discusses the different ways in which the treatment of logical reminders (through counterfactual reasoning) influences the interpretation of QCA results. Second, the paper shows the logical paradoxes which can arise in the application of fsQCA.

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                                                                                                                          • Cooper, B., and J. Glaesser. 2015. Exploring the robustness of set theoretic findings from a large n fsQCA: An illustration from the sociology of education. International Journal of Social Research Methodology 19.4.

                                                                                                                            DOI: 10.1080/13645579.2015.1033799Save Citation »Export Citation »E-mail Citation »

                                                                                                                            Employing QCA to analyze large N-datasets without case knowledge raises some specific challenges which this paper addresses. The paper focuses specifically on changing fuzzy-set theoretic calibrations, simulating errors in measuring conditions/outcome and changing thresholds for assessing the quasi-sufficiency of causal configurations.

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                                                                                                                            • Cooper, B., and J. Glaesser. 2016. Analysing necessity and sufficiency with Qualitative Comparative Analysis: How do results vary as case weights change? Quality & Quantity 50.1: 327–346.

                                                                                                                              DOI: 10.1007/s11135-014-0151-3Save Citation »Export Citation »E-mail Citation »

                                                                                                                              A key component in model specification concerns the number of cases in an analysis. This paper discusses the effect that the distribution of cases in a dataset, and weights of types of cases, can have on results of a QCA analysis. It also offers some suggestions on how to deal with this issue.

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                                                                                                                              • Eliason, S., and R. Stryker. 2009. Goodness-of-fit tests and descriptive measures in fuzzy-set analysis. Sociological Methods and Research 38:102–146.

                                                                                                                                DOI: 10.1177/0049124109339371Save Citation »Export Citation »E-mail Citation »

                                                                                                                                The authors develop goodness-of-fit tests for fuzzy-set analyses to assess the fit between empirical information and theoretical propositions while accounting for measurement error in membership scores. They apply these tests to previous published research and discuss the added value.

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                                                                                                                                • Marx, A. 2010. Crisp set Qualitative Comparative Analysis (csQCA) and model specification. International Journal of Multiple Research Approaches 4.2: 138–158.

                                                                                                                                  DOI: 10.5172/mra.2010.4.2.138Save Citation »Export Citation »E-mail Citation »

                                                                                                                                  This paper argues that QCA is based on an assumption that if there is a model (selection of conditions) specification error (omitted variables, measurement error, etc.) QCA will not be able to generate an explanation for empirical cases and tests this assumption. The paper shows that this assumption is not always valid and can only be taken on board when model specification parameters are taken into account (number of conditions on cases).

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                                                                                                                                  • Marx, A., and A. Dusa. 2011. Crisp-set Qualitative Comparative Analysis (csQCA), contradictions and consistency: Benchmarks for model specification. Methodological Innovations Online 6.2: 103–148.

                                                                                                                                    DOI: 10.4256/mio.2010.0037Save Citation »Export Citation »E-mail Citation »

                                                                                                                                    This paper further develops the idea of balancing the number of conditions on the number of cases and proposes specific guidelines on how many conditions can be included in a QCA analysis given the number of cases one has.

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                                                                                                                                    • Ragin, C. 2005. Core versus tangential assumptions in comparative research. Studies in Comparative International Development 40.1: 33–38.

                                                                                                                                      DOI: 10.1007/BF02686286Save Citation »Export Citation »E-mail Citation »

                                                                                                                                      Discusses some assumptions on which QCA is based and how QCA develops models on the basis of resolving contradictions.

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                                                                                                                                      • Ragin, C. C. 2006. Set relations in social research: Evaluating their consistency and coverage. Political Analysis 14.3: 291–310.

                                                                                                                                        DOI: 10.1093/pan/mpj019Save Citation »Export Citation »E-mail Citation »

                                                                                                                                        Foundational paper which introduces the concepts of coverage and consistency for QCA Consistency assesses the degree to which a subset relation has been approximated, whereas coverage assesses the empirical relevance of a consistent subset.

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                                                                                                                                        • Rihoux, B. and De Meur, G. 2009. Crisp-Set Qualitative Comparative Analysis (csQCA). In Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Edited by B. Rihoux and C. C. Ragin, 33–68. Thousand Oaks, CA: SAGE.

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                                                                                                                                          Introduces the distinctive features and uses of crisp-set QCA as compared to multi-value and fuzzy-set QCA. Good starting point for newcomers to QCA.

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                                                                                                                                          • Schneider, C. Q., and C. Wagemann. 2006. Reducing complexity in Qualitative Comparative Analysis (QCA): Remote and proximate factors and the consolidation of democracy. European Journal of Political Research 45:751–786.

                                                                                                                                            DOI: 10.1111/j.1475-6765.2006.00635.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                            Paper proposes a specific procedure, a two-step approach, to deal with the problem of having too many conditions. This two-step approach is based on identifying remote and proximate conditions influencing the outcome. They apply the procedure to research on consolidation of democracy.

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                                                                                                                                            • Schneider, C. Q., and C. Wagemann. 2013. Doing justice to logical remainders in QCA: Moving beyond the standard analysis. Political Research Quarterly 66.1: 211–220.

                                                                                                                                              DOI: 10.1177/1065912912468269hSave Citation »Export Citation »E-mail Citation »

                                                                                                                                              Paper focuses on the issue of limited diversity and argues it is among the most understudied methodological challenges. It then goes to argue that QCA allows for a more conscious treatment of logical remainders than most other comparative methods. The treatment of logical remainders is of key importance for analyzing models through the truth table.

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                                                                                                                                              • Skaaning, S. 2011. Assessing the robustness of crisp-set and fuzzy-set QCA results. Sociological Methods and Research 40.2: 391–408.

                                                                                                                                                DOI: 10.1177/0049124111404818Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                Paper proposes different ways to address robustness of QCA results with a focus on the calibration of raw data into set-membership values, the frequency of cases linked to the configurations, and the choice of consistency thresholds.

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                                                                                                                                                • Thiem, Alrik. 2015. Parameters of fit and intermediate solutions in multi-value Qualitative Comparative Analysis. Quality & Quantity: International Journal of Methodology 49.2: 657–674.

                                                                                                                                                  DOI: 10.1007/s11135-014-0015-xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                  This is one of the few papers which engages with multi-value QCA (TOSMANA) and aims to expand its potential by introducing two innovations. The first innovation is developing measures of model fit based on the notions of consistency and coverage. The second innovation focuses on the introduction of easy and difficult counterfactuals to deal with limited diversity.

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                                                                                                                                                  Applications of QCA

                                                                                                                                                  Qualitative Comparative Analysis (QCA) over the last three decades has been applied in many studies which have appeared in leading international journals. This section reviews some the different branches of social scientific research in which QCA has been applied. The overview shows a few interesting facts. First of all, QCA has been applied in several disciplines in the social sciences such as sociology (Amenta, et al. 2005; Glaesser 2015; Blackman, et al. 2011), political science (Berg-Schlosser and De Meur 1994, Vis 2011, Thiem 2011), organization studies (Fiss, et al. 2013), management studies (Fiss 2007), governance (Marx 2008), history (Mahoney 2003, Wickham-Crowley 1992), public administration (Sager 2005), social welfare and welfare state research (Emmenegger, et al. 2013; Kvist 2007) and human rights research (Ishida, et al. 2006) Rihoux, et al. 2013 provides an extensive analysis of the applications in different disciplines. Marx, et al. 2014 identifies and discusses the main applications in sociology and political science. Secondly, QCA is now applied to small-N studies (Marx 2008) over medium-N studies (Ishida, et al. 2006) to large-N studies (Glaesser 2015). This shows the diversification of applications and the potential uses. Thirdly, the different branches of QCA are now used in applications. Both crisp-set (Fiss, et al. 2013) as well as fuzzy-set (Vis 2011) applications are now used frequently. Fourthly, QCA is often used in combination with other methods. Some authors first use QCA and then select case for in-depth case research and process tracing (Marx 2008, Glaesser 2015). Other researchers first perform detailed case studies and then apply QCA for cross-case analysis (Wickham-Crowley 1992). Still other researchers apply QCA in combination with correlation based analytic techniques (Amenta, et al. 2005; Mahoney 2003).

                                                                                                                                                  • Amenta, E., N. Caren, and S. Olasky. 2005. Age for leisure? Political mediation and the impact of the pension movement on US old-age policy. American Sociological Review 70.2: 516–538.

                                                                                                                                                    DOI: 10.1177/000312240507000308Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                    This paper applies QCA to social movements research and tests several explanatory models with pooled cross-sectional and time series analyses and fsQCA. Both analytic techniques generate similar results supporting the theoretical claims made by the authors.

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                                                                                                                                                    • Berg-Schlosser, D., and G. De Meur. 1994. Conditions of democracy in interwar Europe: A Boolean test of major hypotheses. Comparative Politics 26:253–279.

                                                                                                                                                      DOI: 10.2307/422112Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                      An early application in comparative politics identifying key (combination) of conditions conducive to the breakdown or survival of democratic regimes in sixteen European states in the interwar period.

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                                                                                                                                                      • Blackman, T., J. Wistow, and D. Byrne. 2011. A Qualitative Comparative Analysis of factors associated with narrowing health inequalities in England. Social Science & Medicine 72.12: 1965–1974.

                                                                                                                                                        DOI: 10.1016/j.socscimed.2011.04.003Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                        Applies QCA to sociology of health and focuses on premature deaths from cancers and cardiovascular disease. It is an application which uses many explanatory conditions (ten to analyze the cancer cases and six to analyze the cardio cases). For each of the cases the study identifies a limited number of causal paths to the outcome.

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                                                                                                                                                        • Emmenegger, P., J. Kvist, and S. E. Skaaning. 2013. Making the most of Configurational Comparative Analysis: An assessment of QCA applications in comparative welfare-state research. Political Research Quarterly 66.1: 185–190.

                                                                                                                                                          DOI: 10.1177/1065912912468269dSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                          Comparative welfare-state research is an area in which QCA has been applied regularly. This paper critically reviews nineteen studies with the aim of identifying their contribution to develop more complex theoretical propositions concerning the development of welfare states. The paper argues that the full potential of QCA so far has been underutilized.

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                                                                                                                                                          • Fiss, P. 2007. A set-theoretic approach to organizational configurations. Academy of Management Review 32:1180–1198.

                                                                                                                                                            DOI: 10.5465/AMR.2007.26586092Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                            Introduces the basics of set-theoretic methods and applies them to organizational and management studies with a specific focus on forging a better match with existing theories in organizational science such as complementarities theory, complexity theory, and the resource-based theories. Highlights the importance of the concept of equifinality for organizational sciences.

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                                                                                                                                                            • Fiss, P., B. Cambré, and A. Marx. 2013. Configurational theory and methods in organizational research. Research in Sociology of Organizations 38.

                                                                                                                                                              DOI: 10.1108/S0733-558X(2013)0000038019Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                              Book provides in thirteen chapters an application of QCA in several sub-disciplines of management studies including corporate governance, social responsibility, and information and communications technology (ICT) governance. In addition, the book engages with the question of what it means to study organizations as configurations.

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                                                                                                                                                              • Glaesser, J. 2015. Young people’s educational careers in England and Germany: Integrating survey and interview analysis via Qualitative Comparative Analysis. Basingstoke, UK: Palgrave Macmillan.

                                                                                                                                                                DOI: 10.1057/9781137355508Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                This book applies QCA to survey data to analyze what shapes the educational career of individuals. QCA is used to apply a cross-case analysis. In addition, this approach is complemented with a within-case analysis using process tracing. The book is a good example of a mixed methods design applying QCA to a large number of cases (large-N design).

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                                                                                                                                                                • Ishida, A., M. Yonetani, and K. Kosaka. 2006. Determinants of linguistic human rights movements: An analysis of multiple causation of LHRs movements using a Boolean approach. Social Forces 84.4: 1937–1955.

                                                                                                                                                                  DOI: 10.1353/sof.2006.0094Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                  Paper applies QCA to a large-N dataset of 159 countries and aims to examine the social background of movements for linguistic human rights. It uses five conditions to explain variation among 159 countries and shows how QCA is able to reduce empirical complexity of a large number of cases while identifying several causal paths to an outcome.

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                                                                                                                                                                  • Kvist, J. 2007. Fuzzy set ideal type analysis. Journal of Business Research 60:474–481.

                                                                                                                                                                    DOI: 10.1016/j.jbusres.2007.01.005Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                    Specific application of fsQCA for the development of ideal types and typologies. This is then applied to the area of welfare state research.

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                                                                                                                                                                    • Mahoney, J. 2003. Long-run development and the legacy of colonialism in Spanish America. American Journal of Sociology 109.1: 50–106.

                                                                                                                                                                      DOI: 10.1086/378454Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                      This paper applies fsQCA to a historical question which analyzes long periods of time in a sophisticated comparative design. Key focus in this paper is applying fuzzy-set techniques to identify necessary conditions for development outcomes. It also combines fsQCA with correlational techniques.

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                                                                                                                                                                      • Marx, A. 2008. Limits to non-state market regulation: A qualitative comparative analysis of the international sport footwear industry and the Fair Labor Association. Regulation and Governance 2.2: 253–273.

                                                                                                                                                                        DOI: 10.1111/j.1748-5991.2008.00037.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                        Paper applies QCA to analyze why seventeen footwear companies pursue social policies. It constructs explanatory conditions on the basis of different theoretical contributions and provides detailed information on how conditions were operationalized and measured. In addition, the paper provides a detailed discussion on case selection and the use of Most Similar Different Outcomes designs in QCA. Finally, it links cross-case analysis with within-case analysis of a specific case.

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                                                                                                                                                                        • Marx, A., B. Rihoux, and C. Ragin. 2014. The origins, development and application of Qualitative Comparative Analysis (QCA): The first 25 years. European Political Science Review 6.1: 115–142.

                                                                                                                                                                          DOI: 10.1017/S1755773912000318Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                          Identifies and discusses the main applications in political science and provides in an annex an extensive overview of all the publications in political science which appeared until 2013.

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                                                                                                                                                                          • Rihoux, B., P. Alamos, D. Bol, A. Marx, and I. Rezsöhazy. 2013. From niche to mainstream method? A comprehensive mapping of QCA applications in journal articles from 1984 to 2011. Political Research Quarterly 66.1: 175–184.

                                                                                                                                                                            DOI: 10.1177/1065912912468269cSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                            This paper provides a first systematic overview of more than three hundred applications published in peer-reviewed journal articles. It shows that the number of applications has increased strongly in the last few years (since 2008) and has been applied in several disciplines in the social sciences.

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                                                                                                                                                                            • Sager, F. 2005. Metropolitan institutions and policy coordination: The integration of land use and transport policies in Swiss urban areas. Governance: An International Journal of Policy, Administration, and Institutions 18.2: 227–256.

                                                                                                                                                                              DOI: 10.1111/j.1468-0491.2005.00274.xSave Citation »Export Citation »E-mail Citation »

                                                                                                                                                                              This paper applies QCA in the discipline of public administration and analyzes how different metropolitan institutional settings affect the quality of political negotiation processes and their subsequent policy decisions. The paper is interesting since it applies QCA to questions related to institutional effectiveness (i.e., institutions as rules) and shows that QCA works especially well to analyze the presence and absence of institutions.

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                                                                                                                                                                              • Thiem, A. 2011. Conditions of intergovernmental armaments cooperation in Western Europe, 1996–2006. European Political Science Review 3.1: 1–33.

                                                                                                                                                                                DOI: 10.1017/S1755773910000251Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                Paper applies fsQCA to explain why cooperation in the areas of defense between fifteen European Union member states has increased after 1989. It is a very interesting application in terms of building models out of distinct theoretical schools and analyzing them via QCA.

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                                                                                                                                                                                • Vis, B. 2011. Under which conditions does spending on active labor market policies increase? A fsQCA analysis of 53 governments between 1985 and 2003. European Political Science Review 3.2: 229–252.

                                                                                                                                                                                  DOI: 10.1017/S1755773910000378Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                  Excellent application of fsQCA to social policy research. Paper analyzes the conditions under which governments increase spending on active labor market policies. On the basis of the data of fifty-three governments from eighteen established democracies between 1985 and 2003, the fuzzy-set qualitative comparative analysis shows that there are different combinations of conditions, or routes, toward activation and that an improving socioeconomic situation is needed for each of them.

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                                                                                                                                                                                  • Wickham-Crowley, T. 1992. Guerillas and revolution in Latin America: A comparative study of insurgents and regimes since 1956. Princeton, NJ: Princeton Univ. Press.

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                                                                                                                                                                                    This application is an example of using QCA to summarize the results from a range of very rich case studies. The book starts out with detailed case studies (twenty-six in total) of revolutionary guerilla activity in Latin America and tries to explain when revolutions occur or are not occurring on the basis of five explanatory factors. The book illustrates how QCA can reduce complexity across twenty-six cases while integrating the findings of detailed case studies.

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                                                                                                                                                                                    QCA Software

                                                                                                                                                                                    Performing a Qualitative Comparative Analysis (QCA) is now possible via a range of software packages. These packages include QCA specific packages such as TOSMANA (Cronqvist and Berg-Schlosser 2009) and fsQCA (Ragin 2008) as well as the integration of QCA in broader data analytic software tools such as R (Thiem and Duşa 2012) and Stata (Longest and Vaisey 2008). This section identifies the main software tools and some introductory texts to these software packages.

                                                                                                                                                                                    • COMPASSS.

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                                                                                                                                                                                      For more information on software and applications, interested researchers can consult the COMPASSS website. COMPASSS stands for COMPArative Methods for Systematic cross-caSe analysis and is a worldwide network bringing together scholars and practitioners who share a common interest in theoretical, methodological, and practical advancements in a systematic comparative case approach in general and QCA in specific. The website contains links toward the software, QCA training courses and seminars, and an overview of all QCA applications.

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                                                                                                                                                                                      • Cronqvist, L., and D. Berg-Schlosser. 2009. Multi-value QCA (mvQCA). In Configurational comparative methods: Qualitative Comparative Analysis (QCA) and related techniques. Edited by B. Rihoux and C. Ragin, 69–86. Thousand Oaks, CA: SAGE.

                                                                                                                                                                                        DOI: 10.4135/9781452226569.n4Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                        Introduces TOSMANA a multi-value version of QCA (mvQCA) which is not based on set-theory but on the use of ordinal or nominal scales. This version of QCA is only used to a very limited extent.

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                                                                                                                                                                                        • Dusa, A. 2010. QCA: Qualitative Comparative Analysis.

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                                                                                                                                                                                          Introduces QCA to R.

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                                                                                                                                                                                          • Duşa, A., and A. Thiem. 2015. Enhancing the minimization of Boolean and multivalue output functions with eQMC. Journal of Mathematical Sociology 39.2: 92–108.

                                                                                                                                                                                            DOI: 10.1080/0022250X.2014.897949Save Citation »Export Citation »E-mail Citation »

                                                                                                                                                                                            This paper introduces an enhanced Quine-McCluskey algorithm to perform Boolean minimization which further extends the existing algorithm.

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                                                                                                                                                                                            • Longest, Kyle C., and S. Vaisey. 2008. Fuzzy: A program for performing Qualitative Comparative Analyses (QCA) in Stata. Stata Journal 8:79–104.

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                                                                                                                                                                                              Paper introduces the new program Fuzzy which allows for the analysis of QCA in Stata. The possibilities for a QCA analysis in Stata are limited but allows for creating configurations (truth table rows), performing a series of statistical tests of the configurations, and reducing the identified configurations through minimization.

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                                                                                                                                                                                              • Ragin, C. C. 2008. fsQCA Manual.

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                                                                                                                                                                                                Manual introduces the original software of QCA as developed by Charles Ragin and collaborators. The software contains all the main features and is freely available.

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                                                                                                                                                                                                • Thiem, A., and A. Duşa. 2012. Qualitative Comparative Analysis with R: A user’s guide. New York: Springer.

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                                                                                                                                                                                                  Introduces readers to QCA in the R environment. This is currently the most advanced QCA software providing many functions for analysis.

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