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Configurational Comparative Methods: QCA and Fuzzy Sets

Charles C. Ragin

Department of Sociology

University of ArizonaTucson, Arizona 85718 USA

Objectives

On the one hand, comparative social science is defined by the existence (or at leastthe presumption) of meaningful "cases." Comparativists treat cases as whole entitiespurposefully selected (e.g., the French Revolution), not as homogeneousobservations drawn haphazardly or randomly from a large pool of equally plausibleselections (e.g., a random selection of cases from the population of all revolutions

assuming such a population could be constructed). This gives comparative work aspecial focus on cases as meaningful in their own right. On the other hand,however, one of the primary goals of comparative social science (and social sciencein general) is to derive general statements about theoretically importantrelationships. Making general statements requires using general concepts. At thelevel of cases, concepts are most often represented through observable variables.Concepts and variables permeate almost all social scientific discussion of cases, nomatter how much or how little homage is paid to their singularity, specificity, ormeaningfulness as cases. Thus, comparative social scientists (especially) need tools

that link case-oriented and variable-oriented discoursetools that help themconstruct a rich dialogue of ideas and evidence.

The analytic challenge of case-oriented research is not simply that the number ofcases is limited, but that researchers gain useful in-depth knowledge of cases that isdifficult to represent using conventional forms of analysis (e.g., representations thatemphasize the net effects of independent variables). The researcher is leftwondering how to represent knowledge of cases in a way that is meaningful andcompact, but which also does not deny their complexity.

Set-theoretic methods such as Qualitative Comparative Analysis (QCA) offer asolution. QCA is fundamentally a case-oriented method that can be applied to small-to-moderate size Ns. It is most useful when researchers have knowledge of eachcase included in an investigation, there is a relatively small number of such cases(e.g., 10-50), and the investigator seeks to compare cases as configurations. Withthese methods it is possible to construct representations of cross-case patterns thatallow for substantial causal heterogeneity and case diversity.

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Fuzzy set analysis can work in tandem with QCA. The use of fuzzy sets is gainingpopularity in the social sciences today because of the close connections it enablesbetween verbal theory, substantive knowledge (especially in the assessment ofdegree of set membership), and the analysis of empirical evidence. Fuzzy sets areespecially useful in case-oriented research, where the investigator has substantialfamiliarity with the cases included in the investigation and seeks to understand casesconfigurationally, that is, as specific combinations of aspects or elements. Usingfuzzy-set methods, case outcomes can be examined in ways that allow for causalcomplexity, where different configurations of causally relevant conditions combineto generate the outcome in question. Also, with fuzzy-set methods it is a possible toevaluate arguments that causal conditions are necessary or sufficient. Examinationsof this type are outside the scope of conventional variable-oriented analysis.

Overview of Lecture Topics

Part 1: BackgroundSocial research as a process of constructing empirically grounded representationsThe case-oriented/variable oriented distinctionThe distinctiveness of configurational comparative researchWhat is QCA?

Part 2: BasicsIntroduction to Boolean algebra and set theoretic methods

Set-theoretic analysis vs. correlational analysisNecessity and sufficiencyConsistency, coverage, coincidenceCase-oriented research strategies for theory building

Part 3: Crisp Set AnalysisOverview of crisp-set QCA (csQCA)Examples of crisp set analysesA set-theoretic approach to counterfactual analysis

The three solutionscomplex, parsimonious, and intermediateEasy versus difficult counterfactualsThe impact of assumptions on counterfactual analysisConsistency and coverage in crisp-set truth table analysis

continued . . .

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Part 4: Fuzzy Set AnalysisFuzzy sets and fuzzy set relationsCalibrating fuzzy setsFuzzy set consistency, coverage, and coincidenceFuzzy-set coverage: raw versus unique versus solution coverageLimited diversity, fuzzy sets, and counterfactual analysisThe fuzzy-set truth table algorithm

Background Reading:

Ragin, Charles C. 1987. The Comparative Method: Moving BeyondQualitative and Quantitative Strategies, Berkeley, CA: University ofCalifornia Press.

Ragin, Charles C. 2000.Fuzzy-Set Social Science, Chicago, IL: Universityof Chicago Press.

Ragin, Charles C. 2008.Redesigning Social Inquiry: Fuzzy-Sets andBeyond.Chicago, IL: University of Chicago Press.

Rihoux, Benoit and Charles C. Ragin (eds.) 2008. ConfigurationalComparative Methods.Thousand Oaks, CA: Sage.

Some Recommended Books:

Brady, Henry and David Collier, 2004. Rethinking Social Inquiry: DiverseTools Shared Standards. Rowman and Littlefield.

King, Gary, Robert O. Keohane, and Sidney Verba. 1994. Designing SocialInquiry: Scientific Inference in Qualitative Research. Princeton, NJ: PrincetonUniversity Press.

Goertz, Gary. 2006. Social Science Concepts: A Users Guide. Princeton,NJ: Princeton University Press.

Mahoney, James and Dietrich Rueschemeyer. 2003. ComparativeHistorical Analysis in the Social Sciences. NY: Cambridge University Press.

Ragin, Charles C., and Howard S. Becker. 1992. What Is a Case? Exploringthe Foundations of Social Inquiry. New York: Cambridge University Press.

Skocpol, Theda. 1984. (editor) Vision and Method in Historical Sociology.

New York: Cambridge University Press.

WWW sites:

http://www.compasss.org (bibliography, working papers, etc.)

http://www.fsqca.com (free QCA software)