Qualitative Comparative Analysis (QCA) is a case-based research method for capturing causal complexity. As Charles Ragin put it in The Comparative Method, QCA is a method for “comparing wholes as configurations of parts” (Ragin 1987, 84).
Here you can find resources on QCA, including my textbook Qualitative Comparative Analysis: An Introduction to Research Design and Application (Georgetown University Press, 2021) and the accompanying R Manual for QCA, as well as training opportunities, replication materials, and video recordings. Below are also some links to QCA reviews and handbook chapters.
How QCA works
QCA helps identify how conditions combine to produce an outcome of interest, and whether different paths can lead to the same result. The method is typically used with 20–30 cases, but it is possible to work with much larger case numbers (up to hundreds of cases). On the lower end, 12 cases can be regarded as a required minimum for useful comparisons with QCA.
QCA rests on set theory and Boolean logic. Relationships are therefore framed in the language of necessary and sufficient conditions. A sufficient condition is a condition that always leads to the outcome, whereas a necessary condition is a condition without which the outcome does not occur. A distinct strength of QCA is its ability to account for causal complexity, which entails three components. Conjunctural causation means that two or more conditions may jointly bring about an outcome, but not individually. Equifinality means that the same outcome may be reached through multiple pathways. Causal asymmetry means that the explanation for an outcome usually cannot be mirrored to account for the non-outcome, which requires a separate analysis.
QCA works with calibrated data. Calibration means that the researcher defines a target set and determines empirical anchors in the raw data, which may be qualitative or quantitative, to assign each case a membership score in that set. Crisp-set QCA works with binary membership (1 or 0), and fuzzy-set QCA allows for degrees of membership between 0 and 1.
The core of QCA is the truth table analysis, which identifies combinations of conditions that are sufficient for the outcome. The truth table displays all logically possible combinations of the included conditions, links these to the empirical cases, and shows which combinations have no cases (logical remainders). Rows that reach the conventional consistency threshold of 0.75 are compared with each other, and redundant elements are eliminated through Boolean minimization. The results are summarized in solution terms. Consistency and coverage are the main measures of fit. Consistency describes the extent to which the data approximate a relationship of necessity or sufficiency, and coverage describes the empirical relevance of a condition or combination of conditions. The analysis is software-based. R has become the standard environment for QCA, with the packages QCA (Adrian Duşa) and SetMethods (Ioana-Elena Oana and Carsten Q. Schneider), while fs/QCA and Tosmana are point-and-click programs that are easy to navigate for beginners.
QCA reviews and handbook chapters
For a comprehensive list of publication entries, see here.
Mello, Patrick A. 2024. Uncovering Causal Complexity with Qualitative Comparative Analysis. In The Handbook of Teaching Qualitative and Mixed Research Methods: A Step-by-Step Guide for Instructors, edited by Alissa Ruth, Amber Wutich, and H. Russell Bernard. London: Routledge, 336–339.
Ide, Tobias, and Patrick A. Mello. 2022. QCA in International Relations: A Review of Strengths, Pitfalls, and Empirical Applications. International Studies Review 24 (1), 1–20.
Mello, Patrick A. 2017. Qualitative Comparative Analysis and the Study of Non-State Actors. In Researching Non-State Actors in International Security: Theory & Practice, edited by Andreas Kruck and Andrea Schneiker. London: Routledge, 123–142.