"By formalizing the logic of qualitative analysis, QCA makes it possible to bring the logic and empirical intensity of qualitative approaches to studies that embrace more than a handful of cases - research situations that normally call for the use of variable-oriented, quantitative methods." - Prof. Charles C. Ragin, Claude Rubinson, Prof. Benoit Rihoux, and Damien Bol


Developed by Charles Ragin in 1987, Qualitative Comparative Analysis (QCA) is a means of analysing the causal contribution of different conditions (e.g. aspects of an intervention and the wider context) to an outcome of interest. To put it another way, QCA is used for analysing data sets by listing and counting all the combinations of variables observed in the data set and then applying the rules of logical inference to determine which descriptive inferences or implications the data supports.

Specifically, QCA starts with the documentation of the different configurations of conditions associated with each case of an observed outcome. QCA is a theory-driven approach, in that the choice of conditions being examined needs to be driven by a prior theory about what matters. The list of conditions may also be revised in the light of the results of the QCA analysis if some configurations are still shown as being associated with a mixture of outcomes. The coding of the presence/absence of a condition also requires an explicit view of that condition and when and where it can be considered present. Dichotomisation of quantitative measures about the incidence of a condition also needs to be carried out with an explicit rationale, and not on an arbitrary basis.

These are then subject to a minimisation procedure that identifies the simplest set of conditions that can account all the observed outcomes, as well as their absence. The results are typically expressed in statements expressed in ordinary language or as Boolean algebra. For example: A combination of Condition A and condition B or a combination of condition C and condition D will lead to outcome E. (In Boolean notation, this is expressed more succinctly as A*B + C*D?E). QCA results are able to distinguish various complex forms of causation, including:

  • Configurations of causal conditions, not just single causes. In the example above, there are 2 different causal configurations, each made up of two conditions.
  • Equifinality, where there is more than one way in which an outcome can happen. In the above example, each additional configuration represents a different causal pathway.
  • Causal conditions which are necessary (to be necessary, a cause must be present, but unless it is also sufficient, the effect will not follow), sufficient, both, or neither, plus more complex combinations (known as INUS causes – insufficient but necessary parts of a configuration that is unnecessary but sufficient), which tend to be more common in everyday life. In the example above, no one condition was sufficient or necessary. But each condition is an INUS type cause.
  • Asymmetric causes - where the causes of failure may not simply be the absence of the cause of success. In the example above, the configuration associated with the absence of E might have been one like this: A*B*X + C*D*X ?e Here, X condition was a sufficient and necessary blocking condition.
  • The relative influence of different individual conditions and causal configurations in a set of cases being examined. In the example above, the first configuration may have been associated with 10 cases where the outcome was E, whereas the second might have been associated with only 5 cases. Configurations can be evaluated in terms of coverage (the percentage of cases they explain) and consistency (the extent to which a configuration is always associated with a given outcome).

QCA is able to use relatively small and simple data sets. There is no requirement to have enough cases to achieve statistical significance, although ideally there should be enough cases to potentially exhibit all the possible configurations. The latter depends on the numbers of conditions present.

Editor's note:

  • Please see the Source section below for further resources, which were used to create this summary.
  • COMPASSS (COMPArative Methods for Systematic cross-caSe analySis) is a website that has been designed to develop the use of systematic comparative case analysis as a research strategy by bringing together scholars and practitioners who share its use as a common interest. The website provides an extensive bibliography which contains journal articles that focus on both methodological work and empirical applications of QCA. It also contains reviews of software applications that can be used for QCA, listings of events and training relevant to QCA, and a range of other resources focused on QCA.
  • The below video features a discussion of QCA by Wendy Olsen, Senior Lecturer in Social Science Research Methods (SED) and in Socio-Economic Research (SOSS) at the University of Manchester.

"What Is Qualitative Comparative Analysis?" [PowerPoint], by David Byrne [no longer available online]; User's Guide to Fuzzy-Set/Qualitative Comparative Analysis 2.0., Ragin, Charles C. Tucson, Arizona: Department of Sociology, University of Arizona, 2006; "Qualitative Comparative Analysis (QCA) as an Approach" [PDF], by Dirk Berg-Schlosser, Gisèle De Meur, Benoît Rihoux, and Charles C. Ragin; Wikipedia, accessed January 2 2014; and BetterEvaluation, accessed January 9 2014. Image credit: Ingo Rohlfing, PhD