Microcredential komex Introduction to Qualitative Comparative Analysis (QCA) using R

Discover how QCA can uncover complex necessary and sufficient conditions through an intensive five-day in-person course combining theory, practical analysis in R, and real-world data.

What Is This Course About?
This five-day, in-person course provides a comprehensive introduction to Qualitative Comparative Analysis (QCA), an innovative set-theoretic method for analyzing small, intermediate, and large numbers of cases. QCA enables researchers to identify necessary and sufficient conditions for outcomes, model causal complexity, and incorporate in-depth case knowledge throughout the analytical process. Combining theoretical instruction with hands-on practice, the course emphasizes the practical application of QCA. Participants will use the freely available R software to replicate a published study using real-world data and will have the opportunity to discuss and apply QCA to their own research projects.

Learning Goals

• Design research projects using Qualitative Comparative Analysis (QCA)
• Understand the theoretical foundations, logic, and methodological principles of QCA
• Independently conduct standard crisp-set and fuzzy-set QCA using the R software environment
• Identify and address common challenges and potential pitfalls in QCA applications

Recommended Readings for the Course

• Oana, I.E., Schneider, C.Q. and E. Thomann. (2021). Qualitative Comparative Analysis (QCA) Using R: A Beginner’s Guide. Cambridge University Press.
• Schneider, C.Q. and C. Wagemann (2012). Set-Theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis. New York: Cambridge University Press.
• Hinterleitner, M., Sager, F. und E. Thomann (2016). The Politics of External Approval: Explaining the IMF’s Evaluation of Austerity Programs. European Journal of Political Research 55(3): 549–567.

Assignments for the Course

• 0 ECTS Credits (pass-fail): confirmation of attendance for course attendance and participation.
• 4 ECTS: doing the pre-course readings, and actively take part in the class sessions during the 5 days, including daily self-study; deliver 4 out of 5 formative daily assignments (not graded) ; and pass 90’ multiple choice exam

Schedule

09.00-10.30h Lecture and exercises
Break
11.00-12.30h Lecture and exercises/Lab
Lunch break
13.30-14.30h Lab/small group work
14.45-15.45h Office hour on zoom (does not take place on Friday)

• Basics of QCA. On day one, we will look at the origins, analytic aims, and variants of QCA, based on a demonstration of a recently published QCA study. We start with the Boolean algebraic foundations of QCA, the notion of causal complexity, and then turn to its set-theoretic foundations. The lab session provides a short introduction to the sample data and R software.
• Understanding the technique. Day two introduces to the logical and technical underpinnings of QCA. We will look at concept structuring techniques, the notions of sets and set membership and how cases can be calibrated into different types of sets. Based on this we introduce you to the notion of set relations and associated parameters of fit with QCA. In the lab session, we calibrate sets. Using R, we will combine sets into complex configurations of sets, and plot set relations.
• Analyses of necessity and sufficiency. On day three, we will do our own basic QCA. We will first look at all steps of the analyses of necessity and sufficiency, explained with the example of an empirical study. We will then analyze simple set relations, construct and inspect a truth table, and perform logical minimization. You will do your first own crisp set QCA by hand. We discuss the core assumption of causal complexity and necessity and sufficiency, as compared to correlations.
• Limited empirical diversity. Day four is dedicated to potential pitfalls in QCA in the face of “noisy” empirical data. We will briefly look at the implications of skewed set membership for these analytic steps. We will talk about limited diversity, its sources, and possibilities for counterfactual arguments with QCA when resorting to conservative, intermediate, and parsimonious solutions types. We will learn about the distinction between “easy” and “difficult” counterfactuals and its implementation via the so-called “Standard Analysis”. In the lab session, we implement the Standard Analysis with R.
• After the analysis. Day five is dedicated to the presentation and interpretation of the results, feeding back into the notion of QCA as an approach. In the lecture, we will discuss the interpretation of parameters of fit and how to make sense of complex QCA results, using empirical, conceptual, and theoretical knowledge. We get to know the principles of post-QCA case selection and look at different possibilities of presenting QCA results, corresponding good practices and transparency requirements. In the lab session, we discover the issue of model ambiguity and then look at different options for exporting and visualizing QCA results.

Who Is Your Instructor?
Eva Thomann is a full professor of Public Administration at the University of Konstanz who specializes in policy implementation research and case-oriented and set-theoretic research design and methods. Publications include  “Qualitative Comparative Analysis (QCA) Using R: A Beginner’s Guide” (2021, Cambridge University Press, with Carsten Q. Schneider and Ioana-Elena Oana), “Customized implementation of European Union food safety policy: United in diversity?” (Palgrave, 2019; IPPA best book award), “Designing Research with Qualitative Comparative Analysis (QCA): Approaches, Challenges, Tools” (2020, Sociological Methods & Research, with Martino Maggetti), and “Causation, inferences, and solution types in configurational comparative methods” (2021, Quality & Quantity, with Tim Haesebrouck). Eva Thomann is the academic convenor of KOMEX and a founding member of the Methods Excellence Network (MethodsNet).

https://www.polver.uni-konstanz.de/en/thomann/team/prof-dr-eva-thomann/
https://www.linkedin.com/in/evathomann/
@evathomann.bsky.social