This workshop provides participants with a thorough introduction to Qualitative Comparative Analysis (QCA). Since its inception (Ragin 1987), QCA has gained recognition among social scientists as a case-based research method that is ideally suited to capture causal complexity. This essentially describes a situation where an outcome results from multiple pathways and different combinations of conditions. Moreover, QCA entails a rigorous and systematic comparison of selected cases and their configurations through Boolean logic and a software-based analytical protocol. Throughout the workshop, emphasis is placed on research design and practical application, so that participants are enabled to successfully apply QCA in their own studies.

Throughout the workshop, participants are introduced to the foundations and advanced functions of QCA, while the course structure follows an ideal-typical research process. Starting with empirical illustrations that show how and for what purposes QCA is being used in the social sciences, the workshop proceeds with presenting the method’s core characteristics. This is followed by sessions on causation, causal complexity, and research design, to provide a basis for thinking about empirical applications. The ensuing sessions engage with QCA as an analytical approach, starting with set theory and concepts like necessary and sufficient conditions, Boolean algebra, truth tables, and fuzzy sets. In calibrating sets, we look into approaches to transform empirical raw data into crisp and fuzzy sets. Next, the course examines various measures of fit that help in evaluating QCA results. The sessions on set-theoretic analysis put all the elements together and show how empirical data is analyzed and interpreted with QCA. During the second half of the workshop we explore advanced topics, which can be tailored based on participants’ background and research interests. Potential topics include multi-method research design, QCA variants, robustness tests in QCA, addressing critiques, and recent developments. Participants also have the opportunity to present and discuss their own work within the group. The workshop sessions are complemented by illustrations and exercises from the social sciences, using the R Software environment and relevant R Packages.

The workshop includes dedicated timeslots for individual consultation with the lecturer, group discussions, and networking opportunities.