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Short Course on Qualitative Comparative Analysis, 23rd Swiss Summer School in Social Science Methods
August 19, 2019 - August 23, 2019700CHF – 1100CHF
The course provides participants with a thorough introduction to Qualitative Comparative Analysis (QCA), both as a research approach and as a data analysis technique. In recent years, this set-theoretic method has gained recognition among social scientists as a methodological approach that holds specific benefits for comparative studies. The course begins by familiarizing participants with the foundations of set theory and the basic concepts of the methodological approach of QCA, including necessary and sufficient conditions, Boolean algebra, and fuzzy logic. The next step is devoted to the calibration of empirical data into crisp and fuzzy sets. Once these essentials are in place, the course moves on to the construction and analysis of truth tables as the core of the QCA procedure. Here, we will also spend time to discuss typical challenges that arise during a truth table analysis, and techniques to overcome such problems. Finally, the course will introduce consistency and coverage as parameters of fit, as well as additional measures to assess the robustness of QCA results.
Besides the technical introduction of QCA and its variants, the course will provide opportunities to discuss general aspects of comparative research design, including criteria for concept building and case selection, and data-related issues. Participants will be given the opportunity to present their own work and to receive individual feedback on their projects.
Throughout the course, participants will conduct set-theoretic analyses within the R software environment (packages “QCA” and “SetMethods”). The software will be introduced on the first day and used for exercises and examples throughout the course, so that participants gain a level of proficiency that enables them to conduct their own QCA analyses upon the completion of the course. Participants are encouraged to bring their own qualitative and/or quantitative data for course exercises (if available, preliminary data is fine). In addition, datasets from published studies will be made available and used for in-course exercises.