Patrick A. Mello
is Visiting Scholar for Public Policy at the Willy Brandt School of Public Policy at the University of Erfurt. He holds a PhD in Political Science from Humboldt University Berlin and has worked at the Technical University of Dresden and the Technical University of Munich, where he is completing his Habilitation at the TUM School of Governance. Mello's substantive research focuses on international security and foreign policy analysis. His methodological research interest lies in comparative methods, with an emphasis on fuzzy-set Qualitative Comparative Analysis (QCA). His book Democratic Participation in Armed Conflict: Military Involvement in Kosovo, Afghanistan, and Iraq (Palgrave Macmillan, 2014) received the dissertation award of the German Political Science Association. His work has appeared in peer-reviewed journals such as the European Journal of International Relations, West European Politics, British Journal of Politics and International Relations, Journal of International Relations and Development, and Contemporary Security Policy. He is currently writing the QCA textbook Qualitative Comparative Analysis: Research Design and Application for Georgetown University Press.
For more information, please visit Patrick A. Mello's website.
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.
Course participants are not required to have any previous knowledge of QCA or the R software environment and its relevant packages. Nonetheless, to start working with the software from day 1, a prior introduction to the basic functions of R and RStudio is strongly advised. Participants will receive several preparatory texts one month ahead of the summer school.