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Qualitative Comparative Analysis (QCA): Performing Basics and Advanced Analyses using R
Course Description

This is a methodological course on set-theoretic methods for the social sciences. While the spectrum of a set-theoretic methods is broad, including techniques such as Mill’s methods or typological theory, this course primarily focuses on the crisp-set and fuzzy-set versions of Qualitative Comparative Analysis (QCA). Invented by Charles Ragin [1987], this technique is still undergoing modifications, improvements, and ramifications. These methods are applied in fields as diverse as political science, public policy, international relations, sociology, business and management studies, or even musicology (see www.compasss.org). This course aims at enabling students to produce a publishable QCA of their own. In order to achieve this, this course provides both the formal set theoretical underpinnings of QCA and the technical and research practical skills necessary for performing a QCA. All applied parts of the course will be performed in the R software environment, using RStudio (Cloud). The course is structured as follows. We start with some basics of formal logic and set theory. Then we introduce the notions of sets and how they are calibrated. After this, we move on to the concepts of causal complexity and of necessity and sufficiency, show how the latter denote subset relations, and then learn how such subset relations can be analyzed with so-called truth tables. We learn how to logically minimize truth tables and what the options for the treatment of so-called logical remainders are. Once students master the current standard analysis practice, we discuss several extensions and possible improvements of QCA. Depending on the needs and interests of participants, we choose several topics from the following list: set-theoretic multi-method research, i.e. the combination of QCA with follow-up within-case analyses; the integration of time into QCA; theory-evaluation in set-theoretic methods; or QCAspecific procedures for robustness tests.

Assessment

 

 

 

File attachments
Course Level
Master’s
Doctoral
Course Open to
Students on-site
Academic Year
2023-2024
Term
Fall
US Credits
2
ECTS Credits
4
Course Code
POLS5223