Skip to content

Introduction to Qualitative Comparative Analysis (QCA) using R

Date
18-20 April, 2024

Instructors
Prof. dr. Eva Thomann (University of Konstanz)

Course fees

  • Free for NIG members
  • €500,- for non-members from an NIG member institution
  • €750,- for third parties

NIG members can cover the fee for this course with the one-time grant that reimburses up to 500 euros. This applies for PhD members who take part in the full program, and who have participated in at least three core curriculum courses, subject to conditions.

Registration will open in the first week of December via this link.

This course introduces students to the nuts and bolts of Qualitative Comparative Analysis (QCA), an innovative set-theoretic technique that allows for comparisons of small, intermediate or large numbers of cases in order to identify necessary and/or sufficient conditions for an outcome. It is an attractive method for scholars who seek to model causally complex patterns and integrate in-depth case knowledge at all stages of the analysis.  We will introduce you to performing QCA with the freely available R software using the user-friendly RStudio environment. We will primarily discuss QCA as a case-oriented approach to small- and intermediate N comparisons. The intensive two-day course has a practical focus and combines theoretical blocks with many hands-on labs entailing exercises and quizzes. We will use real-life data to replicate a published study and discuss examples from the participants’ own research projects in class. The course covers the following topics:

                    QCA: Origin, variants, uses and approaches

                    Set theory and causal complexity

                    Defining, structuring, measuring and calibrating concepts as sets

                    Analyses of necessity and sufficiency

                    Truth tables, limited diversity and counterfactual reasoning

                    Conservative, intermediate and parsimonious solution types

Learning goals

·         Understanding of the logic and technical working of QCA

·         Basic familiarity with the application of QCA in R

·         Familiarity with core QCA readings

·         Ability to integrate a basic QCA in own research project.