C03: Introduction to Qualitative Comparative Analysis (QCA)

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 five-day course has a practical focus and combines theoretical blocks with hands-on exercises. 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
· Potential pitfalls (skewed data, model ambiguities)

This course introduces students to performing Qualitative Comparative Analysis (QCA) with the freely available R software. QCA is an innovative set-theoretic technique that compares small, intermediate or large numbers of cases to identify necessary and/or sufficient conditions for an outcome, model causally complex patterns, and integrate in-depth case knowledge.  The intensive five-day course combines theoretical blocks with hands-on exercises. 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:

Day 1: Basics of QCA

•       Origins, analytic aims, and variants of QCA
•       Causal complexity
•       Necessity and sufficiency
•       Sets and set calibration and membership
•       Lab: Introduction to the R software

 Day 2: Understanding the technique.

•       Logical and technical underpinnings
•       Boolean algebraic foundations of QCA
•       Set relations and parameters of fit
•       Lab:  set calibration, combining sets, plot set relations

Day 3: Analyses of necessity and sufficiency

•       Steps of the analyses of necessity and sufficiency
•       Implications of skewed set membership
•       Lab: Own crisp set QCA by hand; simple set relations; truth table analysis and logical minimization with R

Day 4: Limited empirical diversity

•       Potential pitfalls in QCA in the face of “noisy” empirical data
•       Limited diversity and counterfactuals
•       Conservative, intermediate, and parsimonious solutions types
•       Lab: Standard Analysis with R

Day 5: After the analysis

•      Presentation and interpretation of the results
•      QCA as an approach.
•      Post-QCA case selection
•      Good practices and transparency requirements.
•      Lab: model ambiguity;  exporting and visualizing QCA results

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.