Hi everyone,

Thank you for an engaging start to the short course! Below is a recap of what we covered during Day 1 of the QCA Short Course with Dr. Greckhamer.

We began by introducing Qualitative Comparative Analysis (QCA) as a set-theoretic, case-oriented method designed to capture causal complexity. This is especially useful for medium-N research at the intersection of qualitative richness and cross-case comparison (ideally 12 at a minimum for meaningful comparison). QCA departs from conventional regression-based approaches in three key ways:

1. Conjunctural Causation - Rather than assessing the independent net effect of a single variable, QCA focuses on how combinations of conditions (configurations) jointly produce an outcome.
2. Equifinality - There is no single best path to an outcome. Multiple distinct configurations, comprising different sets of conditions, can lead to the same result.
3. Causal Asymmetry - The presence of an outcome may be explained by a different configuration than its absence. In other words, the causal path to success is not simply the mirror image of the path to failure.

We also covered the early design steps in a QCA study, specifically:

1. Condition and Case Selection - Both must be theoretically justified. Condition selection should reflect meaningful causal ingredients (not just control variables), and sampling should prioritize theoretical relevance, not statistical representativeness. [There should be a reasonable number of conditions - ideally around 8. This should not be treated as the kitchen sink to put a lot of control variables.]
2. Types of Sets - We distinguished between crisp sets (binary membership) and fuzzy sets (degree-based membership), noting that fuzzy sets allow richer nuance and measurement granularity.
3. Calibration - Calibration is the process of assigning set membership scores to cases. This is not a mechanical recoding of variables, but a conceptual act guided by substantive knowledge, benchmarks, and theory. We discussed the importance of selecting meaningful thresholds (full membership, full non-membership, and crossover) in fuzzy-set calibration.