How Combinatorics Exposes Sensitivity Blind Spots in QCA

🔍 The Sensitivity Problem

Qualitative Comparative Analysis (QCA) sensitivity diagnostics have become a central concern in methodological research. Existing studies rely on exhaustive enumeration and most examine QCA's reactivity either to changes in discretionary parameter values or to data-quality problems—but rarely both at once.

🧮 Introducing Combinatorial Computation

A combinatorial computation technique is introduced to evaluate interaction effects between two problems afflicting data quality and two discretionary parameters on the stability of QCA reference solutions. Rather than treating data issues and parameter choices separately, this method systematically explores their joint combinations to assess how they influence solution stability.

📌 Key Findings

• Combinatorial computation reveals interaction effects that are overlooked under the implicit assumption built into exhaustive enumeration.
• The approach permits the formulation of general laws of sensitivity in QCA, showing how stability depends jointly on data-quality problems and discretionary parameter settings.
• The technique is more efficient than standard exhaustive enumeration, enabling broader and faster sensitivity diagnostics across the full combinatorial space.

⚖️ Why It Matters

By challenging an unstated assumption in exhaustive enumeration, combinatorial computation provides a more comprehensive and practical framework for sensitivity diagnostics in QCA. This enhances confidence in QCA reference solutions and offers a repeatable basis for future methodological work.