🤖 AI Summary
This study addresses the critical yet underexplored issue of how calibration and dichotomization thresholds in Qualitative Comparative Analysis (QCA) substantially influence analytical outcomes, while existing approaches lack systematic and efficient tools for sensitivity analysis. To bridge this gap, we introduce TSQCA, an R package that explicitly treats thresholds as analytical variables. TSQCA implements four sweep functions—otSweep, ctSweepS, ctSweepM, and dtSweep—to automate the exploration of multidimensional threshold combinations and their effects on QCA results. Built upon the CRAN QCA package for truth table construction and Boolean minimization, TSQCA employs an S3 object system to standardize output formats and supports automated generation of reproducible Markdown reports and visualizations. This framework significantly enhances the robustness, transparency, and reproducibility of QCA research.
📝 Abstract
Qualitative Comparative Analysis (QCA) requires researchers to choose calibration and dichotomization thresholds, and these choices can substantially affect truth tables, minimization, and resulting solution formulas. Despite this dependency, threshold sensitivity is often examined only in an ad hoc manner because repeated analyses are time-intensive and error-prone. We present TSQCA, an R package that automates threshold-sweep analyses by treating thresholds as explicit analytical variables. It provides four sweep functions (otSweep, ctSweepS, ctSweepM, dtSweep) to explore outcome thresholds, single-condition thresholds, multi-condition threshold grids, and joint outcome-condition threshold spaces, respectively. TSQCA integrates with the established CRAN package QCA for truth table construction and Boolean minimization, while returning structured S3 objects with consistent print/summary methods and optional detailed results. The package also supports automated Markdown report generation and configuration-chart output to facilitate reproducible documentation of cross-threshold results.