Simulation Analysis for Influential Mechanism of Fuzzy Set Data Calibration Toward Consistency Analysis of fsQCA

Fuzzy set qualitative comparative analysis (fsQCA) is a new method to solve complex causal relationship analysis in social science, and data calibration is a core process of fsQCA. Ignoring data calibration will have an impact on the fsQCA consistency analysis, thereby undermining the rigor of the causal mechanism between fsQCA mining conditions and results. This study found that the distribution characteristics of the data did not have a significant impact on the final consistency and the corrected consistency, while the anchor point setting of the crosspoint had a significant impact on the sufficient conditional consistency of fsQCA. When the consistency study is carried out on the crosspoint anchor point setting and calibration transformation, it is found that the effect of crosspoint anchor point setting on consistency is more obvious than that of calibration transformation. The study of the consistency between fuzzy set data calibration and fsQCA can provide useful conclusions and calibration methods for the empirical analysis of fsQCA, reminding researchers that they should avoid using mechanical procedures to perform simple data calibration to obtain misleading results and standardize fuzzy set data. Calibration is beneficial to improve the transparency of fsQCA research and also provides an important reference for fsQCA practitioners to conduct robust analysis of different data-driven methods.