Qualitative Comparative Analysis (QCA) and Calibration: A Novel Approach for Creating Fuzzy Sets from Qualitative Data

Calibration (i.e., the process of transforming raw data to membership scores) is one of the most crucial steps in any study applying Qualitative Comparative Analysis. Even though the literature has dealt with the question of how to calibrate qualitative data, the calibration of qualitative data remains ambivalent and clear guidelines are lacking, resulting in non-uniform and ad hoc approaches. Thus, this paper introduces a so-called “pragmatic approach.” In contrast to the existing literature, it is argued that (i) the right balance of deduction and induction is key for an efficient calibration of qualitative data and that (ii) membership scores should not be part of early stages in the calibration procedure. I use a hypothetical example to demonstrate the suggested pragmatic approach. This new template is an important addition to the existing good practice in Qualitative Comparative Analysis.