When dichotomisation becomes a problem for the analysis of middle-sized datasets

This article aims at illustrating the circumstances in which Qualitative Comparative Analysis (QCA) and its ramifications, fs/QCA and MVQCA, become particularly useful tools of analysis. To this end, we discuss the most pertinent problem which researchers encounter when using QCA: the problem of contradicting observations. In QCA analysis, contradictions arise from the sheer number of cases and the problem of dichotomisation. In order to handle contradictions, the method for analysing middle-sized-N situations should therefore be chosen according to two parameters: the size of a dataset, and the need to preserve raw-data information. While QCA is an apt tool for analysing comparatively small middle-sized datasets with a correspondingly reduced necessity to preserve cluster information, the opposite holds true for fs/QCA. MVQCA strikes a balance between these two methods as it is most suitable for analysing genuinely middle-sized case sets for which some cluster information needs to be preserved.