Navigating the complexities of qualitative comparative analysis: case numbers, necessity relations, and model ambiguities.

Abstract

In recent years, the method of Qualitative Comparative Analysis (QCA) has been enjoying increasing levels of popularity in evaluation and directly neighboring fields. Its holistic approach to causal data analysis resonates with researchers whose theories posit complex conjunctions of conditions and events. However, due to QCA's relative immaturity, some of its technicalities and objectives have not yet been well understood. In this article, I seek to raise awareness of six pitfalls of employing QCA with regard to the following three central aspects: case numbers, necessity relations, and model ambiguities. Most importantly, I argue that case numbers are irrelevant to the methodological choice of QCA or any of its variants, that necessity is not as simple a concept as it has been suggested by many methodologists, and that doubt must be cast on the determinacy of virtually all results presented in past QCA research. By means of empirical examples from published articles, I explain the background of these pitfalls and introduce appropriate procedures, partly with reference to current software, that help avoid them. QCA carries great potential for scholars in evaluation and directly neighboring areas interested in the analysis of complex dependencies in configurational data. If users beware of the pitfalls introduced in this article, and if they avoid mechanistic adherence to doubtful "standards of good practice" at this stage of development, then research with QCA will gain in quality, as a result of which a more solid foundation for cumulative knowledge generation and well-informed policy decisions will also be created.