Necessary Condition Analysis (NCA) and Its Diffusion

Abstract

Necessary condition analysis (NCA) understands cause–effect relations in terms of “necessary but not sufficient.” This means that without the right level of the cause, a certain effect cannot occur. This is independent of other causes; thus, the necessary condition can become a single bottleneck, critical factor, constraint, disqualifier, or so on that blocks the outcome when it is absent. NCA can be used as a stand-alone method or in multimethod research to complement regression-based methods such as multiple linear regression (MLR) and structural equation modeling (SEM), as well as methods like fuzzy set qualitative comparative analysis (fsQCA). The NCA method consists of four stages: formulation of necessary condition hypotheses, collection of data, analysis of data, and reporting of results. Based on existing methodological publications about NCA, guidelines for good NCA practice are summarized. These guidelines show how to conduct NCA with the NCA software and how to report the results. The guidelines support (potential) users, readers, and reviewers of NCA to become more familiar with the method and to understand how NCA should be applied, as well as how results should be reported. NCA’s rapid diffusion and broad applicability in the social, technical, and medical sciences is illustrated by the growth of the number of article publications with NCA, the diversity of disciplines where NCA is applied, and the geographical spread of researchers who apply NCA.