Article Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA Bear F. Braumoeller 1 Abstract Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal com- plexity, especially for scholars with small- and medium-N data sets. This research note explores two key assumptions in fsQCA’s methodology for testing for necessary and sufficient conditions—the cumulation assumption and the triangular data assumption—and argues that, in combination, they produce a form of aggregation bias that has not been recognized in the fsQCA literature. It also offers a straightforward test to help researchers answer the question of whether their findings are plausibly the result of aggregation bias. Keywords interactions, QCA, Charles Ragin, necessary and sufficient conditions, fsQCA, aggregation bias fsQCA has become one of the most prominent methods in the social sciences for capturing causal complexity, especially for scholars with small- and 1 Department of Political Science, The Ohio State University, Columbus, OH, USA Corresponding Author: Bear F. Braumoeller, Department of Political Science, The Ohio State University, 2168 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA. Email: braumoeller.1@osu.edu Sociological Methods & Research 1-10 ª The Author(s) 2016 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0049124116672701 smr.sagepub.com at OHIO STATE UNIVERSITY LIBRARY on October 28, 2016 smr.sagepub.com Downloaded from