Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA

Autor/in	Braumoeller, Bear F.
Titel	Aggregation Bias and the Analysis of Necessary and Sufficient Conditions in fsQCA
Quelle	In: Sociological Methods & Research, 46 (2017) 2, S.242-251 (10 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124116672701
Schlagwörter	Qualitative Research; Comparative Analysis; Social Science Research; Research Methodology; Test Bias; Test Reliability; Test Validity; Mathematical Models; Goodness of Fit + Suchen Sie Ihr Suchwort? Qualitative Forschung; Social scientific research; Sozialwissenschaftliche Forschung; Research method; Forschungsmethode; Testkritik; Testreliabilität; Testvalidität; Mathematical model; Mathematisches Modell
Abstract	Fuzzy-set qualitative comparative analysis (fsQCA) has become one of the most prominent methods in the social sciences for capturing causal complexity, 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01