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Goodness-of-Fit Tests and Descriptive Measures in Fuzzy-Set Analysis

University of Arizona, Tuscon, seliason{at}email.arizona.edu

Robin Stryker

University of Arizona, Tuscon

In this article the authors develop goodness-of-fit tests for fuzzy-set analyses to formally assess the fit between empirical information and various causal hypotheses while accounting for measurement error in membership scores. These goodness-of-fit tests, and the accompanying logic, provide a sound inferential foundation for fuzzy-set methodology. The authors also develop descriptive measures to complement these tests. Examples from Stryker and Eliason (2003) and Mahoney (2003) show how goodness-of-fit tests and descriptive measures may be used to assess individual causal factors as well as conjunctions of factors. The authors show how these tools provide more information in a fuzzy-set analysis than do tests currently in use. In providing this inferential foundation, the authors also show that fuzzy-set methods (a) are no less amenable to falsificationist methods of the Neyman-Pearson type than are standard statistical techniques and (b) may be usefully applied in either an exploratory/inductive or a confirmatory/deductive research design.

Key Words: Fuzzy-set • goodness-of-fit • causal inference • necessity • sufficiency

Sociological Methods & Research, Vol. 38, No. 1, 102-146 (2009)
DOI: 10.1177/0049124109339371


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