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

Scott R. Eliason and Robin Stryker
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Scott R. Eliason: University of Arizona, Tuscon, seliason@email.arizona.edu
Robin Stryker: University of Arizona, Tuscon

Sociological Methods & Research, 2009, vol. 38, issue 1, 102-146

Abstract: 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.

Keywords: Fuzzy-set; goodness-of-fit; causal inference; necessity; sufficiency (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:38:y:2009:i:1:p:102-146

DOI: 10.1177/0049124109339371

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