Goodness-of-fit tests and descriptive measures in fuzzy-set analysis

Scott R. Eliason, Robin Stryker

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)102-146
Number of pages45
JournalSociological Methods and Research
Issue number1
StatePublished - Aug 2009


  • Causal inference
  • Fuzzy-set
  • Goodness-of-fit
  • Necessity
  • Sufficiency

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Sociology and Political Science


Dive into the research topics of 'Goodness-of-fit tests and descriptive measures in fuzzy-set analysis'. Together they form a unique fingerprint.

Cite this