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

Research output: Contribution to journalArticle

33 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)102-146
Number of pages45
JournalSociological Methods and Research
Volume38
Issue number1
DOIs
StatePublished - Aug 2009

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Keywords

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

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Sociology and Political Science

Cite this

Goodness-of-fit tests and descriptive measures in fuzzy-set analysis. / Eliason, Scott R; Stryker, Robin S.

In: Sociological Methods and Research, Vol. 38, No. 1, 08.2009, p. 102-146.

Research output: Contribution to journalArticle

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