Nonparametric item response function estimation for assessing parametric model fit

Jeffrey Douglas, Allan Cohen

Research output: Contribution to journalArticlepeer-review


Methods are developed that investigate the fit of parametric item response models by comparing them to models fitted under nonparametric assumptions. The approach is primarily graphical, but is made inferential through resampling from an estimated parametric model. The identifiability and estimation consistency of item response theory models are discussed and shown to be vital to the interpretation of differences between two fitted item response theory models. Simulation studies and real-data examples illustrate these techniques.

Original languageEnglish (US)
Pages (from-to)234-243
Number of pages10
JournalApplied Psychological Measurement
Issue number3
StatePublished - Sep 2001
Externally publishedYes


  • Goodness of fit
  • Item response function
  • Item response theory
  • Kernel smoothing
  • Nonparametric item response theory
  • Nonparametric regression

ASJC Scopus subject areas

  • General Psychology
  • Psychology (miscellaneous)
  • Social Sciences (miscellaneous)


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