Extended Asymptotic Identifiability of Nonparametric Item Response Models

Yinqiu He ()
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Yinqiu He: University of Wisconsin-Madison

Psychometrika, 2024, vol. 89, issue 3, No 10, 958-973

Abstract: Abstract Nonparametric item response models provide a flexible framework in psychological and educational measurements. Douglas (Psychometrika 66(4):531–540, 2001) established asymptotic identifiability for a class of models with nonparametric response functions for long assessments. Nevertheless, the model class examined in Douglas (2001) excludes several popular parametric item response models. This limitation can hinder the applications in which nonparametric and parametric models are compared, such as evaluating model goodness-of-fit. To address this issue, We consider an extended nonparametric model class that encompasses most parametric models and establish asymptotic identifiability. The results bridge the parametric and nonparametric item response models and provide a solid theoretical foundation for the applications of nonparametric item response models for assessments with many items.

Keywords: nonparametric item response theory; identifiability; asymptotic theory (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09972-7

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