Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models

Sally Paganin, Christopher J. Paciorek, Claudia Wehrhahn, Abel Rodríguez, Sophia Rabe-Hesketh and Perry de Valpine
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Christopher J. Paciorek: University of California, Berkeley
Claudia Wehrhahn: University of California, Santa Cruz
Abel Rodríguez: University of Washington, Seattle
Perry de Valpine: University of California, Berkeley

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 2, 147-188

Abstract: Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT literature has been extremely limited, in good part because of the lack of comprehensive studies and accessible software tools. This article provides guidance for practitioners on semiparametric IRT models and their implementation. In particular, we rely on NIMBLE, a flexible software system for hierarchical models that enables the use of DPMs. We highlight efficient sampling strategies for model estimation and compare inferential results under parametric and semiparametric models.

Keywords: binary IRT models; Dirichlet process mixture; MCMC strategies; NIMBLE (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:2:p:147-188

DOI: 10.3102/10769986221136105

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