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

Autor/inn/en	Paganin, Sally; Paciorek, Christopher J.; Wehrhahn, Claudia; Rodríguez, Abel; Rabe-Hesketh, Sophia; de Valpine, Perry
Titel	Computational Strategies and Estimation Performance with Bayesian Semiparametric Item Response Theory Models
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 2, S.147-188 (42 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Paganin, Sally)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221136105
Schlagwörter	Bayesian Statistics; Item Response Theory; Guidance; Evaluation Methods; Computer Software; Sampling; Algorithms + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Beratung; Algorithm; Algorithmus
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01