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Cited by 398

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Bayesian Estimation of a Multilevel IRT Model Using Gibbs Sampling

Open access
Fox, Jean-Paul, Glas, Cees A. W.·2001·Psychometrika
DOI 10.1007/bf02294839
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Abstract

In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.

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Metadata

Authors
Fox, J.-P., & Glas, C. A. W.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
2001-06
Volume
66
Issue
2
Pages
271-288
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/bf02294839
OpenAlex
W2170454983
Cited by
398
References
36
FWCI
11.63
Type
journal article
Language
en
Open access
green

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