Applying a Weighted Maximum Likelihood Latent Trait Estimator to the Generalized Partial Credit Model

This article applies a weighted maximum likelihood (WML) latent trait estimator to the generalized partial credit model (GPCM). The relevant equations required to obtain the WML estimator using the Newton-Raphson algorithm are presented, and a simulation study is described that compared the properti...

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Bibliographic Details
Published in:Applied psychological measurement 2005-05, Vol.29 (3), p.218-233
Main Authors: Penfield, Randall D., Bergeron, Jennifer M.
Format: Article
Language:English
Subjects:
Computation
Evaluation Methods
Evaluation Research
Item Response Theory
Maximum Likelihood Statistics
Psychological Studies
Psychometrics
Simulation
Stimuli
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Summary:This article applies a weighted maximum likelihood (WML) latent trait estimator to the generalized partial credit model (GPCM). The relevant equations required to obtain the WML estimator using the Newton-Raphson algorithm are presented, and a simulation study is described that compared the properties of the WML estimator to those of the maximum likelihood (ML) and expected a posteriori (EAP) estimators for fixed-length tests composed of polytomous items. The results of the simulation study suggest that the WML estimator maintains better properties than both the ML and EAP estimators.
ISSN:0146-6216
1552-3497
DOI:10.1177/0146621604270412