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.
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