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

Autor/inn/en	Penfield, Randall D.; Bergeron, Jennifer M.
Titel	Applying a Weighted Maximum Likelihood Latent Trait Estimator to the Generalized Partial Credit Model
Quelle	In: Applied Psychological Measurement, 29 (2005) 3, S.218-233 (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621604270412
Schlagwörter	Simulation; Computation; Item Response Theory; Maximum Likelihood Statistics; Psychological Studies; Psychometrics; Stimuli; Evaluation Methods; Evaluation Research + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Item-Response-Theorie; Psychometry; Psychometrie; Anreizsystem; Evaluationsforschung
Abstract	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. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10