Refinement of a Bias-Correction Procedure for the Weighted Likelihood Estimator of Ability. Research Report. ETS RR-07-23

Autor/inn/en	Zhang, Jinming; Lu, Ting
Titel	Refinement of a Bias-Correction Procedure for the Weighted Likelihood Estimator of Ability. Research Report. ETS RR-07-23
Quelle	In: ETS Research Report Series, (2007), (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Item Response Theory; Ability; Error of Measurement; Maximum Likelihood Statistics; Computation; Statistical Bias; Comparative Analysis; Simulation + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Fähigkeit; Fertigkeit; Messfehler; Simulation program; Simulationsprogramm
Abstract	In practical applications of item response theory (IRT), item parameters are usually estimated first from a calibration sample. After treating these estimates as fixed and known, ability parameters are then estimated. However, the statistical inferences based on the estimated abilities can be misleading if the uncertainty of the item parameter estimates is ignored. Instead, estimated item parameters can be regarded as covariates measured with error. Along the line of this measurement-error-model approach, asymptotic expansions of the maximum likelihood estimator (MLE) and weighted likelihood estimator (WLE) of ability were derived by Zhang, Xie, Song, and Lu (2007). In this paper, we propose an estimator of an ability parameter based on the asymptotic formula of the WLE. A simulation study shows that the new estimator effectively reduces the bias of the MLE or WLE of ability caused by the uncertainty of the item parameter estimates not taken into account. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01