Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

Autor/inn/en	Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.
Titel	Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure
Quelle	In: Educational and Psychological Measurement, 66 (2006) 4, S.545-564 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405284031
Schlagwörter	Classification; Computation; Simulation; Item Response Theory; Adaptive Testing; Computer Assisted Testing; Bayesian Statistics; Efficiency; Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Simulation program; Simulationsprogramm; Item-Response-Theorie; Effectiveness; Effektivität; Wirkungsgrad
Abstract	The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following results were found. (a) The Bayesian estimators were more likely to misclassify examinees into an inward category because of their inward biases, when a fixed start value of zero was assigned to every examinee. (b) When moderately accurate start values were available, however, Bayesian estimators produced classifications that were slightly more accurate than was the maximum likelihood estimator or weighted likelihood estimator. Expected a posteriori was the procedure that produced the most accurate results among the three Bayesian methods. (c) All five estimators produced equivalent efficiencies in terms of number of items required, which was 50 or more items except for abilities that were less than -2.00 or greater than 2.00. (Contains 3 tables and 5 figures.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10