Item Selection and Ability Estimation Procedures for a Mixed-Format Adaptive Test

Autor/inn/en	Ho, Tsung-Han; Dodd, Barbara G.
Titel	Item Selection and Ability Estimation Procedures for a Mixed-Format Adaptive Test
Quelle	In: Applied Measurement in Education, 25 (2012) 4, S.305-326 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2012.714686
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Test Items; Selection; Methods; Comparative Analysis; Maximum Likelihood Statistics; Computation; Bayesian Statistics + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Auslese; Method; Methode
Abstract	In this study we compared five item selection procedures using three ability estimation methods in the context of a mixed-format adaptive test based on the generalized partial credit model. The item selection procedures used were maximum posterior weighted information, maximum expected information, maximum posterior weighted Kullback-Leibler information, and maximum expected posterior weighted Kullback-Leibler information procedures. The ability estimation methods investigated were maximum likelihood estimation (MLE), weighted likelihood estimation (WLE), and expected a posteriori (EAP). Results suggested that all item selection procedures, regardless of the information functions on which they were based, performed equally well across ability estimation methods. The principal conclusions drawn about the ability estimation methods are that MLE is a practical choice and WLE should be considered when there is a mismatch between pool information and the population ability distribution. EAP can serve as a viable alternative when an appropriate prior ability distribution is specified. Several implications of the findings for applied measurement are discussed. (Contains 5 tables and 6 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10