Stratified and Maximum Information Item Selection Procedures in Computer Adaptive Testing

Autor/inn/en	Deng, Hui; Ansley, Timothy; Chang, Hua-Hua
Titel	Stratified and Maximum Information Item Selection Procedures in Computer Adaptive Testing
Quelle	In: Journal of Educational Measurement, 47 (2010) 2, S.202-226 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2010.00109.x
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Selection; Methods; Test Items; Comparative Analysis + Suchen Sie Ihr Suchwort? Auslese; Method; Methode; Test content; Testaufgabe
Abstract	In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with completely random item selection (RAN). The comparisons were with respect to error variances, reliability of ability estimates and item usage through CATs simulated under nine test conditions of various practical constraints and item selection space. The results showed that F had an apparent precision advantage over STR and USTR under unconstrained item selection, but with very poor item usage. USTR reduced error variances for STR under various conditions, with small compromises in item usage. Compared to F, USTR enhanced item usage while achieving comparable precision in ability estimates; it achieved a precision level similar to F with improved item usage when items were selected under exposure control and with limited item selection space. The results provide implications for choosing an appropriate item selection procedure in applied settings. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10