A Comparison of Item-Selection Methods for Adaptive Tests with Content Constraints

Autor/in	van der Linden, Wim J.
Titel	A Comparison of Item-Selection Methods for Adaptive Tests with Content Constraints
Quelle	In: Journal of Educational Measurement, 42 (2005) 3, S.283-302 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2005.00015.x
Schlagwörter	Law Schools; Item Analysis; Admission (School); Adaptive Testing; Law School Admission Test + Suchen Sie Ihr Suchwort? Itemanalyse; Law school; Assessment; Admission criteria; Admission procedures; Rechtswissenschaft; Fachbereich; Eignungsprüfung; Eignungstest; Hochschulzulassung
Abstract	In test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test -- a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10