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Bayesian Item Selection Criteria for Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

Wim J. van der Linden
Wim J. van der Linden*
Affiliation:
University of Twente
*
Requests for reprints should be sent to W. J. van der Linden, Department of Educational Measurement and Data Analysis, University of Twente, P.O. Box 217, 7500 AE Enschede, THE NETHERLANDS. E-mail; vanderlinden@edte.utwente.nl
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Abstract

Owen (1975) proposed an approximate empirical Bayes procedure for item selection in computerized adaptive testing (CAT). The procedure replaces the true posterior by a normal approximation with closed-form expressions for its first two moments. This approximation was necessary to minimize the computational complexity involved in a fully Bayesian approach but is no longer necessary given the computational power currently available for adaptive testing. This paper suggests several item selection criteria for adaptive testing which are all based on the use of the true posterior. Some of the statistical properties of the ability estimator produced by these criteria are discussed and empirically characterized.

Keywords

adaptive testingitem response theoryBayesian statisticsitem selection criteria
Type
Original Paper
Information
Psychometrika , Volume 63 , Issue 2 , June 1998 , pp. 201 - 216
Copyright
Copyright © 1998 The Psychometric Society

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Footnotes

Portions of this paper were presented at the 60th annual meeting of the Psychometric Society, Minneapolis, Minnesota, June, 1995. The author is indebted to Wim M. M. Tielen for his computational support.

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