Bayesian Item Selection Criteria for - Adaptive Testing

DOCUMENT RESUME TM 027 361 van der Linden, Wim J. Bayesian Item Selection Criteria for Adaptive Testing. Research Report 96-01. Twente Univ., Enschede (Netherlands). Faculty of Educational Science and Technology. 1996-10-00 32p. Faculty of Educational Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands. Reports Evaluative (142) MF01/PCO2 Plus Postage. Ability; *Adaptive Testing; *Bayesian Statistics; Computation; Computer Assisted Testing; *Criteria; Equations (Mathematics); Error of Measurement; Estimation (Mathematics); *Selection; *Test Items R. J. Owen (1975) proposed an approximate empirical Bayes procedure for item selection in adaptive testing. 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 in adaptive testing. This paper suggests several item selection criteria for adaptive testing that 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. An empirical study with 300 test items showed that the maximum predicted posterior expected information criterion had excellent mean-squared error for more extreme values of theta, and is the criterion elect for application in short adaptive tests. An appendix presents Owen's equations. (Contains 17 references.) (Author/SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** Bayesian Item Selection Criteria forAdaptive Testing Wim J. van der Linden U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCAT NAL RESOURCES INFORMATION CENTER (ERIC) This document has been reproduced as received from the person or organization originating it. O Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 1 TIA Research Report96-01 c)) 2 BEST COPY AVAILABLE LBayesian Item Selection Criteria for Adaptive Testing Wim J. van der Linden Bayesian Item Selection Criteria for Adaptive Testing, Wim J. van der Linden Enschede: University of Twente, Faculty of Educational Science and Technology, December 1996. 29 pages. Bayesian Selection Criteria 3