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Item Selection in Multidimensional Computerized Adaptive Testing—Gaining Information from Different Angles

Published online by Cambridge University Press:  01 January 2025

Chun Wang  and
Hua-Hua Chang
Chun Wang*
Affiliation:
University of Illinois at Urbana-Champaign
Hua-Hua Chang
Affiliation:
University of Illinois at Urbana-Champaign
*
Requests for reprints should be sent to Chun Wang, Department of Psychology University of Illinois at Urbana-Champaign, 603 E. Daniel St., Champaign, IL 61820, USA. E-mail: cwang49@cyrus.psych.uiuc.edu
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Abstract

Over the past thirty years, obtaining diagnostic information from examinees’ item responses has become an increasingly important feature of educational and psychological testing. The objective can be achieved by sequentially selecting multidimensional items to fit the class of latent traits being assessed, and therefore Multidimensional Computerized Adaptive Testing (MCAT) is one reasonable approach to such task. This study conducts a rigorous investigation on the relationships among four promising item selection methods: D-optimality, KL information index, continuous entropy, and mutual information. Some theoretical connections among the methods are demonstrated to show how information about the unknown vector θ can be gained from different perspectives. Two simulation studies were carried out to compare the performance of the four methods. The simulation results showed that mutual information not only improved the overall estimation accuracy but also yielded the smallest conditional mean squared error in most region of θ. In the end, the overlap rates were calculated to empirically show the similarity and difference among the four methods.

Keywords

Kullback–Leibler informationFisher informationmutual informationmultidimensional computerized adaptive testcontinuous entropy

Information

Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 3 , July 2011 , pp. 363 - 384
Copyright
Copyright © 2011 The Psychometric Society

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