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

Chun Wang, Hua Hua Chang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)363-384
Number of pages22
JournalPsychometrika
Volume76
Issue number3
DOIs
StatePublished - Jul 2011

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

  • Fisher information
  • Kullback-Leibler information
  • continuous entropy
  • multidimensional computerized adaptive test
  • mutual information

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