Computerized adaptive testing using a class of high-order item response theory models

Hung-Yu HUANG, Po Hsi CHEN, Wen Chung WANG

Research output: Contribution to journalArticles

8 Citations (Scopus)

Abstract

In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage. Copyright © 2012 The Author(s).
Original languageEnglish
Pages (from-to)689-706
JournalApplied Psychological Measurement
Volume36
Issue number8
DOIs
Publication statusPublished - Nov 2012

Citation

Huang, H.-Y., Chen, P.-H., & Wang, W.-C. (2012). Computerized adaptive testing using a class of high-order item response theory models. Applied Psychological Measurement, 36(8), 689-706.

Keywords

  • Computerized adaptive testing
  • Item response theory
  • Higher order
  • Item exposure control
  • Test security

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