Multidimensional computerized adaptive testing using non-compensatory item response theory models

Chia-Ling HSU, Wen Chung WANG

Research output: Contribution to journalArticlespeer-review

6 Citations (Scopus)


Current multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees' abilities, which shed light on item bank construction for non-compensatory items. Copyright © 2018 The Author(s).
Original languageEnglish
Pages (from-to)464-480
JournalApplied Psychological Measurement
Issue number6
Early online dateOct 2018
Publication statusPublished - 01 Sept 2019


Hsu, C.-L., & Wang, W.-C. (2019). Multidimensional computerized adaptive testing using non-compensatory item response theory models. Applied Psychological Measurement, 43(6), 464-480. doi: 10.1177/0146621618800280


  • Item response theory
  • Non-compensatory models
  • Computerized adaptive testing
  • Item selection methods


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