Computerized classification testing under the generalized graded unfolding model

Wen Chung WANG, Chen Wei LIU

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)


The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree–disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut score/sequential probability ratio test method and (b) evaluated their accuracy and efficiency in classification through simulations. The results indicated that both methods were very accurate and efficient. The more points each item had and the fewer the classification categories, the more accurate and efficient the classification would be. However, the latter method may yield a very low accuracy in dichotomous items with a short maximum test length. Thus, if it is to be used to classify examinees with dichotomous items, the maximum text length should be increased. Copyright © 2011 The Author(s).
Original languageEnglish
Pages (from-to)114-128
JournalEducational and Psychological Measurement
Issue number1
Publication statusPublished - Feb 2011


Wang, W.-C., & Liu, C.-W. (2011). Computerized classification testing under the generalized graded unfolding model. Educational and Psychological Measurement, 71(1), 114-128.


  • Sequential probability ratio test
  • Mutual information
  • Computerized adaptive testing
  • Computerized classification testing
  • Unfolding model


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