Computerized classification testing under the generalized graded unfolding model

Wen Chung Wang*, Chen Wei Liu


研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(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.

頁(從 - 到)114-128
期刊Educational and Psychological Measurement
出版狀態已發佈 - 2011 2月

ASJC Scopus subject areas

  • 教育
  • 發展與教育心理學
  • 應用心理學
  • 應用數學


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