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ERIC Number: EJ914078
Record Type: Journal
Publication Date: 2011-Feb
Pages: 15
Abstractor: As Provided
Reference Count: 25
ISSN: ISSN-0013-1644
Computerized Classification Testing under the Generalized Graded Unfolding Model
Wang, Wen-Chung; Liu, Chen-Wei
Educational and Psychological Measurement, v71 n1 p114-128 Feb 2011
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. (Contains 3 figures and 2 tables.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A