01379nas a2200133   4500008003900000245008500039210006900124300001200193490000700205520094200212100002001154700001801174856005301192       2011                          d00aComputerized Classification Testing Under the Generalized Graded Unfolding Model0 aComputerized Classification Testing Under the Generalized Graded  a114-1280 v713 a<p>The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree&mdash;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.</p>
1 aWang, Wen-Chung1 aLiu, Chen-Wei  uhttp://epm.sagepub.com/content/71/1/114.abstract