01599nas a2200145   4500008003900000245010600039210006900145300001200214490000700226520111100233100002001344700001801364700001801382856005301400       2013                          d00aThe Random-Threshold Generalized Unfolding Model and Its Application of Computerized Adaptive Testing0 aRandomThreshold Generalized Unfolding Model and Its Application   a179-2000 v373 a<p>The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs Sampler) freeware, which adopts a Bayesian approach for estimation. A series of simulations was conducted to evaluate the parameter recovery of the new model and the consequences of ignoring the randomness in thresholds. The results showed that the parameters of RTGUM were recovered fairly well and that ignoring the randomness in thresholds led to biased estimates. Computerized adaptive testing was also implemented on RTGUM, where the Fisher information criterion was used for item selection and the maximum a posteriori method was used for ability estimation. The simulation study showed that the longer the test length, the smaller the randomness in thresholds, and the more categories in an item, the more precise the ability estimates would be.</p>
1 aWang, Wen-Chung1 aLiu, Chen-Wei1 aWu, Shiu-Lien  uhttp://apm.sagepub.com/content/37/3/179.abstract01379nas 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