01240nas a2200145 4500008003900000245009000039210006900129300001200198490000700210520076800217100001900985700001701004700002001021856005301041 2012 d00aComputerized Adaptive Testing Using a Class of High-Order Item Response Theory Models0 aComputerized Adaptive Testing Using a Class of HighOrder Item Re a689-7060 v363 a

In the human sciences, a common assumption is that latent traits have a hierarchical structure. Higher order item response theory models have been developed to account for this hierarchy. In this study, computerized adaptive testing (CAT) algorithms based on these kinds of models were implemented, and their performance under a variety of situations was examined using simulations. The results showed that the CAT algorithms were very effective. The progressive method for item selection, the Sympson and Hetter method with online and freeze procedure for item exposure control, and the multinomial model for content balancing can simultaneously maintain good measurement precision, item exposure control, content balance, test security, and pool usage.

1 aHuang, Hung-Yu1 aChen, Po-Hsi1 aWang, Wen-Chung uhttp://apm.sagepub.com/content/36/8/689.abstract01379nas a2200133 4500008003900000245008500039210006900124300001200193490000700205520094200212100002001154700001801174856005301192 2011 d00aComputerized Classification Testing Under the Generalized Graded Unfolding Model0 aComputerized Classification Testing Under the Generalized Graded a114-1280 v713 a

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.

1 aWang, Wen-Chung1 aLiu, Chen-Wei uhttp://epm.sagepub.com/content/71/1/114.abstract01976nas a2200133 4500008003900000245011600039210006900155300001200224490000700236520150500243100002001748700002101768856005301789 2011 d00aComputerized Classification Testing Under the One-Parameter Logistic Response Model With Ability-Based Guessing0 aComputerized Classification Testing Under the OneParameter Logis a925-9410 v713 a

The one-parameter logistic model with ability-based guessing (1PL-AG) has been recently developed to account for effect of ability on guessing behavior in multiple-choice items. In this study, the authors developed algorithms for computerized classification testing under the 1PL-AG and conducted a series of simulations to evaluate their performances. Four item selection methods (the Fisher information, the Fisher information with a posterior distribution, the progressive method, and the adjusted progressive method) and two termination criteria (the ability confidence interval [ACI] method and the sequential probability ratio test [SPRT]) were developed. In addition, the Sympson–Hetter online method with freeze (SHOF) was implemented for item exposure control. Major results include the following: (a) when no item exposure control was made, all the four item selection methods yielded very similar correct classification rates, but the Fisher information method had the worst item bank usage and the highest item exposure rate; (b) SHOF can successfully maintain the item exposure rate at a prespecified level, without compromising substantial accuracy and efficiency in classification; (c) once SHOF was implemented, all the four methods performed almost identically; (d) ACI appeared to be slightly more efficient than SPRT; and (e) in general, a higher weight of ability in guessing led to a slightly higher accuracy and efficiency, and a lower forced classification rate.

1 aWang, Wen-Chung1 aHuang, Sheng-Yun uhttp://epm.sagepub.com/content/71/6/925.abstract