01899nas a2200157   4500008003900000245009300039210007100132300001200203490000700215520139800222100001401620700002101634700001601655700001701671856005301688       2012                          d00aA Mixture Rasch Model–Based Computerized Adaptive Test for Latent Class Identification0 aMixture Rasch Model–Based Computerized Adaptive Test for Latent   a469-4930 v363 a<p>This study explored a computerized adaptive test delivery algorithm for latent class identification based on the mixture Rasch model. Four item selection methods based on the Kullback&ndash;Leibler (KL) information were proposed and compared with the reversed and the adaptive KL information under simulated testing conditions. When item separation was large, all item selection methods did not differ evidently in terms of accuracy in classifying examinees into different latent classes and estimating latent ability. However, when item separation was small, two methods with class-specific ability estimates performed better than the other two methods based on a single latent ability estimate across all latent classes. The three types of KL information distributions were compared. The KL and the reversed KL information could be the same or different depending on the ability level and the item difficulty difference between latent classes. Although the KL information and the reversed KL information were different at some ability levels and item difficulty difference levels, the use of the KL, the reversed KL, or the adaptive KL information did not affect the results substantially due to the symmetric distribution of item difficulty differences between latent classes in the simulated item pools. Item pool usage and classification convergence points were examined as well.</p>
1 aHong Jiao1 aMacready, George1 aLiu, Junhui1 aCho, Youngmi  uhttp://apm.sagepub.com/content/36/6/469.abstract01669nas a2200169   4500008003900000245009500039210007100134300000900205490000700214520113900221100001701360700001401377700002201391700001901413700001601432856005101448       2008                          d00aComparability of Computer-Based and Paper-and-Pencil Testing in K–12 Reading Assessments0 aComparability of ComputerBased and PaperandPencil Testing in K–1  a5-240 v683 a<p>In recent years, computer-based testing (CBT) has grown in popularity, is increasingly being implemented across the United States, and will likely become the primary mode for delivering tests in the future. Although CBT offers many advantages over traditional paper-and-pencil testing, assessment experts, researchers, practitioners, and users have expressed concern about the comparability of scores between the two test administration modes. To help provide an answer to this issue, a meta-analysis was conducted to synthesize the administration mode effects of CBTs and paper-and-pencil tests on K&mdash;12 student reading assessments. Findings indicate that the administration mode had no statistically significant effect on K&mdash;12 student reading achievement scores. Four moderator variables&mdash;study design, sample size, computer delivery algorithm, and computer practice&mdash;made statistically significant contributions to predicting effect size. Three moderator variables&mdash;grade level, type of test, and computer delivery method&mdash;did not affect the differences in reading scores between test modes.</p>
1 aShudong Wang1 aHong Jiao1 aYoung, Michael, J1 aBrooks, Thomas1 aOlson, John  uhttp://epm.sagepub.com/content/68/1/5.abstract