01651nas a2200157   4500008003900000245010700039210006900146300001000215490000700225520113900232100001501371700001901386700002101405700001501426856005201441       2009                          d00aConstraint-Weighted a-Stratification for Computerized Adaptive Testing With Nonstatistical Constraints0 aConstraintWeighted aStratification for Computerized Adaptive Tes  a35-490 v693 a<p>a-stratification is a method that utilizes items with small discrimination (a) parameters early in an exam and those with higher a values when more is learned about the ability parameter. It can achieve much better item usage than the maximum information criterion (MIC). To make a-stratification more practical and more widely applicable, a method for weighting the item selection process in a-stratification as a means of satisfying multiple test constraints is proposed. This method is studied in simulation against an analogous method without stratification as well as a-stratification using descending-rather than ascending-a procedures. In addition, a variation of a-stratification that allows for unbalanced usage of a parameters is included in the study to examine the trade-off between efficiency and exposure control. Finally, MIC and randomized item selection are included as baseline measures. Results indicate that the weighting mechanism successfully addresses the constraints, that stratification helps to a great extent balancing exposure rates, and that the ascending-a design improves measurement precision.</p>
1 aYing Cheng1 aChang, Hua-Hua1 aDouglas, Jeffrey1 aFanmin Guo  uhttp://epm.sagepub.com/content/69/1/35.abstract