01780nas a2200145   4500008003900000245008400039210006900123300001200192490000700204520131700211100001601528700002301544700001401567856005301581       2015                          d00aa-Stratified Computerized Adaptive Testing in the Presence of Calibration Error0 aaStratified Computerized Adaptive Testing in the Presence of Cal  a260-2830 v753 aa-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson–Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items.1 aCheng, Ying1 aPatton, Jeffrey, M1 aShao, Can  uhttp://epm.sagepub.com/content/75/2/260.abstract01791nas a2200133   4500008003900000245009200039210006900131300001200200490000700212520135400219100001601573700001501589856005301604       2015                          d00aThe Effect of Upper and Lower Asymptotes of IRT Models on Computerized Adaptive Testing0 aEffect of Upper and Lower Asymptotes of IRT Models on Computeriz  a551-5650 v393 aIn this article, the effect of the upper and lower asymptotes in item response theory models on computerized adaptive testing is shown analytically. This is done by deriving the step size between adjacent latent trait estimates under the four-parameter logistic model (4PLM) and two models it subsumes, the usual three-parameter logistic model (3PLM) and the 3PLM with upper asymptote (3PLMU). The authors show analytically that the large effect of the discrimination parameter on the step size holds true for the 4PLM and the two models it subsumes under both the maximum information method and the b-matching method for item selection. Furthermore, the lower asymptote helps reduce the positive bias of ability estimates associated with early guessing, and the upper asymptote helps reduce the negative bias induced by early slipping. Relative step size between modeling versus not modeling the upper or lower asymptote under the maximum Fisher information method (MI) and the b-matching method is also derived. It is also shown analytically why the gain from early guessing is smaller than the loss from early slipping when the lower asymptote is modeled, and vice versa when the upper asymptote is modeled. The benefit to loss ratio is quantified under both the MI and the b-matching method. Implications of the analytical results are discussed.1 aCheng, Ying1 aLiu, Cheng  uhttp://apm.sagepub.com/content/39/7/551.abstract