%0 Journal Article %J Applied Psychological Measurement %D 2003 %T Computerized adaptive testing using the nearest-neighbors criterion %A Cheng, P. E. %A Liou, M. %K (Statistical) %K Adaptive Testing %K Computer Assisted Testing %K Item Analysis %K Item Response Theory %K Statistical Analysis %K Statistical Estimation computerized adaptive testing %K Statistical Tests %X Item selection procedures designed for computerized adaptive testing need to accurately estimate every taker's trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract) %B Applied Psychological Measurement %V 27 %P 204-216 %G eng %0 Journal Article %J Applied Psychological Measurement %D 2000 %T A comparison of item selection rules at the early stages of computerized adaptive testing %A Chen, S-Y. %A Ankenmann, R. D. %A Chang, Hua-Hua %K Adaptive Testing %K Computer Assisted Testing %K Item Analysis (Test) %K Statistical Estimation computerized adaptive testing %X The effects of 5 item selection rules--Fisher information (FI), Fisher interval information (FII), Fisher information with a posterior distribution (FIP), Kullback-Leibler information (KL), and Kullback-Leibler information with a posterior distribution (KLP)--were compared with respect to the efficiency and precision of trait (θ) estimation at the early stages of computerized adaptive testing (CAT). FII, FIP, KL, and KLP performed marginally better than FI at the early stages of CAT for θ=-3 and -2. For tests longer than 10 items, there appeared to be no precision advantage for any of the selection rules. (PsycINFO Database Record (c) 2005 APA ) (journal abstract) %B Applied Psychological Measurement %V 24 %P 241-255 %G eng %0 Journal Article %J Applied Psychological Measurement %D 2000 %T Estimation of trait level in computerized adaptive testing %A Cheng, P. E. %A Liou, M. %K (Statistical) %K Adaptive Testing %K Computer Assisted Testing %K Item Analysis %K Statistical Estimation computerized adaptive testing %X Notes that in computerized adaptive testing (CAT), a examinee's trait level (θ) must be estimated with reasonable accuracy based on a small number of item responses. A successful implementation of CAT depends on (1) the accuracy of statistical methods used for estimating θ and (2) the efficiency of the item-selection criterion. Methods of estimating θ suitable for CAT are reviewed, and the differences between Fisher and Kullback-Leibler information criteria for selecting items are discussed. The accuracy of different CAT algorithms was examined in an empirical study. The results show that correcting θ estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items. (PsycINFO Database Record (c) 2005 APA ) %B Applied Psychological Measurement %V 24 %P 257-265 %G eng