TY - JOUR T1 - Computerized adaptive testing using the nearest-neighbors criterion JF - Applied Psychological Measurement Y1 - 2003 A1 - Cheng, P. E. A1 - Liou, M. VL - 27 ER - TY - JOUR T1 - Computerized adaptive testing using the nearest-neighbors criterion JF - Applied Psychological Measurement Y1 - 2003 A1 - Cheng, P. E. A1 - Liou, M. KW - (Statistical) KW - Adaptive Testing KW - Computer Assisted Testing KW - Item Analysis KW - Item Response Theory KW - Statistical Analysis KW - Statistical Estimation computerized adaptive testing KW - Statistical Tests AB - 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) VL - 27 ER - TY - JOUR T1 - Estimation of trait level in computerized adaptive testing JF - Applied Psychological Measurement Y1 - 2000 A1 - Cheng, P. E. A1 - Liou, M. KW - (Statistical) KW - Adaptive Testing KW - Computer Assisted Testing KW - Item Analysis KW - Statistical Estimation computerized adaptive testing AB - 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 ) VL - 24 ER -