%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 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