@article {75, title = {Computerized adaptive testing using the nearest-neighbors criterion}, journal = {Applied Psychological Measurement}, volume = {27}, number = {3}, year = {2003}, pages = {204-216}, abstract = {Item selection procedures designed for computerized adaptive testing need to accurately estimate every taker{\textquoteright}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)}, keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Statistical Analysis, Statistical Estimation computerized adaptive testing, Statistical Tests}, author = {Cheng, P. E. and Liou, M.} } @article {505, title = {Computerized adaptive testing using the nearest-neighbors criterion}, journal = {Applied Psychological Measurement}, volume = {27}, year = {2003}, pages = {204-216}, author = {Cheng, P. E. and Liou, M.} } @article {74, title = {Estimation of trait level in computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {24}, number = {3}, year = {2000}, pages = {257-265}, abstract = {Notes that in computerized adaptive testing (CAT), a examinee{\textquoteright}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 )}, keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Statistical Estimation computerized adaptive testing}, author = {Cheng, P. E. and Liou, M.} }