01931nas a2200229   4500008004100000245007200041210006900113300001200182490000700194520116000201653001801361653002101379653003001400653001801430653002501448653002501473653005701498653002201555100001501577700001201592856009701604       2003                        eng d00aComputerized adaptive testing using the nearest-neighbors criterion0 aComputerized adaptive testing using the nearestneighbors criteri  a204-2160 v273 aItem 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)10a(Statistical)10aAdaptive Testing10aComputer Assisted Testing10aItem Analysis10aItem Response Theory10aStatistical Analysis10aStatistical Estimation computerized adaptive testing10aStatistical Tests1 aCheng, P E1 aLiou, M  uhttp://www.iacat.org/content/computerized-adaptive-testing-using-nearest-neighbors-criterion01705nas a2200181   4500008004100000245007300041210006900114300001100183490000700194520110100201653002101302653003001323653002501353653001501378100001701393700001501410856009801425       2001                        eng d00aOutlier measures and norming methods for computerized adaptive tests0 aOutlier measures and norming methods for computerized adaptive t  a85-1040 v263 aNotes that the problem of identifying outliers has 2 important aspects: the choice of outlier measures and the method to assess the degree of outlyingness (norming) of those measures. Several classes of measures for identifying outliers in Computerized Adaptive Tests (CATs) are introduced. Some of these measures are constructed to take advantage of CATs' sequential choice of items; other measures are taken directly from paper and pencil (P&P) tests and are used for baseline comparisons. Assessing the degree of outlyingness of CAT responses, however, can not be applied directly from P&P tests because stopping rules associated with CATs yield examinee responses of varying lengths. Standard outlier measures are highly correlated with the varying lengths which makes comparison across examinees impossible. Therefore, 4 methods are presented and compared which map outlier statistics to a familiar probability scale (a p value). The methods are explored in the context of CAT data from a 1995 Nationally Administered Computerized Examination (NACE). (PsycINFO Database Record (c) 2005 APA )10aAdaptive Testing10aComputer Assisted Testing10aStatistical Analysis10aTest Norms1 aBradlow, E T1 aWeiss, R E  uhttp://www.iacat.org/content/outlier-measures-and-norming-methods-computerized-adaptive-tests