TY - JOUR T1 - Outlier measures and norming methods for computerized adaptive tests JF - Journal of Educational and Behavioral Statistics Y1 - 2001 A1 - Bradlow, E. T. A1 - Weiss, R. E. KW - Adaptive Testing KW - Computer Assisted Testing KW - Statistical Analysis KW - Test Norms AB - Notes 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 ) VL - 26 ER - TY - JOUR T1 - Bayesian identification of outliers in computerized adaptive testing JF - Journal of the American Statistical Association Y1 - 1998 A1 - Bradlow, E. T. A1 - Weiss, R. E. A1 - Cho, M. AB - We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test (CAT). The vec-tor of responses yi of person i from the CAT comprise a multivariate response vector. Multivariate observations may be outlying in manydi erent directions and we characterize speci c directions as corre- sponding to outliers with different interpretations. We develop a class of outlier statistics to identify different types of outliers based on a con-trol chart type methodology. The outlier methodology is adaptable to general longitudinal discrete data structures. We consider several procedures to judge how extreme a particular outlier is. Data from the National Council Licensure EXamination (NCLEX) motivates our development and is used to illustrate the results. VL - 93 ER -