Bayesian Identification of Outliers in Computerized Adaptive Tests

We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test #CAT#. The vector of responses y i of person i from the CAT comprise a multivariate response vector. Multivariate observations may be outlying in many di#erent directions and we characterize speci#c directions as corresponding to outliers with di#erentinterpretations. We develop a class of outlier statistics to identify di#erenttypes of outliers based on a control 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. # Eric T. Bradlow is Assistant Professor of Marketing and Statistics, Wharton School of Business, UniversityofPennsylvania, Philadelphia, PA 19104-6371. Part of this work was performed while he...