Bayesian Identification of Outliers in Computerized Adaptive Tests
研究了在计算机自适应测试中识别异常作答模式的问题，基于控制图方法开发了一类异常统计量来区分不同类型的异常值，并用全国性考试数据验证。
Abstract We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test (CAT). The vector Y of responses of an examinee from a CAT is a multivariate response vector. Multivariate observations may be outlying in many different directions, and we characterize specific directions as corresponding to outliers with different interpretations. We develop a class of outlier statistics to identify different types of outliers based on a control chart–type methodology. The outlier methodology is adaptable to general longitudinal discretes data structures. We consider several procedures to judge how extreme a particular outlier is. Data from a nationally administered CAT examination motivates our development and is used to illustrate the results.