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Bayesian Procedures for Identifying Aberrant Response-Time Patterns in Adaptive Testing

机译:在自适应测试中识别异常响应时间模式的贝叶斯程序

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摘要

In order to identify aberrant response-time patterns on educational and psychological tests, it is important to be able to separate the speed at which the test taker operates from the time the items require. A lognormal model for response times with this feature was used to derive a Bayesian procedure for detecting aberrant response times. Besides, a combination of the response-time model with a regular response model in an hierarchical framework was used in an alternative procedure for the detection of aberrant response times, in which collateral information on the test takers’ speed is derived from their response vectors. The procedures are illustrated using a data set for the Graduate Management Admission Test® (GMAT®). In addition, a power study was conducted using simulated cheating behavior on an adaptive test.
机译:为了识别教育和心理测验中异常的反应时间模式,重要的是能够将测验者的操作速度与测验所需的时间区分开。具有此功能的对数响应时间的对数正态模型用于导出用于检测异常响应时间的贝叶斯程序。此外,在另一种程序中,将响应时间模型与常规响应模型在分层框架中结合使用,以检测异常的响应时间,其中从应试者的响应向量中得出有关应试者速度的附带信息。使用“研究生管理入学考试”(sup>®(GMAT ®)的数据集来说明这些过程。另外,在自适应测试中使用模拟的作弊行为进行了功率研究。

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