In continuous testing programs, some items are repeatedly used across test administrations, and statistical methods are often used to evaluate whether items become compromised due to examinees’ preknowledge. In this study, we proposed a residual method to detect compromised items when a test can be partitioned into two subsets of items: secure items and possibly compromised items. We derived the standard error of the residual statistic by taking the sampling error in both ability and item parameter estimate into account. The simulation results suggest that the Type I error is close to the nominal level when both sources of error are adjusted, and item parameter error can be ignored only when the item calibration sample size is much larger than the evaluation sample size. We also investigated the performance of the residual method when not using information from secure items in both simulation and real data analyses.

中文翻译：

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使用安全物品中的信息检测损坏的物品

在连续测试程序中，某些项目在测试管理部门之间反复使用，并且经常使用统计方法来评估项目是否由于应试者的先知而受到损害。在这项研究中，我们提出了一种残差方法，可以将测试划分为两个子集：安全项目和可能的受害项目，以检测受害项目。通过考虑能力和项目参数估计中的抽样误差，我们得出了剩余统计量的标准误差。仿真结果表明，当两个误差源均被调整时，I类误差接近标称水平，并且仅当项目校准样本量远大于评估样本量时，项目参数误差才可以忽略。