Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

沃拉克等人。(2015) 建议使用擦除检测指数 (EDI) 来检测个别考生的欺诈性擦除。Wollack and Eckerly (2017) 和 Sinharay (2018) 扩展了 Wollack 等人的指数。(2015) 提出了三种 EDI，用于在总体或组级别检测欺诈性擦除。本文跟进 Wollack 和 Eckerly (2017) 和 Sinharay (2018) 的研究，并通过结合线性混合效应模型文献中的经验最佳线性无偏预测器（例如 McCulloch 等人， ., 2008)。模拟研究表明，新的 EDI 比 Wollack 和 Eckerly (2017) 和 Sinharay (2018) 的指数具有更大的功率。此外，新指数具有令人满意的第一类错误率。还包括一个真实的数据示例。