There is a long history of examining assessments used in college admissions or personnel selection for predictive bias, also called differential prediction, to determine whether a selection system predicts comparable levels of performance for individuals from different demographic groups who have the same assessment scores. We expand on previous research that has considered predictive bias in individual predictor variables to (a) examine magnitudes of differential prediction in multipredictor selection systems and (b) explore how differences in prediction generalize across samples. We also share updated methods for computing standardized effect sizes for categorically moderated regression models that facilitate the meta-analysis of differential prediction effects. Our findings highlight the importance of analyzing composite predictors when testing for predictive bias in compensatory selection systems and demonstrate the generalizability of long-observed differential prediction trends by race/ethnicity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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关于具有多个预测因子的选择系统中预测偏差的评估。

长期以来，人们一直在检查大学招生或人员选拔中使用的预测偏差（也称为差异预测）的评估，以确定选拔系统是否可以预测来自不同人口群体的具有相同评估分数的个人的可比绩效水平。我们扩展了先前的研究，这些研究考虑了个体预测变量中的预测偏差，以 (a) 检查多预测器选择系统中差异预测的大小，以及 (b) 探索预测差异如何在样本间泛化。我们还分享了用于计算分类调节回归模型的标准化效应大小的更新方法，这些模型有助于对差异预测效应进行荟萃分析。我们的研究结果强调了在测试补偿选择系统中的预测偏差时分析复合预测变量的重要性，并证明了长期观察到的种族/民族差异预测趋势的普遍性。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。