Test Bias, Differential Prediction, and a Revised Approach for Determining the Suitability of a Predictor in a Selection Context
质疑传统测验偏差检验方法，指出回归线截距差异反映差异预测而非测验偏差，并提出新程序识别差异来源，对选拔测验的使用者具有参考价值。
The most commonly used and accepted model of assessing bias in a selection context is that proposed by Cleary in which predictor-criterion regression lines are tested for both slope and intercept equality. With this approach, any difference in intercepts or slopes is considered an indication of bias. We argue that differing regression lines intercepts is indicative of differential prediction but not test bias. We describe several fundamentally different potential causes of differences in groups’ regression line intercepts, many of which are unrelated to test properties. We argue that differential prediction because of such sources should not preclude the use of the test in selection contexts. We propose a new procedure to potentially identify the source of regression line differences and illustrate this framework using a job incumbent sample.