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.

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A new regression-based method of assessing test bias is proposed. We highlight two different potential causes of differences in groups' regression line intercepts. Intercepts differing due to mean criterion score differences are not interpreted as predictive test bias. Using both simulated and employee data, we illustrate this new approach. Test bias is a systematic error in how a test measures members of a particular group (Camilli & Shepard, 1994). Test bias is a fundamentally important issue in testing as pervasive and systematic sources of error can lead to erroneous inferences regarding the interpretation and use of test scores. Test bias may preclude comparisons across groups and is cause for considerable concern in decision making contexts such as employee selection (U.S. EEOC, 1978). The most common method of assessing test bias is the Cleary (1968) method, which involves testing for both slope and intercept regression line differences across groups (Sackett & Wilk, 1994). In this approach, the test serves as the predictor variable and some measure of performance serves as the criterion.