Effects of predictor weighting methods on incremental validity.
研究了在预测变量加权方式（如回归加权、单位加权和多重门槛）下，增量效度结论的稳健性，发现某些情况下第二个预测变量的增量价值可能消失甚至降低预测效度。
It is common to add an additional predictor to a selection system with the goal of increasing criterion-related validity. Research on the incremental validity of a second predictor is generally based on forming a regression-weighted composite of the predictors. However, in practice predictors are commonly used in ways other than regression-weighted composites, and we examine the robustness of incremental validity findings to other ways of using predictors, namely, unit weighting and multiple hurdles. We show that there are settings in which the incremental value of a second predictor disappears, and can even produce lower validity than the first predictor alone, when these alternatives to regression weighting are used. First, we examine conditions under which unit weighting will negate gain in predictive power attainable via regression weights. Second, we revisit Schmidt and Hunter's (1998) summary of incremental validity of predictors over cognitive ability, evaluating whether the reported incremental value of a second predictor is different when predictors are unit weighted rather than regression weighted. Third, we analyze data reported in the published literature to discern the frequency with which unit weighting might affect conclusions about whether there is value in adding a second predictor to a first. Finally, we shift from unit weighting to multiple hurdle selection, examining conditions under which conclusions about incremental validity differ when regression weighting is replaced by multiple-hurdle selection. (PsycINFO Database Record