Differential prediction and the use of multiple predictors: The omitted variables problem
成果类型：
Article
署名作者：
Sackett, PR; Laczo, RM; Lippe, ZP
署名单位：
University of Minnesota System; University of Minnesota Twin Cities
刊物名称：
JOURNAL OF APPLIED PSYCHOLOGY
ISSN/ISSBN：
0021-9010
DOI：
10.1037/0021-9010.88.6.1046
发表日期：
2003
页码：
1046-1056
关键词：
摘要：
Moderated regression is widely used to examine differential prediction by race or gender. When using multiple predictors in a selection system, guidance as to whether, differential prediction analysis should be conducted on each predictor individually, or on the set of predictors in combination, is lacking. Analyzing predictors individually creates the possibility of an omitted variable problem. Army Project A data were used to examine differential prediction by race with the use of personality measures for 79 predictor-criterion combinations. Traditional analysis indicated predictive bias by intercept in 45 instances and by slope in 7 instances; the inclusion of an Armed Services Vocational Aptitude Battery general factor as an additional predictor changed the conclusion in 32 cases for the intercept and in 3 cases for the slope.
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