Diversity Shrinkage: Cross-Validating Pareto-Optimal Weights to Enhance Diversity via Hiring Practices
Diversity Shrinkage: Cross-Validating Pareto-Optimal Weights to Enhance Diversity via Hiring Practices
复制标题
多样性收缩：交叉验证帕累托最优权重，通过招聘实践增强多样性
DOI：
10.1037/apl0000240
复制
发表时间：
2017
影响因子：
9.9
通讯作者：
Daniel A. Newman
中科院分区：
文献类型：
--
作者：
Q. Song;Serena Wee;Daniel A. Newman
为了减少人员选择的不利影响并改善多样性结果，一种很有前途的技术是De Corte，Lievens和Sackett(2007)的帕累托最优加权策略。De Corte等人的S策略已经在(A)认知和非认知(如人格)测试(de Corte，Lievens，&Sackett，2008)和(B)特定认知能力分测试(Wee，Newman，&Joseph，2014)的组合上得到了证明。这两项研究都说明了帕累托加权(与单位加权相反)如何导致多样性结果的实质性改善(即多样性的改善)，有时会使少数族裔申请者获得的工作机会增加一倍以上。目前的工作解决了该技术的一个关键限制--在帕累托最优解中存在收缩的可能性，特别是差异收缩。利用蒙特卡罗模拟，改变样本量和预测器组合，得到交叉验证的帕累托最优解。尽管当样本量等于或低于500时，对于认知和非认知预测因子的组合来说，多样性萎缩是相当大的，但当样本量至少为100时，对于特定认知分测试预测因子的组合，多样性萎缩通常可以忽略不计。当帕累托最优解建议实质性的多样性改善时，多样性收缩更大。当样本量至少为100时，交叉验证的帕累托最优权重通常优于单位权重，这表明尽管多样性缩小，多样性的改善通常是可能的。讨论了帕累托最优权重的含义、不利影响、验证研究的样本量，以及优化多样性-工作绩效的权衡。
To reduce adverse impact potential and improve diversity outcomes from personnel selection, one promising technique is De Corte, Lievens, and Sackett’s (2007) Pareto-optimal weighting strategy. De Corte et al.’s strategy has been demonstrated on (a) a composite of cognitive and noncognitive (e.g., personality) tests (De Corte, Lievens, & Sackett, 2008) and (b) a composite of specific cognitive ability subtests (Wee, Newman, & Joseph, 2014). Both studies illustrated how Pareto-weighting (in contrast to unit weighting) could lead to substantial improvement in diversity outcomes (i.e., diversity improvement), sometimes more than doubling the number of job offers for minority applicants. The current work addresses a key limitation of the technique—the possibility of shrinkage, especially diversity shrinkage, in the Pareto-optimal solutions. Using Monte Carlo simulations, sample size and predictor combinations were varied and cross-validated Pareto-optimal solutions were obtained. Although diversity shrinkage was sizable for a composite of cognitive and noncognitive predictors when sample size was at or below 500, diversity shrinkage was typically negligible for a composite of specific cognitive subtest predictors when sample size was at least 100. Diversity shrinkage was larger when the Pareto-optimal solution suggested substantial diversity improvement. When sample size was at least 100, cross-validated Pareto-optimal weights typically outperformed unit weights—suggesting that diversity improvement is often possible, despite diversity shrinkage. Implications for Pareto-optimal weighting, adverse impact, sample size of validation studies, and optimizing the diversity-job performance tradeoff are discussed.