Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices

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 noncognitiv...

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Main Authors: SONG, Q. Chelsea, WEE, Serena, NEWMAN, Daniel A.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/soss_research/2395
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spelling sg-smu-ink.soss_research-36522019-05-22T06:42:29Z Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices SONG, Q. Chelsea WEE, Serena NEWMAN, Daniel A. 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. 2017-12-01T08:00:00Z text https://ink.library.smu.edu.sg/soss_research/2395 info:doi/10.1037/apl0000240 Research Collection School of Social Sciences eng Institutional Knowledge at Singapore Management University Adverse impact Cognitive ability/intelligence Cross-validation Diversity Pareto-optimal weighting Applied Behavior Analysis Industrial and Organizational Psychology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Adverse impact
Cognitive ability/intelligence
Cross-validation
Diversity
Pareto-optimal weighting
Applied Behavior Analysis
Industrial and Organizational Psychology
spellingShingle Adverse impact
Cognitive ability/intelligence
Cross-validation
Diversity
Pareto-optimal weighting
Applied Behavior Analysis
Industrial and Organizational Psychology
SONG, Q. Chelsea
WEE, Serena
NEWMAN, Daniel A.
Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
description 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.
format text
author SONG, Q. Chelsea
WEE, Serena
NEWMAN, Daniel A.
author_facet SONG, Q. Chelsea
WEE, Serena
NEWMAN, Daniel A.
author_sort SONG, Q. Chelsea
title Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
title_short Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
title_full Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
title_fullStr Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
title_full_unstemmed Diversity shrinkage: Cross-validating Pareto-optimal weights to enhance diversity in hiring practices
title_sort diversity shrinkage: cross-validating pareto-optimal weights to enhance diversity in hiring practices
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/soss_research/2395
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