Diversity tradeoff curves in personnel selection: Evaluating local study, meta-analysis, Bayes-analysis, and ensemble machine learning

One major advancement toward reducing adverse impact is the diversity-validity tradeoff curve methodology (De Corte, Lievens, & Sackett, 2007). The Pareto-optimal tradeoff curve provides sets of selection predictor weights that can often substantially enhance diversity (increase adverse impact ratio and number of minority job offers) with no loss of job performance, in comparison to unit weights (Wee, Newman, & Joseph, 2014). A chief limitation of this diversity-enhancing approach is the tendency for tradeoff curves to shrink, leading to lesser job performance and diversity outcomes upon cross-validation (Song, Wee, & Newman, 2017). Typical selection scenarios considered in Pareto-optimal shrinkage papers involve using a single local validity study as the calibration sample (see Rupp et al., 2020). The current project proposes to evaluate tradeoff curve shrinkage (both validity shrinkage and diversity shrinkage) using four types of validity evidence/calibration studies: (a) a local validity study, (b) a meta-analysis (Schmidt & Hunter, 1977), (c) a Bayes-analysis with empirical priors (Newman, Jacobs, & Bartram, 2007), and (d) an ensemble machine learning approach (Zhou, 2012). This dissertation consists of three studies. Study 1 examines conditions under which each approach performs best, offering recommendations on ideal methods for diversity improvement (reducing shrinkage and maximizing cross-validity) while using shrunken tradeoff curves in local selection settings. Study 2 evaluates effects of meta-analytic publication bias on the performance of meta-analysis, empirical Bayes-analysis, and ensemble learning. Finally, Study 3 considers potential biases due to the ignored violation of the assumption of independence between validities and artifacts.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Human Res & Industrial Rels

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Tang, Chen

Contributors dc:contributor

• Newman, Daniel A.
• Drasgow, Fritz
• Rounds, James
• Song, Q. Chelsea

• Personnel Selection
• Adverse Impact
• Cross-validation
• Shrinkage
• Diversity
• Pareto-optimization

Statement dc:rights

• Copyright 2023 Chen Tang

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120523

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120523