{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120523"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120523","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Diversity tradeoff curves in personnel selection: Evaluating local study, meta-analysis, Bayes-analysis, and ensemble machine learning","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Tang, Chen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Human Res & Industrial Rels","degree_department":null,"school":null,"contributors":["Newman, Daniel A.","Drasgow, Fritz","Rounds, James","Song, Q. Chelsea"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Personnel Selection","Adverse Impact","Cross-validation","Shrinkage","Diversity","Pareto-optimization"],"languages":["en","eng"],"rights":["Copyright 2023 Chen Tang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120523","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Newman, Daniel A.","Drasgow, Fritz","Rounds, James","Song, Q. Chelsea"]},{"key":"dc:creator","label":"Author","values":["Tang, Chen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-19"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Human Res & Industrial Rels"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Personnel Selection","Adverse Impact","Cross-validation","Shrinkage","Diversity","Pareto-optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Chen Tang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120523"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Chen Tang, accepted the attached license on 2023-04-17 at 20:49.","The student, Chen Tang, submitted this Dissertation for approval on 2023-04-18 at 12:41.","This Dissertation was approved for publication on 2023-04-19 at 07:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19023 on 2023-09-01 at 17:20:43","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Diversity tradeoff curves in personnel selection: Evaluating local study, meta-analysis, Bayes-analysis, and ensemble machine learning"]}]}],"canonical_facts":{"dc:contributor":["Newman, Daniel A.","Drasgow, Fritz","Rounds, James","Song, Q. Chelsea"],"dc:creator":["Tang, Chen"],"dc:date":["2023-05","2023-04-19"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Chen Tang, accepted the attached license on 2023-04-17 at 20:49.","The student, Chen Tang, submitted this Dissertation for approval on 2023-04-18 at 12:41.","This Dissertation was approved for publication on 2023-04-19 at 07:18.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19023 on 2023-09-01 at 17:20:43","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120523"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Chen Tang"],"dc:subject":["Personnel Selection","Adverse Impact","Cross-validation","Shrinkage","Diversity","Pareto-optimization"],"dc:title":["Diversity tradeoff curves in personnel selection: Evaluating local study, meta-analysis, Bayes-analysis, and ensemble machine learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Human Res & Industrial Rels"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}