Validity and adverse impact potential of predictor composite formation
Previous research on the validity and adverse impact (AI) of predictor composite formation focused on the merits of regression-based or ad hoc composites. We argue for a broader focus. Ad hoc chosen composites are usually not Pareto-optimal, whereas the regression-based composite represents only one...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2008
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/5630 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6629/viewcontent/Validity_Adverse_Impact_Potentian_PCF_sv.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.lkcsb_research-6629 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.lkcsb_research-66292019-08-27T02:47:51Z Validity and adverse impact potential of predictor composite formation DE CORTE, Wilfried LIEVENS, Filip SACKETT, Paul R. Previous research on the validity and adverse impact (AI) of predictor composite formation focused on the merits of regression-based or ad hoc composites. We argue for a broader focus. Ad hoc chosen composites are usually not Pareto-optimal, whereas the regression-based composite represents only one element from the total set of Pareto-optimal composites and can, therefore, provide only limited information on the potential for validity and AI reduction of forming predictor composites when both validity and AI are of concern. In that case, other Pareto-optimal composites may provide a better benchmark to decide on the merits of the predictor composite formation. We summarize a method to determine the set of Pareto-optimal composites and apply the method to a representative collection of selection predictors. The application shows that the assessment of the AI and validity of predictor composite formation can differ substantially from the one arrived at when considering only regression-based composites. 2008-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/5630 info:doi/10.1111/j.1468-2389.2008.00423.x https://ink.library.smu.edu.sg/context/lkcsb_research/article/6629/viewcontent/Validity_Adverse_Impact_Potentian_PCF_sv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Human Resources Management 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 |
Human Resources Management Industrial and Organizational Psychology |
spellingShingle |
Human Resources Management Industrial and Organizational Psychology DE CORTE, Wilfried LIEVENS, Filip SACKETT, Paul R. Validity and adverse impact potential of predictor composite formation |
description |
Previous research on the validity and adverse impact (AI) of predictor composite formation focused on the merits of regression-based or ad hoc composites. We argue for a broader focus. Ad hoc chosen composites are usually not Pareto-optimal, whereas the regression-based composite represents only one element from the total set of Pareto-optimal composites and can, therefore, provide only limited information on the potential for validity and AI reduction of forming predictor composites when both validity and AI are of concern. In that case, other Pareto-optimal composites may provide a better benchmark to decide on the merits of the predictor composite formation. We summarize a method to determine the set of Pareto-optimal composites and apply the method to a representative collection of selection predictors. The application shows that the assessment of the AI and validity of predictor composite formation can differ substantially from the one arrived at when considering only regression-based composites. |
format |
text |
author |
DE CORTE, Wilfried LIEVENS, Filip SACKETT, Paul R. |
author_facet |
DE CORTE, Wilfried LIEVENS, Filip SACKETT, Paul R. |
author_sort |
DE CORTE, Wilfried |
title |
Validity and adverse impact potential of predictor composite formation |
title_short |
Validity and adverse impact potential of predictor composite formation |
title_full |
Validity and adverse impact potential of predictor composite formation |
title_fullStr |
Validity and adverse impact potential of predictor composite formation |
title_full_unstemmed |
Validity and adverse impact potential of predictor composite formation |
title_sort |
validity and adverse impact potential of predictor composite formation |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2008 |
url |
https://ink.library.smu.edu.sg/lkcsb_research/5630 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6629/viewcontent/Validity_Adverse_Impact_Potentian_PCF_sv.pdf |
_version_ |
1770574020483219456 |