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Designing pareto-optimal systems for complex selection decisions

Celina Druart and Wilfried De Corte UGent (2011) 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts.
abstract
Complex selection decisions handle situation involving multiple jobs and an applicant pool where some of the applicants simultaneously apply for several of the available jobs. Practitioners and researchers interested in the outcomes of such complex selection decisions, based on specific predictors and their characteristics, are provided with two decision aids for designing predictor composites that offer a Pareto-optimal balance between the goals of quality and work force diversity. As shown in the illustrative applications, these developments provide a coherent set of tools for addressing both practical and substantive issues related to the selection uality/adverse impact quandary.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Personnel selection, I-O Psychology, Complex selection systems
in
26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts
publisher
Ghent University, Department of Data Analysis
place of publication
Ghent, Belgium
conference name
26th Annual Society for Industrial and Organizational Psychology Conference (SIOP)
conference location
Chicago, IL, USA
conference start
2011-04-14
conference end
2011-04-16
language
English
UGent publication?
yes
classification
C3
copyright statement
I have retained and own the full copyright for this publication
id
1220776
handle
http://hdl.handle.net/1854/LU-1220776
date created
2011-05-10 14:41:02
date last changed
2017-01-02 09:53:00
@inproceedings{1220776,
  abstract     = {Complex selection decisions handle situation involving multiple jobs and an applicant pool where some of the applicants simultaneously apply for several of the available jobs. Practitioners and researchers interested in the outcomes of such complex selection decisions, based on specific predictors and their characteristics, are provided with two decision aids for designing predictor composites that offer a Pareto-optimal balance between the goals of quality and work force diversity. As shown in the illustrative applications, these developments provide a coherent set of tools for addressing both practical and substantive issues related to the selection uality/adverse impact quandary.},
  author       = {Druart, Celina and De Corte, Wilfried},
  booktitle    = {26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts},
  keyword      = {Personnel selection,I-O Psychology,Complex selection systems},
  language     = {eng},
  location     = {Chicago, IL, USA},
  publisher    = {Ghent University, Department of Data Analysis},
  title        = {Designing pareto-optimal systems for complex selection decisions},
  year         = {2011},
}

Chicago
Druart, Celina, and Wilfried De Corte. 2011. “Designing Pareto-optimal Systems for Complex Selection Decisions.” In 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts. Ghent, Belgium: Ghent University, Department of Data Analysis.
APA
Druart, Celina, & De Corte, W. (2011). Designing pareto-optimal systems for complex selection decisions. 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts. Presented at the 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Ghent, Belgium: Ghent University, Department of Data Analysis.
Vancouver
1.
Druart C, De Corte W. Designing pareto-optimal systems for complex selection decisions. 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts. Ghent, Belgium: Ghent University, Department of Data Analysis; 2011.
MLA
Druart, Celina, and Wilfried De Corte. “Designing Pareto-optimal Systems for Complex Selection Decisions.” 26th Annual Society for Industrial and Organizational Psychology Conference (SIOP), Abstracts. Ghent, Belgium: Ghent University, Department of Data Analysis, 2011. Print.