Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2010
Abstract
The paper proposes a procedure for designing Pareto-optimal selection systems considering validity, adverse impact and constraints on the number of predictors from a larger subset that can be included in an operational selection system. The procedure determines Pareto-optimal composites of a given maximum size thereby solving the dual task of identifying the predictors that will be included in the reduced set and determining the weights with which the retained predictors will be combined to the composite predictor. Compared with earlier proposals, the simultaneous consideration of both tasks makes it possible to combine several strategies for reducing adverse impact in a single procedure. In particular, the present approach allows integrating (a) investigating a large number of possible predictors (such as multitest battery of ability tests, or a collection of ability and nonability measures); (b) explicit predictor weighting within feasible test procedures of a given limited size.
Discipline
Organizational Behavior and Theory
Research Areas
Organisational Behaviour and Human Resources
Publication
International Journal of Selection and Assessment
Volume
18
Issue
3
First Page
260
Last Page
270
ISSN
0965-075X
Identifier
10.1111/j.1468-2389.2010.00509.x
Publisher
Wiley: 24 months
Citation
DE CORTE, Wilfred; SACKETT, Paul; and LIEVENS, Filip.
Selecting predictor subsets: Considering validity and adverse impact. (2010). International Journal of Selection and Assessment. 18, (3), 260-270.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5571
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1111/j.1468-2389.2010.00509.x