Selecting predictor subsets: Considering validity and adverse impact
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 m...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/5571 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6570/viewcontent/subset.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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