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Adding a second predictor to selection systems may not always increase validity. Alternative methods like unit weighting or multiple hurdles can diminish or negate the added predictive power of a second predictor.

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Area of Science:

  • Industrial-Organizational Psychology
  • Human Resources Management
  • Quantitative Psychology

Background:

  • Criterion-related validity is often enhanced by adding predictors to selection systems.
  • Research typically assumes regression-weighted composites for evaluating incremental validity.
  • Practical applications frequently deviate from regression-weighted composites, using unit weighting or multiple hurdles instead.

Purpose of the Study:

  • To examine the robustness of incremental validity findings when predictors are used via unit weighting or multiple hurdles.
  • To investigate conditions where alternative predictor usage methods negate the gains from regression weighting.
  • To re-evaluate existing findings on the incremental validity of a second predictor over cognitive ability under different weighting schemes.

Main Methods:

  • Analyzing conditions under which unit weighting reduces predictive power compared to regression weights.
  • Revisiting and re-analyzing data from Schmidt and Hunter (1998) using unit weighting.
  • Examining published literature for the frequency of unit weighting's impact on predictor value conclusions.
  • Analyzing multiple hurdle selection as an alternative to regression weighting.

Main Results:

  • Unit weighting can negate or even reduce predictive validity compared to regression weighting.
  • The incremental validity of a second predictor can disappear or become negative under unit weighting.
  • Conclusions about the value of a second predictor may differ significantly when using unit weighting or multiple hurdles instead of regression weighting.

Conclusions:

  • The assumption of regression weighting is critical for accurately assessing incremental validity.
  • Alternative predictor usage methods like unit weighting and multiple hurdles can lead to different conclusions about predictor utility.
  • Practitioners should carefully consider the method of predictor combination when evaluating the value of additional selection tools.