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Using order statistics to assess the sampling variability of personnel selection utility estimates

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  • Wilfried De Corte

Abstract

Virtually all models for the utility of personnel selection are based on the average criterion score of the predictor selected applicants. This paper indicates how standard results from the theory on order statistics can be used to determine the expected value, the standard error and the sampling distribution of the average criterion score statistic when a finite number of employees is selected. Exact as well as approximate results are derived and it is shown how these results can be used to construct intervals that will contain, with a given probability 1 - f , the average criterion score associated with a particular implementation of the personnel selection. These interval estimates are particularly helpful to the selection practitioner because they can be used to state the confidence level with which the selection payoff will be above a specific value. In addition, for most realistic selection scenarios, it is found that the corresponding utility interval estimate is quite large. For situations in which multiple selections are performed over time, the utility intervals are, however, smaller.

Suggested Citation

  • Wilfried De Corte, 2000. "Using order statistics to assess the sampling variability of personnel selection utility estimates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 703-713.
    • Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:703-713
    DOI: 10.1080/02664760050081889
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    1. Cronshaw, Steven F. & Alexander, Ralph A. & Wiesner, Willi H. & Barrick, Murray R., 1987. "Incorporating risk into selection utility: Two models for sensitivity analysis and risk simulation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 40(2), pages 270-286, October.
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    Cited by:

    1. Oliver Fabel & Razvan Pascalau, 2007. "Recruitment of Overeducated Personnel: Insider-Outsider Effects on Fair Employee Selection Practices," TWI Research Paper Series 18, Thurgauer Wirtschaftsinstitut, Universität Konstanz.

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