Theory and Practice for the use of Cut-Scores for Personnel Decisions

David T. Chuang, James J. Chen and Melvin R. Novick

Journal of Educational and Behavioral Statistics, 1981, vol. 6, issue 2, 129-152

Abstract: Cut-scores are commonly used in industrial personnel selection, academic selection, minimum competence certification testing, and professional licensing, using simple and multiple-person/multiple-job category decision paradigms. Previous approaches have proposed cut-score solutions in a variety of applications using threshold, normal ogive, linear and discrete utility functions. This paper considers these results by investigating conditions on the posterior, likelihood and utility functions required for setting a cut-score in a Bayesian decision approach. Generalizing and extending results of Lehmann, Karlin, Ferguson and others, it is shown that cut-scores are appropriate in a wide range of applications, but they are less than universally appropriate. Following this, a general paradigm and computational algorithm for cut-score solutions is developed under the assumption that the conditions for a cut-score have been satisfied.

Keywords: Cut scores; Monotone utility; Monotone likelihood ratio; Monotone posterior ratio; Stochastically increasing; Quota-free selection; Restricted selection (search for similar items in EconPapers)
Date: 1981
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:6:y:1981:i:2:p:129-152

DOI: 10.3102/10769986006002129

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