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dc.contributor.authorSturman, Michael C.
dc.contributor.authorCheramie, Robin A.
dc.contributor.authorCashen, Luke H.
dc.description.abstractIt has been widely accepted that past performance is a good predictor of future performance. The exact strength of that relationship, however, has been unclear. Knowing the predictive power of past performance on future performance is particularly important for employers who make hiring decisions based in part on internal candidates’ performance record. Generally, some of the internal candidates’ performance would be measured at different points of time (e.g., 6 months, 12 months, and 24 months ago). Others under consideration will be external candidates, whose employment information is derived from selection devices such as structured interviews and intelligence tests. This paper uses a meta-analysis to examine 20 previously published studies on the stability of job performance over time. It provides an estimate of the relationship between existing performance measures and future performance, and models the nature of this relationship as a function of the elapsed time between measures. The findings show conclusively that, in general, past performance is, indeed, a good predictor of future performance for a variety of job types (i.e., exempt, nonexempt, and those that are evaluated subjectively). Using a hypothetical selection scenario, this report also demonstrates how that information can be used to compare multiple internal and external candidates.
dc.rightsRequired Publisher Statement: © Cornell University. This report may not be reproduced or distributed without the express permission of the publisher
dc.subjectjob performance
dc.subjectmotivated employees
dc.subjectmulti-unit hospitality companies
dc.subjectperformance change
dc.titleHow to Compare Apples to Oranges Balancing Internal Candidates’ Job-performance Data with External Candidates’ Selection-test Results
dc.description.legacydownloadsSturman_2002_Apples.pdf: 92 downloads, before Aug. 1, 2020.
local.authorAffiliationSturman, Michael C.: Cornell University

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