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Organizational Research Methods
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Criterion-Related Validity in Multiple-Hurdle Designs: Estimation and Bias

Jorge L. Mendoza

David E. Bard

Michael D. Mumford

Siew C. Ang

University of Oklahoma

Employee selection often involves a series of sequential tests (or hurdles). However, validation strategies under this complex design are not found in the literature. Missing is a discussion of the statistical properties important in establishing criterion-related validity in multiple-hurdle designs. The authors address this gap in the literature by suggesting a general statistical model for range restriction corrections. Because the multiple-hurdle design includes as special cases predictive and concurrent designs, the corrections apply also to these designs. The general correction model is based on algorithms from the missing data literature. Two missing data procedures are examined: the estimation-maximization procedure and the Bayesian multiple imputation (MI) procedure. These procedures are large-sample equivalent and often yield similar results. The MI procedure, however, has the added advantage of providing easily obtainable standard errors. A hypothetical example of a multiple-hurdle design is used to illustrate the procedures.

Key Words: selection • range restriction • multiple-hurdle design • missing data • corrections

Organizational Research Methods, Vol. 7, No. 4, 418-441 (2004)
DOI: 10.1177/1094428104268752
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