Criterion-Related Validity in Multiple-Hurdle Designs: Estimation and Bias
研究了多障碍选拔设计中效标关联效度的统计估计方法，提出了基于缺失数据算法的范围限制校正模型，对人力资源研究者有用。
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.