A meta-analysis of 117 studies evaluated the effects of behavior modeling training (BMT) on 6 training outcomes, across characteristics of training design. BMT effects were largest for learning outcomes, smaller for job behavior, and smaller still for results outcomes. Although BMT effects on declarative knowledge decayed over time, training effects on skills and job behavior remained stable or even increased. Skill development was greatest when learning points were used and presented as rule codes and when training time was longest. Transfer was greatest when mixed (negative and positive) models were presented, when practice included trainee-generated scenarios, when trainees were instructed to set goals, when trainees' superiors were also trained, and when rewards and sanctions were instituted in trainees' work environments.

The standard Pearson correlation coefficient is a biased estimator of the true population correlation, rho, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, rc, has been recommended, and a standard formula based on asymptotic results for estimating its standard error is also available. In the present study, the bootstrap standard-error estimate is proposed as an alternative. Monte Carlo simulation studies involving both normal and nonnormal data were conducted to examine the empirical performance of the proposed procedure under different levels of rho, selection ratio, sample size, and truncation types. Results indicated that, with normal data, the bootstrap standard-error estimate is more accurate than the traditional estimate, particularly with small sample size. With nonnormal data, performance of both estimates depends critically on the distribution type. Furthermore, the bootstrap bias-corrected and accelerated interval consistently provided the most accurate coverage probability for rho.

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