Bootstrap estimates of standard errors in validity generalization.

Abstract

Bootstrapping is introduced as a method for approximating the standard errors of validity generalization (VG) estimates. A Monte Carlo study was conducted to evaluate the accuracy of bootstrap validity-distribution parameter estimates, bootstrap standard error estimates, and nonparametric bootstrap confidence intervals. In the simulation study we manipulated the sample sizes per correlation coefficient, the number of coefficients per VG analysis, and the variance of the distribution of true correlation coefficients. The results indicate that the standard error estimates produced by the bootstrapping procedure were very accurate. It is recommended that the bootstrap standard-error estimates and confidence intervals be used in the interpretation of the results of VG analyses