A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model and represents a partial effect size in the correlation family. The derivations presented in this article provide the effect size and its variance. Standard errors and confidence intervals can be computed for individual r[subscript sp] values. Also, meta-analysis of the semipartial correlations can proceed in a similar fashion to typical meta-analyses, where weighted analyses can be used to explore heterogeneity and to estimate central tendency and variation in the effects. The authors provide an example from a meta-analysis of studies of the relationship of teacher verbal ability to school outcomes. (Contains 3 tables and 2 figures.)