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A new approach to representing data from multiple regression designs is presented in this dissertation. The index, denoted as rsp, is the semi-partial 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 dissertation provide the partial effect size and its variance. Standard errors and confidence intervals can be computed for individual rsp values. Also, meta-analysis of the semi-partial correlations can proceed in a similar fashion to typical meta-analyses weighted analyses can be used to explore heterogeneity and to estimate central tendency and variation in the effects. A simulation study is presented to study the behavior of this index and its variance.