PSYC 394 Lecture Notes - Lecture 3: Meta-Analysis, Homoscedasticity, Standard Deviation
Document Summary
* includes theory, computational, and result interpretation questions (in apa format) Semi-partial correlation measures the relation between a predictor and outcome, controlling for the relation between that predictor and any others already in the model. In other words, it measures the unique contribution of a predictor in explaining the variance of the outcome. Variance in y ea ba aa ca da. R2 value represents the shared vari- ance between x and y for a linear regression. Using the model for multiple regression, an r2 value between y and x1 would be determined by dividing the total variance of y by that which it shares with x1 (the predictor variable): R2 = shared variance / total variance = (a + c) / (a + b + c + e) In semi-partial correlation, c would be removed to isolate only variance that is not shared by other predictor variables: Semi-partial x1 = a / (a + b + c + e)
