The t values here, expressed as F values, are 2.7782 = 7.717 and 2.4212 = 5.861. For the multiple R2 and each of the predictors, I enter into the .sav file the values for F and degrees of freedom and ran the syntax. In the r2 column, first row, is the R2 for the overall model. For each predictor in that same column is the pr2 for that predictor. Coefficientsa Correlations Model Partial Part 1 (Constant) GRE_Q .472 .384 GRE_V .422 .334 The squared partial correlations are .4722 = .2228 for and .4222 = .1781 for GRE_V, within rounding error of the values in the r2 column in the .sav file. The confidence intervals for the predictors in that file are confidence intervals for pr2. Jiah Yoo, a doctoral student in Social Psychology and Personality at the University of Wisconsin, wrote I think eta squared (aka the squared semipartial correlation coefficient) might be a “, better indicator of the effect size. Would there be a way to obtain a CI for squared semipartial correlation? I reported the squared semipartial correlation as the effect size in my paper and one of reviewers asked for its CI. I with Jiah, I generally prefer the semipartial to the partial. I probably could modify Smithson’s SPSS syntax to get confidence intervals for the semipartial, but I am not motivated to do so since the solution is already available with SAS’ GLM procedure, and the syntax is very easy. Here I bring into SAS the same SPSS data set used above. Then I submit this code: proc GLM; model GPA =GRE_Q GRE_V / ss3 EFFECTSIZE alpha=0.1; run; quit; Here is part of the output: Proportion of Variation Accounted for Eta-Square 0.49 Omega-Square 0.44 90% Confidence Limits (0.22,0.61) The total model R2 is .49, with a confidence interval running from .22 to .61 Source DF Type III SS Mean F Pr > F Variation Accounted For Partial Variation Accounted For Square Value Semipartial Semipartial Conservative Partial Partial 90% Eta-Square 90% Confidence Limits
The t values here, expressed as F values, are 2.7782 = 7.717 and 2.4212 = 5.861. For the multiple R2 and each of the predictors, I enter into the .sav file the values for F and degrees of freedom and ran the syntax. In the r2 column, first row, is the R2 for the overall model. For each predictor in that same column is the pr2 for that predictor. Coefficientsa Model Correlations Partial Part 1 (Constant) GRE_Q .472 .384 GRE_V .422 .334 The squared partial correlations are .4722 = .2228 for and .4222 = .1781 for GRE_V, within rounding error of the values in the r2 column in the .sav file. The confidence intervals for the predictors in that file are confidence intervals for pr2. Jiah Yoo, a doctoral student in Social Psychology and Personality at the University of Wisconsin, wrote “, I think eta squared (aka the squared semipartial correlation coefficient) might be a better indicator of the effect size. Would there be a way to obtain a CI for squared semipartial correlation? I reported the squared semipartial correlation as the effect size in my paper and one of reviewers asked for its CI. I with Jiah, I generally prefer the semipartial to the partial. I probably could modify Smithson’s SPSS syntax to get confidence intervals for the semipartial, but I am not motivated to do so since the solution is already available with SAS’ GLM procedure, and the syntax is very easy. Here I bring into SAS the same SPSS data set used above. Then I submit this code: proc GLM; model GPA =GRE_Q GRE_V / ss3 EFFECTSIZE alpha=0.1; run; quit; Here is part of the output: Proportion of Variation Accounted for Eta-Square 0.49 Omega-Square 0.44 90% Confidence Limits (0.22,0.61) The total model R2 is .49, with a confidence interval running from .22 to .61 Source DF Type III SS Mean Square F Value Pr > F Variation Accounted For Partial Variation Accounted For Semipartial Eta-Square Semipartial Conservative 90% Partial Partial 90% Confidence Limits
CI-R2-SPSS.docx