G* Power does not provide any direct application for estimating sample size requirements or statistical power for partial or semipartial (“part”) correlation analysis. Various workarounds are suggested, ranging from the impossibly complex to simply ignoring the presence of the covariates and using the same G*Power app that is used for Pearson correlation. The procedure I favor is to use the G*Power app for estimating sample size (in an a prior power analysis) or power (in a post hoc power analysis) for test of the significance of the individual predictors in multiple regression analysis. This approach is based on the fact that the tests of significance of the individual predictors in SPSS multiple regression are also the tests of the significance of the related partial and semipartial correlations.
Here is the G*Power procedure:
Tests > Correlation and regression > Linear multiple regression: Fixed model, single regression coefficient
Type of power analysis: a priorI
Tail(s): chose one-tail if you’ve predicted the sign of the partial or semipartial correlation or chose two-tail if you’re equally interested in a correlation in either direction
Effect size f-squared: Use .02 for a weak population effect, .15 for a medium strength effect, or .35 for a strong population effect
Alpha err prob: your chosen level of significance, typically .05
Power (1 – beta err prob): your chosen level of statistical power, typically .80 which gives you a Type II error probability of .20
Number of predictors: set equal to the total number of variables in the analysis (X, Y, and all covariates) minus one.
Then click “Calculate”