Say I'm using GPower to calculate the number of participants needed in a multiple regression to detect one of the predictor's unique effect. I want to be able to detect a correlation of .3. What you typically do is select:
- Test family: F tests
- Statistical test: Fixed model, R2 increase
- Type of power analysis: A priori (but I guess for my question, it really doesn't matter)
GPower ask for an effect size (f2), and has a tool to convert R2 to f2. In the toolbox, you can select a "Direct" input, which is partial R2. So from what I understand, GPower uses partial correlation has an input.
Is it strictly for partial correlation, or is it OK to use this for part correlation as well? I know both return the same p-value (it's just two different beta-to-correlation transformation), so I'm not even sure if it is relevant at all.
Thanks in advance for any insights on this question!
The partial correlation does not involve the control of a third variable. As far as I know, R2 is the value of the correlation or prediction squared.
I think Gpower needs a partial correlation specifically, but it will fit both a correlation and a partial correlation. For instance, the best scenario is partial correlation = correlation (other predictors have no effects), and the worst is partial correlation
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