This question already has an answer here:
Per reviewer request, I need to do power analysis for a logistic regression model with multiple dummy variables. I have four groups: Control, (Treatment) A, B, and C. The hypothesis is that group A and B do NOT differ from the control group, but group C does. I tested this hypothesis by running a logistic regression model with 3 dummy variables with the control group being the baseline group, along with 3 control variables.
The results supported the hypothesis, but one of the reviewers recommended conducting power analysis, saying that the non-significant differences between control and group A, and between control and group B might be a result of lack of power.
I tried to conduct power analysis using G*Power, but because there are no true values of Pr(Y=1|X=1)H0 and Pr(Y=1|X=1)H1 as I'm dealing with political perception as the dependent variable, I couldn't do the analysis.
I found that power analysis simulation using R could be a solution, but unfortunately I don't have enough knowledge about R to adapt the code provide here (Simulation of logistic regression power analysis - designed experiments) to my study design.
Any help or suggestions would be greatly appreciated.
p.s.) I am just wondering what follows is a valid alternative method: - A meta-analysis study suggested that the mean correlation of the effect in question is about 5. - Use the value to calculate R2 and use the R2 value to calculate Cohen's f2. - Estimate effect size (i.e., small, medium, large) based on the f2 value. - If, for example, the effect size is large, plug .40 in the "Effect size f" box in G*Power and select ANCOVA to calculate power.