A priori Power Analysis

Hey Guys,

I am a newbie and have a question on power analysis.

I would like to measure the effect of an intervention on various measures and have several control variables. I believe it is the best to use a multiple linear regression model with a dummy variable for the treatment.

Therefore, I have a linear model y = beta_0 + beta_1 * x_1 + ... beta_k * x_k and would like to calculate the required sample size. I have 3 independent variables (one of them is the treatment dummy, and two variables for moderating effects) plus a few control variables. The hypotheses I would like to test are whether some of the betas are larger than 0. The estimated effect size is weak to medium (suggested Cohen's d of around 0.4).

I am using G*Power and don't know which option to choose for the linear multiple regression. These are the following options:

1) t-Test: Fixed model, single regression coefficient
2) F-Test: Fixed model, R2 deviation from zero
3) F-Test: Fixed model, R2 increase
4) Exact: Random model

Can anybody explain the differences to me? Which option should I choose? The calculated sample sizes are very different depending on the option I choose...

I appreciate your help!
Last edited:

thanks for your reply.

Just two more questions/points:

The power analysis is a priori to calculate the required sample. Depending on the model I choose, the sample can be around 30-40 or even 150. I can't just gather all data first and then check whether 30-40 would have been enough. Therefore, I need to run the analysis first, but don't know which model to choose.

Concerning random/fixed effects I am bit confused, because I do not have panel data. My data will only be measured once. Hence, I think I don't know in which way to distinguish between the two options?