- Thread starter LucieR
- Start date

Maybe I read or heard somewhere that power analysis' are often a simplification of the model that generates the data. As you start getting in the weeds with more complicated models, a power analysis for that specific model is harder to calculate. So, you do your best with what's tractable.

If you have specific questions we'd be more than happy to help but without specific details that's about all I can say.

Could this be an alternative option - https://jakewestfall.shinyapps.io/two_factor_power/? The only issue is that you need the variance estimates to calculate the VPC which I can only get post-hoc. Is there any way around this so that I can determine the appropriate sample size using this app beforehand?

Could this be an alternative option - https://jakewestfall.shinyapps.io/two_factor_power/? The only issue is that you need the variance estimates to calculate the VPC which I can only get post-hoc. Is there any way around this so that I can determine the appropriate sample size using this app beforehand?

@Jake think you could help here?

Yes, all my mixed model power apps are for all categorical predictors only. Not only that, but they are for balanced, orthogonal designs. Those are the only special cases in which the math works out simply enough that you can get easy analytic power calculations. If you're dealing with continuous predictors and especially correlated continuous predictors, then I recommend simulation.

Yes, all my mixed model power apps are for all categorical predictors only. Not only that, but they are for balanced, orthogonal designs. Those are the only special cases in which the math works out simply enough that you can get easy analytic power calculations. If you're dealing with continuous predictors and especially correlated continuous predictors, then I recommend simulation.

I'm literally just spitballling and do think pure simulation is the ideal approach though.

With that said it might be interesting to see how different the results are between the full simulation compared to splitting the continuous predictor into a binary variable at least for the case of power or sample size estimation.

That approximation strategy is just for the case of a single predictor. Like I hinted earlier, things are much more difficult with multiple predictors, especially if they are correlated, for reasons I won't list here but which I'm sure you can have fun imagining.

(4) Power Calculation for a Multilevel Linear Regression | Statistics Help @ Talk Stats Forum