I attended a training class from SAS about experimental design in marketing. They advocated the use of their GLMPOWER proc for power analysis for designing experiments.
GLMPOWER is a power analysis procedure for ascertaining the required sample size for a general linear model (main effects, interactions and/or specific contrasts between factor levels).
For this class, interest rests on designing experiments with regards to binary outcomes (response or no response) analyzed using logistic regression. Given the large sample sizes typically witnessed in marketing, they said an approximation to this problem could be handled by GLMPOWER, which assumes the response is a Gaussian distributed continuous variable.
One of the parameters in the GLMPOWER procedure is a standard deviation STDDEV defined as "the error standard deviation, or root MSE" of the model being postulated.
This error standard deviation is not the same as the standard deviation of "Y" is it? They state that it is.
Further, after stating this, they use SQRT(p(1-p)) as an estimate of this value, where p is the pooled response rate (number of responses / number of attempts) of the experiment. Where does this come from and does it sound like the right value to use?
It was well stated that these are approximations only given the assumptions of GLMPOWER for a binary outcome, but can anyone help with the reasoning?