I was asked to do a correction of p-values for my analyses, but I'm not sure what p-values I am to correct, and presuming I'm using Bonferroni, what is the number that I am dividing the .05 level with. I have done three sets of multiple regression analyses, each with seven different time points for a total of 21 models. The method was backwards regression, independent variables remaining in the model at p < .05. The sets are comprised of the same group of independent variables entered, but the dependent variables differ (3 total x 7 time points). Due to the nature of backwards regression, obviously the final predictors in the model differ. Just to make the case clearer, by seven time points I mean the same variables have been measured at seven time points (i.e. A1, B1 to X1; A2, B2 to X2 etc).
So, what I really want to know is what is the correct way to go about this. Do I need to adjust the model ANOVA's p-values printed by SPSS, or the individual regression coefficients? Why this baffles me is the fact that in essence, a given time point's (potential) predictors are used for three different analyses. Then again, there are 7 actual regression analyses done for each set. This is of importance as some of the individual regression coefficients are close to .05 and if corrected, won't remain significant, whereas all the models even if corrected with what I think is the right level of alpha here, will remain significant.
Thank you in advance.