I am doing final revisions to my dissertation, which includes a large number of regressions. I have six outcome variables (DVs) and I am testing the effect of four primary (correlated) IVs, but I am testing them separately to determine the unique contribution to the model of each IV. Therefore, I have 24 regressions.
One of my committee members would like me to use Bonferroni correction to control for Type I error; however, I am struggling with determining the number to use to determine the significance threshold. I guess the main thing I'm struggling with is that while I include these 24 regressions in my dissertation, any publication that is derived from my dissertation will not include all of these analyses, so setting the Bonferroni correction to .05/24=.002 seems (1) extremely conservative and (2) would be less conservative in a subsequent journal publication, as I would be correcting for fewer tests.
Are there other ways to approach determining the number of "tests" that alleviate Type I error concerns and make theoretical sense without being so conservative? I've been struggling to find some useful resources/articles for the last few days that account for a situation like mine, but most discussions of Bonferroni (or Holm-Bonferroni or other derivatives) focus on treatment of a single regression or analysis rather than a range of analyses across a dataset. I also realize that this is one of the biggest criticisms of Bonferroni--that the idea of constantly adjusting the alpha with each test would (in theory) require us to put off publishing anything until we had exhausted a given dataset.
Any links to useful articles or advice on how to approach my current problem would be greatly appreciated!