I have a data frame with different outcome measures (DV1:4) for participants some partcipant with additional IVs 1:2.
n=100;df <- data.frame(participant=1:n,DV1=rnorm(n),DV2=rnorm(n) ,DV3=rnorm(n),DV4=rnorm(n),IV1=rep(seq(1,2),n/2),IV1=rep(seq(2,1),n/2))
My initital thought was to use a
t.test() for the different contrasts and run some post-hoc correction for multiple comparisons. I might opt for something more liberal than bonferoni correction (
What are Hommel Hochberg corrections?)
While investigating this correction I found that (Does one need to adjust for multiple comparisons when using MANOVA?) It's not necessary to correct for multiple comparisons in a multivariate model as it does it implicitly.
What would be the best option then?, to do multiple t tests or construct a multivariate model and do posthoc comparisons (of cause correcting for multiple comparisons)
I surpose that I could still control the direction of the alternative hypothesis by way of planned contrasts. And that there are different assumptions of the multivariate model to keep in mind.
Sorry if this has been asked before and Thanks for reading.