Hi everyone.

When running a GLMM, I need to turn the data from wide format to the long format (stacked).

When checking for assumptions like normality, do I check them for the stacked variable (e.g., outcomemeasure_time) or for each variable separately (e.g., outcomemeasure_baseline, outcomemeasure_posttest, outcomemeasure_followup)?

Also, when identifying covariates via correlations (Pearson's or Spearman's), do I use the seperate variables or the stacked one?

Normality: say the outcomemeasure_baseline normality is violated but normality for the others weren't (ouecomemeasure_posttest and outcomemeasure_followup). Normality for the stacked variable is also not violated. In this case when running the GLMM, do I adjust for normality violations because normality for one of the seperate measures was violated?

Covariates: say age was identified as a covariate for outcomemeasure_baseline but not the others (separately: ouecomemeasure_posttest and outcomemeasure_followup OR the stacked variable). In this case, do I include age as a covariate since it was identified as one for one of the seperate variables?

Thank you so much in advance!