I ran an Anova using the car package.
A has 2 factor levels, B has 3 factor levels, and C has 2 factor levels (data from two data sets was included in the analysis. C is Experiment 1/2)
I found that there is a significant effect of A and B but no significant interaction. I now wanted to do post hoc comparisons among the levels of factor B separately for each level of factor A, which is of interest to me. I did another Anova for each level of factor A (y~B+C) and now want to do a post hoc comparison to see differences among the 3 levels of factor B under each condition. However, there is clear heteroscedasticity in the residuals, which I corrected for using the white.adjust argument in the Anova. Is there a post hoc test that can be used for heteroscedastic data in e.g. the emmeans package?
Should I rather use weighted regression (e.g. gls) to resolve the heteroscedasticity issue and then do e.g. tukey adjusted comparisons?
Any advice is much appreciated!