Imagine the following model:
lmer(DV ~ Control Variable A + Control Variable B + Control Variable C + Variable of Interest A + Variable of Interest B + Variable of Interest C + Variable of Interest A x Variable of Interest B + Variable of Interest A x Variable of Interest C + (1|Subject) + (1|Item)
(I only included random intercepts for the purpose of the example.)
If a model with all possible random slopes does not converge, what's the best way to decide which random slopes to include. Should it only be based on theory, and which effects are more likely to vary by subject/item?
Edit: I am fitting an
lmer() model using
lme4. I know that might make this seem like a stackoverflow issue, but I'm really interested in the theory of how to decide which random slopes to include when I can't include all of them (as Barr er al., 2013 recommend). It is a linear mixed effects model for data in which participants responded to various stimuli. Thus the model includes random subject and item intercepts. The variables of interest were manipulated within subjects but not within items. There are 120 observations per participant. I used whatever optimized is the default for lmer().