But I recently realized something. Most of the variables that I've
tested as fixed effects are properties of the subject (e.g. Race,
Gender, etc.). Is it correct to be using a random effect
Subject that is nested within (partially-crossed) fixed effects
like Gender and Race?
Yes. I
I have a large dataset where each Subject answered seven similar
Items, which are binary yes/no questions. So I've always used Subject
and Item random effects in my models, fit with lmer(), e.g.:
model-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1|
Item_ID),data,binomial)
But I recently
