I'm looking for an example of a data set that can be fitted using a GLMM with at least 3 separate random effects. I've taken a look at a few books on GLMM's including the one by McCulloch and Searle, but their examples always stop at 2 random effects. I don't know why I'm having so much trouble wrapping my head around a practical example of 3 or more random effects but I am.
In the national youth tobacco survey for example, counties were the PSU's. Within sampled counties, schools were then randomly selected along with students. So if I understand it correctly it would make sense to perhaps treat both counties and schools as random. I suppose if they sampled classrooms within each school we could then have 3 nested random effects - one for each of county, school and classroom. Is that correct?
Any help would be greatly appreciated.