[R-sig-ME] Adding Level for non-repeated measurements
D@v|d@Du||y @end|ng |rom q|mrbergho|er@edu@@u
Fri Mar 19 08:57:37 CET 2021
> I have a cross-sectional (i.e., non-repeated measurements) dataset from
> students ("stud_id") nested within many schools ("sch_id").
> 1- Given above, should we possibly add an additional random-effect for
> "stud_id"? If yes, why?
> 2- Given above, should we also allow residuals in each school (e_ij) to
> correlate? If yes, why? (I have a bit of a conceptual problem understanding
> this part given the cross-sectional nature of our study.)
I think this is more a slightly-harder-than-elementary stats question rather than a "technical" query. If this was some types of
GLMM, then the answer to 1 would be yes eg poisson GLMM then an individual-specific random effect adds in one type of
extra-poisson variation. This is not the case for the gaussian (hopefully you see why). As to 2, consider how the *variance* of your
measurement could be different within each school.
More information about the R-sig-mixed-models