I've built a mixed effects logistic regression model using glmer().
I'm trying to measure clause transitivity (2 possibilities: transitive/intransitive)
Each observation is a clause and clauses are nested within households and, in turn, socio-economic status. So those are my random variables, which -I understand- should be nested.
My fixed effects are word order, speaker (adult, child), addressee (target child, other participant) and language.
The formula looks like this:
Transitivity ~ order + speaker + addressee + language + (1|household/ses).
As a result I'm getting convergence problems I can't solve. So I was wondering, on a theoretical level, if I could treat 'household' and 'ses' as two non-nested random effects (as below). Or perhaps I should drop household as a random effect (supposing that ses variability is greater than individual household variability)?
Transitivity ~ order + speaker + addressee + language + (1|household) + (1|ses).
Other important info:
Number of observations: 2207
number of households = 24 (4 of ses1, 8 of ses2, 12 of ses3)
numbers of ses = 3 (ses1: 659 clauses, ses2: 576 clauses, ses3: 972 clauses)
** Fixed effects
levels of word order = 3
levels of speaker = 2
levels of addressee = 2
levels of language = 3 (language 1: 1562 clauses, language 2: 256 clauses, language 3: 389 clauses)