I'm rephrasing a question I asked before - any input would be hugely appreciated, as I haven't found similar examples online.
Consider a design where random effect 'a' is fully crossed with random effect 'b' and random effect 'c' is fully crossed with random effect 'd'. Random effect 'c' is nested within a x b combinations.
'a' are language editors, 'b' are sentences to be edited, 'c' are the edited versions of these sentences (i.e. edited by 'a'), and 'd' are judges who provided quality scores on 'c', the edited versions. Each instance of 'c' received a different ID in the data, though they may turn out to be identical (i.e. in case editors edit the same instance of 'b' in the same way). The DV is quality scores related to 'c' (provided by 'd') and predictors relate to 'a' and 'b'.
My questions are: - Do I need 'c' as a random effect in the model, when each instance of 'c' is already represented by a x b combinations? - Is it OK to give different IDs to each 'c' even when there's the possibility of these being identical (i.e. if the same 'b' happens to be edited in the same way by different editors)?