Hi all, I've recently been exploring beyond my established comfort zone with mixed models, and am looking for some correction or reassurance. I am working with experimental data on social perceptions of linguistic variation. I've got two types of dependent variables: ratings on a 6 point scale (e.g. not at all intelligent-very intelligent), which I've been treating as linear variables and binary variables, based on whether a given term was selected as a good description of a speaker (e.g. hardworking).
The independent variables (well, some of them) were: speaker (8) recording (nested, 4 for each speaker) -- which recording was being responded to (ING) (3) -- crossed with recording, indicates which guise of the variable (ING) was used (e.g. working or workin') two measures of listener mood pleasant and arousal The structure of the experiment was such that every subject heard one recording (which represented also one (ING) guise) from each speaker. In the past with similar data, I have been using nlme for linear mixed models, and using subject id as a random effect. (ING) effects and the interaction of (ING) with the other variables, such as speaker, is the main point of interest. I have two questions. 1) Is it more appropriate to build in both subject id and the recording choice as random effects, rather than only including just the subject id? I am treating speakers as fixed effects, deliberately-- I have no expectation that these particular speakers are representative of anyone except themselves. But the recordings within each speaker were randomly assigned to listeners. 2) When doing an analysis of the binary variables, how can I tell whether overdispersion and/or zero-inflation is an issue for me? Bringing these two questions together, I have been looking at using lmer for both the "linear" and the binary variables, with something like these: lmer(intellect~speaker*ining*(pleasant_mood+mood_arousal)+(1|subject_id)+(1|recording), data=whitenoise) lmer(hardworking~speaker*ining*(pleasant_mood+mood_arousal)+(1|subject_id)+(1|recording), family = binomial, data=whitenoise, method="AGQ") Does this make sense, do I need the "recording" term? And how can I determine if I need to be concerned about zero-inflation and if so, is glmmADMB my only option for the binary variables (a pain, since I mostly use Macs)? Many thanks, Kathryn _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
