Kathryn Campbell-Kibler wrote: > 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). > > ... > > lmer(intellect~speaker*ining*(pleasant_mood+mood_arousal)+(1|subject_id)+(1|recording), > data=whitenoise)
Hi Kathryn, One other point I neglected to mention. Technically it is not really correct to treat data on a 6-point scale with a linear model, because the error in your data cannot be normally distributed. This problem will probably be worst in cases where the predicted response rate is close to the extreme values, where the distribution is likely to be skewed. Ordinal regression would probably be the most natural approach, but the bad news is that I believe there is no current means within R to include mixed effects in an ordinal regression model. Best Roger -- Roger Levy Email: [EMAIL PROTECTED] Assistant Professor Phone: 858-534-7219 Department of Linguistics Fax: 858-534-4789 UC San Diego Web: http://ling.ucsd.edu/~rlevy _______________________________________________ R-lang mailing list [email protected] https://ling.ucsd.edu/mailman/listinfo.cgi/r-lang
