Thank you for replying so quickly! 🙂
I try to explain my problem better.
I ran a mixed-design experiment with one between-subject factor (biological sex: two levels) and two within-subject factors (four levels each). I measured several continuous response variables, which I have already analysed with a standard anova, and a nominal (non-ordinal) categorical response variable. This response variable has five possible outcomes (which would be even more, if I used the raw data without any kind of grouping).
Statistical analysis of this categorical response variable is not crucial for the study. What is more, I have realised that the needed statistical tools go beyond my current knowledge. Basically, I am exploring the viability of such analysis. For this purpose, I was wondering whether and how I could adapt this line of code from your 2016 presentation
m2 <- mixed(resp2 ~ cond * validity * believability + (believability * validity|id) + (1|content),
d, family = binomial, method = "LRT")
when the reponse variable is categorical, non-ordinal, and non-binary.
Unfortunately, my knowledge of GLMMs is still superficial (cheeky understatement). It is not clear to me whether your answer implies that I can just replace resp2 in the code above with a response variable such as mine.
Francesco
