I am building a model of certain conversational phenomena comparing across languages: e.g. would people in language A backchannel (for instance, say Hmm, or nod while the other person is speaking) more than in language B.
We have 4 conversations per each pair, 2 spontaneous ones, and 2 aimed at solving lab tasks (with the two tasks being quite different, but sharing the task-oriented nature). So I want to model
- backchannel as a function of conversation (Session, factor w 4 levels) as a function of task (factor w 2 levels), and both conversation and task as a function of language (factor w 2 levels).
- varying effects of speaker (interlocutor) nested within pair (each interlocutor appears only within one pair and speaks only one language)
- get estimates per each conversation and task (for practical reasons)
My basic (incorrect) model would be something like
Backchannel ~ 0 + Session : Language + Task:Language + (0 + Session + Task | Pair / Interlocutor)
Using a non-linear model I could transform it to a more proper:
bf(Backchannel ~ 0 + bSess * Session,
bSess ~ 0 + (bTask * Task) : (bLangSess * Language) + (1 | Pair / Interlocutor),
bTask ~ 0 + bLangTask * Language + (1 | Pair / Interlocutor),
bLangSess + bLangTask ~ 1,
nl = TRUE)
Where the coefficient of session is generated from task and language plus some variation, and the coefficient of task is also generated from language.
Does this seem correct conceptually?
Additionally, when trying to get the priors to be defined for this I get a
“Error: The parameter ‘bTask’ is not a valid distributional or non-linear parameter. Did you forget to set ‘nl = TRUE’?”
I’m probably committing a silly error, but I can’t really see it now.