Hi Everyone!
I am conducting a mixed model logistic regression analysis
of some simple experimental data in which
participants made dichotomous choices. My independent
variable has two levels. What I am really
interested in is the ability of the independent variable
to predict the dependent variable, with the
random effects in there simply to allow the best test of this.
I am starting from the point of view that I should ?keep it maximal?.
That being said my two questions are:
* Is there something horrible about keeping in
random effects that are highly (or perfectly!) correlated
with one another?
* If so?would you retain the term that gives you
the best model, based on AIC or some other value?
* Should you always remove random effects that have
0 variance associated with them?
It seems like the intuitive answer to both of these is yes.
But in this paper I am working on, there are several
experiments, and for simplicity?s sake it would be much easier
to always just have the maximally
complex random effects structure that managed to converge,
instead of giving details in each case about
which terms were removed and why. But is there something
horrible wrong with that??