I have two data sets collected from two different sets of participants on their behaviour. EDIT: Both have the same response variables (Propensity to behave in a certain way - Yes or No). But they have some common (sex, age) and some different explanatory variables (education, origin etc) because they were two different surveys and two different set of participants. I have fitted generalised linear mixed effects models (GLMM) with participant ID as random effect separately for these data sets.
Why I would like to apply model averaging here is because I would like to take all the available information into my model from both data sets rather than fitting separate models for the two data sets.
Can I apply model averaging for the best Generalised Linear Mixed-effects models selected from the two different data sets or is it applied only for nested models?
Edit: What I mean by model averaging is averaging across model parameters instead of selecting one best model so that the model uncertainty issue can be addressed. (Eg: AIC and BIC-based model averaging - where weights are applied to the parameters, (Bates and Granger 1969), Bayesian model averaging (BMA) (Raftery et al. 1997))