Correlations in a Multilevel Model I'm currently running a daily diary study, where participants first complete a baseline survey and then complete the same survey each day for 10 days. My data has a nested structure (days nested within persons), where the measures assessed in the daily surveys are at level 1 (within-person level) and the measures assessed in the baseline survey are at level 2 (between-person level). I'm trying to identify which variables to control for in my multilevel regressions. I understand that we generally want to control for variables that correlate with the predictors and outcome in the model. I want to assess if I should control for any level 2 variables in my model (e.g., gender, age, ethnicity, etc.) but I am unsure how to obtain correlations between level 2 and level 1 variables. I was wondering if anyone may have an idea of something I could try?

To my knowledge, there is no clear-cut way of obtaining basic correlations between L1 and L2 variables. You can run preliminary multilevel regression(s) with your outcome as the dependent and your L2 potential covariate(s) as predictors to see if they are related. But I don't see a problem in just adding the covariates that are theoretically meaningful into your main model. One way to analyze and report data like this is to run and report several models, for instance one with no covariates, one with demographic covariates, and one with demographic + other possible covariates, and report all of these in parallel in the same table.
