Dear all,

I have unbalanced panel data with repeated sampling over 6 years, hence I am employing mixed linear regressions. I want to incorporate various covariates, which differ either at level 1 (observation level) or level 2 (individual level). I want to assess the effect of reading on cognitive test scores in later life. My level 1 predictors are, among others, age, wealthgroup, and the spread between observations due to the unbalanced nature. The level 1 predictors may change over time within each individual. Mylevel 2 predictors are constant for each individual and are education level and gender. My code for the mixed model with the random intercept is

Code:
mixed testscore reading age wealthgroup spread education gender || mergeid_num:,
I now also want to add random slopes. My first indication was to set the spread between observations as a random slope with:

Code:
mixed testscore reading age wealthgroup spread education gender || mergeid_num: spread, covariance(unstructured)
Adding the spread as a random slope significantly improves the fit of my model. However, my question now is whether it is possible (and whether it makes sense) to add all level 1 predictors as random slopes. I have tried the following code, but even after an hour of computation time, no output was given

Code:
mixed testscore reading age wealthgroup spread education gender || mergeid_num: spread age wealthgroup, covariance(unstructured)
1. Is it possible to add multiple random slopes?
2. Does it make sense to add multiple slopes?
3. If I cannot/should not add multiple random slopes, how do I deal with the fact that my level 1 predictors vary over time?

Thanks all!