# How do I interpret the random intercepts of a multilevel mixed-effects model?

I am using a three-level mixed-effects model in which:

1. individuals (level 1) are nested in
2. primary sampling units (PSUs) or enumeration areas (level 2) which in turn are nested in
3. countries (level 3).

Using Stata's xtmixed command I get fixed effects for all my variables in standard regression coefficients and random effects for countries and PSUs as a sum of variance and co-variance parameters.

Questions:

• If my random-effects parameters are for country (in SD): 0.192*** (SE 0.030) and for PSU (SD): 0.620*** (0.020), how do I interpret them?
• Should I just report what proportion of the variance is at the PSU level and at the country level?
• Do these random-effects parameters say anything about the fixed-effect regression coefficients?
• Can these parameters be used to predict the intercept of the DV?
• Looking at the random-effects parameters can I justify my mixed-effects model building?