Dear all,


I have a three-level cross-country survey data that is not panel. The levels are country, survey, and individual respondents. Ten countries and six years.
My dependent variable is binary variable. I am interested in interaction between the two variables one of which is on individual level and the other one is on country level.

Given a relatively low number of clusters, what would be the best model to choose:
  • a multilevel model with random effects for two upper levels: country and year? Perhaps there is a correction on a small number of clusters that I could include? (aka Kenward-Roger correction - I was unable find a version of the same correction for non-linear models)
meprobit y x1##x2 i.gender age [pweight = dweight] || country: || election:
  • a fixed effects model with clustered errors - if so, should the error be clustered on a country level only?
probit y x1##x2 i.gender age i.year i.country [pweight = dweight], cluster(country)
  • or a fixed effects model w/o clustered errors, since with only 10 countries the appropriateness of using cluster(country) is questionable as well.
probit y x1##x2 i.gender age i.year i.country [pweight = dweight], robust



I am really looking forward to any advice and literature suggestions.



Maria