Hierarchical linear models using Stata

Some surveys collect data of individuals who are nested within hierarchical organizations or countries. These data are useful, for instance, for ranking countries according to a major outcome adjusted for covariates. Reporting only means produces rankings that are biased. So it is necessary to incorporate covariates and acknowledge the hierarchical structure of the data. From the perspective of ordinary regression, such structuring constitutes a statistical problem because it violates the assumption that observations are independent and identically distributed. In such a context, a hierarchical, or multilevel, linear model can be fit so that the hierarchical nature of the data is explicitly modeled. In this presentation, we will briefly discuss the strengths and limitations of hierarchical models for ranking countries.