An Introduction to Multi-Level Models (Using Stata)

Multilevel models are a class of models that are used for data that are clustered into hierarchically organized groups, violating the conditional independence assumption about the error term from the classical linear regression model. For example, political data is frequently observed for individuals that are grouped by region or country, and there may be slightly different effects for individuals according to which region or country they are from. Multilevel models not only provide a means to correctly estimate causal models when data is hierarchically clustered, but also provide more direct ways to investigate the effects of different levels themselves on the causal process.