Multilevel Analysis Essay ⋆ Political Science Essay Examples ⋆ EssayEmpire

Multilevel models are extensions to regression methods designed to explicitly take into account the grouped (or clustered) structure frequently seen in observed and experimental data. Examples of such data in political science abound. In voting research, for instance, voters are nested within districts, which themselves may be nested within states. In comparative political economy, country-years are nested within countries. The hierarchical structure of data such as these is one reason why multilevel models are also known as hierarchical models. It is important, however, to note that multilevel models can also have non-nested grouping factors (e.g., individuals may belong to parties that cut across districts). Other names for multilevel models include random effects models, random coefficient models, and mixed effects models.

The fundamental idea of multilevel modeling is that individual observations in any particular group or cluster are more similar to each other than observations across groups. Furthermore, even though one might include covariates in a model that explain variation across clusters, they are unlikely to explain all such variation. When this happens, correlation in the error structure is introduced. Multilevel modeling is one way to take into account this correlation while still allowing for explanatory variables at the different levels of the model. When using multilevel models in practice, it is important to understand that many assumptions underlie the method, and they should be taken into consideration when constructing and interpreting these models.

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