Fitting Multilevel Models When Predictors and Group Effects Correlate

Bafumi, Joseph; Gelman, Andrew E.

Random effects models (that is, regressions with varying intercepts that are modeled with error) are avoided by some social scientists because of potential issues with bias and uncertainty estimates. Particularly, when one or more predictors correlate with the group or unit effects, a key Gauss-Markov assumption is violated and estimates are compromised. However, this problem can easily be solved by including the average of each individual-level predictors in the group-level regression. We explain the solution, demonstrate its effectiveness using simulations, show how it can be applied in some commonly-used statistical software, and discuss its potential for substantive modeling.



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March 12, 2010


Presented at the Annual Meeting of the Midwest Political Science Association, Chicago, April 20-23, 2006.