title:         Fitting Multilevel Models When Predictors and Group Effects Correlate
authors:       Joseph Bafumi
entrydate:     2006-04-27 15:39:36
keywords:      Multilevel models, random effects, fixed effects, unit effects, group effects, Gauss-Markov
abstract:      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.

http://polmeth.wustl.edu/retrieve.php?id=594

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