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Generalized linear mixed models are commonly employed to tackle a wide variety of data analysis problems in a multitude of settings. In these data settings understanding the statistical results can be a bigger issue than generating the numbers from the analysis in the first place. Communicating the results of an analysis can be a challenge as at times there is not a clear picture of what is going on and one may see different results between a simple aggregate analysis and the results of a regression analysis. The goal of this monograph is to bridge this gap and develop tools for understanding this sort of data analysis. This document addresses 3 aims: to review statistical methods for the analysis of count data in hierarchical settings; to examine graphical methods for presenting findings and evaluating confounding; and to illustrate these methods with actual data.