Hierarchical, correlated data structure: A hierarchical linear models (HLM) approach (Raudenbush and Bryk, 2002) will be used to account for the correlation induced by repeated measurements over time for students nested within schools. School will be modeled as a random effect. These HLMs will be implemented in SAS using generalized linear mixed models, which can be applied to analyze binary, count and continuous repeated measurement data, collected at unequal time intervals following a hierarchical structure (McCulloch and Searle, 2001; Liang and Zeger 1986). These models address the correlated nature of multiple measurements that come from each subject over time. We anticipate that generalized linear models will be appropriate for the data, but will also explore using the method of generalized estimating equations (GEE) (McCulloch and Searle, 2008). The GEE method does not require assumptions of normality and equal variances, but uses less information than linear mixed models since only the first two moments of the distribution are employed in GEE.