The purpose of the study was to assess and demonstrate the use of covariate balance diagnostics for samples matched with propensity scores in multilevel settings. A Monte Carlo simulation was conducted that assessed the ability of different balance measures to identify the correctly specified propensity score model and predict bias in treatment effect estimates. The balance diagnostics included absolute standardized bias (ASB) and variance ratios calculated across the pooled sample (pooled balance measures) as well as the same balance measures calculated separately for each cluster and then summarized across the sample (within-cluster balance measures). The results indicated that overall across conditions, the pooled ASB was most effective for predicting treatment effect bias but the within-cluster ASB (summarized as a median across clusters) was most effective for identifying the correctly specified model. However, many of the within-cluster balance measures were not feasible with small cluster sizes. Empirical illustrations from two distinct datasets demonstrated the different approaches to modeling, matching, and assessing balance in a multilevel setting depending on the cluster size. The dissertation concludes with a discussion of limitations, implications, and topics for further research.