This chapter discusses the methods for analyzing nested data with a focus on two statistical methods: multilevel modeling (MLM) and structural equation modeling (SEM). It describes two inappropriate methods for analyzing nested data: aggregation and disaggregation. Aggregation and disaggregation have many downsides, both statistically and in terms of interpretation of results. In contrast to aggregation and disaggregation, MLM methods that explicitly account for the nested structure of data provide the best of all worlds. The chapter covers the basic equations of MLM and also discusses its uses with nested data. Both MLM and SEM can be used to analyze nested data. Research designs that result in nested data can be broken down into two broad categories: cross-sectional designs and longitudinal designs. One of the most popular multilevel models for longitudinal data designs is the latent growth curve model.