Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to change. Specifically, this article outlines how to build and estimate "piecewise growth models" (PGM) using SAS PROC MIXED in order to estimate discontinuous growth models over time in a hierarchical linear model (HLM) framework. Details of data coding schemes, model parameterization, analysis using SAS PROC MIXED, and interpretation of parameter estimates will be discussed using a simulated education intervention data set.