Presently, there is a broader set of options available for including a temporal dimension in studies, depending on the specific goals of the research and the data structure used to investigate proposed theoretical models. This chapter develops several practical research applications of the basic two-level growth model using IBM SPSS Mixed. First, it extends the basic two-level growth model to include a grouping structure as a third level. Second, the chapter briefly introduces fixed-effects (FE) longitudinal models, where the focus is on time-varying covariates only, and time-invariant (i.e., level-2) predictors are eliminated. This approach is often the choice when there may be limited numbers of subjects available or where important covariates defining differences between individuals may not be available. If there are omitted variables, those variables will likely introduce bias into the analysis. In situations where omitted variables are correlated with the variables in the model, then a fixed-effects approach typically controls for this bias. Third, the chapter illustrates a piecewise growth model, which provides a means of examining two or more different growth trends within one model. The example provides a piecewise growth model to investigate institutional trends in admitting freshman student-athletes before and after introducing a policy to raise admission standards.