This chapter delves more deeply into mixed models and provides two hands-on examples. First, a dataset is constructed using random number generation functions. This dataset is then analyzed with mixed models. Because the dataset is hand-generated, the “ground truth” of the data can be compared to the model output. The reader is walked through interpreting the output of a mixed model. The second example introduces mixed logistic regression, a form of mixed model suitable for binary categorical data. In this case, a discourse analysis dataset of “ugly selfies” is analyzed. The chapter also discusses remaining topics in mixed models, such as convergence issues and shrinkage.