Mixed effects models are powerful techniques for controlling for non-independence of data or repeated measures, and can be harnessed for both normal and non-normal data structures. Chapter 8 teaches readers how to code, assess, interpret, and troubleshoot both linear and generalized linear mixed models using the same RxP dataset which has been used throughout the book, although now it is viewed through a new lens. Readers are taught how to code likelihood ratio tests to calculate statistical significance and how to use multiple packages, such as lme4 and glmmTMB.