Linear mixed models are a statistical tool used for analyzing data over time. This chapter focuses on how to set up and analyze these models, specifically looking at fixed effects and random effects. The researchers outline three specific cases of linear mixed models and explain how to make statistical inferences based on them. They also discuss the maximum likelihood estimator, which helps estimate fixed effects. It's noted that with small sample sizes, this estimator may underestimate variance. The chapter also covers other statistical procedures related to linear mixed models. Finally, an example is provided to show how these models can be used in real-world research.