Longitudinal data involves collecting measurements from multiple subjects over time. Linear mixed effects models are used to analyze this data, incorporating random effects and variance structures. Popular models include random intercepts, unstructured variances, and linear time trends. Group comparisons can also be made using these models. The goal of the research is to describe autoregressive linear mixed effects models for analyzing longitudinal data. The researchers discuss mean and variance structures and estimation methods like maximum likelihood.