Multi-level/Mixed Effects Models

These readings and walkthroughs show how we can extend the linear model to analyses of “hierarchical data”, in which observations are clustered in higher-level groups (e.g. trials within participants, or students within schools). We see how these methods lend themselves well to longitudinal data, and we present one of the more traditional approaches to studying non-linear change over time. The assumptions underlying these models are discussed, along with certain considerations that are important to bear in mind especially for observational data.

Readings and walkthroughs are presented with accompanying R code.