In 2019, Rights and Sterba introduced a set of effect size measures for linear multilevel models in the article Quantifying Explained Variance in Multilevel Models: An Integrative Framework for Defining R-Squared Measures. This workshop discusses the concepts of within-cluster and between-cluster variance in multilevel models, and how these new R-squared measures quantify how the components of the model explain some proportion of these variances. We will also discuss how to use their R package, r2mlm, to estimate these R-squared effect sizes and the interpretation of the output and graphs. Some background in both multilevel models and R is assumed for this workshop.
