Abstract
Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (2019) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package - r2mlm - that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.
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