Answers are grounded in this paper and cite their sources.
This article demonstrates how r2_mlm computes R-squared measures in mixed models, suggesting improved accuracy in multilevel modeling.
The article by Rights and Sterba (2023, Multivariate Behavioral Research 58: 340-367) provides a comprehensive framework for computing R 2 measures for (linear) multilevel models. In this article, we introduce r2_mlm , a postestimation command for mixed that computes R 2 measures using Rights and Sterba’s framework. We explain how this R 2 framework works and demonstrate how r2_mlm can be used to compute various R 2 measures for models fit by mixed . One of the most useful features of r2_mlm is that it will produce warning messages if the user specifies the model in a way that may lead to conflation bias (an easily overlooked issue). Finally, we walk through a simple example and explain how to interpret the various R 2 measures.
No takes yet. Share an insight, caveat, or question.
Gambino et al. (2025) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: