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Quantifying explained variance in cross-sectional and longitudinal multilevel models with any number of levels
Multilevel models (MLMs) are commonly used by psychologists to analyze nested data (e.g., students nested within schools or repeated observations nested within persons). To aid researchers in quantifying explained variance for these models, Rights & Sterba (2019, 2020) recently developed an integrative framework for R-squared computation that subsumed existing measures, clarified equivalencies among existing measures, and filled gaps by supplying new measures to answer key substantive questions. This original framework, however, did not readily accommodate modeling choices common to longitudinal contexts, nor did it generalize to hierarchical data structures beyond two levels. In this talk, I first describe this recent framework of Rights & Sterba, and proceed to delineate generalizations of this framework to accommodate longitudinal models and/or models with three or more levels. I also discuss a recently developed R package (r2mlm) to further aid researchers in implementing this framework and computing R-squared measures in practice.
Dr Right's research is primarily focused on addressing methodological complexities and developing statistical methods for multilevel/hierarchical data contexts (e.g., patients nested within clinicians, students nested within schools, or repeated measures nested within individuals).
Specifically, he is currently pursuing several interrelated programs of research: (1) developing R-squared measures and methods for multilevel models; (2) addressing unappreciated consequences of conflating level-specific effects in analysis of multilevel data; (3) delineating relationships between multilevel models and other commonly used models, such as mixture models; and (4) advancing model selection and comparison methods for latent variable models. To aid researchers in applying my work, I develop software (primarily in R) that is openly available for public use.