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• W4225087125 abstract "Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and level-specific versions of MLM R-squared measures have not been clarified, despite such relationships becoming increasingly important to understand when there are more levels. Furthermore, the impact of predictor centering strategy on R-squared computation and interpretation has not been explicated for MLMs with any number of levels. To fill these gaps, we extend the Rights and Sterba two-level MLM R-squared framework to three or more levels, providing a general set of measures that includes preexisting three-level measures as special cases and yields additional results not obtainable from existing measures. We mathematically and pedagogically relate total and level-specific R-squareds, and show how all total and level-specific R-squared measures in our framework can be computed under any centering strategy. Finally, we provide and empirically demonstrate software (available in the r2mlm R package) to compute measures and graphically depict results." @default.
• W4225087125 created "2022-04-30" @default.
• W4225087125 creator A5074457520 @default.
• W4225087125 creator A5079379686 @default.
• W4225087125 date "2022-04-27" @default.
• W4225087125 modified "2023-10-14" @default.
• W4225087125 title "R-squared Measures for Multilevel Models with Three or More Levels" @default.
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• W4225087125 doi "https://doi.org/10.1080/00273171.2021.1985948" @default.
• W4225087125 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35476605" @default.
• W4225087125 hasPublicationYear "2022" @default.
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