Just like \(η^2\) in ANOVA, \(r^2\) is interpreted as the amount of variance explained in the outcome variance, and the cut scores are the same as well: 0.01, 0.09, and 0.25 for small, medium, and lar...Just like \(η^2\) in ANOVA, \(r^2\) is interpreted as the amount of variance explained in the outcome variance, and the cut scores are the same as well: 0.01, 0.09, and 0.25 for small, medium, and large, respectively. Notice here that these are the same cutoffs we used for regular \(r\) effect sizes, but squared (0.102 = 0.01, 0.302 = 0.09, 0.502 = 0.25) because, again, the \(r^2\) effect size is just the squared correlation, so its interpretation should be, and is, the same.
Just like \(η^2\) in ANOVA, \(r^2\) is interpreted as the amount of variance explained in the outcome variance, and the cut scores are the same as well: 0.01, 0.09, and 0.25 for small, medium, and lar...Just like \(η^2\) in ANOVA, \(r^2\) is interpreted as the amount of variance explained in the outcome variance, and the cut scores are the same as well: 0.01, 0.09, and 0.25 for small, medium, and large, respectively. Notice here that these are the same cutoffs we used for regular \(r\) effect sizes, but squared (0.102 = 0.01, 0.302 = 0.09, 0.502 = 0.25) because, again, the \(r^2\) effect size is just the squared correlation, so its interpretation should be, and is, the same.
