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 ```I ran a set of hierarchical linear regressions on my full dataset (N = 351). I ran the same regressions on a subset of the data (N = 151). I compared the results to see if the same regressions were significant for the subset AND the full dataset. Most of them were. Some of them were not. For the ones that were not, I want to compare the R squares, because I know that is a measure of effect size in regression. My question is this: what constitutes a significant difference of R square? For example, for one of my tests the R square value for the full dataset was .17, for the same test on the partial dataset the R square value was .11. Are these values far enough apart to suggest that the findings are the different? I've done about 20 of these regressions, so a general answer ("a change in R square value of .2 would mean a significant difference")would be more useful than the answer for the specific example above. Alternatively, I know that there are guidelines for Cohen's d measure of effect size, such that a certain number would count as a "large effect." Are there any such guidelines for R square?```