How do I calculate effect size for mixed model regression in SPSS or R programming?
Effect size for the overall fit of the model, or of the individual coefficients? And do you want standardized measures? The raw effect sizes for the components are likely printed (for R, you need to say which package that you are using).
Dear Muhammad
Please, use the following link for same discussion, I hope will be useful for you:
https://www.researchgate.net/post/How_to_calculate_effect_size_from_Linear_Mixed_Model_in_SPSS
Good Luck
In R, I would recommend using the emmeans package, basically reporting the difference in estimated marginal means as the effect size. This could be in table format or plot. I suspect for your model, this would make the most sense. I don't think there's any traditional effect size statistic that would be applicable.
Salvatore S. Mangiafico Does the difference in emmeans follow d cohen's rule for effect size or other rules of thumb?
Thanks
Cosimo Tuena , the emmeans output will return an estimate for the emmean and and SE. I suppose one could try to convert the SE to a standard deviation and calculate a statistic something like Cohen's d. But I've never seen anywhere that this would be valid or necessarily make sense.
Muhammad Waseem Khan , you may find Thom S Baguley 's article on effect size interesting. RG has a free PDF of the pre-print manuscript here:
Article Standardized or simple effect size: What should be reported?
HTH.
Standardised effect size isn't necessarily a sensible thing to use. However, if you are going to calculate it you need to think about what you are trying to do (e.g., what you might want for a power calculation or to interpret practical importance of an effect are likely completely different quantities). In addition in some models (generalised linear models, multilevel models) scaling in terms of variance is complicated because the variance in question is typically a function of the value of a predictor and because the total variance to be explained may change as you add or remove predictors.
That said, a simple approach, which has the virtue of transparency is to scale the observed effect in terms of the raw SD of X and Y (measured on the raw data not estimated from the model).
Standardizing regression models is not a trivial process, as there are several methods which can give very different results (see https://easystats.github.io/parameters/articles/standardization.html for a comparison). Cosimo Tuena regarding emmeans, it does not give you standardized differences by default (unless your outcome variable is), but the `estimate` R package can compute them (see https://easystats.github.io/estimate/articles/estimate_contrasts.html).
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