after researching on how to get estimated effect sizes from linear mixed effects models in R, I still do not understand how this can be properly done. Is there any function in R that does that? I am not lazy but I am working both with lmer and glmer on three types of data (reading times, reaction times, accuracy) and I really do not understand what there is to be done. People say that the estimated fixed effects coefficients are the effect size, or talk about standardization but I do not really understand what that implies really. I also do not really understand whether computing the effect size is different depending on coding scheme of the fixed effects (deviation vs treatment coding). Westfall and colleagues (2014) mention how the effect size could be calculated (the estimated slope coefficient for a given fixed effect / summed variances of all varying intercepts and slopes and residual variance) but I worry that the computing mode depends on the coding scheme, on whether people conduct lmer vs glmer analyses. On the other hand, how should one best report these results? It's all very confusing to me and I would definitely appreciate your help.
Westfall, J., Kenny, D. A. and Judd, C. M. (2014). Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli. Journal of Experimental Psychology: General 143(5): 2020–2045, DOI: https://doi.org/10.1037/xge0000014
There seems to exist a function that computes this, which is implemented in the
lme.dscore. I am nevertheless interested in your input regarding the question that I posted and also with regard to prior experience with this function. Many thanks and happy Monday!