I have a data set from a repeated measures experimental design with different sets of stimuli. I want to know how strong the association between the continuous dependent variable and the continuous predictor is while accounting for the interindividual and interstimulus variation.
lmer model description in
R looks like this
dv ~ pred + (1 | subject) + (1 | stimulus)
Question 1: I understand that it is non-trivial to calculate R squared for random intercept-slope models. Is the same true for random intercept models? Is there an R-implementation of any of the available methods?
Question 2: If I z-transform my dependent variable and predictor, will the parameter estimate for the fixed effect reflect the strength of the association such as it would in an ordinary regression model? Update: I think I phrased this questions too vaguely. I was wondering if scaling variables would yield standardized regression estimates. I found this question has been answered before in another question.
Question 3: Is there an entirely different/more appropriate way to quantify the association strength while controlling for the interindividual and interstimulus variation?