I would like to know if there is a significant effect of condition on Score1, and how closely correlated Score2 is at approximating Score1, for the following dataset:
I am using a random intercept with random slopes model in R: data1.model=lmer(Score1 ~ Condition+(1+Condition|Subject), data=data1,REML=FALSE)
Score2 is an indirect measure of Score1, in this type of experiment, Score2 is often the only available measurement and thus it is used as an approximation of Score1. However, in this experiment we have been able to measure both variables. So my question is, how effective is Score2 at approximating Score1? I think I am looking for the amount of variance in Score2 that can be explained by Score1.
I have looked at this problem using rmcorr: Bakdash, Jonathan Z., and Laura R. Marusich. "Repeated measures correlation." Frontiers in psychology 8 (2017): 456.
However, I think a mixed model may offer more flexibility as I have a third variable (Score3) influencing the Score2 measurement and I would like to find the amount of variance in Score2 that can be explained by Score1, while controlling for the third variable (which is also measured at each trial).
Any direction on this problem would be much appreciated!