I am certain this question has been asked several times, yet I can't seem to find the correct code or explanation. (will remove it if I find a solution)
I have a rather simple design, yet I can't seem to code things correctly in R. I have one repeated measures factor (Group: A, B, C), and 4 dependent measures (Accuracy, Bias, Confidence, Intensity).
I would like to predict Accuracy using the three other DVs, while controlling for Group and Subject.
My issue is both from a theoretical point and from a coding point. First, my predicting model would ideally be used to predict ALL Groups (e.g., I will be able to claim that using Intensity is a good predictor of Accuracy under all groups). However, from my analyses (ANOVAs) I know that there are significant differences between the Groups on all ratings, therefore, I may run into a Simpsons' paradox, where I claim that Intensity is a predictor, yet for Group A and B it is negative, but looks positive overall, or it simply becomes useless because the predictions all difer based on Group.
Would I run a model that predicts Accuray based on the other DVs at each Group level? Or is there a way to make an "overall/parsimonious" predicting model?
Now for the coding. I am using NLME, where I think the code should either be:
m1 <- lme(Accuracy ~ Bias + Intensity + Confidence, random=~Group|Subjects, data = my_data)
m2 <- lme(Accuracy ~ Bias:Group + Intensity:Group + Confidence:Group, random=~1|Subjects, data = my_data)
Thank you in advance!