(2014-Jun-11, 23:33:21)nooffensebut Wrote: So, if my model can achieve an R^2 of 0.942, then adding in those indirect effects could add up to 0.058 to the coefficient of determination, and you want me to think that such a miniscule change would so profoundly transform my model that, in its current form, “there is no possibility to say which [variable] has the strongest effect.”

This is because you believe R^2 tells you the predictive power of the direct effects of the variables in the model while in reality it tells you the predictive power of the variables included in the model. In other words, it still regards the total effect.

It's interesting that each time I try to constrain the indirect paths in SEM model to zero, the r² systematically decreases. And I'm thinking that if r² regards only the direct paths, as you imply, this can never happen.

I believe I had a good paper on this subject about the irrelevance of r² in multiple regression, but I'm busy and I cannot find it among the messy millions of documents I have in my computer. (but I'll edit the post later if I find it)

Quote:that MR underestimates the true explanatory power of a variable because it only includes direct effects

My earlier comment was clear : the total effect can be either lower, higher, similar than the direct path.

I will probably come here later, because right now, I find it impossible to understand the 2nd part of your comment.