How to predict the degree to which an extraneous variable will attenuate a correlation? Assume there is a predictor x (a video-recorded job simulation) that correlates r=.3 (Pearson r) with a criterion y (later job performance). 
Assume a new grading process is used and it is noticed that there is a correlation of .25 between the order of grading and the grade, such that job candidates who are graded later are given somewhat higher grades than those who are graded earlier.  (This correlation is statistically significant.)
Assuming the two effects are independent (i.e., the proficiency of the job candidates is unrelated to the order of grading effect), how might I go about predicting the degree to which the correlation of .3 is attenuated by the correlation of .25?
 A: What if you look at the partial explained variance. For example run first the intial regression. And then run a regression adding the effect of grading order. I guess you could say something about the change in explained variance 
A: The implication seems to be that the grades are made into noisy predictors by the order of the grading, so what is the correlation between the "denoised" grades and the job prediction? But I don't quite know how one would model the way the grades receive that noise, and even if you do make such a model, I believe you need the actual data on job performance to evaluate the new correlation. 
This is because one could imagine that the order of grading and the grades themselves are correlated in a way that makes the grades less accurate, but still not actually worse for the purpose of predicting performance. For instance imagine that the actual best workers were simultaneously the ones graded last, so that your correlation wouldn't at all be attenuated. This is unlikely of course, but because of this issue, I don't see how to untie the relationship without criterion data to test on. 
