As a part of my dissertation I want to compare a focal, new, variable against other well-known predictors of my outcome, so I plan to run a hierarchical regression in SPSS with the focal variable entered on the last step. The problem is that my focal variable is highly correlated (r=.84 based on my previous data) with one of the well-known predictors of my outcome variable, yet conceptually my new variable is distinct and theoretically important. Because of the high correlation it seems unwise to include both variables in the same regression model, but still, I'd like to be able to compare the predictive power of the two variables.

Thus, I plan to run the regression with my focal variable first (excluding the well-known predictor), then run a separate regression on the same sample, with my focal variable extracted and replaced by that other well-known/highly correlated predictor, but I'm unsure how to statistically compare these two models (or, rather, the predictive power of the two highly correlated predictor variables, given that the models are otherwise the same)

My question is how might I statistically compare these two models? That is, how can I determine whether or not there is a significant difference in either the overall effect size between the two models, or in predictor strength? Would it make sense to obtain the partial correlation coefficients and use those in a Steiger's Z test? Is there a better way?

Thanks in advance for any help!