This paper has illustrated how researchers‘ conceptualizations and assessments of variable importance can be enhanced by viewing MR results through multiple lenses. As it has shown that each ―lens‖ has distinct advantages and limitations, and that multiple statistical measures complement each other in the perspectives they provide regarding regression findings, we hope that this paper will encourage researchers to look beyond‖ beta weights and employ the other measures discussed in our guidebook. Ideally, this practice would become a matter of routine among researchers, in our peer-reviewed journals, and in teaching MR within graduate-level statistics curricula. The hope is that our data-driven example will allow researchers to write up their own findings in a similar manner and thus will be able to better represent the richness of their regression findings, and how these findings are impacted by such issues as suppression and patterns of shared variance that go undiscovered through heavy reliance on beta weights alone.