Variance Explained: Why Size Does Not (Always) Matter

Abstract

I examine the role of explaining variance in the construction of explanatory theory. Explaining variance can be an insufficient basis for evaluating a theory (Lieberson, 1985). Starting with this insight, I suggest that mod- els that provide explanations of variance do not necessarily provide good explanations of causal mechanisms. I then explore the utility of process models and theories (Mohr, 1982) relative to variance theories. I clarify the role of stochastic processes in such model building and discuss the implications of such processes for evaluating explanatory `adequacy'. Under some conditions, explaining variance may be neither a necessary nor a sufficient condition for good explanatory theory. I then identify some implications of this argument for developing and analyzing explanatory theory. These arguments are applied to two examples: (1) meta-analysis and (2) the disposition versus situation debate (a variant on the nature vs. nurture argument) to illustrate the implications of this process theory point of view.