This article examines the multiplicative combination of belief strength by outcome evaluation in the expectancy–value model of attitudes. Because linear transformation of a belief strength measure results in a nonlinear transformation of its product with outcome evaluation, use of unipolar or bipolar scoring must be empirically justified. Also, the claim that the Belief × Evaluation product fails to explain significant variance in attitudes is found to be baseless. In regression analyses, the main effect of belief strength takes account of the outcome's valence, and the main effect of outcome evaluation incorporates the outcome's perceived likelihood. Simulated data showed that multiplication adds substantially to the prediction of attitudes only when belief and evaluation measures cover the full range of potential scores.