Abstract
Models of communication processes sometimes require the computation of the difference between two variables. For example, information insufficiency is the difference between what people know and what they think they need to know about an issue, and it can motivate information seeking and processing. Common methods that compute this differential may bias model estimates as a function of the correlation between the differentiated variables and other variables in the model. This article describes the general form of Cohen and Cohen's (1983) analysis of partial variance for computing differentials and analyzes simulated data to contrast that method with two alternative methods. The discussion recommends the use of the general form of the Cohen and Cohen method in other areas of communication research, such as studies of third-person perception.
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