COMMONALITY ANALYSIS: A METHOD FOR DECOMPOSING EXPLAINED VARIANCE IN MULTIPLE REGRESSION ANALYSES

Authors


David R. Seibold (Ph.D. Michigan State University, 1975) and Robert D. McPhee (Ph.D. Michigan State University, 1978) are assistant professors in the Department of Speech Communication at the University of Illinois, Urbana, IL 61801. This paper accepted for publication December 20, 1978.

Abstract

Commonality analysis is a procedure for decomposing R2 in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors. Commonality analysis thus sheds additional light on the magnitude of an obtained multivariate relationship by identifying the relative importance of all independent variables, findings which can be of theoretical and practical significance. In this paper we offer a brief explication of commonality analysis; a step-by-step discussion of how communication researchers may perform commonality analyses using output from computer-assisted statistical analysis programs like SPSS; and we provide an extended example illustrating a commonality analysis.

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