# In Defense of Multiplicative Terms In Multiple Regression Equations

**Robert Friedrich**

CiteWeb id: 20160000026

CiteWeb score: 1034

Though the inclusion of multiplicative terms in multiple regression equations is often prescribed as a method for assessing interaction in multivariate relationships, the technique has been criticized for yielding results that are hard to interpret, unreliable (as a result of multicollinearity between the multiplicative term and its constituent variables), and even meaningless. An interpretation of a multiple regression equation with a multiplicative term in conditional terms reveals all these criticisms to be unfounded. In fact, it is better analytic strategy to include a multiplicative term than to exclude one. Complicated as quantitative political analysis may seem to the uninitiated, one of the most telling criticisms made against it is that it often oversimplifies an exceedingly complicated political reality. The penchant for simplicity and generality of explanation is, of course, one of the driving forces of science, and unfortunately, it sometimes drives too far. But oversimplification sometimes also occurs because political researchers do not know about or hesitate to use techniques that would allow them to detect more complicated patterns of relationship in data. A prime example of this is the technique considered in the following pages: the inclusion of multiplicative terms in multiple regression equations. Perhaps the most common simplification in quantitative analysis is the assumption of additivity-the assumption that the effect of an independent variable on a dependent variable is always the same, regardless of the level of other variables. The familiar multiple regression equation

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