Interaction, nonlinearity, and multicollinearity: Implications for multiple regression

used to test for the presence of interactions. When an interaction term is composed of correlated variables, linearity and additivity become con-founded. The result of this confounding is that an interaction term in MHMR may be statistically significant only because of its overlap with unmeasured nonlinear terms. I recommend that squared terms be used as covariates in such situations and show that the resulting loss of power with respect to the test of significance for the interaction term is limited to that associated with the loss of degrees of freedom and is therefore negligible if it exists at all. As the social sciences have developed, hypothesized relationships have become more complex. One of the more obvious indicators of this increase in complexity is the increase in the number of moderating relationships that have been hypothesized over the past several decades (Althauser, 1971; Cohen, 1978; Cronbach, 1987). One of the most common methods for testing moderating rela-tionships or interactions has been and is moderated hierarchical multiple regression (MHMR). Although this method of analysis has a long history of criticism (e.g., Sock