Multiple Regression: Testing And Interpreting Interactions

This web page calculates simple intercepts and simple slopes, the region of significance, and computes specific values to facilitate the plotting of significant two-way interactions in ordinary least squares (OLS) regression. The interaction can be between two dichotomous variables, two continuous variables, or a dichotomous and a continuous variable. We assume that the user is sufficiently knowledgeable in the testing, probing, and interpretation of interactions in multiple regression (e.g., Aiken & West, 1991; Bauer & Curran, 2004; Cohen, Cohen, West & Aiken, 2003). A more extensive treatment of interaction effects can be found here.

Multiple Regression: Testing And Interpreting Interactions

Approximately one-third of international business (IB) articles include conditional hypotheses, yet the vast majority risk errors in testing or interpreting the results. Scholars typically restrict their empirical analysis to the coefficient of the interaction term in the regression, exposing themselves to the hazard of overstating or understating results. To mitigate the risk of misstating, we advocate that IB scholars also evaluate the statistical significance of the marginal effect of the primary independent variable over the range of values of the moderating variable. We demonstrate that overstating results can occur when the interaction term coefficient is statistically significant but the marginal effect is not significantly different from zero for some value(s) of the moderating variable. Understating can occur when the interaction term coefficient is not statistically significant, but the marginal effect is statistically different from zero for some value(s) of the moderating variable. In this article, we describe, using simulated data, these two possibilities associated with testing conditional hypotheses, and offer practical guidance for IB scholars. 350c69d7ab