Negligible interaction test for continuous predictors

Abstract

Behavioral science researchers are often interested in whether there is negligible interaction among continuous predictors of an outcome variable. For example, a researcher might be interested in demonstrating that the effect of perfectionism on depression is very consistent across age. In this case, the researcher is interested in assessing whether the interaction between the predictors is too small to be meaningful. Unfortunately, most researchers address the above research question using a traditional association-based null hypothesis test (e.g. regression) where their goal is to fail to reject the null hypothesis of no interaction. Common problems with traditional tests are their sensitivity to sample size and their opposite (and hence inappropriate) hypothesis setup for finding a negligible interaction effect. In this study, we investigated a method for testing for negligible interaction between continuous predictors using unstandardized and standardized regression-based models and equivalence testing. A Monte Carlo study provides evidence for the effectiveness of the equivalence-based test relative to traditional approaches.