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.

行为科学研究人员通常对结果变量的连续预测变量之间是否存在可忽略不计的相互作用感兴趣。例如，研究人员可能有兴趣证明完美主义对抑郁症的影响在各个年龄段都非常一致。在这种情况下，研究人员有兴趣评估预测变量之间的交互是否太小而无意义。不幸的是，大多数研究人员使用传统的基于关联的零假设检验（例如回归）来解决上述研究问题，他们的目标是无法拒绝没有交互的零假设。传统测试的常见问题是它们对样本大小的敏感性以及它们相反的（因此是不适当的）假设设置以找到可忽略的交互效应。在这项研究中，我们研究了一种使用非标准化和标准化的基于回归的模型和等价测试来测试连续预测变量之间可忽略不计交互的方法。Monte Carlo 研究为基于等效性的测试相对于传统方法的有效性提供了证据。