West S G, Aiken L S, Krull J L
Department of Psychology, Arizona State University, Tempe 85287-1104, USA.
J Pers. 1996 Mar;64(1):1-48. doi: 10.1111/j.1467-6494.1996.tb00813.x.
Theories hypothesizing interactions between a categorical and one or more continuous variables are common in personality research. Traditionally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative multiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predictors on the outcome in the presence of interactions. We discuss the structuring of the regression equation, the selection of a coding system for the categorical variable, and the importance of centering the continuous variable. We present in detail the interpretation of the effects of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illustrate two- and three-dimensional graphical displays of the results and present methods for conducting post hoc tests following a significant interaction. The application of multiple regression techniques is illustrated through the analysis of two data sets. We show how multiple regression can produce all of the information provided by traditional but less optimal ANOVA procedures.
在人格研究中，假设分类变量与一个或多个连续变量之间存在相互作用的理论很常见。传统上，此类假设一直通过对方差分析（ANOVA）进行非最优调整来检验。本文介绍了一种基于多元回归的替代方法，该方法具有更强的效力，并能防止在存在相互作用的情况下得出关于个体预测变量对结果影响的虚假结论。我们讨论了回归方程的构建、分类变量编码系统的选择以及连续变量中心化的重要性。我们详细阐述了根据为分类变量选择的编码系统，对个体预测变量及其相互作用的效应进行解释。我们展示了结果的二维和三维图形显示，并介绍了在显著相互作用后进行事后检验的方法。通过对两个数据集的分析来说明多元回归技术的应用。我们展示了多元回归如何能够产生传统但不太理想的方差分析程序所提供的所有信息。
