Hayes Andrew F, Matthes Jörg
School of Communication, Ohio State University, Columbus, Ohio 43210, USA.
Behav Res Methods. 2009 Aug;41(3):924-36. doi: 10.3758/BRM.41.3.924.
Researchers often hypothesize moderated effects, in which the effect of an independent variable on an outcome variable depends on the value of a moderator variable. Such an effect reveals itself statistically as an interaction between the independent and moderator variables in a model of the outcome variable. When an interaction is found, it is important to probe the interaction, for theories and hypotheses often predict not just interaction but a specific pattern of effects of the focal independent variable as a function of the moderator. This article describes the familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models and introduces macros for SPSS and SAS to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression. A script version of the SPSS macro is also available for users who prefer a point-and-click user interface rather than command syntax.
研究人员常常假设存在调节效应，即自变量对结果变量的效应取决于调节变量的值。这种效应在统计上表现为结果变量模型中自变量和调节变量之间的交互作用。当发现存在交互作用时，探究该交互作用很重要，因为理论和假设通常不仅预测交互作用，还预测焦点自变量作为调节变量的函数的特定效应模式。本文介绍了在线性模型中探究交互作用时常用的选点法以及鲜为人知的约翰逊 - 奈曼技术，并介绍了用于SPSS和SAS的宏，以简化计算并便于在普通最小二乘法和逻辑回归中探究交互作用。对于更喜欢点击式用户界面而非命令语法的用户，还提供了SPSS宏的脚本版本。
