Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations
Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations
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DOI：
10.3758/brm.41.3.924
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发表时间：
2009-08-01
影响因子：
5.4
通讯作者：
Matthes, Joerg
中科院分区：
文献类型：
--
作者：
Hayes, Andrew F.;Matthes, Joerg
研究人员经常假设调节效应，其中自变量对结果变量的影响取决于调节变量的值。这种效应在统计学上表现为结果变量模型中自变量和调节变量之间的相互作用。当发现相互作用时，重要的是要探测相互作用，因为理论和假设通常不仅预测相互作用，而且预测作为调节剂函数的焦点自变量的特定效应模式。本文介绍了常见的pick-a-point方法和不太常见的Johnson-Neyman技术，用于探测线性模型中的相互作用，并介绍了SPSS和SAS的宏，以简化计算并方便探测普通最小二乘法和逻辑回归中的相互作用。SPSS宏的脚本版本也可用于喜欢点击式用户界面而不是命令语法的用户。
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.