Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations

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...

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Bibliographic Details
Published in:Behavior research methods 2009-08, Vol.41 (3), p.924-936
Main Authors: Hayes, Andrew F., Matthes, Jörg
Format: Article
Language:English
Subjects:
Behavioral Research - methods
Behavioral Research - statistics & numerical data
Behavioral Science and Psychology
Behavioral sciences
Cognitive Psychology
Data analysis
Estimates
Linear Models
Logistic Models
Mathematical models
Methods
Psychology
Regression analysis
Software
Standard deviation
Studies
Variables
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Summary: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.
ISSN:1554-351X
1554-3528
DOI:10.3758/BRM.41.3.924