Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behavior Research Methods Springer Journals

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

Behavior Research Methods , Volume 41 (3) – Aug 1, 2009

Loading next page...
 
/lp/springer-journals/computational-procedures-for-probing-interactions-in-ols-and-logistic-OAhcLdDvI4

References (23)

Publisher
Springer Journals
Copyright
Copyright © Psychonomic Society, Inc. 2009
eISSN
1554-3528
DOI
10.3758/brm.41.3.924
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Behavior Research MethodsSpringer Journals

Published: Aug 1, 2009

Keywords: Ordinary Little Square; Moderator Variable; Political Ideology; Conditional Effect; Ordinary Little Square Regression

There are no references for this article.