Interactions in Linear Regression Analysis

Map Unavailable

Date(s) - 05/13/2016 - 05/14/2016
All Day

Categories No Categories

Start Date: 3/20/2015

End Date: 3/21/2015


Temple University Center City

1515 Market Street



Contact: 610-642-1941

Taught by Andrew Hayes, Ph.D. 

The specification and interpretation of interactions is one of the more confusing and problematic areas of regression analysis. Two variables X and W interact in explaining some outcome Y if the effect of X on Y depends on the value of W. Interaction is also called moderation. If X’s effect on Y depends on W, then W is a moderator of the effect of X on Y.

The identification and analysis of moderators is important in nearly all areas of science. Is psychotherapy more effective in treating depression when combined with an anti-depressive drug or when used by itself? Does a marketing campaign increase sales more among customers who are loyal to the brand or among those who are not? Does watching The Daily Show increase knowledge of current political events more for people who are interested in politics or those who are not? These are all questions about whether one variable’s effect is moderated by another.

Many researchers make fundamental errors in specifying and intepreting interactions. During their statistics training, most researchers are exposed to factorial analysis of variance, and it is in this context that concept of interaction is often introduced. But ANOVA is just a special case of linear regression with X and W as categorical variables. Researchers familiar with ANOVA but not the more general analysis of interaction in linear regression often resort to undesirable practices when their X or W (or both) is a continuum, such as categorizing the data prior to analysis.


Leave a Reply