# Interactions in Linear Regression Analysis

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Date/Time
Date(s) - 05/13/2016 - 05/15/2016
All Day

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Start Date: 5/13/2016

End Date: 5/14/2016

Location:

Temple University Center City

1515 Market Street

Website: http://statisticalhorizons.com/seminars/public-seminars/interactions-spring-2016

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 interpreting 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.

By the end of this class, students will understand the analysis of interaction in linear regression and be able to use it in their own research. The course covers two-way interaction between continuous and dichotomous variables, between two continuous variables, and between multicategorical (i.e., more than two categories) and continuous variables. Also included are methods for visualizing interactions, and methods of probing interactions such as the pick-a-point approach (also called simple slopes or spotlight analysis) and the Johnson-Neyman technique (also called a floodlight analysis).

With the two-way case covered, the course shifts to models with more than one moderator, including moderated-moderation, or three-way interaction. The estimation and interpretation of models with multiple moderators, including visualization and probing of three-way interactions is the focus of this part of the course. Also covered is the comparison of conditional effects (simple slopes) defined by different values of two moderators.

Computer applications will include the use of SPSSs regression routine as well as SASs PROC REG but will emphasize the PROCESS macro for SPSS and SAS developed by the instructor that greatly simplifies the analysis, probing, and visualization of interactions and that aids interpretation.

This is a hands-on course with many opportunities for participants to practice the methods they learn.