Interactions in Regression Analysis

In person in Calgary July 18-19, 2025, as part of Rocky Mountain Methodology Academy

$950 (Canadian dollars) + 5% GST

Register through the RMMA 2025 page

Moderation analysis is used to understand or test hypothesis about the contingencies or boundary conditions of effects. Sometimes also called “interaction,” one variable’s effect on a second is moderated by a third variable if that third variable influences the size of the first variable’s effect on the second. Although this topic is often covered in regression analysis classes, there are subtleties in visualization and interpretation that are often neglected, leading researchers underinformed and underprepared to be able to properly implement moderation analysis in their own research. This class is dedicated exclusively to the fundamentals and more advanced issues in setting up and testing, visualizing, probing, and interpreting moderation models in ordinary least squares regression, with an emphasis on implementation in the PROCESS macro for SPSS, SAS, and R invented by the course instructor. This class is a companion to the instructor's books Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd Edition) and Regression Analysis and Linear Models: Concepts, Applications, and Implementation, both published by The Guilford Press. 

INSTRUCTOR: Dr. Andrew F. Hayes, PhD

In this course, you will learn about the underlying principles and the practical applications of moderation analysis using ordinary least squares regression. The topics covered include

  • The fundamentals of moderation analysis in OLS regression.
  • The distinction between conditional and unconditional effects.
  • Interpretation of regression coefficients when the product of two variables is included in a model.
  • Probing interactions using the pick-a-point approach/spotlight analysis and the Johnson-Neyman technique/floodlight analysis.
  • Visualizing interactions.
  • Debunking of various myths of moderation analysis, such as the need to mean-center or standardize variables prior to analysis.
  • Simplifying a moderation analysis with the use of the PROCESS macro for SPSS, SAS, and R.

This course meets in person in Calgary July 18-19, 2025, between 9am and 5pm each day.

Computer applications will focus on the use of ordinary least squares regression and the PROCESS macro for SPSS, SAS and R, developed by the instructor, that makes the analyses described in this class much easier than they otherwise would be. This is a hands-on course, so maximum benefit results when learners can follow along with analyses using a laptop or desktop computer with a recent version of SPSS Statistics (version 27 or later), SAS (release 9.3 or later, with PROC IML installed) or R (version 3.6 or later; base module only. No packages are used in this course). Learners can choose which statistical package they prefer to use. STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for STATA. 

This course will be helpful for researchers in any field – including psychology, sociology, education, business, human development, social work, public health, communication and others that rely on social science methodology – who want to learn how to test whether effects one variable's effect on another is moderated using widely-used software such as SPSS, SAS and R.

Learners are recommended to have familiarity with the practice of multiple regression analysis and elementary statistical inference. No knowledge of matrix algebra is required or assumed, nor is matrix algebra used in the delivery of course content. Learners should also have some experience with the use of SPSS, SAS or R, including opening and executing data files and programs.

By the end of this course, you will…

  • acquire an understanding of how to build flexibility into a regression model that allows a variable’s effect to be a function of another variable in a model.
  • have the ability to visualize and probe interactions in regression models.
  • be able to estimate and interpret regression models involving a multicategorical causal agent or moderator.
  • see the equivalence between regression with interactions and factorial analysis of variance.
  • understand how scaling of variables influence parameter estimates and their interpretation.
  • be able to apply the methods discussed in this course using readily available statistical software.
  • be in a position to talk and write in an informed way about the contingencies of effects in regression models.

A certificate of completion from the Canadian Centre for Research Analysis and Methods is provided at the end of the course.

 

The modeling strategies discussed in this course can be completed using ordinary least squares regression analysis. As a consequence, all topics and examples are based on the analysis of dependent variables that are quantitative and roughly continuous in nature. This course does not cover dichotomous or count outcomes, nor does it discuss multilevel models or cross-level interactions. However, understanding the estimation and interpretation of moderation models in the context or OLS regression is a prerequisite to applying these ideas to more complicated modeling problems and many of the fundamentals discussed in this class generalize to other kinds of modeling strategies. Thus, at the end of this course, participants will be well prepared to further their own education in these more advanced areas.