Introduction to Mediation, Moderation and Conditional Process Analysis

Self-paced online | Offerings dates TBD

Statistical mediation and moderation analyses are among the most widely used data analysis techniques in social science, health and business research. Mediation analysis is used to test hypotheses about various intervening mechanisms by which causal effects operate. Moderation analysis is used to examine and explore questions about the contingencies or conditions of an effect, also called “interaction.”  Increasingly, moderation and mediation are being integrated analytically in the form of what has become known as “conditional process analysis,” used when the goal is to understand the contingencies or conditions under which mechanisms operate. An understanding of the fundamentals of mediation and moderation analysis is in the job description of almost any empirical scholar. In this course, you will learn about the underlying principles and the practical applications of these methods using ordinary least squares (OLS) regression analysis and the PROCESS macro for SPSS, SAS and R, invented by the course instructor and widely used in the behavioral sciences. This course is a companion to the instructor’s book Introduction to Mediation, Moderation, and Conditional Process Analysis, published by The Guilford Press.  An overview of the course can be viewed at https://www.youtube.com/watch?v=w7iOnsrI2dI

This introductory course is recommended to all levels of learners prior to taking Mediation, Moderation and Conditional Process Analysis: A Second Course.

Instructor: Dr. Andrew F. Hayes, PhD 

In this course, you will learn about the underlying principles and the practical applications of mediation, moderation and conditional process analysis. It covers six broad topics:

  1. Direct, indirect and total effects in a mediation model
  2. Estimation and inference in single mediator models using ordinary least squares regression
  3. Estimation and inference in mediation models with more than one mediator
  4. Moderation or “interaction” in ordinary least squares regression
  5. Testing, interpreting, probing, and visualizing interactions
  6. The integration of mediation and moderation: Conditional process analysis

This online course consists of a collection of 16 modules in the form of videos and exercises that can be completed with a time commitment of about 6-8 hours/week. You can participate at your own convenience; there are no set times when you are required to be online during the offering period, and you can rewind the videos and review modules completed at your leisure. Questions can be sent to the instructor and others in the class through a discussion board on the course delivery platform, and the instructor will offer regular opportunities for (optional) synchronous interaction via Zoom at various times during the course. The course can be accessed with any recent web browser on almost any computing platform, including iPhone, iPad and Android devices.  Although the course runs for 3 weeks, everyone will have access to the content through the course portal for an additional two weeks beyond the end of the course.

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 23 or later), SAS (release 9.2 or later, with PROC IML installed) or R (version 3.6; 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 apply the methods of moderation and mediation analysis 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.

Upon completing this course, you will be able to:

  • statistically partition one variable’s effect on another into its primary pathways of influence, direct and indirect
  • understand modern approaches to inference about indirect effects in mediation models
  • test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators
  • understand how to build flexibility into a regression model that allows a variable’s effect to be a function of another variable in a model
  • visualize and probe interactions in regression models (e.g. using the simple slopes/spotlight analysis and Johnson-Neyman/floodlight analysis approaches)
  • integrate models involving moderation and mediation into a conditional process model
  • estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects
  • determine whether a mechanism is dependent on a moderator variable
  • apply the methods discussed in this course using the PROCESS procedure for SPSS, SAS and R
  • talk and write in an informed way about the mechanisms and contingencies of causal effects

In this course, we focus primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. We do not cover complex models involving dichotomous outcomes, latent variables, nested data (i.e., multilevel models) or the use of structural equation modeling. We also do not address the "counterfactual" or "potential outcomes" approaches to mediation analysis or discuss directed acyclic graphs (DAGs).

This course can be combined with Mediation, Moderation, and Conditional Process Analysis: A Second Course and delivered as a private or semi-private course at a time of your choosing for your group of 10 or more. Contact ccram@ucalgary.ca to express your interest or for more information.