Introduction to Mediation, Moderation, and Conditional Process Analysis

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 course meets in person usually over two days from 9.00am to 5.00pm each day. If you are interested in an online version of this topic, consider Mediation, Moderation, and Conditional Process Analysis: The Complete 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

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

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).

"The Introduction course to Mediation, Moderation and Conditional Process Analysis is useful for anyone interested in learning the basics of these analyses and how to use PROCESS. I found the examples and interpretation of results from the output helpful and I am applying what I learned to my current research projects."

"I found this class so helpful - I am using mediation analysis for my dissertation and this class gave me the foundation which I was lacking. I highly recommend this class!"

"Thank you for offering such a great course – it was very informative and applicable!"