Mediation, Moderation, and Conditional Process Analysis: A Second Course

This second course on mediation, moderation and conditional process analysis continues where the introductory course concludes. Upon completing this learning program, you will have a more detailed understanding of the following topics:

1. serial mediation and serial moderated mediation
2. mediation, moderation and conditional process analysis with a multi-categorical cause or moderator
3. estimating, probing and interpreting models with two moderators
4. testing, visualizing and probing three-way interaction (moderated moderation)
5. partial, conditional and moderated moderated mediation
6. using PROCESS and the creation of custom models in PROCESS

This course meets in person usually over two days from 9am to 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 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, political science, public health, communication and others that rely on social science methodology – who want to learn how to apply the latest methods in moderation and mediation analysis using readily-available software packages such as SPSS, SAS and R. Because this is an advanced course, participants should either be familiar with the contents of the first edition of Introduction to Mediation, Moderation, and Conditional Process Analysis and the statistical procedures discussed therein, or should have taken the first course through through CCRAM or other vendors in the recent past. Participants should also have experience using syntax in SPSS, SAS or R and a good working knowledge of multiple linear regression. No knowledge of matrix algebra is required or assumed, nor is matrix algebra ever used in the course. Some prior use of PROCESS is desirable but not required, as a review of the use of PROCESS syntax is included in one of the course modules.

Upon completing this course, you will be able to:

• estimate and interpret mediation models with mediators operating in serial
• conduct a conditional process analysis with models with more than one mediator (serial and parallel)
• understand the concept of differential dominance and appreciate its value in theory and research
• estimate and interpret relative direct, indirect and total effects in a mediation model with a multi-categorical (more than 2 groups) independent variable
• test, visualize, probe and interpret moderation (interaction) in a model with a multi-categorical independent variable or moderator
• conduct a conditional process analysis with a multi-categorical independent variable
• distinguish mathematically and in use the additive (dual moderation) and multiplicative (moderated moderation) model that includes two moderators of the effect of a variable
• test, visualize and interpret partial, conditional and moderated moderated mediation
• use PROCESS in more advanced ways, such as modifying a numbered model and creating a custom model

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).