Take a course from Andrew F. Hayes on the use of PROCESS. Currently scheduled courses open to the public can be found below.
Mediation, Moderation, and Conditional Process Analysis courses I [preview video] and II [preview video], taught by Andrew F. Hayes, will be offered through the Haskayne School of Business at the University of Calgary. These courses will be online and are open to the public. Course I begins January 11 2022. Course II begins February 15th 2022.
Introduction to Mediation, Moderation, and Conditional Process Analysis
January 11 to February 1 2022
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. A copy of the book is not required to benefit from the course, but it could be helpful to reinforce understanding.
Delivery and Technology. 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. Content is delivered asynchronously through prerecorded video, with occasional open synchronous office hours for meeting with the instructor through Zoom. This is a handson course, so maximum benefit results when students 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). Students should have good familiarity with the basics of ordinary least squares regression as well as the use of SPSS, SAS, or R. STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for STATA.
Prerequisites. Participants should have a basic working knowledge of the principles and practice of multiple regression and elementary statistical inference. No knowledge of matrix algebra is required or assumed, nor is matrix algebra ever used in the course. Some familiarity with the use of SPSS, SAS, or R is assumed.
Topics covered:
The course contains 16 modules, described below, that span about 15 hours in total. Also included are seven activities dispersed in various places throughout the course to test your understanding.
Delivery and Technology. 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. Content is delivered asynchronously through prerecorded video, with occasional open synchronous office hours for meeting with the instructor through Zoom. This is a handson course, so maximum benefit results when students 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). Students should have good familiarity with the basics of ordinary least squares regression as well as the use of SPSS, SAS, or R. STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for STATA.
Prerequisites. Participants should have a basic working knowledge of the principles and practice of multiple regression and elementary statistical inference. No knowledge of matrix algebra is required or assumed, nor is matrix algebra ever used in the course. Some familiarity with the use of SPSS, SAS, or R is assumed.
Topics covered:
 Path analysis: Direct, indirect, and total effects in mediation models.
 Estimation and inference about indirect effects in single mediator models.
 Models with multiple mediators
 Estimation of moderation and conditional effects.
 Probing and visualizing interactions.
 Conditional process analysis (“moderated mediation”)
 Quantification of and inference about conditional indirect effects.
 Testing a moderated mediation hypothesis and comparing conditional indirect effects
The course contains 16 modules, described below, that span about 15 hours in total. Also included are seven activities dispersed in various places throughout the course to test your understanding.
Introduction

An overview of the course and the software requirements and things available on the course portal that you will need to complete the course. [watch this video]

Module 1

Estimation and interpretation of the simple mediation model; total, direct, and indirect effects; unstandardized and completely standardized effects; path analysis tracing rules, illustration of computations using SPSS/SAS/R syntax.

Module 2

Statistical inference about total, direct, and indirect effects in a mediation model; an overview of the mechanics of bootstrapping and the reasons why bootstrap confidence intervals are preferred to more classical inferential techniques.

Module 3

Introduction to the PROCESS tool for SPSS, SAS, and R; common questions about bootstrapping and PROCESS.

Module 4

The uses and limitations of data analysis in causal inference; the designanalysistheory tripod of inference; confounding in a mediation model, and how to account for confounds through the inclusion of covariates in a PROCESS command.

Module 5

An example of the application of mediation analysis using PROCESS when the causal antecedent X is dichotomous; partially standardized effect relative to the completely standardized effect; reasons to avoid completely standardized measures of effects when X is dichotomous.

Module 6

An overview of the influential but now outdated "causal steps" procedure for assessing mediation (also known as the "Baron and Kenny" approach); a critique of the concepts of complete and partial mediation

Module 7

The parallel multiple mediator model; reasons for estimating such a model with more than one mediator; path analysis rules in a parallel mediation model; implementation in PROCESS; the comparison of indirect effects through different mediators.

Module 8

The fundamentals of moderation; the distinction between conditional and unconditional effects; how to set up a regression model to allow one variable's effect to depend on another variable in the model; the symmetry property of interactions; interpretation of regression coefficients.

Module 9

An example of a linear moderation analysis; how to visualize a model, interpreting regression coefficients; simplification of the analysis using PROCESS.

Module 10

Probing moderation using the pickapoint approach/spotlight analysis and the JohnsonNeyman technique/floodlight analysis; the distinction between testing for moderation and probing moderation; probing options available in PROCESS.

Module 11

Debunking of two widelybelieved myths about moderation analysis: the need to "mean center" or standardized focal predictor and moderator, and that to test a moderation hypothesis, a model should be built in stages using hierarchical variable entry.

Module 12

The generalization of principles of moderation analysis discussed in the course to this point to models with a continuous focal predictor and a dichotomous moderator.

Module 13

Additional generalization of the principles of moderation analysis to quantitative focal predictors and quantitative moderators; good and bad approaches to producing standardized regression coefficients in a moderation analysis.

Module 14

The integration of mediation and moderation analysis as "conditional process analysis”; the conditional indirect effect and a test of moderated mediation; the index of moderated mediation; example analysis of a second stage conditional process model using PROCESS; the similarities and differences between the use of PROCESS and a structural equation modeling program.

Module 15

Illustrating, using PROCESS, the analysis of a first stage conditional process model that includes moderation of the direct and indirect effect of X.

Module 16

Various places to go for additional information about mediation, moderation, and conditional process analysis.

What is Not Covered. In this course, we focus primarily on research designs that are experimental or crosssectional 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).
Mediation, Moderation, and Conditional Process Analysis: A Second Course
February 15 to March 8 2022
Statistical mediation and moderation analyses are among the most widely used data analysis techniques. Mediation analysis is used to test 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.” Conditional process analysis is the integration of mediation and moderation analysis and used when one seeks to understand the conditional nature of processes (i.e., “moderated mediation”)
In Introduction to Mediation, Moderation, and Conditional Process Analysis: A RegressionBased Approach, Andrew Hayes describes the fundamentals of mediation, moderation, and conditional process analysis using ordinary least squares regression. He also explains how to use PROCESS, a freelyavailable and handy tool he invented that brings modern approaches to mediation and moderation analysis within convenient reach. This online coursea second coursepicks up where the first edition of the book and the first course introductory course leaves off. After a review of basic principles, it covers material in the second edition of the book as well as new material in neither edition.
Delivery and Technology. 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. Content is delivered asynchronously through prerecorded video, with occasional open synchronous office hours for meeting with the instructor through Zoom. This is a handson course, and maximum benefit results when students 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). Students should have familiarity with the basics of ordinary least squares regression as well as the use of SPSS, SAS, or R. STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for STATA.
Prerequisites. Participants should have a basic working knowledge of the principles and practice of multiple regression and elementary statistical inference. No knowledge of matrix algebra is required or assumed, nor is matrix algebra ever used in the course. Some familiarity with the use of SPSS, SAS, or R is assumed. Because this is a second 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 (A 90 minute review of fundamentals is provided in this course). This course is a companion to the instructor’s book Introduction to Mediation, Moderation, and Conditional Process Analysis, published by The Guilford Press. A copy of the book is not required to benefit from the course, but it could be helpful to reinforce understanding.
Topics Covered:
 Combining parallel multiple mediation with moderation.
 Differential dominance
 Serial mediation and serial moderated mediation
 Mediation, moderation, and conditional process analysis with a multicategorical cause or moderator
 Additive and multiplicative dual moderation (moderated moderation or "threeway interaction")
 Partial, conditional, and moderated moderated mediation
 Creation of custom models in PROCESS and the editing of preprogrammed models
The course contains 10 modules, described below, plus a set of review modules that span about 15 hours in total. Also included are six activities dispersed in various places throughout the course to test your understanding.
Introduction

An overview of the course and the software requirements and the things available on the course portal that you will need to complete the course [watch this video].

Review of Fundamentals

Review of the fundamentals of mediation, moderation, and conditional process as discussed in a prior introductory course, as well as a review of the use of PROCESS. Work through this review if you have not taken the first course, you took the first course more than a year or so ago, you have never used PROCESS syntax, or if you feel you would like to freshen up on the fundamentals.

Setting up PROCESS

Preparing SPSS, SAS, and R for the use of PROCESS; a description of some of the differences in the syntax structure between the SPSS, SAS, and R versions.

Module 1

Conditional process analysis with two mediators configured in parallel form using PROCESS; the concept of "differential dominance."

Module 2

The serial mediation model, including path analysis tracing rules for partitioning an effect into direct and indirect components when a mediator is allow to affect other mediator; illustration of serial mediation analysis using PROCESS; custom assignment of covariates to equations using PROCESS; model pruning.

Module 3

A conditional process model that blends serial mediation with moderation; more on model pruning to simplify an unnecessarily complex model.

Module 4

Mediation analysis when the independent variable X is multicategorical; representing a multicategorical variable in a regression model; estimation of relative total, direct, and indirect effects of a multicategorical X; how to determine whether X's effect on Y is mediated; example analysis using PROCESS.

Module 5

Moderation analysis when the focal predictor or moderator is multicategorical; conditioning a multicategorical X's effect on a moderator; probing and visualizing the model using features available in PROCESS.

Module 6

Conditional process analysis when X is multicategorical; an illustration using PROCESS and how to test for moderation of mediation and quantify the relationship between moderator and the size of relative indirect and direct effects that are conditional on a moderator.

Module 7

Moderation analysis with more than one moderator; the additive multiple moderator model that allows X's effect on Y to vary linearly but independently by moderators W and Z; moderated moderation or “threeway” interaction that allows the moderation of X's effect by moderator W to depend on moderator Z; techniques for visualizing and probing, facilitated by PROCESS.

Module 8

Creating a custom model in PROCESS from scratch; editing and customizing a preprogrammed, numbered model.

Module 9

Conditional process models with one of the two paths defining an indirect effect moderated simultaneously by two variables; visualization and interpretation; partial moderated mediation and how to test a partial moderated mediation hypothesis using PROCESS.

Module 10

Conditional process models with two moderators of the indirect effect effect, one operating on the first stage and one on the second stage; moderated moderated mediation and conditional moderated mediation; visualizing conditional indirect effects; testing for moderation of moderated mediation and conditional moderated mediation using PROCESS.

What is Not Covered. In this course, we focus primarily on research designs that are experimental or crosssectional 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).