# Mediation, Moderation, and Conditional Process Analysis

**Prerequisites (knowledge of topic)**

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.

**Hardware**

Participants are strongly encouraged to bring their own laptops (Mac or Windows)

**Software**

Computer applications will focus on the use of OLS regression and the PROCESS macro for SPSS and SAS developed by Andrew F. Hayes (processmacro.org) that makes the analyses described in this class much easier than they otherwise would be. Because this is a hands-on course, participants are strongly encouraged to bring their own laptops (Mac or Windows) with a recent version of SPSS Statistics (version 19 or later) or SAS (release 9.2 or later) installed. SPSS users should ensure their installed copy is patched to its latest release. SAS users should ensure that the IML product is part of the installation. R and STATA users can benefit from the course content, but PROCESS makes these analyses much easier and is not available for R or STATA.

**Course content**

Statistical mediation and moderation analyses are among the most widely used data analysis techniques in social science, health, and business fields. 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 and SAS.

Topics covered in this five-day course include:

- Path analysis: Direct, indirect, and total effects in mediation models.
- Estimation and inference about indirect effects in single mediator models.
- Models with multiple mediators
- Mediation analysis in the two-condition within-subject design.
- Estimation of moderation and conditional effects.
- Probing and visualizing interactions.
- Conditional Process Analysis (also known as “moderated mediation”)
- Quantification of and inference about conditional indirect effects.
- Testing a moderated mediation hypothesis and comparing conditional indirect effects

As an introductory-level 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, models with more than two repeated measures, nested data (i.e., multilevel models), or the use of structural equation modeling.

This course will be helpful for researchers in any field—including psychology, sociology, education, business, human development, political science, public health, communication—and others who want to learn how to apply the latest methods in moderation and mediation analysis using readily-available software packages such as SPSS and SAS.

**Structure**

The schedule for the course will be partially determined by previous experience of the students, and their existing familiarity with mediation and moderation. The below schedule is a rough approximation of the schedule for the course.

Day 1

- Path analysis: Direct, indirect, and total effects in mediation models.
- Estimation and inference about indirect effects in single mediator models.

Day 2

- Models with multiple mediators
- Mediation analysis in the two-condition within-subject design.

Day 3

- Estimation of moderation and conditional effects.
- Probing and visualizing interactions.
- Moderation analysis in the two-condition within-subject design

Days 4 & 5

- Estimation of conditional process models (also known as “moderated mediation”)
- Quantification of and inference about conditional indirect effects.
- Testing a moderated mediation hypothesis and comparing conditional indirect effects

**Literature**

This course is a companion to Andrew Hayes’s book Introduction to Mediation, Moderation, and Conditional Process Analysis (IMMCPA), published by The Guilford Press. The content of the course overlaps the book to some extent, but many of the examples are different, and this course includes material not in the first edition of the book. A copy of the book is not required to benefit from the course, but it could be helpful to reinforce understanding.

Beyond IMMCPA additional materials include:

Montoya, A. K., & Hayes, A. F. (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework. *Psychological Methods, 22*(1), 6-27*. *

Hayes, A. F. (2015). An index and test of linear moderated mediation. *Multivariate Behavioral Research, 50*, 1-22.

**Mandatory:**

No materials are mandatory, but students will benefit greatly from reading Andrew Hayes’s book Introduction to Mediation, Moderation, and Conditional Process Analysis (IMMCPA), published by The Guilford Press

**Supplementary / voluntary:**

Introduction to Mediation, Moderation, and Conditional Process Analysis (IMMCPA), published by The Guilford Press

Montoya, A. K., & Hayes, A. F. (2017). Two-condition within-participant statistical mediation analysis: A path-analytic framework. *Psychological Methods, 22*(1), 6-27*. *

Hayes, A. F. (2015). An index and test of linear moderated mediation. *Multivariate Behavioral Research, 50*, 1-22.

**Mandatory readings before course start:**

N/A

**Examination part**

60% of assessment will be based daily homework assignments and 40% will be based on a written final examination at the end of the course. The exam and homework will be a combination of multiple choice questions and short-answer/fill in the blank questions, along with some interpretation of computer output. Homework questions will be posted at the end of class on Monday – Thursday and turned in the following day. Students will take the final exam home on the last day of class and return it to the instructor within one week.

**Supplementary aids**

For homework and during the examination students will be allowed to use all course materials, such as PDFs of PowerPoint slides, student notes taken during class, and any other materials distributed or student-generated during class. Although the book mentioned in “Literature” is not a requirement of the course nor is it necessary to complete the assignments, students may use the book if desired.

A computer is not required to complete the assignments, though students may use a computer if desired, for example as a storage and display device for class notes provided to them during class.

**Examination content**

The topics of the homework and exam may include how to quantify and interpret path analysis models, calculate direct, indirect, and total effects, and determine whether evidence of a mediation effect exists in a data set based on computer output provided or other information. Also covered will be the testing moderation of an effect, interpreting evidence of interaction, and probing interactions. Students will be asked to generate or interpret conditional indirect effects from computer output given to them and/or determine whether an indirect effect is moderated. Students may be asked to construct computer commands that will conduct certain analyses. All questions will come from the content listed in “Course Content” above.

**Literature**

Although the book mentioned in “Literature” is not a requirement of the course nor is it necessary to complete the assignments, students may use the book if desired.