Introduction to Mediation, Moderation, and Conditional
A 2-Week Online Seminar Taught by
Andrew Hayes, Ph.D.
To see a sample and overview of the course, click here.
Although these concepts are fairly simple, the statistical issues that arise in estimating and testing mediation and moderation effects turn out to be rather complex and subtle. Andrew Hayes has been one of the leading contributors to the literature on these methods. He has studied and written about methods of estimating mediation and moderation effects and developed software tools that greatly ease the computational burdens on the researcher.
In this seminar, you will learn about the underlying principles and the practical applications of these methods. The seminar is divided roughly into three parts:
1. Partitioning effects into direct and indirect components, and how to quantify and test hypotheses about indirect effects.
2. Estimating, testing, probing, and visualizing interactions in linear models.
3. Integrating moderation and mediation analysis by discussing how to test whether a mechanism (an indirect effect) is moderated.
Computer applications will focus on the use of OLS regression and the PROCESS macro for SPSS, SAS, and R. Participants in this online seminar will get a copy of PROCESS for R (in beta form) before it is released to the general public toward the end of 2020.
This seminar will feature all of the content included in the 2-day live version of the seminar. You may participate at your own convenience; there are no set times when you are required to be online.
Because this is a hands-on course, participants are strongly encouraged to use their own laptop or desktop computer (Mac or Windows) with a recent version of SPSS Statistics (version 23 or later), SAS (release 9.2 or later, with PROC IML installed), or R (v3.6 or later; base R; no special packages are needed) installed. Participants can choose which statistics package they want to use while working through the course material.
You should have good familiarity with the basics of ordinary least squares regression as well as the use of SPSS, SAS, or R, including opening and executing data files and programs. You are also encouraged to have your own data available to apply what you’ve learned.
WHO SHOULD Register?
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. 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.
- A Path analysis primer: Direct, indirect, and total effects
- Estimation and inference in single mediator models
- Estimation and inference in models with more than one mediator
- Moderation/interaction in OLS regression
- Probing and visualizing interactions
- Conditional Process Analysis (Moderated mediation)
- 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.
- know how test competing theories of mechanisms statistically through the comparison of indirect effects in models with multiple mediators.
- acquire an understanding of how to build flexibility into a regression model that allows a variable’s effect to be a function of another variable in a model.
- have the ability to visualize and probe interactions in regression models (e.g., using the simple slopes/spotlight analysis and Johnson-Neyman/floodlight analysis approaches).
- have learned how to integrate models involving moderation and mediation into a conditional process model.
- have learned how to estimate the contingencies of mechanisms through the computation and inference about conditional indirect effects.
- know how to determine whether a mechanism is dependent on a moderator variable.
- be able to apply the methods discussed in this course using the PROCESS procedure for SPSS, SAS, and R.
- be in a position to talk and write in an informed way about the mechanisms and contingencies of causal effects.
“I have used PROCESS already for many years, and I was initially hesitant about taking this class. However, Dr. Hayes does a fabulous job in teaching regression fundamentals and how to use them to understand and use PROCESS. This class really deepened my knowledge of PROCESS and gave me more confidence in my understanding of regression analysis, mediation, and moderation. Thank you!”
Colleen Kirk, New York Institute of Technology
“This course provided a clear and concise introduction to regression-based Mediation, Moderation, and Conditional Process Analysis. A knowledgeable expert capable of providing complex concepts in a simple, effective manner punctuated this exceptional learning experience.”
Melissa Miller, Rutgers University
“This seminar explained the concept really well and in detail. It’s easily understandable even for beginners.”
Huiying Jin, Rutgers University
“This class covered just the right amount of material to allow me to perform moderation and mediation analysis while following the instructions in Hayes’s book. The instructor was both thorough and clear, making the class easy to follow and enjoy.”
Kelly McClure, La Salle University
“This course greatly expanded my understanding of mediation and moderation and the potential for integrating these ideas. This course is a ‘must’ for anyone interested in learning more about the nuances and practicalities of conducting conditional process analysis.”
Timothy Cleary, Rutgers University
“Instructor, instruction, and tools are all excellent! I think all levels of students will benefit from this workshop.”
Anita Delahay, Carnegie Mellon University
“This course was well worth the price. The foundational review of concepts was helpful in understanding how PROCESS works. Andrew was generous with is time in answering all questions. An excellent experience.”
Kristen Abraham, University of Detroit Mercy
“This course is a gold mine. Dr. Hayes is able to take knowledge you already have about regression and statistical inference and show you the incredibly sophisticated models that can be created and tested in just a few additional steps. Dr. Hayes’s explanations are also so gradual that you don’t realize the wealth of knowledge you’ve acquired until later. Highly recommended for anyone looking for a relatively easy way to enhance your analysis and research.”
Olivia Podolak, University of Toronto, Scarborough