Course Description

Leading expert Dr. Andrew Hayes, PhD will guide the learner through topics in the statistical analysis of mechanisms responsible for causal effects as well as their contingencies, popularly known as mediation and moderation analysis, as well as their integration as conditional process analysis. This introductory course is recommended to all levels of learners prior to taking Mediation, Moderation and Conditional Process Analysis: A Second Course.


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 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


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.

Course Details

This online course consists of a collection of 16 modules in the form of videos and exercises that can be completed with a time commitment of about 6-8 hours/week. You can participate at your own convenience; there are no set times when you are required to be online during the offering period, and you can rewind the videos and review modules completed at your leisure. Questions can be sent to the instructor and others in the class through a discussion board on the course delivery platform, and the instructor will offer regular opportunities for (optional) synchronous interaction via Zoom at various times during the course. The course can be accessed with any recent web browser on almost any computing platform, including iPhone, iPad and Android devices.  Although the course runs for 3 weeks, everyone will have access to the content through the course portal for an additional two weeks beyond the end of the course.

The course is delivered through CCRAM's online course portal at http://ccram.digitalchalk.com/. After paying tuition, you should set up a Digital Chalk account in order to get access to the course content.

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


Instructor bio

Dr. Andrew Hayes is a quantitative methodologist and holds a PhD in Psychology from Cornell University as well as a BA in Psychology from San Jose State University. His research and writing on applied statistical methods has been published in such journals as Psychological Methods, Multivariate Behavioral Research, Behavior Research Methods, British Journal of Mathematical and Statistical Psychology, Psychological Science, Journal of Educational and Behavioral Statistics, American Behavioral Scientist, Communication Monographs, Journal of Communication and Australasian Marketing Journal, among many others.

He is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis (2018) and Regression Analysis and Linear Models (2017), both published by The Guilford Press, and Statistical Methods for Communication Science (2005), published by Routledge. He also invented the PROCESS macro for SPSS, SAS and R (processmacro.org) that is widely used by researchers examining the mechanisms and contingencies of effects. He teaches courses on applied data analysis and also conducts online and in-person workshops on statistical analysis to multidisciplinary audiences throughout the world, most frequently to faculty and graduate students in business schools but also in education, psychology, social work, communication, public health and government researchers. His work has been cited over 200,000 times according to Google Scholar and he has been designated a Highly Cited Researcher by Clarivate Analytics in 2019, 2020, 2021, 2022 and 2023. Visit his website to learn more (afhayes.com).



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.


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