Statistics workshop for UCalgary researchers

Haskayne School of Business is pleased to welcome Andrew Hayes, PhD to our faculty. Starting March 2021, UCalgary researchers will be able to access Dr. Hayes’ statistical workshop Introduction to Mediation, Moderation and Conditional Process Analysis, free of charge via Haskayne Executive Education.

Introduction to Mediation, Moderation and Conditional Process Analysis

Dates: March 1 – 31

Delivery: Asynchronous online modules, with opportunities for feedback and engagement with the instructor.

Instructor: Andrew F. Hayes, PhD, Haskayne School of Business

Andrew F. 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), 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 125,000 times according to Google Scholar and he has been designated a Highly Cited Researcher by Clarivate Analytics in 2019 and 2020. Visit his website (www.afhayes.com) to learn more.

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

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. 

By the end of this course, you will:

  • Be able to statistically partition one variable’s effect on another into its pathways of influence, direct and indirect
  • Understand historical and modern approaches to inference about indirect effects in causal models
  • Know how to 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
  • 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 readily available statistical software
  • Be in a position to talk and write in an informed way about the mechanisms and contingencies of causal effects

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 moderation and mediation analysis in their research.

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.

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. Because this is a hands-on course, 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 (although a brief overview of OLS regression will be the first topic of the course), 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.

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.

  • 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

As an introductory-level program, the focus is primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. This program does not cover complex models involving dichotomous outcomes, latent variables, nested data (i.e., multilevel models) or the use of structural equation modeling. "Counterfactual" or "potential outcomes" approaches to mediation analysis are not addressed, nor is there a discussion of directed acyclic graphs (DAGs).

Questions? Please contact execed@haskayne.ucalgary.ca.