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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.

 

LEARNING STATEMENT
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

 

SUMMARY
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

TIME COMMITMENT AND COURSE DELIVERY
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. The course can be accessed with any recent web browser on almost any computing platform, including iPhone, iPad and Android devices.

 

COMPUTING
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.

 

WHO WILL BENEFIT?
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.

 

LEARNING OUTCOMES
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

Prerequisites

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.

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Type
Online
Dates
Jan 11, 2022 to Feb 01, 2022
Hours
18.0
Delivery Options
Course Fees
General Enrolment non-credit $625.00 Click here to get more information
Student Enrolment non-credit $531.25 Click here to get more information
Potential Price Adjustments
Section Notes

Delivery:

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

Start Date: January 11, 2022

Program Delivery: Online, asynchronous

Commitment: 3 weeks* 

Investment: $625 (Canadian dollars) 

Instructor: Dr. Andrew Hayes, PhD

Registration Deadline: January 10, 2022

*Content access will be extended one week past the course close to February 8. After this date, content cannot be accessed. 

**University of Calgary Registrants - please contact execed@haskayne.ucalgary.ca to obtain coupon code to apply against general enrolment selection. Categorized as Faculty, Staff, Student Undergraduate, Student Graduate, Post-Doc. Must have active @ucalgary.ca email/ID

**Economically Developing Nations - Funding relief or supports are available, please contact us directly at execed@haskayne.ucalgary.ca 

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