In "HSK 849 Mediation, Moderation and Conditional Process Analysis: The Complete Course", leading expert Dr. Andrew Hayes, PhD will guide learners through key topics in causal analysis focusing on introduction to advanced applications and methods of the mediation, moderation and conditional process analysis. This course is offered on-demand in online, asynchronous format.
- Direct, indirect and total effects in a mediation model
- Estimation and inference in single mediator models using ordinary least squares regression
- Estimation and inference in models with more than one mediator
- Moderation or “interaction” in ordinary least squares regression
- Testing, interpreting, probing and visualizing interactions
- The integration of mediation and moderation: Conditional process analysis
- Serial mediation and serial moderated mediation.
- Mediation, moderation and conditional process analysis with a multi-categorical cause or moderator
- Estimating, probing, and interpreting models with two moderators
- Testing, visualizing, and probing three-way interaction (moderated moderation)
- Partial, conditional and moderated moderated mediation
- Using PROCESS and the creation of custom models in PROCESS
Statistical mediation and moderation analyses are among the most widely used data analysis techniques. Mediation analysis is used to test 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.” Conditional process analysis is the integration of mediation and moderation analysis and is used when one seeks to understand the conditional nature of processes (i.e. “moderated mediation”)
In his book, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach, Dr. Hayes describes the fundamentals of mediation, moderation and conditional process analysis using ordinary least squares regression. He also explains how to use PROCESS, a freely available and handy tool he invented that brings modern approaches to mediation and moderation analysis within convenient reach. This online course picks up where the first introductory course leaves off. After a review of basic principles, it covers material in the second edition of the book not covered in the first course, as well as new material not available in the book.
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.
This course is delivered in online format asynchronously through pre-recorded video through the user interface at http://ccram.digitalchalk.com. Learners can begin the course when registration is completed, and access to the content is provided for 100 days after registration. There is no limit to the number of times a video can be viewed, and controls for pausing, forwarding, and rewinding are provided on the user interface. Each course also contains a number of downloadable activities and self-assessments, with corresponding videos describing the answers to each activity and assessment task.
All data files, statistical code, activities, and PDFs of content of the videos can be downloaded and saved to the learner's storage medium for viewing even after the end of the access period. Videos cannot be downloaded or viewed offline.
Learners can interact with the instructor through email. In HSK 849, an online discussion forum and occasional open group office hours through Zoom are provided for learners and the instructor to interact in real time during the learner's access period.
This course is taught in English. There are no foreign language subtitles or closed captioning available on the videos, and all course materials are provided in English.
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, which 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.
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 190,000 times according to Google Scholar and he has been designated a Highly Cited Researcher by Clarivate Analytics in 2019, 2020, 2021, and 2022. 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.