Mediation, Moderation, and Conditional Process Analysis

How to estimate detailed direct, indirect, and total effects for complex, intertwined mediating and moderating effects.
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  • Lectures 24
  • Length 3 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 12/2015 English

Course Description

Mediation, Moderation, and Conditional PROCESS Analysis is a methodological statistical technique developed by Dr. Andrew F. Hayes which is applied to ordinary least squares (OLS) regression. PROCESS simplifies the 'untangling' of the analyses of combined direct, indirect and total effects driven by simultaneous mediating and moderating influences in an OLS regression model. However, PROCESS can also be directly applied to estimate the mediating and moderating effects of latent variables in PLS path modeling and in covariance-based SEM.

In 2013, Dr. A.F. Hayes developed and published a comprehensive statistical approach to estimate and interpret intertwined, cascading, mediating and moderating, direct, indirect, and total effects, and the comparisons of significant differences between them, as embedded in a series of 77 templates for linear models. Dr. Hayes comprehensively integrated these techniques into a coherent set of SAS and SPSS 'PROCESS' scripts to estimate these complicated effects in linear regression models. We extended these SAS and SPSS scripts with R scripts and a GUI interface and applied this approach directly to both OLS regression and to PLS path modeling results. In this course, we provide our complimentary GUI-based desktop application, allstatGUI, that reliably calculates these estimates using participants' own data and OLS models. This course explains the conceptual basis of PROCESS and demonstrates how these complicated mediating and moderating effects can be reliably estimated on your own data and models using the allstatGUI application developed by Mr. Dean Lim and Dr. Geoffrey Hubona.

All software is included with the course materials. The course is structured as a tutorial which begins with simple mediation models and then progresses through a series of moderation examples. All of the models and data and analyses are provided with the course materials. A course participant should then be able to apply the PROCESS analysis approach to their own data and models which may contain a mixture of combined mediating and moderating effects.

Anyone who regularly works with regression models would benefit from this course. This includes graduate students, faculty and quantitative and data analysis professionals. However, please note that both our allstatGUI application and the PROCESS application are written in the visual RGtk2 language in R which has been noted to have problems running on a Mac computer. So if you only have a Mac computer available to you, you might have problems getting the free software that comes with the course materials to run properly.

What are the requirements?

  • Students will need to install and run the latest no-cost version of R and RStudio software, although ample instruction to do so is provided.

What am I going to get from this course?

  • Apply the PROCESS technique to separately estimate complex, combined direct, indirect and total mediating and moderating effects in OLS models and in PLS path models using their own data and models.
  • Have a wider range of precise analytical tools to more finitely estimate individual direct, indirect and total mediating and moderating effects, as well as the significant differences among them.
  • Install and use our R-based allstatGUI and MEDMOD (PROCESS) software applications on their own data and models.

What is the target audience?

  • Anyone who works with ordinary least squares (OLS) regression models will benefit from this course.
  • SAS and SPSS users of PROCESS scripts who wish to transfer these skills over to using R software.
  • Graduate students, faculty and quantitative and data analysis professionals who work with linear modeling will benefit from this course.
  • Persons who work with partial least squares (PLS) path modeling and who wish to apply the PROCESS technique to latent variables in their own PLS path models.
  • People who only have available Mac computers might have problems running the GUI-based RGtk2-written R PROCESS software that accompanies the course materials.

What you get with this course?

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30 day money back guarantee.

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Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to Mediation, Moderation, and PROCESS
01:59

Introduction to Mediation, Moderation, and Conditional Process Analysis describes the foundation of mediation and moderation analysis as well as their analytical integration in the form of "conditional process analysis", sometimes called "PROCESS" in abbreviation.

Mediation-Moderation Introduction
Preview
05:46
Example PROCESS Templates
Preview
06:58
VIMGUI, Rcmdr, and Rattle ALLSTATGUI Applications
Preview
10:30
MEDMOD Application and PROCESS
09:33
Section 2: Mediation PROCESS Examples
08:12

In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable.

Mediation Concepts (part 2)
08:35
Model 4 Simple Mediation Example (part 1)
09:10
Model 4 Simple Mediation Example (part 2)
07:30
Presumed Media Influence (PMI) Study Examples #2, #3 (part 1)
07:25
Presumed Media Influence (PMI) Study Examples #2, #3 (part 2)
09:04
Presumed Media Influence (PMI) Study Examples #2, #3 (part 3)
07:49
Mediated Examples #4, #5 using PLS Path Modeling (part 1)
09:26
Mediated Examples #4, #5 using PLS Path Modeling (part 2)
09:41
Mediated Examples #4, #5 using PLS Path Modeling (part 3)
06:41
Mediated Examples #4, #5 using PLS Path Modeling (part 4)
02:59
Section 3: Moderation PROCESS Examples
09:15

In statistics and regression analysis,moderation occurs when the relationship between two variables depends on a third variable.

Moderation (part 2)
08:21
Moderation (part 3)
08:03
First Moderation PROCESS Example (part 1)
Preview
07:20
First Moderation PROCESS Example (part 2)
07:34
Second Moderation PROCESS Example (part 1)
07:42
Second Moderation PROCESS Example (part 2)
08:09
Bonus Lecture: SmartPLS versus PLS-GUI Features
03:53

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Instructor Biography

Geoffrey Hubona, Ph.D., Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

Instructor Biography

Dean Lim, SEM Statistical Programmer

I am an avid user and statistical programmer of all things related to SEM especially the newer Partial Least Squares SEM. I believe PLS-SEM will be the future of big data as related to marketing. I am also a data analyst and CMRP marketing researcher.

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