Date(s) - 06/06/2016 - 06/07/2016
Categories No Categories
Start Date: 6/6/2016
End Date: 6/7/2016
Queensland University of Technology
Gardens Point Campus, 2 George Street
Taught by Andrew Hayes, Ph.D.
Although these concepts are fairly simple, the statistical issues that arise in estimating and testing mediation and moderation effects turn out to be rather complex and subtle. Andrew Hayes has been among the leading recent contributors to the literature on these methods. He has developed
In this seminar, you will learn about the underlying principles and the practical applications of these methods. The seminar is divided roughly into three parts:
1. Partitioning effects into direct and indirect components, and how to quantify and test hypotheses about indirect effects.
2. Estimating, testing, probing, and visualizing interactions in linear models.
3. Integrating moderation and mediation by discussing how to estimate conditional indirect effects, determine whether an indirect effect is moderated (moderated mediation) and whether moderated effects are mediated (mediated moderation).
Computer applications will focus on the use of OLS regression and computational modeling tools for SPSS and SAS (including the PROCESS add on developed by Hayes). When appropriate, some Mplus code will be provided for those interested, but structural equation modeling and Mplus will not be the emphasis of this seminar.
Because this is a hands-on course, participants are strongly encouraged to bring their own laptops (Mac or Windows) with a recent version of SPSS Statistics (version 19 or later) or SAS (release 9.2 or later) installed. SPSS users should ensure their installed copy is patched to its latest release. SAS users should ensure that the IML product is part of the installation. You should have good familiarity with the basics of ordinary least squares regression (although an overview of OLS will be the first topic of the course), as well as the use of SPSS or SAS. You are also encouraged to bring your own data to apply what youve learned.