Event Phone: 1-610-715-0115
- Conditional Process Analysis
October 27, 2017 - October 28, 2017
9:00 am - 5:00 pm
Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.
A 2-Day Seminar Taught by Andrew Hayes, Ph.D.
In both the social sciences and the health sciences, the study of mechanisms has become increasingly important. In this seminar, Andrew Hayes describes the fundamentals of conditional process analysis, a class of models that allows mechanisms (i.e., indirect effects in a path model) to vary systematically as a function of one or more moderator variables. Starting with a review of the fundamentals of moderation and mediation analysis, the course focuses on models based on ordinary least squares regression, as implemented in the popular PROCESS add-on for SPSS and SAS.
To elaborate, the effect of X on Y is mediated if all or part of that effect operates indirectly through a mediator M. It is moderated if its strength or sign depends on a moderator W. A conditional process model integrates mediation and moderation phenomena into a single, integrated analytical model. Sometimes known as an analysis of moderated mediation, conditional process analysis is used when an investigator is interested in understanding the contingencies of the mechanisms that produce effects. Under what circumstances does a mechanism (an indirect effect) exist and when does it not? When, for whom, or under what circumstances is the mechanism strong versus weak, or positive versus negative?
The course builds in complexity as it progresses, advancing from models with a single moderator of a single path to more complex models with multiple moderators or multiple indirect effects. Parallels are drawn between the regression-based perspective described in Hayes’ book, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Perspective and multiple group structural equation modeling. In particular, you’ll learn how to emulate an observed variable multiple group structural equation model using the latest release of PROCESS. Strategies are offered for how to approach complex analyses and how to modify a model based on evidence acquired during analysis.