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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 3-Day Livestream Seminar Taught by Amanda Montoya, Ph.D.

Mediation and moderation analyses are widely used in many different fields. Mediation analysis tests hypotheses about mechanisms or processes by which effects occur. Moderation analysis examines questions of contingencies (e.g., for whom, or when), commonly described as an “interaction”.

Moderation and mediation can be combined analytically to investigate questions of contingencies in mechanisms. This is called conditional process analysis or moderated mediation. These statistical approaches help researchers generate theoretical models of how and when, which can then be statistically evaluated using observed data.

This course covers methods for statistical mediation, moderation, and conditional process analysis using ordinary least squares (OLS) regression and the PROCESS macro, available for SPSS, SAS, and R.

By the end of this class you will be able to:

• • Define and calculate direct, indirect, and total effects in mediation models.
  • Estimate and conduct inference on indirect effects in single mediator models.
  • Generalize previous concepts to multiple mediator models.
  • Estimate models with moderation and conditional effects.
  • Probe and visualize interactions.
  • Define conditional process analysis (AKA “moderated mediation.”)
  • Quantify and conduct inference on conditional indirect effects.
  • Test a moderated mediation hypothesis and compare conditional indirect effects.

This introductory seminar focuses primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. It does not cover complex models involving dichotomous outcomes, latent variables, models with repeated measures, nested data (i.e., multilevel models), or the use of structural equation modeling.