Mediation, Moderation, and Conditional Process Analysis
A 4-Day Remote 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.
Starting August 17, we are offering this seminar as a 4-day synchronous*, remote workshop for the first time. Each day will consist of a 3-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional lab session will be held Tuesday and Thursday afternoons, where you can review the exercise results with the instructor and ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions.
MORE DETAILS ABOUT THE COURSE CONTENT
Topics covered in the course include:
- Path analysis: Direct, indirect, and total effects in mediation models.
- Estimation and inference about indirect effects in single mediator models.
- Models with multiple mediators.
- Estimation of moderation and conditional effects.
- Probing and visualizing interactions.
- Conditional Process Analysis (also known as “moderated mediation”).
- Quantification of and inference about conditional indirect effects.
- Testing a moderated mediation hypothesis and comparing 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.
Because this is a hands-on course, participants are strongly encouraged to use their own computer (Mac or Windows) with one of the following packages installed: SPSS Statistics (version 23 or later), SAS (release 9.2 or later, with PROC IML installed), or R (v3.6 or later; base R; no special packages are needed).
Participants can choose which statistics package they want to use while working through the course material. You should have good familiarity with the basics of OLS regression as well as the use of SPSS, SAS, or R, including opening and executing data files and programs. You are also encouraged to have your own data available to apply what you’ve learned
WHO SHOULD Register?
This seminar will be helpful for researchers in any field—including psychology, sociology, education, business, human development, political science, public health, communication—and others who want to learn how to apply the latest methods in moderation and mediation analysis using readily-available software packages such as SPSS, SAS and R.
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. Participants should be familiar with linear regression in one of the following software packages: SPSS, SAS, or R.
Day 1: Introduction to Mediation Analysis
- Review of basic OLS regression concepts
- Introduction to path analysis
- Estimation and inference for total, direct, and indirect effects
- Assumptions and causality in mediation analysis
- Writing clear and compelling mediation results
Day 2: Mediation Analysis with Multiple Mediators
- Advantages of estimating models with multiple mediators
- Path analysis for parallel mediator models
- Estimation and inference for specific indirect effects
- Path analysis for serial mediator models
- Estimation and inference for serial indirect effects
- Considerations in causality for models with multiple mediators
Day 3: Introduction to Moderation Analysis
- Estimation of models with a single moderator
- Symmetry of moderation models
- Estimation of and inference in moderation parameters
- Estimation of and inference for conditional effects
- Probing and visualizing interactions
- Writing clear and compelling mediation results
Day 4: Conditional Process Analysis
- Advantages of combining mediation and moderation
- Path analysis for conditional process models with a single mediator and single moderator
- Estimation of and inference for conditional indirect effects
- Estimation of and inference for the index of moderated mediation
- Comparing tests of moderated mediation to conditional indirect effects
- Writing clear and compelling conditional process analysis results