Full course description
SHORT COURSE DESCRIPTION
Mediation and moderation are two of the most important models in science. Exploring if effects exist is simply not rigorous enough given modern theory and methodology. More compelling is to explore why effects exist (mediation) or when/for whom effects differ (moderation). In this course, you will receive an introduction to the theory and mathematics underlying mediation and moderation models. We will start a review of regression and simple mediation analysis before moving on to parallel and serial multiple mediation models (and how to compare indirect effects). We will then cover moderation analysis, starting with a review of how to test and visualize interaction effects, misconceptions about moderation, and then single and multiple moderator models. The session will conclude with a brief introduction to conditional indirect effects (i.e., the combination of mediation and moderation). We will cover examples from multiple substantive disciplines, and you will work with data from published studies to reproduce and critique the results. You will learn how to do these analyses by hand or by using the popular PROCESS macro (available for R, SPSS, and SAS). Code scripts and outputs for all analyses will be provided. This two-day short course does not require prior knowledge of mediation or moderation, but a working knowledge of linear regression is assumed.
DATES AND TIMES
Nov 6-7, 2025 (Thurs-Fri)
9am-5pm Eastern Time (UTC-4)
The instructor will determine timing of lunch break, as well as morning and afternoon breaks.
COURSE FEES
Professional: $375
Full-time student*: $195
*Full-time students need to submit student status proof at CILVR-STUDENT to request a discount code prior to registration.
*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: CILVR--HDQM Affiliate.
HOW TO REGISTER
Prior to registration, participants not affiliated with UMD need to get a valid UMD associate account in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details about the UMD associate account, please click here.
For UMD affiliated participants, you may register using your existing UMD directory ID.
To request the promotional code prior to registration:
- Full-time students can submit the student status proof at CILVR-STUDENT to request a student discount code prior to registration. Note that it may take 2-3 business days for your request to be processed.
- HDQM department registrants can request the HDQM discount code by submitting the following form: CILVR--HDQM Affiliate. Note that it may take 2-3 business days for your request to be processed.
TARGET AUDIENCE
Graduate students, emerging researchers, continuing researchers
REQUISITE KNOWLEDGE
Participants should have a foundational knowledge up through multiple regression.
SOFTWARE
Participants should have access to R/RStudio installed to maximize their experience. However, code for SPSS and SAS will be provided for all analyses.
LOCATION AND PLATFORM
· The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/).
· This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).
· Typically within 24 hours, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.
IMPORTANT COURSE DETAILS
Platform: Participants who are not affiliated with UMD need to get a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.
Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.
Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.
Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).
Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.
Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.
THE INSTRUCTOR
Dr. Jacob Coutts is a Lecturer of Social Data Science and Psychology at the University of Maryland. In the classroom, he uses active learning techniques to make learning statistics practical and fun. His research interests are mainly in mediation (and its application to dyadic data), moderation, conditional process analysis, resampling methods, and power analysis. His main goals are to make advancements in quantitative methodology and make these advancements available to other researchers through software development (e.g., macros, R packages). He obtained his B.S. in Psychological Sciences at Northern Arizona University, followed by an M.S. in Quantitative Psychology, a master's in Applied Statistics (M.A.S.), and a Ph.D. in Quantitative Psychology at Ohio State University. He may be reached at jjcoutts@umd.edu. (Link to personal website: https://jjcoutts.com)
REFUND POLICY
Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.
CONTACT
For any further questions, please contact mediation.cilvr@gmail.com.
To request a copy of the payment receipt, please contact the OES office at oes-finance@umd.edu.
CILVR Short Course Series
- An (Intermediate) Introduction to Mediation and Moderation Analysis (Nov 6-7, 2025)
- Missing Data Analysis (Dec 4-5, 2025)
- Structural Equation Modeling: From Beginner to Intermediate (Jan 5-7, 2026)
- Introduction to Longitudinal Structural Equation and Latent Growth Modeling (Jan 12-14, 2026)
- Introduction to Mixture Modeling (Mar 11-13, 2026)
- Introduction to Machine Learning for the Social Sciences (May 7-8, 2026)
Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)
CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Measurement, Statistics and Evaluation (EDMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. EDMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.