Webinar Series
Modeling Individual and Group Outcomes: An Introduction to Mixed-Effects Models
Brent Kaplan (Advocates for Human Potential (AHP))
Date: October 22, 2025
Time: 1:00 PM Eastern
Abstract: Behavior analysts frequently work with repeated‑measures and single‑case data, yet traditional approaches often rely on aggregating observations or visual inspection alone. This intermediate‑level webinar introduces mixed‑effects (multilevel) modeling as an analytic framework that preserves individual differences while estimating population‑level effects. Attendees will build intuition about concepts underlying these models, learn the fundamentals of specifying and interpreting mixed-effects models, see examples applied to behavioral data, and understand how these models can help characterize both group and individual trends without requiring data aggregation. The session will demystify key concepts (e.g. distinguishing fixed vs. random effects) and compare mixed-effects analyses to visual inspection and simpler statistics. Attendees will learn when mixed models are appropriate and how to specify and interpret a basic model. The session emphasizes practical takeaways over mathematical derivations, and will provide attendees resources to begin learning about and implementing these techniques in their own work.
Instruction Level: Intermediate
CE Available: BACB/IBAO
Learning Objectives:
At the conclusion of this session, participants will be able to:
- Define mixed-effects modeling and distinguish between fixed effects and random effects in an analysis.
- Identify data scenarios (e.g. repeated measures or hierarchical datasets) where mixed-effects models are an appropriate analytic approach.
- Explain how incorporating random effects in a model accounts for individual differences and addresses limitations of traditional analyses that rely on data aggregation.
Target Audience: Behavior analysts, behavioral researchers, and behavioral economists interested in data analysis, especially those working with repeated measures designs.
Biography: Dr. Brent A. Kaplan is a data scientist at Advocates for Human Potential (AHP) whose work integrates behavioral science, data analytics, and software development for behavioral health. He leverages his expertise to extract meaningful insights, inform business decisions, and create end-to-end software tools and procedures that streamline data analytic processes. Brent also works as a statistical research consultant (www.codedbx.com) where he specializes in behavioral economics and human decision-making. His expertise helps him design rigorous experiments and conduct analyses using both frequentist and Bayesian methods.
Brent received the 2024 B. F. Skinner New Basic Researcher Award from Division 25 of the American Psychological Association and the 2018 Outstanding Dissertation Award from Division 28 of the American Psychological Association. He has authored more than 70 peer‑reviewed publications in topics of behavioral economic demand and discounting, public policy, and statistical methods, and has served as co‑investigator on NIH‑funded R01 projects focused on addiction and behavioral health. He maintains two open-source R packages, beezdemand and beezdiscounting, for conducting behavioral economic analysis, and a web app, shinybeez, that makes conducting these analyses user-friendly. Brent completed a postdoctoral fellowship at the Addiction Recovery Research Center (Virginia Tech) and most recently was an Assistant Professor in the Department of Family and Community Medicine at the University of Kentucky. He earned his Ph.D. in Behavioral Psychology from the University of Kansas.
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