Browse the online program to view session details, scheduling, and speakers.

The APS Annual Convention includes extended educational sessions that offer attendees the opportunity to learn research methods and techniques from prominent psychological scientists.

Workshops are open to Convention registrants only and require additional registration fees. Workshops can be added when you register for the APS Convention.

If you’ve already registered and would like to add a workshop, please follow these instructions to add a Workshop to your 2026 APS Annual Convention Registration purchase.

Workshop Registration Fees:

Wednesday 27 May

Bayesian SEM in blavaan 

09:00 – 11:50 (9:00 AM – 11:50 AM) 

Presenter: Mauricio Garnier-Villarreal, Vrije Universiteit Amsterdam, The Netherlands 

Bayesian methods have gain popularity, and user friendly open-source software like blavaan have made them accessible. We will go over Bayesian inference, BSEM. Followed by details steps of the process, like model definition, prior specification, overall and local model fit evaluation, model comparison, with extensive example code.

Prerequisites:

Linear regression, and basics of SEM 

Introduction to Structural Equation Modeling in the Psychological Sciences

09:00 – 12:50 (9:00 AM – 12:50 PM)

Presenter: Timothy Hayes, Florida International University, USA

Structural Equation Modeling (SEM) combines common factor analysis with multiple regression to allow researchers to assess true score relations among constructs of theoretical interest. This workshop presents an overview of the logic, implementation, and interpretation of SEMs. Topics covered include: path analysis, confirmatory factor analysis, and structural regression analysis. 

Prerequisites:

• A standard graduate course in linear regression analysis. 
• Software packages used: lavaan (R) and Mplus. 

Multilevel Modeling

13:00 – 16:50 (1:00 – 4:50 PM)

Presenter: Ethan M. McCormick, University of Delaware, USA 

Abstract: This workshop aims to develop theoretical knowledge and practical skills (primarily in R) with multilevel models, focusing on considerations for real data analysis. These models are commonly used with nested data structures, including children nested within classrooms or families, or with longitudinal data. Frequentist and Bayesian perspectives will be addressed.

Prerequisites:

• Participants should be familiar with standard linear regression.  
• Prior experience in R is helpful, but not strictly necessary.  
• Participants should come with R installed, and with the packages `lme` and `brms` installed. 

Thursday 28 May

Item Response Theory: Core Principles and Applied Techniques for Psychological Science

09:00 – 11:50 (9:00 AM – 11:50 AM)

Presenters:

• Brian C. Leventhal, James Madison University, USA   
• Autumn N. Wild, James Madison University, USA 

This workshop introduces item response theory (IRT), a class of psychometric models describing interactions between people and test/survey questions. Through lectures, discussions, and interactive examples, we will explore the basic tenets of IRT, common dichotomous and polytomous IRT models, and applications in psychological science. 

This course will be taught at an introductory level but assumes a foundational knowledge of statistics. Participants should be familiar with basic statistical concepts, such as the distinction between parameters and statistics, and the interpretation of scatterplots and line graph. 

Variable Selection Methods for Psychological Research

09:00 – 11:50 (9:00 AM – 11:50 AM)

Presenter: Sierra Bainter, University of Miami, USA

Variable selection methods are useful when a researcher isn’t sure which predictors belong in their model. In this workshop we will review and compare classic and modern approaches to address this age-old problem, including LASSO regularization and Bayesian methods. We will also discuss missing data and approaches for longitudinal data.

Prerequisites: 

• Familiarity with linear regression 
• Bring a laptop with up-to-date versions of R and Rstudio (optional)

Latent Class Analysis in R

09:00 – 12:50 (9:00 AM – 12:50 PM)

Presenters:

• Marcos Jiménez, Vrije Universiteit Amsterdam, The Netherlands
• Mauricio Garnier-Villarreal, Vrije Universiteit Amsterdam, The Netherlands  

This workshop introduces Latent Class Analysis (LCA) in R using the open-source latent package. Participants will learn how to specify, customize, estimate, and interpret latent class models. We will handle common estimation issues, and compare solutions across model specifications. The session combines practical examples, reproducible workflows, and guidance for applied research.

Prerequisites: 

• R and RStudio installed onto laptops. 
• Basic knowledge of R is required. 
• The development version of Latent should be installed from https://github.com/Marcosjnez/latent. Instructions for installation are available from the GitHub repository. 
• Basic familiarity with latent variable modeling is helpful, but prior experience with LCA is not required. 

Bayesian Estimation for Multilevel Models: Overcoming Computational Hurdles and Enhancing Statistical Inference

09:00 – 12:50 (9:00 AM – 12:50 PM)

Presenters:

• J.P.  Laurenceau, University of Delaware, USA 
• Niall Bolger, Columbia University, USA  

The analysis of longitudinal data often requires multilevel models that can handle complex structures, numerous random effects, and non-normal outcomes. In these scenarios, traditional maximum likelihood (ML) estimation is increasingly prone to convergence failures. This day-long, practical, hand-on workshop introduces Bayesian estimation as the essential alternative for the modern psychological scientist, bridging the gap between computational necessity and theoretical clarity. We will examine the rise of “computational frequentism,” where researchers adopt Bayesian methods to resolve practically the limitations of ML in several contexts. We will then pivot to the substantive benefits of the Bayesian framework. Participants will learn how combining prior information with likelihood functions creates posterior distributions that offer richer, more intuitive insights than standard frequentist point estimates.  Lecture notes, example datasets, and analysis code will be made available.

Prerequisites: 

• Bring a laptop with R (brms) and the free demo version of Mplus pre-loaded to follow along. 
• Have good working knowledge of multiple regression and ideally some working knowledge of standard frequentist multilevel modeling (MLM).  

Key Learning Objectives:  

1. Solving Convergence Issues: Strategies for using Bayesian estimators for complex multilevel models.  
2. Epistemological Clarity: Moving beyond the limitations of null-hypothesis significance testing to making probability statements about hypotheses.  
3. Practical Application: A tutorial on specifying models in R (brms) and Mplus, covering prior specification, posterior interpretation, and reporting standards (e.g., Highest Density Intervals).  

Suggested Readings:

• Laurenceau, J.-P., DiGiovanni, A. M., & Bolger, N. (2026). Intensive longitudinal methods: Toward a psychological science of daily life. Annual Review of Psychology, 77, 513– 541. https://doi.org/10.1146/annurev-psych-040325-025418   
• Levy, R, & McNeish, D. (2023). Perspectives on Bayesian inference and their implications for data analysis. Psychological Methods, 28(3), 719–39. https://doi.org/10.1037/met0000443

Categorical Data Analysis: New Methods for Marginal Effects, Moderation, and Mediation

14:00 – 17:50 (2:00 – 5:50 PM) 

Presenter: Trenton D. Mize, Purdue University, USA 

Categorical models for binary, ordinal, nominal, and count outcomes are widely used but frequently misinterpreted. This workshop covers contemporary advances in estimating marginal effects for meaningful effect sizes, improved tests of moderation and interaction, and mediation analysis using marginal effects. All methods will be implemented in R, and full replication code will be provided.

Prerequisites: 

• A solid foundation in linear regression.  
• Some familiarity with categorical models such as logit and probit is helpful but not required.  
• Those who wish to implement the replication code should have the latest version of R installed. 

Introduction to Causal Mediation Analysis

14:00 – 15:50 (2:00 – 3:50 PM)

Presenter: Amanda Kay Montoya, University of California, Los Angeles, USA 

Causal mediation analysis describes and evaluates assumptions necessary to estimate causal effects, such as indirect effects. This workshop introduces researchers to these methods and how they can be used in concert with statistical mediation analysis in psychological research, using real data examples and example code in R. 

Prerequisites:

• Familiarity with statistical mediation analysis as typically used in psychology research (i.e., estimation of separate regression equations, calculation of the indirect effect as the product of two effect, bootstrapping for inference of indirect effects). 
• Working familiarity (basic data handling) with R. No specific package familiarity required.

Safeguarding Scholarship in Emerging Autocracies

14:00 – 17:50 (2:00 – 5:50 PM)

Presenters:

• Stephan Lewandowsky, University of Bristol, United Kingdom
• Vera Kempe, Abertay University, Dundee, United Kingdom 
• Christina Pagel, University College London, United Kingdom 

Drawing on the Anti-Autocracy Handbook ( https://sks.to/autocracy), this workshop helps scholars recognise the “3 Ps” of autocratization: populism, polarization, and post-truth. Participants will assess personal risk to choose safe activities to counter autocratization. Through active listening and collaborative exercises, attendees will build collective strategies to defend academic freedom and democratic accountability.