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 and will be available in the registration form soon.
Workshop Registration Fees:
APS Professional Member | $75.00 |
APS Professional Member- Developing Country | $5.00 |
APS Student Member | $25.00 |
APS Student Member – Developing Country | $5.00 |
Thursday, May 22
Modern Variable Selection Methods for Psychological Research
Thursday, May 22, 9:00 am – 11:00 am
Presenter: Sierra Bainter, University of Miami
Abstract: Variable selection methods are useful when a researcher isn’t sure which predictors to include in their model. In this workshop we will review and compare modern approaches to address this age-old problem, including LASSO and Bayesian variable selection methods.
Prerequisites: Familiarity with linear regression. Bring a fully charged laptop with up-to-date versions of R and Rstudio (optional).
Multilevel Modeling for Longitudinal Data in Stata
Thursday, May 22, 9:00 am – 12:00 pm
Presenter: Meghan Cain, StataCorp LLC
Abstract: We will begin this workshop by exploring longitudinal data and learning how to characterize and format it appropriately for analysis. Then, we will fit multilevel models to our data, focusing on growth curve models. All models will be conceptualized, fit, and interpreted.
Prerequisites: Basic knowledge of multilevel modeling will be helpful. All registered attendees will receive a temporary Stata license, which they should download before the workshop.
Multilevel Modeling
Thursday, May 22, 9:00 am – 12:00 pm
Presenter: Ethan M. McCormick, University of Delaware
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. modeling will be helpful.
Towards Responsible Development and Use of Generative AI for Psychological Safety and Inclusion
Thursday, May 22, 9:00 am – 1:00 pm
Presenters:
Jina Suh, Microsoft Research
Emily Tseng, Microsoft Research
Ebele Okoli, Microsoft Accessibility
Esther Howe, University of Washington
Denae Ford Robinson, Microsoft Research
Alice Qian Zhang, Carnegie Mellon University
Hong Shen, Carnegie Mellon University
Paola Pedrelli, Massachusetts General Hospital
Bolor-Erdene Jagdagdorj, Microsoft AI Red Team
Abstract: General-purpose generative conversational AI agents now serve as sources of social-emotional support, yet their psychological risks remain poorly understood. This workshop convenes diverse stakeholders to “red team” public AI agents and uncover potential psychological harms—ranging from emotional distress and cognitive distortions to unhealthy dependencies and reinforcement of harmful behaviors.
Session Details: This workshop invites interdisciplinary collaboration among technologists, psychologists, and individuals with lived experiences to propose pathways to developing a comprehensive taxonomy of risks, clarifying contextual factors, and informing evidence-based design principles that uphold psychological well-being in responsible AI development.
Getting Started With Registered Reports
Thursday, May 22, 1:00 pm – 3:00 pm
Presenters:
Amanda Montoya, University of California, Los Angeles
William Krenzer, Duke University
Abstract: A registered report is a multi-stage publication that is accepted at a journal prior to data collection and analysis, removing the opportunity for publication bias.
Session Details: This workshop will introduce registered reports, help researchers identify journals, find examples, and provide guidance for writing and navigating the review and publication process.
Introduction to Structural Equation Modeling in the Psychological Sciences
Thursday, May 22, 1:00 PM – 5:00 PM
Presenter: Timothy B. Hayes, Florida International University
Abstract: 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; download software package – lavaan ® and Mplus. A fully charged laptop.
Friday, May 23
An Introduction to Item Response Theory
Friday, May 23, 10:30 am – 1:30 pm
Presenter: Brian Leventhal, James Madison University
Abstract: 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.
Prerequisites: 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 graphs.
Data Science for Psychologists
Friday, May 23, 2:00 pm – 5:00 pm
Presenter: S. Mason Garrison, Wake Forest University
Abstract: Data Science for Psychologists introduces you to the principles of data science. In this hands-on workshop, you will gain a strong foundation in R and the tidyverse, including data wrangling, modeling, visualization, and communication.
Prerequisites: This workshop is suitable for both beginners and those seeking to expand their research toolbox. No prior R coding experience is required.
* Fully charge laptops are required and should have the latest version of R and RStudio installed.
* Additional resources are available here.
Saturday, May 24
The Art of the Elevator Pitch
Saturday, May 24, 10:30 am – 12:00 pm
Presenter: Tamara E. Spence, Stories of Science
Abstract: The elevator pitch. We talk about it often. We say it is important for networking and sharing our research. And yet when it comes to actually doing it, we find ourselves using jargon, spending too much time on details that aren’t important, or not giving the right context for our audience. The elevator pitch is, at its core, a story that should be compelling, focused, and clear.
Session Details: This workshop is intended to guide participants step by step through building their own compelling, clear, and focused elevator pitch—specifically around their research—and then honing it to 60 seconds. By the end of the session, participants will have a draft of a 60-second elevator pitch and 1-2 short anecdotes that they can use to illustrate their research.
Data Storytelling Training
Saturday, May 24, 1:30 pm – 2:30 pm
Presenter: Lisa Cantrell, Stories of Science
Abstract: How do you make a research presentation compelling? One of the biggest secrets in science communication is this: the same narrative strategies that Hollywood uses for creating compelling movies are those that we should be using to talk about our research findings. In this workshop, participants experience demos of research presentations told with and without storytelling components and then discuss how a story format pushes the audience’s thinking forward about the research.
Session Details: Participants will have the opportunity to practice sharing their data story in small groups and by the end of the session, participants will have a draft of their own research data story and a method for turning their future studies into data stories for presentations at conferences, job talks, and speaking engagements.