APS
2024 APS Annual Convention
Machine Learning in Psychology: Unveiling Complex Dynamics in Development, Relationships, and Data Analysis
This symposium features four studies demonstrating machine learning (ML)’s role in analyzing early childhood development, relationship quality, individual life trajectories, and handling missing data. These studies collectively illustrate ML’s ability to deepen the understanding of human development and relationship dynamics, charting new frontiers for research advancements.
Chairs & Discussants
- Ying ZhangChair
Clarkson University
Presentations
- Family Ties and Romantic Lives: Decoding Childhood Influences on African Americans’ Relationships in AdulthoodAlaysia Brown, Brady Eisert, Xiaoran Sun
- Exploring Self-Regulation in Early Childhood: Advancing Predictive Analysis with Machine LearningYing Zhang, Siva Korakutty, Razza Rachel, Alisha Ohl, David Schelly, Soumyabrata Dey
- Transforming Developmental Trajectory Analysis: The Impact of Large Language Models on Predicting Life CoursesNilam Ram, Jordan Weiss, Alaleh Azhir, David Rehkoff
- Missing Data Handling in Lasso RegressionKevin Grimm, Yibin (Amanda) Ni