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RT4: Modeling Complex Data and Patterns: Bayesian, Longitudinal, and Machine Learning Approaches
Fri, April 10, 7:45 to 9:15am PDT (7:45 to 9:15am PDT), JW Marriott Los Angeles L.A. LIVE, Floor: Ground Floor, Gold 4Session Type: Roundtable Session
Abstract
This roundtable highlights methodological advances in longitudinal and predictive modeling across educational and developmental research. The papers explore Bayesian Moderated Nonlinear Factor Analysis for latent growth models, Bayesian variable selection methods for longitudinal data, a hybrid Bayesian framework for modeling cyclical patterns, cross-classified simulations examining how curricular complexity predicts degree completion, and machine learning applications for forecasting student outcomes. Collectively, these studies integrate simulation, Bayesian inference, and predictive analytics to improve estimation accuracy, model interpretability, and decision-making in education. The session underscores how modern computational and Bayesian approaches can illuminate complex developmental and institutional processes while guiding evidence-based policy and practice.
Sub Unit
Papers
Examining Moderation Effects in Latent Growth Model using A Bayesian Moderated Nonlinear Factor Analysis - Suyoung Kim, University of Chicago; Jiwon Kim, Northwestern University; Brian T. Keller, University of Missouri
Evaluating Bayesian Variable Selection Approaches for Nonlinear Random Effects Models - Yue Zhao, University of Minnesota; Nidhi Kohli, University of Minnesota; Eric F. Lock, University of Minnesota
Modeling Cyclic Patterns Using a Two-Stage Hybrid Bayesian Approach - Han Du, University of California - Los Angeles; Brian T. Keller, University of Missouri; Lijuan Wang, University of Notre Dame
A Cross-Classified Model Using Curricular Complexity to Predict Graduations - Michael D. Toland, University of Toledo; David Dueber, University of Toledo; Gregory Heileman, University of Arizona; Steven Dandeneau, Colorado State University; Erin Helbig, Damour Systems; Hayden Free, Damour Systems
Applying Machine Learning Approaches with Administrative Data to Investigate Student Level Outcomes: An Application with Kindergarten Entry Assessments - Kristen E. Wright, University of North Carolina at Charlotte; Kyle T. Cox, University of North Carolina - Charlotte; Richard G. Lambert, University of North Carolina - Charlotte