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Machine Learning Algorithms, New Modeling Approaches, and Simulation Methods to Understand Model Performance
Fri, April 12, 4:55 to 6:25pm, Philadelphia Marriott Downtown, Floor: Level 4, Room 403Session Type: Symposium
Abstract
This symposium links six papers on new methods for estimating latent variables and mixture models. Across four studies, Monte Carlo simulations were employed to expand and improve mixture models. The first paper investigated trifactor mixtures, followed by a study identifying optimal automated indicator selection methods for latent class analysis.The third paper used simulations for class enumeration. In the fourth paper, researchers introduced MplusAutomation for simulations with various models, including mixture modeling. The final two papers focus on novel methods for mixture models. The fifth paper proposes a novel two-part mixture model to address challenges with observed or latent semi-continuous response variables. The final paper introduces a new approach to estimating patterns of growth trajectories for individuals in latent classes.
Sub Unit
Chair
Papers
Trifactor Mixture Modeling for Multi-Informant Assessment: A Simulation Study - Courtney Howard-Kirby, University of South Florida; Eunsook Kim, University of South Florida; Yan Wang, University of Massachusetts Lowell
A Comparison of Methods of Automated Indicator Selection for Latent Class Analysis - Walter L. Leite, University of Florida; Zuchao Shen, University of Georgia; Eric Wright, University of Florida
Monte Carlo Simulation Methods for Class Enumeration With Latent Class Analysis Models - Tiffany A. Whittaker, University of Texas at Austin
Facilitating Model Comparison and Evaluating the Impact of Model Specification Using Simulation Studies With MplusAutomation - Netasha Pizano, University of California - Santa Barbara; Dina Naji Arch, University of California - Santa Barbara; Delwin Carter, University of California - Santa Barbara
A General Two-Part Mixture Modeling for Semi-Continuous Response Variables - Katherine E. Masyn, Georgia State University; Aprile Benner, University of Texas at Austin
Novel Approach Growth Mixture Modeling Using Natural Cubic Smoothing Splines - Raj Karl Wahlquist, University of Minnesota; Katerina M. Marcoulides, University of Minnesota; Laura Trinchera, NEOMA Business School