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Advancing Mixture Modeling Methodology: Model Specification, Class Enumeration, and Measurement Invariance for Complex Data Structures
Sat, April 11, 9:45 to 11:15am PDT (9:45 to 11:15am PDT), InterContinental Los Angeles Downtown, Floor: 5th Floor, Echo ParkSession Type: Symposium
Abstract
This symposium presents five methodological papers that advance mixture modeling for cross-sectional, longitudinal, and multilevel designs. The papers address critical issues in model specification, class enumeration, and measurement invariance. The first paper examines the consequences of unmodeled differential item functioning (DIF) in latent class analysis. The second investigates the impacts of ignoring longitudinal measurement non-invariance in latent transition analysis (LTA), followed by a comparison of traditional LTA with random-intercept LTA to guide model choice when trait-like stability is present. The fourth paper evaluates how hierarchical data structures affect enumeration and parameter recovery in latent profile analysis. The final paper introduces a Bayesian cross-classified growth mixture model to account for student mobility. Collectively, the papers provide practical guidance for applied researchers.
Sub Unit
Chair
Papers
Unmodeled DIF in Latent Class Analysis: Consequences for Class Enumeration - Dina Naji Arch, University of California - Santa Barbara; Karen L. Nylund-Gibson, University of California - Santa Barbara
Investigating the Impacts of Longitudinal Measurement Non-Invariance in Latent Transition Analysis - Boshi Wang, Georgia State University; Katherine E. Masyn, Georgia State University
When Is RI-LTA Necessary? A Monte Carlo Comparison of Latent Transition Models - Delwin Carter, University of California - Santa Barbara; Karen L. Nylund-Gibson, University of California - Santa Barbara
The Role of Class Separation in Class Enumeration for Latent Profile Analysis with Nested Data - Angela D. Starrett, University of South Carolina; Katherine E. Masyn, Georgia State University
Developing a Bayesian Cross-Classified Growth Mixture Model to Account for Student Mobility - Audrey J. Leroux, Georgia Institute of Technology; Tonghui Xu, University of Houston; Yan Wang, University of Massachusetts Lowell