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Methodology and Application in Structural Equation Modeling
Mon, April 25, 11:30am to 1:00pm PDT (11:30am to 1:00pm PDT), Manchester Grand Hyatt, Floor: 3rd Level, Harbor Tower, Hillcrest ABSession Type: Paper Session
Abstract
Different innovations to Structural Equation Modeling are investigated. Among papers in this session, authors explored goodness-of-fit of models in SEM framework, examined characteristics of Bayesian fit indices across different simulation conditions, proposed an unbiased asymptotic distribution free (ADF) estimator to improve model fit statistics for non-normal data. New estimators were proposed based on statistical learning. Moreover, impact of item communality and sample size was investigated on the variability of factor loadings. A multilevel bifactor model was used to address design features to examine test dimensionality.
Sub Unit
Chair
Papers
Performance of Model Fit Indices in Bayesian Confirmatory Factor Analysis - Kelly D. Edwards, University of Virginia; Timothy R. Konold, University of Virginia
40-Year-Old ADF Estimator Reliably Improves SEM Statistics for Nonnormal Data - Han Du, University of California - Los Angeles; Peter Bentler
Predicting College Outcomes Using High School GPA and SAT Scores - Kramer Anketell Dykeman, University of California - Davis; Michal Kurlaender, University of California - Davis
Item Communality, Sample Size, and Variability in Latent Factor Loadings Across Simulated Samples - Christopher Shank, Western Michigan University; Brooks Applegate, Western Michigan University
Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model - Hope O. Akaeze, Michigan State University; Frank Lawrence; Jamie Wu, Michigan State University