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RT1: Measurement and Related Issues in Quantitative Research Designs, Analysis, and Simulation
Sun, April 12, 1:45 to 3:15pm PDT (1:45 to 3:15pm PDT), JW Marriott Los Angeles L.A. LIVE, Floor: Gold Level, Gold 3Session Type: Roundtable Session
Abstract
This roundtable presents five studies that advance modern psychometric and quantitative modeling. The papers examine Monte Carlo simulation performance under the IRT 3PL model, neural network and eye-tracking approaches for assessing cognitive engagement, Bayesian estimation of MIMIC models with small samples and outlier contamination, zero-inflated IRT models for addressing item-level disparities, and innovative solutions to Heywood cases in CFA. Collectively, these studies tackle persistent challenges in estimation, model fit, and parameter recovery across diverse methodological contexts. Together, they demonstrate how simulation, Bayesian inference, and machine learning can enhance the validity, precision, and interpretability of quantitative research in educational and psychological measurement.
Sub Unit
Papers
How Well Does Monte Carlo Simulation Perform in Generating Datasets Under the IRT 3PL Model? - Ergul Demir, Ankara University
Cognitive Engagement Measurement in Online Learning Using Eye-Tracking Data and Back Propagation Neural Networks - jialin zhou, East China Normal University; Zhan Chen, East China Normal University; Yaofeng Xue, East China Normal University; 扣琪 王, East China Normal University; yiwei gou, East China Normal University
Bayesian Estimation of MIMIC Models Under Small-Sample Conditions and Latent Outlier Contamination - Nancy Alila, University of Georgia
Testing for Item Level Disparities Using Zero-Inflation Models in NAFS - Fathima Mohrrag Jaffari, National Center for Assessment; Georgios D. Sideridis, Harvard University
Possible Solutions to Heywood Cases in Confirmatory Factor Analysis - Yijun Cheng, University of Washington; Weicong Lyu, University of Macau; Oscar L. Olvera Astivia, University of Washington