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When Sharp Statistics Meet Complex Data
Fri, April 9, 4:10 to 5:40pm EDT (4:10 to 5:40pm EDT), Division D, Division D - Section 2 Paper and Symposium SessionsSession Type: Paper Session
Abstract
This session sits at the crux of statistical rigor and complex Data. The papers in this session utilize simulation studies to explore the robustness of statistical methods when data are nonnormal, nonlinear, heterogenous, or curved. Evaluations of fit, power, error, and bias are discussed throughout the session.
Sub Unit
Chair
Papers
Detection and Estimation of Effect Heterogeneity: Simulation Evidence Motivated by Large-Scale Education Data - Bryan Keller, Teachers College, Columbia University; Tianyang (Terry) Zhang, Teachers College, Columbia University
A Cautionary Note on the Use of the Third-Order Polynomial Method in Simulation Studies - Oscar L. Olvera Astivia, University of Washington
Conceptualizing a Nonlinear Meta-Analysis in Education Research - Denis Dumas, University of Denver; Daniel McNeish, Arizona State University; Garrett J Roberts, University of Denver
Distributionally Weighted Least Squares in Structural Equation Modeling - Han Du, University of California - Los Angeles; Peter M. Bentler, University of California
Path Analysis With Nonnormal Continuous Data: A Monte Carlo Simulation - Nnamdi Chika Ezike, University of Arkansas at Fayetteville; Chunhua Cao, The University of Alabama; Xinya Liang, University of Arkansas; Wen-Juo Lo, University of Arkansas; Paula Castro Abbott, University of Arkansas at Fayetteville; Ji Li, University of Arkansas for Medical Sciences