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Cross-Classified and Multiple Membership Random Effects Models: Demonstrations, Evaluations, and Extensions
Mon, April 11, 11:45am to 1:15pm, Convention Center, Floor: Level One, Room 103 ASession Type: Structured Poster Session
Abstract
This structured poster session includes eleven methodological studies focused on cross-classified and multiple membership random effects models (CCREMs and MMREMs). Every study includes a real data analysis demonstrating focal concepts and many of them also include simulation studies evaluating model estimation. Five of the studies demonstrate and evaluate extensions to commonly used multilevel models (including scenarios with longitudinal data, propensity score matching, meta-analysis and two mediation designs) so that they can be used with cross-classified data. Four studies assess robustness when the CCREM or MMREM is mis-specified. One study investigates reasonable sample size thresholds for decent estimation of a CCREM and MMREM combination model and the final study demonstrates use of a design effect intended for use with cross-classified data.
Sub Unit
Papers
Estimating a Piecewise Growth Curve Model With Longitudinal Data That Contains Individual Mobility Across Clusters - Audrey J. Leroux, Georgia State University
Bayesian Estimation of the Indirect Effect in a Mediation Model With Cross-Classified Data - Anita Israni, The University of Texas - Austin; Susan Natasha Beretvas, The University of Texas - Austin
Testing the Mediated Effect in Cross-Classified Multilevel Data - Wen Luo, Texas A&M University - College Station
Quasi-Experimental Program Evaluation by Combining Propensity Score Matching With Cross-Classified Random Effects Models - Walter L. Leite, University of Florida; Zachary Kendall Collier, University of Florida
Cross-Classified Meta-Analytic Models - Belen Fernandez-Castilla, Katholieke Universiteit Leuven; Eline Belmans, KU Leuven; Wim Van den Noortgate, Katholieke Universiteit Leuven
Modeling Cross-Classified Data With and Without the Crossed Factors Random Effects Interaction - Myriam Lopez Wallace, The University of Texas - Austin; Susan Natasha Beretvas, The University of Texas - Austin
The Impact of Ignoring Multiple Membership in Three-Level Multiple Membership Random Effects Modeling - Hyeonjeong Jeon, Chungnam National University; Hyewon Chung, Chungnam National University
The Impact of Weights' Specifications With the Multiple Membership Random Effects Model - Jennifer Lynn Galindo, Tri-Lin Integrated Services, Inc.; Susan Natasha Beretvas, The University of Texas - Austin
The Impact of Ignoring Cross-Classified Data Structures When Estimating Cross-Classified Logistic Models - Weijia Ren, Westat
The Impact of Cluster Size and Intraclass Correlation on Cross-Classified Multiple Membership Random Effects Models - Daniel L. Murphy, Pearson
Using Design Effects to Determine the Necessity of Applying Cross-Classified Random Effects Models - Hok Chio (Mark) Lai, University of Cincinnati; Oiman Kwok, Texas A&M University - College Station