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34.094-5 - Partially Clustered Data and Sample Size Investigations With Various Types of Multilevel Models
Sat, April 6, 2:15 to 3:45pm, Fairmont Royal York Hotel, Floor: Convention Floor, Concert HallSession Type: Roundtable Session
Abstract
This collection of papers addresses methods for handling partially clustered data and issues related to sample size and multivariate group comparisons, multilevel SEM, and detecting treatment effects in CRTs.
Sub Unit
Papers
A Partially Nested Multilevel Model for Analysis of District Data Including Charter or Private Schools - Brian P. Shaw, The Ohio State University - Columbus; Ann A. O'Connell, The Ohio State University
Small Sample Performance of Multilevel and Traditional Methods for Multivariate Group Comparisons With Incomplete Data - Keenan A. Pituch, The University of Texas at Austin; Tiffany Ann Whittaker, The University of Texas at Austin; Megha Joshi, The University of Texas at Austin; Ryoungsun Park, SiFive; Molly Elizabeth Cain, The University of Texas at Austin
Unbiased Estimation of Multilevel Structural Equation Models in Small to Moderate Sample Sizes: A Limited Information Estimator - Benjamin Kelcey, University of Cincinnati; Kyle T. Cox, University of Cincinnati; Nianbo Dong, University of North Carolina at Chapel Hill
Using Optimal Sample Allocation to Improve the Chance of Detecting Treatment Effects in Cluster-Randomized Trials - Zuchao Shen, University of Georgia; Benjamin Kelcey, University of Cincinnati