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Complexity in Data: Simulations and Applications in Complex, Multivariate, Multilevel, and Nonnormal Models (Table 2)
Sat, April 26, 11:40am to 1:10pm MDT (11:40am to 1:10pm MDT), The Colorado Convention Center, Floor: Ballroom Level, Four Seasons Ballroom 4Session Type: Roundtable Session
Abstract
The collection of papers in this roundtable brings attention to complexity in data. The papers are focused on use of mobile-log data to clarify relationships between student performance and task difficulty; power in partially-nested multilevel designs, factored-regression models when missing data on a non-linear predictor is present; and challenges in detecting overdispersion in models for various forms of discrete outcomes. Studies shared in this session include simulation studies as well as model review and applied, empirical examples of the various methodologies.
Sub Unit
Chair
Papers
Preschooler’s Planning Time on Problem-Solving Performances Across Different Task Difficulties: Insights From Mobile Log Data - Surina He, University of Alberta; Okan Bulut, University of Alberta; Ying Cui, University of Alberta
Longitudinal, Repeated Measures in Multisite Partially Nested Experiments: Estimation, Inference, and Power - Yanli Xie, Florida State University; Benjamin Kelcey, University of Cincinnati; Wei Li, University of Florida; Fangxing Bai, Montana State University
Factored Regression Approach for a Continuous Non-Normal Incomplete Predictor - Jiwon Kim, Northwestern University; Suyoung Kim, University of Chicago
Maximum Likelihood Estimation of Non-Normal Random Effects and Random Errors in Nonlinear Random Effects Models - Yue Zhao, University of Minnesota; Nidhi Kohli, University of Minnesota
Detecting Overdispersion in Discrete Data - Nivedita Bhaktha, Indian Institute of Technology Kanpur; Ann A. O'Connell, Rutgers University; Latif Kadir, The Ohio State University