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46.028 - Quantitative Methods for the Analysis of Social Network Data in Education
Sat, April 18, 8:15 to 9:45am, Marriott, Floor: Fifth Level, ScottsdaleSession Type: Symposium
Abstract
Social network data is becoming increasingly more common in education research and the purpose of this symposium is to both summarize current research on social network methodology and to showcase how these methods can address substantive research questions in education and promote on-going education research. Each presentation introduces exciting cutting-edge methodological research focusing on different aspects of social network analysis that will be of interest to both methodologists and education researchers.
Sub Unit
Chair
Papers
Modeling Networks With Ordinal Data - Andrew C. Thomas, Carnegie Mellon University
Temporal Latent Space Models for Social Networks - Samrachana Adhikari, Carnegie Mellon University; Brian W. Junker, Carnegie Mellon University
Goodness of Fit and Model Selection for Network Models - Beau Dabbs, Carnegie Mellon University; Andrew C. Thomas, Carnegie Mellon University; Brian W. Junker, Carnegie Mellon University
Hierarchical Network Models for Interventions on Social Networks - Tracy Sweet, University of Maryland - College Park
Hierarchical Mixed-Membership Stochastic Blockmodel With Network-Level Covariates - Qiwen Zheng, University of Maryland - College Park; Tracy Sweet, University of Maryland - College Park