Network analysis shifts the research focus from individuals to their connections and so brings both theoretical and methodological innovations. Interest in network analysis has EXPLODED especially recently, due to new advances in statistical modeling and the rapid growth of network data. This course covers the major methods for collecting and analyzing network data. Selected topics include basic network analysis (study designs, data collection, network plots, and various measures at node, dyad, group, and network levels), the exponential random graph model for modeling network formation that can account for receiver effects, sender effects, and endogenous tie formation processes such as homophily, transitivity, and preferential attachment, dynamic network models including the temporal exponential random graph model for modeling network dynamics and the stochastic actor‐oriented model for modeling the co-evolution of networks and behaviors that helps to separate peer selection from peer influence, and social network-based interventions (types of network interventions and methods to choose strategic nodes and groups from social networks). Materials (lecture slides, lab code, and recordings) for optional topics including causal network analysis and meta network analysis may also be acquired for self-study. Examples and code will be provided. Knowledge of R and logistic regression is required.
Day 1: Basic Network Analysis (Study designs, data collection, network plots, and various measures)
Day 2: Exponential Random Graph Model (Theories, specification, estimation, and diagnostics)
Day 3: Dynamic Network Analysis and Social Network-Based Interventions
Optional Topics: Causal Network Analysis and Meta Network Analysis
Dr. Weihua An is Associate Professor (tenured) of Sociology & Quantitative Theory and Methods and associated faculty of the East Asian Studies Program, the Goizueta Business School, and the Rollins School of Public Health at Emory University. He received a Ph.D. in Sociology and an A.M. in Statistics from Harvard University and was a doctoral fellow and a postdoc fellow at Harvard Kennedy School. His research advances theories and methods for network analysis and causal inference with applications to studying inequality and social policy (e.g., immigration, housing, education, and redistributive policy), health (e.g., peer effects, perceived risk, social network-based interventions, and pandemics), and organizations. He has published widely in both methodological and substantive journals and has also authored multiple statistical software including "fglsnet", "LARF", and "keyplayer" in R and "DIDMatch' in Stata, which have received over 130K downloads in total. He has served or is serving on the editorial boards of American Sociological Review, Journal of Machine Learning Research, Social Science Research, Sociological Methodology, and Sociological Methods and Research and has edited several special issues at top journals including the latest Methodological Advances in Quantitative Social Science (Social Science Research 50th Anniversary Series). He is a recipient of the Faculty Teaching Award from Emory Sociology and has advised over 20 dissertations and multiple honors theses. He is an instructor for the NIDA-funded program “Training in Advanced Data Analytics and Computational Sciences” at Emory and also has extensive experience in leading methods training programs for researchers and working professionals. Dr. An has studied social network analysis for over 16 years and has published on a range of topics, including causal network analysis (e.g., instrumental variable methods, social network-based interventions, and multilevel meta network analysis), network formation (e.g., theories on status differential and differential homophily), combing peer-reports and self-reports to improve data measurement, big and text network analysis (e.g., the “blocking-bridging-stacking” method), and assessing the joint effects of network and neighborhood, etc.
Livestreaming (TBA) and on-demand. Zoom class with recordings available.