NETWORK ANALYSIS
Description
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 (data collection, centrality, and structure), the exponential random graph model for modeling network formation, meta network analysis for combining and comparing estimates from multiple random network models, the stochastic actor‐oriented model for analyzing network dynamics and network effects, and social network-based interventions. Optional materials (Slides, code, and recordings) for causal network analysis may also be acquired for self-study. The seminar will provide a delicate balance of major methods and diverse applications as well as readily useful computer code. Knowledge of R and logistic regression is required.
Agenda (Request syllabus)
Day 1: Basic Network Analysis (Data collection, centrality, and structure)
Day 2: Network Formation (Exponential random graph model and meta network analysis)
Day 3: Dynamic Network Analysis and Social Network-Based Interventions
Optional Topics: Causal Network Analysis (Experiments, instrumental variables, etc.)