May 11-15, 2020
Chapel Hill, North Carolina
Instructor: Doug Steinley
Software Demonstrations: R
Note that this workshop will be held the same week as our Structural Equation Modeling workshop
Register for the Workshop
*To be eligible, participant must be actively enrolled in a degree-granting graduate or professional school program at the time of the workshop. Post-doctoral fellows are not eligible for the student rate.
Network Analysis is a five-day workshop taught by Doug Steinley that focuses on the application and interpretation of techniques for modeling connections between observations (e.g., actors) within a network. Examples include social networks among peers, connectivity networks in fMRI data, symptom networks in diagnostic data, and management networks within the workplace. In this workshop we first introduce the basic concepts of network analysis, such as centrality measures, and the descriptive, structural analysis of network data. After covering these foundations, we discuss special considerations for two burgeoning areas of application: brain network analysis for examining connectivity and psychometric network models for examining the structure of item responses (e.g., symptom networks). Last, we explore how we can use exponential random graph models (ERGMs) to obtain inferential tests of specific hypotheses about network characteristics in both cross-sectional and longitudinal data. Throughout, we demonstrate visualization techniques for leveraging the true power of network data — understanding how observations are interrelated with each other.