This three-part webinar series, taught by Dr. Robert A Hanneman from the University of California, Riverside, will provide an introduction to analyzing network data and will review some of the most important concepts from the network perspective. The series will also address ways in which the network perspective can inform common types of health services research. Practical guidance on the common statistical software for analyzing networks will be covered, in addition to advanced concepts that are useful for developing strategies to implement change in networks.
System Requirements: This webinar series will be conducted using Adobe Connect, and can be accessed through any major internet browser. The latest version of Adobe Flash Player is required to participate in this webinar series. Download Adobe Flash Player for free.
Date: Thursday, March 31, 2011 1:00 p.m.-2:30 p.m. (EDT).
By the end of Part I of the series, participants will be able to understand:
- How network analysis may apply to a range of problem types in health policy analysis
- The key differences between the network perspective on data and the conventional case-by-variable approach
- Some of the language for describing data arrays containing the kinds of data that are used in network analysis
- Some of the features of UCINET for managing and working with network data
- How network data strctures are commonly represented as various types of graphs
- How to develop skills in seeing patterns in graphs of social network data
Date: Thursday, April 14, 2011, 1:00 p.m.-2:30 p.m. (EDT).
By the end of Part II of the series, participants will understand approaches to calculating basic metrics, including:
- Connection in graphs such as size, density, transivity
- Distance in a graph
- Sub-structures in graphs
- Individual centrality and graph centralization
Analysis of Social Networks Part III: Beyond the Basics
Date: Thursday, April 28, 2011, 1:00 p.m.-2:30 p.m. (EDT).
By the end of Part III of the series, participants will be familiar with:
- Common approaches to identifying "key" or "critical" nodes and connections in social networks, and use UCINET tools to locate them.
- Ways to describe the extent to which ties in a graph relate to categorical attributes of the actors.
- The ideas of regular and strutural equivalence and equivalence classes.