Nebraska MAP Academy’s Spring 2021 Methodology Applications Series Presentation on Network Analysis is March 12 at 12 PM
[EDIT from March 17, 2021: The full video recording from Dr. Smith’s Methodology Applications Series presentation is now available on the Nebraska MAP Academy website! Click here to watch the presentation.]
The following event is FREE and OPEN to the public.
On Friday, March 12, 2021, at 12 Noon (CT), Jeffrey Smith, Associate Professor of Sociology and Worlds of Connections Advisory Board member, will lead a virtual presentation on the use of network analysis to measure social cohesion. Dr. Smith’s presentation will be the second in the Spring 2021 Methodology Applications Series, hosted by the Nebraska Academy for Methodology, Analytics, and Psychometrics (MAP Academy).
The presentation, entitled “Using Network Analysis to Measure Social Cohesion: Complete Versus Sampled Network Data,” will cover the basic theoretical, methodological, and substantive underpinnings of network analysis and how it has been used to measure social cohesion. Discussion will also explore the differences between census, or complete, network data and sampled network data for measuring social cohesion.
“Using Network Analysis to Measure Social Cohesion: Complete Versus Sampled Network Data” will be presented via Zoom videoconference. Click here to view the presentation.
To learn more about this event, visit the Nebraska MAP Academy website.
Biography
Jeffrey Smith’s research explores methodological and substantive problems related to networks, individuals and the broader social context. Methodologically, his work focuses on a core problem in network studies: How can network structure be measured when there is incomplete information, either because there is missing data or because the dataset itself comes from a sample?
Substantively, his work pushes a contextual approach to studying social networks — how network features, such as hierarchy, cohesion or group structure, vary across contexts, such as schools or neighborhoods.