Spring 2021 Methodology Applications Series:

Using Network Analysis to Measure Social Cohesion:
Complete Versus Sampled Network Data
Jeffrey Smith

Jeffrey Smith

The MAP Academy invites you to a presentation of the Spring 2021 Methodology Applications Series,
featuring Jeffrey Smith, associate professor of sociology.

Friday, March 12, 2021 12:00-1:30 p.m. Zoom videoconference

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Network analysis has experienced a dramatic increase in popularity over the past 15 years, with network techniques utilized by a wide range of academic disciplines (including sociology, anthropology, physics, biology, computer science, economics, political science and more). Network analysis focuses on the relationships connecting actors, rather than on the properties of the actors themselves.

The presentation will cover the basic theoretical, methodological and substantive underpinnings of network analysis, and will discuss how network analysis has been used to measure an important latent construct — social cohesion. Particular focus will be on differences between census, or complete, network data, and sampled network data, showing how each can be used to measure social cohesion.

Relative costs and benefits of each data type (census versus sampled) in the context of measuring social cohesion also will be discussed.


Date, Time, & Location
Friday, March 12, 2021
12:00-1:30 PM
Zoom videoconference

Presentation: Using Network Analysis to Measure Social Cohesion: Complete Versus Sampled Network Data

This virtual presentation is free and open to the public.

Join the Zoom videoconference March 12, 2021.


Jeffrey Smith

Associate Professor of Sociology

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 data itself come 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.

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