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Egocentric Network Research

Instructor: Brea Perry

This 4-day (Mon-Thu) workshop provides an introduction to egocentric social network analysis, a research design used to understand the structure, function, and composition of network ties around an individual. Both sociocentric (i.e., whole) network analysis and egocentric network analysis share the basic assumption that behaviors, beliefs, attitudes, and values of individuals are shaped through contact and communication with others. However, these two methods are distinct in a number of important ways:

Unbounded versus bounded networks. Sociocentric SNA collects data on ties between all members of a socially or geographically-bounded group and has limited inference beyond that group. Egocentric SNA assesses individuals' personal community networks across any number of social settings using name generators, and is therefore less limited in theoretical and substantive scope.

Focus on individuals rather than groups. Focus on local versus global social contexts. Sociocentric SNA focuses on the network structure of one whole group, and how position in these networks affect outcomes. In contrast, egocentric SNA is concerned with how people's own unique “groups” (i.e., personal networks) shape their individual outcomes (e.g., health, voting behavior, employment opportunities), or vice-versa.

Flexibility in data collection. Because sociocentric SNA uses as its sampling frame a census of a particular bounded group, data collection is very time-consuming, expensive, and targeted to a specific set of research questions. In contrast, because egocentric SNA uses individuals as cases, potential sampling frames and data collection strategies are virtually limitless. Egocentric data collection tools can easily be incorporated into large-scale or nationally-representative surveys being fielded for a variety of other purposes.

While no single course could cover the entire breadth of the field, we will examine the most fundamental methodological issues and practical concerns that arise in egocentric network research. This course requires no prior knowledge of egocentric SNA. We will begin with an introduction to the foundational concepts of egocentric SNA, highlighting linkages to theories commonly used in the social and health sciences (e.g., social capital). The rest of the course will cover methodological considerations and statistical techniques for egocentric SNA. In addition to covering data collection strategies (e.g., name generators, name interpreters), measures, and modeling in a lecture format, participants will learn to use software to code and analyze ego network data in daily lab sessions. In the labs, the instructors will demonstrate the software and participants will follow along on their machines. 

The course will be conducted using R (see our software page), but code will also be provided for Stata.