Egocentric Social Network Analysis (Bloomington, IN)


Egocentric social network analysis (SNA) is a methodological tool 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:

  1. 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.
  2. Focus on individual rather than group outcomes. Sociocentric SNA often focuses on network structures of groups as predictors of group-level outcomes (e.g. concentration of power, resource distribution, information diffusion). In contrast, egocentric SNA is concerned with how people's patterns of interaction shape their individual-level outcomes (e.g. health, voting behavior, employment opportunities).
  3. Flexibility in data collection. Because sociocentric SNA must use 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 Stata and E-NET software packages in daily lab sessions. These sessions will primarily focus on interactive use of Stata and E-NET in a computer lab, providing hands-on practice exercises using a range of substantive topics. E-NET is a free software package for egocentric network analysis and visualization created by the developers of UCI-NET.

Fee: Members = $1700; Non-members = $3200

Tags: networks, social networks

Course Sections