Feasibility Analysis Framework for Respondent-Driven Sampling Based on Egocentric Social Network Data and Chain-Referral Attitudes

• Activity Number: 627
• Type: Invited
• Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Statistical and Applied Mathematical Sciences Institute
• Author(s): Elena A. Erosheva*+ and Krista J. Gile and Mark S. Handcock and Karen Fredriksen-Goldsen
• Companies: University of Washington and University of Massachusetts, Amherst and University of California, Los Angeles and University of Washington
• Keywords: synthetic networked populations ; clustering ; RDS estimators

Abstract

Respondent-Driven Sampling (RDS) is a sampling technique for networked populations. It is a popular method for researchers studying hard-to-reach or hidden populations. RDS exploits social networks by starting with a small set of individuals and allowing the respondents at each wave to recruit the next wave of the sample from their contacts. Estimators based on RDS data are justified by strong assumptions about the sampling design and the population network structure. In this talk, we present statistical methodology for RDS feasibility analysis. The framework relies on the creation of synthetic networked populations using information from egocentric data. The analysis involves a comprehensive simulation study for assessing RDS feasibility informed by important relational structures, such as clustering and homophily. We apply these new methods to egocentric observational data on social networks and chain-referral attitudes of older GLBT adults from the study Caring and Aging with Pride to systematically study the quality of potential RDS estimators in this hard-to-reach population.