Estimating Hidden Population Size Using Respondent-Driven Sampling Data

• Activity Number: 154
• Type: Invited
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: General Methodology
• Author(s): Mark S. Handcock*+ and Krista Jennifer Gile
• Companies: University of California at Los Angeles and University of Massachusetts
• Keywords: Bayesian Statistics ; survey sampling ; network ; epidemiology ; social science ; likelihood

Abstract

The study of hard-to-reach or otherwise "hidden" populations presents many challenges to existing survey methodologies. Examples include injection drug users and unregulated workers.

These populations are characterized by the difficulty in sampling from them using standard probability methods. Typically, a sampling frame for the target population is not available, and its members are rare or stigmatized in the larger population so that it is prohibitively expensive to contact them through the available frames. Hard-to-reach populations in the US and elsewhere are under-served by current sampling methodologies.

Respondent-Driven sampling (RDS) is one approach to collect data from networked populations. Most analysis of RDS data has focused on estimating aggregate characteristics of the target population, such as disease prevalence. However, RDS is often conducted in settings where the population size is unknown and of great independent interest. We present an approach to estimating the size of a target population based on the data collected through RDS.

This is joint work with Krista J. Gile (UMass-Amherst) and Corinne M. Mar (University of Washington).