Abstract #300988

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JSM 2003 Abstract #300988
Activity Number: 290
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #300988
Title: Making Unbiased Estimates About Hidden Populations Using Respondent-driven Sampling
Author(s): Matthew J. Salganik*+ and Douglas Heckathorn
Companies: Cornell University and Cornell University
Address: Dept. of Sociology, Ithaca, NY, 14853,
Keywords: hidden populations ; snowball sampling ; chain-referral sampling ; link-tracing design ; social networks
Abstract:

Injection drug users and men who have sex with men are two populations of great interest to academic researchers and public health officials because the behavior of these hidden populations can affect the spread of many diseases like HIV/AIDS. Unfortunately, current statistical procedures for making estimates about the composition and behavior of these groups are plagued with problems. We further develop a new technique called respondent-driven sampling, which uses a chain-referral procedure to collect a sample from the target population. We avoid many of the known problems with making inference from chain-referral samples by not attempting to estimate directly from the sample to the population. Rather, we use the sample to make estimates about the social network connecting the population and then use these estimates about the social network to make estimates about the population. We will show that our procedure produces asymptotically unbiased estimates of the proportion of the population falling into one of two groups.

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