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Activity Number: 379 - Achieving Adequate Representation When Surveying Rare Populations
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Journal of Survey Statistics and Methodology
Abstract #300017
Title: Exploring Mechanisms of Recruitment and Recruitment Cooperation in Respondent-Driven Sampling
Author(s): Sunghee Lee* and Ai Rene Ong and Michael Elliott
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Respondent driven sampling; Rare population; Nonresponse

Respondent driven sampling (RDS) is a sampling method designed for hard-to-sample groups with strong social ties. RDS starts with a small number of arbitrarily selected participants (“seeds”). Seeds are issued recruitment coupons, which are used to recruit from their social networks. Waves of recruitment and data collection continue until meeting desired sample sizes. Under the assumptions of random recruitment, with-replacement sampling, and a sufficient number of waves, the probability of selection for each participant converges to be proportional to their network size. With recruitment noncooperation, however, recruitment can end abruptly, leading to unstable sample sizes causing operational difficulties; noncooperation can void the Markovian recruitment assumptions, leading to selection bias. We analyze data from an in-person RDS study targeting illicit substance users and a Web-RDS study targeting an ethnic minority group and explore predictors of recruitment cooperation, associations between recruiter and recruits, and details within recruitment dynamics.

Authors who are presenting talks have a * after their name.

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