Respondent-Driven Sampling – Testing Assumptions: Sampling with Replacement

Barash Vladimir D. (), Cameron Christopher J. (), Spiller Michael W. () and Heckathorn Douglas D. ()
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Barash Vladimir D.: Graphika Inc., 116 West 23rd Street, 5th Floor, New York NY 10011, U.S.A.
Cameron Christopher J.: Cornell University – Sociology, 344 Uris Hall Ithaca, New York 14853, U.S.A.
Spiller Michael W.: Cornell University – Sociology, 344 Uris Hall, Ithaca, New York 14853, U.S.A.
Heckathorn Douglas D.: Cornell University – Sociology, 344 Uris Hall Ithaca, New York 14853, U.S.A.

Journal of Official Statistics, 2016, vol. 32, issue 1, 29-73

Abstract: Classical Respondent-Driven Sampling (RDS) estimators are based on a Markov Process model in which sampling occurs with replacement. Given that respondents generally cannot be interviewed more than once, this assumption is counterfactual. We join recent work by Gile and Handcock in exploring the implications of the sampling-with-replacement assumption for bias of RDS estimators. We differ from previous studies in examining a wider range of sampling fractions and in using not only simulations but also formal proofs. One key finding is that RDS estimates are surprisingly stable even in the presence of substantial sampling fractions. Our analyses show that the sampling-with-replacement assumption is a minor contributor to bias for sampling fractions under 40%, and bias is negligible for the 20% or smaller sampling fractions typical of field applications of RDS.

Keywords: Respondent-driven sampling; hidden populations; sampling with replacement (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:32:y:2016:i:1:p:29-73:n:2

DOI: 10.1515/jos-2016-0002

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