Methods for Inference from Respondent-Driven Sampling Data

@inproceedings{Gile2018MethodsFI,
  title={Methods for Inference from Respondent-Driven Sampling Data},
  author={Krista Gile and Isabelle S. Beaudry and Mark S. Handcock and Miles Q. Ott},
  year={2018}
}
Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to join the sample. Although the method is often effective at attaining large and varied samples, its reliance on convenience samples, social network contacts, and participant decisions makes it subject to a large number of statistical concerns. This… 

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TLDR
It is indicated that the convenience sample of seeds can induce bias, and the number of sample waves typically used in RDS is likely insufficient for the type of nodal mixing required to obtain the reputed asymptotic unbiasedness.

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TLDR
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Respondent-driven sampling (RDS) is a widely used method for generating chain-referral samples from hidden populations. It is an extension of the snowball sampling method and can, given that some a

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TLDR
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