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ABSTRACT

Respondent-Driven sampling (RDS) is a network-based sampling method devised to overcome challenges with sampling hard-to-reach human populations. The sampling starts with a limited number of individuals who are asked to recruit a small number of their contacts. Every surveyed individual is subsequently given the same opportunity to recruit additional members of the target population until a pre-established sample size is achieved. In this talk, we discuss typical sources of non-sampling errors affecting the inference from RDS data and methods to address them. In particular, we discuss the misclassification of the outcome variable, nonrandom recruitment behaviors, and biases in the seed selection.

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