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A new inference approach for respondent driven sampling

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Respondent driven sampling (RDS) is an approach to sampling design utilizing the networks of social relationships that connect members of the target population to facilitate sampling by chain referral methods. Although RDS can lead to biased sampling, most RDS studies typically measure each participant’s degree (i.e., number of acquaintances) and use inverse-probability weighting in an attempt to correct for this bias (based on the fundamental RDS assumption that the probability of sampling an individual is proportional to his degree). However, this assumption is both naive and unsupported and here we present an entirely new and alternative inference approach.

This talk is part of the Worms and Bugs series.

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