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Activity Number: 469 - 2022 GSS/SRMS/SSS Student Paper Competition Award Winners
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #321006
Title: Sample Size Estimation in Respondent-Driven Sampling
Author(s): Yibo Wang* and Michael Elliott and Sunghee Lee
Companies: University of Michigan and University of Michigan and University of Michigan, Institute for Social Research
Keywords: Respondent-Driven Sampling; sample size; recruitment success
Abstract:

Sample size is a necessary feature of quantitative studies. Typically determined by available funding, sample size defines the level of precision of the resulting statistical inference. In contrast to most sample surveys, respondent-driven sampling (RDS) studies do not start with a sample of a fixed size drawn from a sampling frame; rather, they depend on recruitment chains branching from a small number of "seeds" with whom RDS recruitment begins. Although researchers may have a target sample size when they start the study, the peer recruitment of RDS brings uncertainty to the resulting sample size, essentially making it a random variable over which researchers have little control. This study aims to calculate distributions of sample sizes by modeling individual recruitment through a Bernoulli distribution and decomposing the size recursively, resulting in an exhaustive enumeration of all possible scenarios of peer recruitment. To expedite the computational process, we propose an approximation algorithm which estimates the sample size distribution by trimming off unlikely scenarios with negligibly small probabilities of occurrence.


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

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