<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Respondent driven sampling (RDS) is a sampling method designed for hard-to-sample groups with strong social ties. RDS starts with a small number of arbitrarily selected participants (“<italic>seeds</italic>”). Seeds are issued recruitment coupons, which are used to recruit from their social networks. Waves of recruitment and data collection continue until reaching a sufficient sample size. Under the assumptions of random recruitment, with-replacement sampling, and a sufficient number of waves, the probability of selection for each participant converges to be proportional to their network size. With recruitment noncooperation, however, recruitment can end abruptly, causing operational difficulties with unstable sample sizes. Noncooperation may void the recruitment Markovian assumptions, leading to selection bias. Here, we consider two RDS studies: one targeting Korean immigrants in Los Angeles and in Michigan; and another study targeting persons who inject drugs in Southeast Michigan. We explore predictors of coupon redemption, associations between recruiter and recruits, and details within recruitment dynamics. While no consistent predictors of noncooperation were found, there was evidence that coupon redemption of targeted recruits was more common among those who shared social bonds with their recruiters, suggesting that noncooperation is more likely to be a feature of recruits not cooperating, rather than recruiters failing to distribute coupons.</p>
</abstract>

Respondent driven sampling (RDS) is a sampling method designed for hard-to-sample groups with strong social ties. RDS starts with a small number of arbitrarily selected participants (“seeds”). Seeds are issued recruitment coupons, which are used to recruit from their social networks. Waves of recruitment and data collection continue until reaching a sufficient sample size. Under the assumptions of random recruitment, with-replacement sampling, and a sufficient number of waves, the probability of selection for each participant converges to be proportional to their network size. With recruitment noncooperation, however, recruitment can end abruptly, causing operational difficulties with unstable sample sizes. Noncooperation may void the recruitment Markovian assumptions, leading to selection bias. Here, we consider two RDS studies: one targeting Korean immigrants in Los Angeles and in Michigan; and another study targeting persons who inject drugs in Southeast Michigan. We explore predictors of coupon redemption, associations between recruiter and recruits, and details within recruitment dynamics. While no consistent predictors of noncooperation were found, there was evidence that coupon redemption of targeted recruits was more common among those who shared social bonds with their recruiters, suggesting that noncooperation is more likely to be a feature of recruits not cooperating, rather than recruiters failing to distribute coupons.

Exploring Mechanisms of Recruitment and Recruitment Cooperation in Respondent Driven Sampling

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Respondent driven sampling (RDS) is a sampling method designed for hard-to-sample groups with strong social ties. RDS starts with a small number of arbitrarily...

Exploring Mechanisms of Recruitment and Recruitment Cooperation in Respondent Driven Sampling

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 339 - 360

Eingereicht: 01. Nov. 2018

Akzeptiert: 01. Nov. 2019

DOI: https://doi.org/10.2478/jos-2020-0018

Schlüsselwörter
Respondent driven sampling, sampling hard-to-reach population, nonresponse error

© 2020 Sunghee Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Exploring Mechanisms of Recruitment and Recruitment Cooperation in Respondent Driven Sampling

Online veröffentlicht: 15. Juni 2020

Seitenbereich: 339 - 360

Eingereicht: 01. Nov. 2018

Akzeptiert: 01. Nov. 2019

DOI: https://doi.org/10.2478/jos-2020-0018

SchlüsselwörterRespondent driven sampling, sampling hard-to-reach population, nonresponse error

© 2020 Sunghee Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Respondent driven sampling, sampling hard-to-reach population, nonresponse error