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Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling technique for surveying hard-to-reach communities that takes advantage of community members' social networks to reach potential participants. As a network-based…

Respondent-driven sampling (RDS) is a commonly used substitute for random sampling when studying hidden populations, such as injecting drug users or men who have sex with men, for which no sampling frame is known. The method is an extension…

统计方法学 · 统计学 2012-05-01 Xin Lu , Jens Malmros , Fredrik Liljeros , Tom Britton

Researchers in many scientific fields make inferences from individuals to larger groups. For many groups however, there is no list of members from which to take a random sample. Respondent-driven sampling (RDS) is a relatively new sampling…

应用统计 · 统计学 2012-01-10 Xin Lu , Linus Bengtsson , Tom Britton , Martin Camitz , Beom Jun Kim , Anna Thorson , Fredrik Liljeros

Respondent-driven sampling (RDS) is a sampling scheme used in socially connected human populations lacking a sampling frame. One of the first steps to make design-based inferences from RDS data is to estimate the sampling probabilities. A…

统计方法学 · 统计学 2025-03-19 Alejandro Sepulveda-Peñaloza , Isabelle S. Beaudry

Sampling hidden populations is particularly challenging using standard sampling methods mainly because of the lack of a sampling frame. Respondent-driven sampling (RDS) is an alternative methodology that exploits the social contacts between…

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections…

统计方法学 · 统计学 2016-10-24 Ashton M. Verdery , Jacob C. Fisher , Nalyn Siripong , Kahina Abdesselam , Shawn Bauldry

Respondent-Driven Sampling (RDS) is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As such, an RDS sample has a graphical component which…

Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention. Respondent-driven…

社会与信息网络 · 计算机科学 2015-12-03 Lin Chen , Forrest W. Crawford , Amin Karbasi

Respondent-driven sampling (RDS) is a chain-referral method for sampling members of a hidden or hard-to-reach population such as sex workers, homeless people, or drug users via their social network. Most methodological work on RDS has…

统计方法学 · 统计学 2015-08-03 Forrest W. Crawford

Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent…

应用统计 · 统计学 2026-03-03 Adam Visokay , Laura Boudreau , Rachel M. Heath , Tyler H. McCormick

Respondent-Driven Sampling (RDS) is a chain-referral design used for collecting data from hidden or hard-to-reach populations through their social networks. In RDS, respondents recruit their peers from the population of interest. As such,…

统计方法学 · 统计学 2026-04-14 Vanesa Reinoso , Danilo Alvares , Jonathan Acosta , Isabelle S. Beaudry

Respondent-driven sampling (RDS) is a link-tracing network sampling strategy for collecting data from hard-to-reach populations, such as injection drug users or individuals at high risk of being infected with HIV. The mechanism is to find…

统计计算 · 统计学 2012-10-24 Sergiy Nesterko , Joseph Blitzstein

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially groups most at-risk for HIV/AIDS. Data are collected through a peer-referral process in which current sample members…

统计方法学 · 统计学 2012-09-28 Krista J. Gile , Lisa G. Johnston , Matthew J. Salganik

Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture-recapture and multiplier methods have become standard tools for inference of hidden…

统计方法学 · 统计学 2015-05-01 Forrest W. Crawford , Jiacheng Wu , Robert Heimer

Respondent-driven sampling (RDS) is frequently used when sampling hard-to-reach and/or stigmatized communities. RDS utilizes a peer-driven recruitment mechanism where sampled individuals pass on participation coupons to at most $c$ of their…

物理与社会 · 物理学 2014-11-19 Jens Malmros , Fredrik Liljeros , Tom Britton

Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their mem- bers unwilling to identify themselves. Obtaining…

社会与信息网络 · 计算机科学 2013-08-30 Christopher M. Homan , Vincent Silenzio , Randall Sell

Respondent driven sampling (RDS) is a method often used to estimate population properties (e.g. sexual risk behavior) in hard-to-reach populations. It combines an effective modified snowball sampling methodology with an estimation procedure…

统计方法学 · 统计学 2013-08-19 Jens Malmros , Naoki Masuda , Tom Britton

Respondent-driven sampling (RDS) is widely used to study hidden or hard-to-reach populations by incentivizing study participants to recruit their social connections. The success and efficiency of RDS can depend critically on the nature of…

统计方法学 · 统计学 2025-01-06 Justin Weltz , Angela Yoon , Yichi Zhang , Alexander Volfovsky , Eric Laber

Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure…

应用统计 · 统计学 2009-04-14 Krista J. Gile , Mark S. Handcock

Unveiling individuals' preferences for connecting with similar others (choice homophily) beyond the structural factors determining the pool of opportunities, is a challenging task. Here, we introduce a robust methodology for quantifying and…

物理与社会 · 物理学 2024-01-25 Sina Sajjadi , Samuel Martin-Gutierrez , Fariba Karimi
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