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相关论文: Sampling from Networks: Respondent-Driven Sampling

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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…

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

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

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 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 a link-tracing procedure for surveying hidden or hard-to-reach populations in which subjects recruit other subjects via their social network. There is significant research interest in detecting clustering…

应用统计 · 统计学 2015-11-18 Forrest W. Crawford , Peter M. Aronow , Li Zeng , Jianghong Li

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

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 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

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 procedure to sample from hard-to-reach populations. It has been widely used in several countries, especially in the monitoring of HIV/AIDS and other sexually transmitted infections. Hard-to-reach…

应用统计 · 统计学 2012-06-26 Leonardo S. Bastos , Adriana A. Pinho , Claudia Codeço , Francisco I. Bastos

Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a sampling technique used for `hard-to-reach' populations that aims to leverage individuals' social relationships to reach potential participants. While the methodological…

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 currently widely used for the study of HIV/AIDS-related high risk populations. However, recent studies have shown that traditional RDS methods are likely to generate large variances and may be severely…

统计方法学 · 统计学 2012-10-17 Xin Lu

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 both a sampling strategy and an estimation method. It is commonly used to study individuals that are difficult to access with standard sampling techniques. As with any sampling strategy, RDS has…

应用统计 · 统计学 2023-09-29 Jessica P. Kunke , Adam Visokay , Tyler H. McCormick

Respondent-driven sampling (RDS) is an approach to sampling design and analysis which utilizes the networks of social relationships that connect members of the target population, using chain-referral methods to facilitate sampling. RDS…

统计方法学 · 统计学 2015-08-19 Yakir Berchenko , Jonathan Rosenblatt , Simon D. W. Frost

Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased…

统计方法学 · 统计学 2016-03-15 Jens Malmros , Luis E. C. Rocha

Respondent-driven sampling (RDS) is a method of chain referral sampling popular for sampling hidden and/or marginalized populations. As such, even under the ideal sampling assumptions, the performance of RDS is restricted by the underlying…

统计方法学 · 统计学 2017-11-02 Mohammad Khabbazian , Bret Hanlon , Zoe Russek , Karl Rohe
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