Estimating uncertainty in respondent-driven sampling using a tree bootstrap method

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates rem...
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Autor*in:	Baraff, Aaron J [verfasserIn] McCormick, Tyler H Raftery, Adrian E

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Uncertainty Social networks Sampling techniques Drug use Bootstrap method Estimating techniques Confidence intervals

Übergeordnetes Werk:	Enthalten in: Proceedings of the National Academy of Sciences of the United States of America - Washington, DC : NAS, 1877, 113(2016), 51, Seite 14668-14673
Übergeordnetes Werk:	volume:113 ; year:2016 ; number:51 ; pages:14668-14673

Links:	Volltext Link aufrufen

DOI / URN:	10.1073/pnas.1617258113

Katalog-ID:	OLC1989957994

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520			\|a Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.
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spelling	10.1073/pnas.1617258113 doi PQ20170301 (DE-627)OLC1989957994 (DE-599)GBVOLC1989957994 (PRQ)c1281-ab068ceb2e46cb221d36658a841068bbabbaca0ef3776173020d17b0629d94870 (KEY)0583363920160000113005114668estimatinguncertaintyinrespondentdrivensamplingusi DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Baraff, Aaron J verfasserin aut Estimating uncertainty in respondent-driven sampling using a tree bootstrap method 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS. Uncertainty Social networks Sampling techniques Drug use Bootstrap method Estimating techniques Confidence intervals McCormick, Tyler H oth Raftery, Adrian E oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 51, Seite 14668-14673 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:51 pages:14668-14673 http://dx.doi.org/10.1073/pnas.1617258113 Volltext http://search.proquest.com/docview/1853314706 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 51 14668-14673
allfields_unstemmed	10.1073/pnas.1617258113 doi PQ20170301 (DE-627)OLC1989957994 (DE-599)GBVOLC1989957994 (PRQ)c1281-ab068ceb2e46cb221d36658a841068bbabbaca0ef3776173020d17b0629d94870 (KEY)0583363920160000113005114668estimatinguncertaintyinrespondentdrivensamplingusi DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Baraff, Aaron J verfasserin aut Estimating uncertainty in respondent-driven sampling using a tree bootstrap method 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS. Uncertainty Social networks Sampling techniques Drug use Bootstrap method Estimating techniques Confidence intervals McCormick, Tyler H oth Raftery, Adrian E oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 51, Seite 14668-14673 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:51 pages:14668-14673 http://dx.doi.org/10.1073/pnas.1617258113 Volltext http://search.proquest.com/docview/1853314706 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 51 14668-14673
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source	Enthalten in Proceedings of the National Academy of Sciences of the United States of America 113(2016), 51, Seite 14668-14673 volume:113 year:2016 number:51 pages:14668-14673
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author	Baraff, Aaron J
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topic_title	500 DNB 570 AVZ LING fid BIODIV fid Estimating uncertainty in respondent-driven sampling using a tree bootstrap method Uncertainty Social networks Sampling techniques Drug use Bootstrap method Estimating techniques Confidence intervals
topic	ddc 500 ddc 570 fid LING fid BIODIV misc Uncertainty misc Social networks misc Sampling techniques misc Drug use misc Bootstrap method misc Estimating techniques misc Confidence intervals
topic_unstemmed	ddc 500 ddc 570 fid LING fid BIODIV misc Uncertainty misc Social networks misc Sampling techniques misc Drug use misc Bootstrap method misc Estimating techniques misc Confidence intervals
topic_browse	ddc 500 ddc 570 fid LING fid BIODIV misc Uncertainty misc Social networks misc Sampling techniques misc Drug use misc Bootstrap method misc Estimating techniques misc Confidence intervals
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title	Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
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title_full	Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
author_sort	Baraff, Aaron J
journal	Proceedings of the National Academy of Sciences of the United States of America
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author_browse	Baraff, Aaron J
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title_sort	estimating uncertainty in respondent-driven sampling using a tree bootstrap method
title_auth	Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
abstract	Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.
abstractGer	Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.
abstract_unstemmed	Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.
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container_issue	51
title_short	Estimating uncertainty in respondent-driven sampling using a tree bootstrap method
url	http://dx.doi.org/10.1073/pnas.1617258113 http://search.proquest.com/docview/1853314706
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author2	McCormick, Tyler H Raftery, Adrian E
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up_date	2024-07-03T23:22:15.892Z
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