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Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling

BACKGROUND: Respondent-driven sampling (RDS) is a method successfully used to research hard-to-access populations. Few studies have explored the use of the Internet and social media with RDS, known as Web-based RDS (WebRDS). This study explored the use of combining both “traditional” RDS and WebRDS...

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Autores principales: Hildebrand, Janina, Burns, Sharyn, Zhao, Yun, Lobo, Roanna, Howat, Peter, Allsop, Steve, Maycock, Bruce
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705369/
https://www.ncbi.nlm.nih.gov/pubmed/26704736
http://dx.doi.org/10.2196/jmir.4762
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author Hildebrand, Janina
Burns, Sharyn
Zhao, Yun
Lobo, Roanna
Howat, Peter
Allsop, Steve
Maycock, Bruce
author_facet Hildebrand, Janina
Burns, Sharyn
Zhao, Yun
Lobo, Roanna
Howat, Peter
Allsop, Steve
Maycock, Bruce
author_sort Hildebrand, Janina
collection PubMed
description BACKGROUND: Respondent-driven sampling (RDS) is a method successfully used to research hard-to-access populations. Few studies have explored the use of the Internet and social media with RDS, known as Web-based RDS (WebRDS). This study explored the use of combining both “traditional” RDS and WebRDS to examine the influences on adolescent alcohol use. OBJECTIVE: This paper reports on the recruitment processes and the challenges and enablers of both RDS and WebRDS. It details comparative recruitment data and provides a summary of the utility of both methods for recruiting adolescents to participate in an online survey investigating youth alcohol norms. METHODS: Process evaluation data collected from research staff throughout the study were used to assess the challenges and solutions of RDS and WebRDS. Pearson chi-square test (Fisher’s exact test if applicable) was used to compare the differences in sociodemographics and drinking behavior between data collected by RDS and WebRDS. RESULTS: Of the total sample (N=1012), 232 adolescents were recruited by RDS and 780 by WebRDS. A significantly larger proportion of Aboriginal or Torres Strait Islander (P<.001) participants who spoke English as their main language at home (P=.03), and of middle and lower socioeconomic status (P<.001) was found in the RDS sample. The RDS sample was also found to have a higher occurrence of past 7-day drinking (P<.001) and past 7-day risky drinking (P=.004). No significant differences in gender, age, past month alcohol use, and lifetime alcohol use were observed between the RDS and WebRDS samples. This study revealed RDS and WebRDS used similar lengths of chains for recruiting participants; however, WebRDS conducted a faster rate of recruitment at a lower average cost per participant compared to RDS. CONCLUSIONS: Using WebRDS resulted in significant improvements in the recruitment rate and was a more effective and efficient use of resources than the traditional RDS method. However, WebRDS resulted in partially different sample characteristics to traditional RDS. This potential effect should be considered when selecting the most appropriate data collection method.
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spelling pubmed-47053692016-01-12 Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling Hildebrand, Janina Burns, Sharyn Zhao, Yun Lobo, Roanna Howat, Peter Allsop, Steve Maycock, Bruce J Med Internet Res Original Paper BACKGROUND: Respondent-driven sampling (RDS) is a method successfully used to research hard-to-access populations. Few studies have explored the use of the Internet and social media with RDS, known as Web-based RDS (WebRDS). This study explored the use of combining both “traditional” RDS and WebRDS to examine the influences on adolescent alcohol use. OBJECTIVE: This paper reports on the recruitment processes and the challenges and enablers of both RDS and WebRDS. It details comparative recruitment data and provides a summary of the utility of both methods for recruiting adolescents to participate in an online survey investigating youth alcohol norms. METHODS: Process evaluation data collected from research staff throughout the study were used to assess the challenges and solutions of RDS and WebRDS. Pearson chi-square test (Fisher’s exact test if applicable) was used to compare the differences in sociodemographics and drinking behavior between data collected by RDS and WebRDS. RESULTS: Of the total sample (N=1012), 232 adolescents were recruited by RDS and 780 by WebRDS. A significantly larger proportion of Aboriginal or Torres Strait Islander (P<.001) participants who spoke English as their main language at home (P=.03), and of middle and lower socioeconomic status (P<.001) was found in the RDS sample. The RDS sample was also found to have a higher occurrence of past 7-day drinking (P<.001) and past 7-day risky drinking (P=.004). No significant differences in gender, age, past month alcohol use, and lifetime alcohol use were observed between the RDS and WebRDS samples. This study revealed RDS and WebRDS used similar lengths of chains for recruiting participants; however, WebRDS conducted a faster rate of recruitment at a lower average cost per participant compared to RDS. CONCLUSIONS: Using WebRDS resulted in significant improvements in the recruitment rate and was a more effective and efficient use of resources than the traditional RDS method. However, WebRDS resulted in partially different sample characteristics to traditional RDS. This potential effect should be considered when selecting the most appropriate data collection method. JMIR Publications Inc. 2015-12-24 /pmc/articles/PMC4705369/ /pubmed/26704736 http://dx.doi.org/10.2196/jmir.4762 Text en ©Janina Hildebrand, Sharyn Burns, Yun Zhao, Roanna Lobo, Peter Howat, Steve Allsop, Bruce Maycock. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.12.2015. https://creativecommons.org/licenses/by/2.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/ (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Hildebrand, Janina
Burns, Sharyn
Zhao, Yun
Lobo, Roanna
Howat, Peter
Allsop, Steve
Maycock, Bruce
Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title_full Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title_fullStr Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title_full_unstemmed Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title_short Potential and Challenges in Collecting Social and Behavioral Data on Adolescent Alcohol Norms: Comparing Respondent-Driven Sampling and Web-Based Respondent-Driven Sampling
title_sort potential and challenges in collecting social and behavioral data on adolescent alcohol norms: comparing respondent-driven sampling and web-based respondent-driven sampling
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4705369/
https://www.ncbi.nlm.nih.gov/pubmed/26704736
http://dx.doi.org/10.2196/jmir.4762
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