<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This article presents the results of a pilot feasibility study comparing two alternative recruitment approaches based on Respondent Driven Sampling using initial seeds selected from a US nationally representative panel, AmeriSpeak, to augment the number of lesbian, gay, bisexual, and transgender (LGBT) respondents to a short web survey on smoking, discrimination, and health. In the nomination condition after completing the survey both LGBT and non-LGBT seeds were invited to share the names and email address of up to four LGBT persons they knew. In the recruitment condition, seeds were given four unique PINs and links to the survey to distribute to LGBT persons. Both conditions were successful in producing new LGBT respondents. The recruitment condition was much more productive. LGBT seeds (and their recruits) were much connected to and willing to contact other LGBT people they knew to participate in a survey. Comparisons of characteristics and responses from the initial samples and the LGBT referrals as well as comparisons to LGB samples from a large national survey are presented. Results demonstrate the promise of this hybrid technique for increasing the number of LGBT respondents through referrals from an initial probability based sample.</p></abstract>

This article presents the results of a pilot feasibility study comparing two alternative recruitment approaches based on Respondent Driven Sampling using initial seeds selected from a US nationally representative panel, AmeriSpeak, to augment the number of lesbian, gay, bisexual, and transgender (LGBT) respondents to a short web survey on smoking, discrimination, and health. In the nomination condition after completing the survey both LGBT and non-LGBT seeds were invited to share the names and email address of up to four LGBT persons they knew. In the recruitment condition, seeds were given four unique PINs and links to the survey to distribute to LGBT persons. Both conditions were successful in producing new LGBT respondents. The recruitment condition was much more productive. LGBT seeds (and their recruits) were much connected to and willing to contact other LGBT people they knew to participate in a survey. Comparisons of characteristics and responses from the initial samples and the LGBT referrals as well as comparisons to LGB samples from a large national survey are presented. Results demonstrate the promise of this hybrid technique for increasing the number of LGBT respondents through referrals from an initial probability based sample.

Test of a Hybrid Method of Sampling the LGBT Population: Web Respondent Driven Sampling with Seeds from a Probability Sample

NORC at the University of Chicago – AmeriSpeak

Journal of Official Statistics

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

This article presents the results of a pilot feasibility study comparing two alternative recruitment approaches based on Respondent Driven Sampling using...

Test of a Hybrid Method of Sampling the LGBT Population: Web Respondent Driven Sampling with Seeds from a Probability Sample

Published Online: Dec 11, 2019

Page range: 731 - 752

Received: Jan 01, 2019

Accepted: Aug 01, 2019

DOI: https://doi.org/10.2478/jos-2019-0031

Keywords
LGBT population, respondent driven sampling, network sampling

© 2019 Stuart Michaels et al., published by Sciendo

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

Test of a Hybrid Method of Sampling the LGBT Population: Web Respondent Driven Sampling with Seeds from a Probability Sample

Published Online: Dec 11, 2019

Page range: 731 - 752

Received: Jan 01, 2019

Accepted: Aug 01, 2019

DOI: https://doi.org/10.2478/jos-2019-0031

KeywordsLGBT population, respondent driven sampling, network sampling

© 2019 Stuart Michaels et al., published by Sciendo

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

Keywords
LGBT population, respondent driven sampling, network sampling