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Diagnostics for respondent-driven sampling

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  • Krista J. Gile
  • Lisa G. Johnston
  • Matthew J. Salganik

Abstract

type="main" xml:id="rssa12059-abs-0001"> Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for human immunodeficiency virus or acquired immune deficiency syndrome. Data are collected through a peer referral process over social networks. RDS has proven practical for data collection in many difficult settings and has been adopted by leading public health organizations around the world. Unfortunately, inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher and not fully observable. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to understand their RDS data better and encourage future statistical research on RDS sampling and inference.

Suggested Citation

  • Krista J. Gile & Lisa G. Johnston & Matthew J. Salganik, 2015. "Diagnostics for respondent-driven sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 241-269, January.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:1:p:241-269
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.178.issue-1
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    Citations

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    Cited by:

    1. Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    2. Lee Sunghee & Suzer-Gurtekin Tuba & Wagner James & Valliant Richard, 2017. "Total Survey Error and Respondent Driven Sampling: Focus on Nonresponse and Measurement Errors in the Recruitment Process and the Network Size Reports and Implications for Inferences," Journal of Official Statistics, Sciendo, vol. 33(2), pages 335-366, June.
    3. Larissa Jennings Mayo-Wilson & Muthoni Mathai & Grace Yi & Margaret O Mak’anyengo & Melissa Davoust & Massah L Massaquoi & Stefan Baral & Fred M Ssewamala & Nancy E Glass & NAHEDO Study Group, 2020. "Lessons learned from using respondent-driven sampling (RDS) to assess sexual risk behaviors among Kenyan young adults living in urban slum settlements: A process evaluation," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-22, April.
    4. Johanna Jonsson & Mart Stein & Gun Johansson & Theo Bodin & Susanne Strömdahl, 2019. "A performance assessment of web-based respondent driven sampling among workers with precarious employment in Sweden," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    5. Lee Sunghee & Ong Ai Rene & Elliott Michael, 2020. "Exploring Mechanisms of Recruitment and Recruitment Cooperation in Respondent Driven Sampling," Journal of Official Statistics, Sciendo, vol. 36(2), pages 339-360, June.
    6. Eleni D. Rompoti & Alexis D. Ioannides, 2023. "“Pseudo-Contracted” Workers as a Means of Bypassing Labour Law in Greece," Administrative Sciences, MDPI, vol. 13(11), pages 1-27, November.
    7. Fatemi, Samira & Salehi, Mostafa & Veisi, Hadi & Jalili, Mahdi, 2018. "A fuzzy logic based estimator for respondent driven sampling of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 42-51.
    8. Lucinda Platt & Renee Luthra & Tom Frere-Smith, 2015. "Adapting chain referral methods to sample new migrants," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 33(24), pages 665-700.
    9. Luis E. C. Rocha & Anna E. Thorson & Renaud Lambiotte & Fredrik Liljeros, 2017. "Respondent-driven sampling bias induced by community structure and response rates in social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 99-118, January.
    10. Héctor Mullo & Ismael Sánchez-Borrego & Sara Pasadas-del-Amo, 2020. "Respondent-Driven Sampling for Surveying Ethnic Minorities in Ecuador," Sustainability, MDPI, vol. 12(21), pages 1-17, November.

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