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Network model-assisted inference from respondent-driven sampling data

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  • Krista J. Gile
  • Mark S. Handcock

Abstract

type="main" xml:id="rssa12091-abs-0001"> Respondent-driven sampling is a widely used method for sampling hard-to-reach human populations by link tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to compute the sampling weights for traditional design-based inference directly, and likelihood inference requires modelling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared with existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of the prevalence of human immunodeficiency virus in a high-risk population.

Suggested Citation

  • Krista J. Gile & Mark S. Handcock, 2015. "Network model-assisted inference from respondent-driven sampling data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 619-639, June.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:3:p:619-639
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-3
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    Cited by:

    1. 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.
    2. Yongren Shi & Christopher J. Cameron & Douglas D. Heckathorn, 2019. "Model-Based and Design-Based Inference: Reducing Bias Due to Differential Recruitment in Respondent-Driven Sampling," Sociological Methods & Research, , vol. 48(1), pages 3-33, February.
    3. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    4. Malmros Jens & Masuda Naoki & Britton Tom, 2016. "Random Walks on Directed Networks: Inference and Respondent-Driven Sampling," Journal of Official Statistics, Sciendo, vol. 32(2), pages 433-459, June.

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