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Inferring bivariate association from respondent‐driven sampling data

Author

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  • Dongah Kim
  • Krista J. Gile
  • Honoria Guarino
  • Pedro Mateu‐Gelabert

Abstract

Respondent‐driven sampling (RDS) is an effective method of collecting data from many hard‐to‐reach populations. Valid statistical inference for these data relies on many strong assumptions. In standard samples, we assume observations from pairs of individuals are independent. In RDS, this assumption is violated by the sampling dependence between individuals. We propose a method to semi‐parametrically estimate the null distributions of standard test statistics in the presence of sampling dependence, allowing for more valid statistical testing for dependence between pairs of variables within the sample. We apply our method to study characteristics of young adult illicit opioid users in New York City.

Suggested Citation

  • Dongah Kim & Krista J. Gile & Honoria Guarino & Pedro Mateu‐Gelabert, 2021. "Inferring bivariate association from respondent‐driven sampling data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 415-433, March.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:2:p:415-433
    DOI: 10.1111/rssc.12465
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    References listed on IDEAS

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    3. Miles Q. Ott & Krista J. Gile & Matthew T. Harrison & Lisa G. Johnston & Joseph W. Hogan, 2019. "Reduced bias for respondent‐driven sampling: accounting for non‐uniform edge sampling probabilities in people who inject drugs in Mauritius," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1411-1429, November.
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    5. Ashton M Verdery & Ted Mouw & Shawn Bauldry & Peter J Mucha, 2015. "Network Structure and Biased Variance Estimation in Respondent Driven Sampling," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-27, December.
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