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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

Author

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  • Lee Sunghee

    (Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, United States of America)

  • Suzer-Gurtekin Tuba

    (Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, United States of America)

  • Wagner James

    (Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, United States of America)

  • Valliant Richard

    (Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48104, United States of America)

Abstract

This study attempted to integrate key assumptions in Respondent-Driven Sampling (RDS) into the Total Survey Error (TSE) perspectives and examine TSE as a new framework for a systematic assessment of RDS errors. Using two publicly available data sets on HIV-at-risk persons, nonresponse error in the RDS recruitment process and measurement error in network size reports were examined. On nonresponse, the ascertained partial nonresponse rate was high, and a substantial proportion of recruitment chains died early. Moreover, nonresponse occurred systematically: recruiters with lower income and higher health risks generated more recruits; and peers of closer relationships were more likely to accept recruitment coupons. This suggests a lack of randomness in the recruitment process, also shown through sizable intra-chain correlation. Self-reported network sizes suggested measurement error, given their wide dispersion and unreasonable reports. This measurement error has further implications for the current RDS estimators, which use network sizes as an adjustment factor on the assumption of a positive relationship between network sizes and selection probabilities in recruitment. The adjustment resulted in nontrivial unequal weighting effects and changed estimates in directions that were difficult to explain and, at times, illogical. Moreover, recruiters’ network size played no role in actual recruitment. TSE may serve as a tool for evaluating errors in RDS, which further informs study design decisions and inference approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:offsta:v:33:y:2017:i:2:p:335-366:n:3
    DOI: 10.1515/jos-2017-0017
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    References listed on IDEAS

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    1. Gile, Krista J., 2011. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 135-146.
    2. 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.
    3. Zheng, Tian & Salganik, Matthew J. & Gelman, Andrew, 2006. "How Many People Do You Know in Prison?: Using Overdispersion in Count Data to Estimate Social Structure in Networks," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 409-423, June.
    4. 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.
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