Respondent-Driven Sampling in Online Social Networks

  • Christopher M. Homan
  • Vincent Silenzio
  • Randall Sell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)

Abstract

Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their members unwilling to identify themselves. Obtaining accurate statistical data about such communities is important because, for instance, they often have different health burdens from the greater population, and without good statistics it is hard and expensive to effectively reach them for prevention or treatment interventions. Online social networks (OSN) have the potential to transform RDS for the better. We present a new RDS recruitment protocol for (OSNs) and show via simulation that it outperforms the standard RDS protocol in terms of sampling accuracy and approaches the accuracy of Markov chain Monte Carlo random walks.

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References

  1. [AQHM+06]
    Abdul-Quader, A.S., Heckathorn, D.D., McKnight, C., Bramson, H., Nemeth, C., Sabin, K., Gallagher, K., Des Jarlais, D.C.: Effectiveness of respondent-driven sampling for recruiting drug users in New York City: findings from a pilot study. Journal of Urban Health 83(3), 459–476 (2006)CrossRefGoogle Scholar
  2. [BSPar]
    Bernhardt, A., Spiller, M., Polson, D.: All work and no pay: Violations of employment and labor laws in Chicago, Los Angeles and New York City. Social Forces (to appear)Google Scholar
  3. [GH10]
    Gile, K., Handcock, M.: Respondent-driven sampling: an assessment of current methodology. Sociological Methodology 40(1), 285–327 (2010)MathSciNetCrossRefGoogle Scholar
  4. [GH11]
    Gile, K.J., Handcock, M.S.: Network model-assisted inference from respondent-driven sampling data. arXiv preprint arXiv:1108.0298 (2011)Google Scholar
  5. [Gil11]
    Gile, K.J.: Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. Journal of the American Statistical Association 106(493), 135–146 (2011)MathSciNetCrossRefGoogle Scholar
  6. [GJS12]
    Gile, K.J., Johnston, L.G., Salganik, M.J.: Diagnostics for respondent-driven sampling. arXiv preprint arXiv:1209.6254 (2012)Google Scholar
  7. [GKBM11]
    Gjoka, M., Kurant, M., Butts, C., Markopoulou, A.: A walk in facebook: Uniform sampling of users in online social networks. Technical Report 0906.0060v4, arXiv (February 2011)Google Scholar
  8. [GS10]
    Goel, S., Salganik, M.J.: Assessing respondent-driven sampling. Proceedings of the National Academy of Sciences of the United States of America 107(1515), 6743–6747 (2010)CrossRefGoogle Scholar
  9. [Hec97]
    Heckathorn, D.: Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems 44(2), 174–199 (1997)CrossRefGoogle Scholar
  10. [Hec07]
    Heckathorn, D.: Extensions of respondent-driven sampling: Analyzing continuous variables and controlling for differential recruitment. Sociological Methodology 37(1), 151–207 (2007) (in press)MathSciNetCrossRefGoogle Scholar
  11. [HG10]
    Handcock, M.S., Gile, K.J.: Modeling social networks from sampled data. The Annals of Applied Statistics 4(1), 5–25 (2010)MathSciNetMATHCrossRefGoogle Scholar
  12. [HJ01]
    Heckathorn, D., Jeffri, J.: Finding the beat: Using respondent-driven sampling to study jazz musicians. Poetics 28(4), 307–329 (2001)CrossRefGoogle Scholar
  13. [MJK+08]
    Malekinejad, M., Johnston, L.G., Kendall, C., Kerr, L.R.F.S., Rifkin, M.R., Rutherford, G.W.: Using respondent-driven sampling methodology for HIV biological and behavioral surveillance in international settings: a systematic review. AIDS and Behavior 12, 105–130 (2008)CrossRefGoogle Scholar
  14. [SH04]
    Salganik, M.J., Heckathorn, D.D.: Sampling and estimation in hidden populations using respondent-driven sampling. Sociological methodology 34(1), 193–240 (2004)CrossRefGoogle Scholar
  15. [TG11]
    Tomas, A., Gile, K.J.: The effect of differential recruitment, non-response and non-recruitment on estimators for respondent-driven sampling. Electronic Journal of Statistics 5, 899–934 (2011)MathSciNetCrossRefGoogle Scholar
  16. [Tho92]
    Thompson, S.: Sampling. John Wiley & Sons, New York (1992)MATHGoogle Scholar
  17. [VH08]
    Volz, E., Heckathorn, D.: Probability based estimation theory for respondent driven sampling. Journal of Official Statistics 24(1), 79–97 (2008)Google Scholar
  18. [Wej09]
    Wejnert, C.: An empirical test of respondent-driven sampling: Point estimates, variance, degree measures, and out-of-equilibrium data. Sociological Methodology 39(1), 73–116 (2009)CrossRefGoogle Scholar
  19. [WH08]
    Wejnert, C., Heckathorn, D.: Web-based network sampling: Efficiency and efficacy of respondent-driven sampling for online research. Sociological Methods & Research 37(1), 105–134 (2008)MathSciNetCrossRefGoogle Scholar
  20. [WS02]
    Walters, K., Simoni, J.: Health survey of two-spirited native americans (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christopher M. Homan
    • 1
  • Vincent Silenzio
    • 2
  • Randall Sell
    • 3
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.University of Rochester Medical CenterRochesterUSA
  3. 3.Drexel UniversityPhiladelphiaUSA

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