IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v32y2016i2p433-459n11.html
   My bibliography  Save this article

Random Walks on Directed Networks: Inference and Respondent-Driven Sampling

Author

Listed:
  • Malmros Jens

    (Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden.)

  • Masuda Naoki

    (Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.)

  • Britton Tom

    (Department of Engineering Mathematics, University of Bristol, Merchant Venturers Building, Woodland Road, Clifton, Bristol BS8 1UB, United Kingdom.)

Abstract

Respondent-driven sampling (RDS) is often used to estimate population properties (e.g., sexual risk behavior) in hard-to-reach populations. In RDS, already sampled individuals recruit population members to the sample from their social contacts in an efficient snowball-like sampling procedure. By assuming a Markov model for the recruitment of individuals, asymptotically unbiased estimates of population characteristics can be obtained. Current RDS estimation methodology assumes that the social network is undirected, that is, all edges are reciprocal. However, empirical social networks in general also include a substantial number of nonreciprocal edges. In this article, we develop an estimation method for RDS in populations connected by social networks that include reciprocal and nonreciprocal edges. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. The proposed estimators are evaluated on artificial and empirical networks and are shown to generally perform better than existing estimators. This is the case in particular when the fraction of directed edges in the network is large.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:433-459:n:11
    DOI: 10.1515/jos-2016-0023
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jos-2016-0023
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jos-2016-0023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Chien-Min Huang & F. Jay Breidt, 2023. "A dual-frame approach for estimation with respondent-driven samples," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 65-81, April.
    4. 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.
    5. Lisa Avery & Alison Macpherson & Sarah Flicker & Michael Rotondi, 2021. "A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    6. 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.
    7. Florence Samkange-Zeeb & Ronja Foraita & Stefan Rach & Tilman Brand, 2019. "Feasibility of using respondent-driven sampling to recruit participants in superdiverse neighbourhoods for a general health survey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 451-459, April.
    8. 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.
    9. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    10. Merli, M. Giovanna & Moody, James & Smith, Jeffrey & Li, Jing & Weir, Sharon & Chen, Xiangsheng, 2015. "Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling," Social Science & Medicine, Elsevier, vol. 125(C), pages 79-93.
    11. Barash Vladimir D. & Cameron Christopher J. & Spiller Michael W. & Heckathorn Douglas D., 2016. "Respondent-Driven Sampling – Testing Assumptions: Sampling with Replacement," Journal of Official Statistics, Sciendo, vol. 32(1), pages 29-73, March.
    12. Nicky McCreesh & Andrew Copas & Janet Seeley & Lisa G Johnston & Pam Sonnenberg & Richard J Hayes & Simon D W Frost & Richard G White, 2013. "Respondent Driven Sampling: Determinants of Recruitment and a Method to Improve Point Estimation," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    13. Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    14. Ali Mirzazadeh & Yea-Hung Chen & Jess Lin & Katie Burk & Erin C Wilson & Desmond Miller & Danielle Veloso & Willi McFarland & Meghan D Morris, 2021. "Progress toward closing gaps in the hepatitis C virus cascade of care for people who inject drugs in San Francisco," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-11, April.
    15. Thespina J. Yamanis & M. Giovanna Merli & William Whipple Neely & Felicia Feng Tian & James Moody & Xiaowen Tu & Ersheng Gao, 2013. "An Empirical Analysis of the Impact of Recruitment Patterns on RDS Estimates among a Socially Ordered Population of Female Sex Workers in China," Sociological Methods & Research, , vol. 42(3), pages 392-425, August.
    16. Mart L Stein & Vincent Buskens & Peter G M van der Heijden & Jim E van Steenbergen & Albert Wong & Martin C J Bootsma & Mirjam E E Kretzschmar, 2018. "A stochastic simulation model to study respondent-driven recruitment," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    17. Hernández, Hugo & Quiroz, Gabriel & Zambrano, Omar & Zanoni, Wladimir, 2023. "Measuring Labor Market Discrimination against LGTBQ+ in the Case of Ecuador: A Field Experiment," IDB Publications (Working Papers) 12977, Inter-American Development Bank.
    18. Zanoni, Wladimir & Fabregas, Raissa, 2024. "The Migrant Penalty in Latin America: Experimental Evidence from Job Recruiters," IDB Publications (Working Papers) 13804, Inter-American Development Bank.
    19. Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
    20. Schonlau, Matthias & Liebau, Elisabeth, 2012. "Respondent-Driven Sampling," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(1), pages 72-93.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:32:y:2016:i:2:p:433-459:n:11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.