IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2603.00365.html

Randomized Recruitment Driven Sampling

Author

Listed:
  • Adam Visokay
  • Laura Boudreau
  • Rachel M. Heath
  • Tyler H. McCormick

Abstract

Surveys are critical inputs for research and policy, yet, enumerating a sampling frame is logistically infeasible or financially nonviable in many circumstances, such as during pandemics, natural disasters, or armed conflict. Respondent Driven Sampling (RDS) does not require a sampling frame, yet non-random peer recruitment often introduces substantial bias, particularly under high homophily. We introduce and evaluate Randomized Recruitment Driven Sampling (RRDS), a cellphone-based adaptation of RDS that incorporates researcher-controlled randomization into each recruitment wave. While standard RDS is necessary for stigmatized groups where network transparency is infeasible, RRDS is designed for low-stigma populations that become difficult to access due to logistical barriers. In these contexts, RRDS enforces the random recruitment assumption that traditional RDS relies upon but rarely achieves. Through simulation and an experiment surveying Bangladeshi garment workers during the COVID-19 pandemic, we demonstrate that RRDS produces less biased estimates and improved confidence interval coverage compared to traditional RDS. RRDS offers a scalable, remote-compatible alternative for studying low-stigma groups in challenging contexts where large-scale probability sampling is unsafe or infeasible.

Suggested Citation

  • Adam Visokay & Laura Boudreau & Rachel M. Heath & Tyler H. McCormick, 2026. "Randomized Recruitment Driven Sampling," Papers 2603.00365, arXiv.org.
    • Handle: RePEc:arx:papers:2603.00365
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2603.00365
    File Function: Latest version
    Download Restriction: no
    ---><---

    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. Casey F. Breen & Dennis M. Feehan, 2025. "New Data Sources for Demographic Research," Population and Development Review, The Population Council, Inc., vol. 51(1), pages 539-573, March.
    3. Laura E. Boudreau & Sylvain Chassang & Ada Gonzalez-Torres & Rachel M. Heath, 2023. "Monitoring Harassment in Organizations," NBER Working Papers 31011, National Bureau of Economic Research, Inc.
    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.
    5. Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2020. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," American Economic Review, American Economic Association, vol. 110(8), pages 2454-2484, August.
    6. A K B Green & T H McCormick & A E Raftery, 2020. "Consistency for the tree bootstrap in respondent-driven sampling," Biometrika, Biometrika Trust, vol. 107(2), pages 497-504.
    7. Laura Boudreau & Sylvain Chassang & Ada González-Torre & Rachel Heath, 2023. "Monitoring Harassment in Organizations," Working Papers 311, Princeton University, Department of Economics, Center for Economic Policy Studies..
    8. Laura Boudreau & Sylvain Chassang & Ada González-Torre & Rachel Heath, 2023. "Monitoring Harassment in Organizations," Working Papers 2022-19, Princeton University. Economics Department..
    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. Cai, Xiqian & Chen, Shuai & Cheng, Zhengquan, 2024. "The #MeToo Movement and Judges' Gender Gap in Decisions," GLO Discussion Paper Series 1453, Global Labor Organization (GLO).
    2. Nugent, Jeffrey B., 2023. "A new institutional perspective on business associations: Filling a gap between firms and states in the dynamic analysis of Richard Day," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 49-59.
    3. Cai, Xiqian & Chen, Shuai & Cheng, Zhengquan, 2024. "The #Metoo Movement and Judges' Gender Gap in Decisions," IZA Discussion Papers 17115, IZA Network @ LISER.
    4. 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.
    5. Steven Wilkins Reeves & Shane Lubold & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Model-Based Inference and Experimental Design for Interference Using Partial Network Data," Papers 2406.11940, arXiv.org.
    6. Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    7. 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.
    8. Rhys Murrian & Paul A. Raschky & Klaus Ackermann, 2024. "Friends, Key Players and the Adoption and Use of Experience Goods," Monash Economics Working Papers 2024-17, Monash University, Department of Economics.
    9. Mathieu Lambotte & Sandrine Mathy & Anna Risch & Carole Treibich, 2022. "Spreading active transportation: peer effects and key players in the workplace," Post-Print hal-03702684, HAL.
    10. Björkegren, Daniel & Karaca, Burak Ceyhun, 2022. "Network adoption subsidies: A digital evaluation of a rural mobile phone program in Rwanda," Journal of Development Economics, Elsevier, vol. 154(C).
    11. 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.
    12. Evan Sadler, 2025. "Seeding a Simple Contagion," Econometrica, Econometric Society, vol. 93(1), pages 71-93, January.
    13. de Janvry, Alain & Rao, Manaswini & Sadoulet, Elisabeth, 2025. "Seeding the seeds: Role of social structure in agricultural technology diffusion," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    14. Larissa Jennings Mayo-Wilson & Muthoni Mathai & Grace Yi & Margaret O Mak’anyengo & Melissa Davoust & Massah L Massaquoi & Stefan Baral & Fred M Ssewamala & Nancy E Glass & NAHEDO Study Group, 2020. "Lessons learned from using respondent-driven sampling (RDS) to assess sexual risk behaviors among Kenyan young adults living in urban slum settlements: A process evaluation," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-22, April.
    15. Ayushi Narayan, 2024. "The limits of using grievance procedures to combat workplace discrimination," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 63(1), pages 26-42, January.
    16. 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.
    17. Tiziano Arduini & Edoardo Rainone, 2024. "Partial identification of treatment response under complementarity and substitutability," Temi di discussione (Economic working papers) 1473, Bank of Italy, Economic Research and International Relations Area.
    18. Roland Pongou & Guy Tchuente & Jean-Baptiste Tondji, 2020. "An Economic Model of Health-vs-Wealth Prioritization During COVID-19: Optimal Lockdown, Network Centrality, and Segregation," Working Papers 2009E Classification-E61,, University of Ottawa, Department of Economics.
    19. Áureo de Paula & Imran Rasul & Pedro C L Souza, 2025. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(4), pages 2691-2729.
    20. 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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2603.00365. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.