Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation



  • NIH-National Institute of Child Health and Human Development


  • Duke University Department of Sociology

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  • Ongoing

Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation

The main aims of this research are to obtain valid estimates of the prevalence of sexually transmitted disease (STD) infection and risk behaviors in a hidden population, female sex workers in China, sampled with different strategies including Respondent Driven Sampling (RDS) and to improve RDS methodology and procedures using data collected as part of this multiple data collection effort. This will lead to the production of more accurate information on this population, a better understanding of its impact on the larger population health dynamics, and guidelines for researchers using RDS on steps to improve RDS estimation for representation of other hidden populations.

* Merli, M. Giovanna Merli, Jim Moody, Jeff Smith, Jing Li, Sharon Weir and Xiangsheng Chen. Challenges to Recruiting Representative Samples of Female Sex Workers in China using Respondent Driven Sampling: How Much of the Network Do We See? [also presented as Smith J., Merli M.G. and J. Moody Network Sampling Coverage in RDS: How Much of the Network Do We See? at H2R--Survey Methods for Hard To Reach Populations, October 31-November 3, 2012, New Orleans]. Social Science & Medicine. In Press. * Yamanis Nina, M. Giovanna Merli (corresponding author), W. Whipple Neely, Felicia F. Tian, James Moody, Xiaowen Tu and Ersheng Gao. 2013. An empirical analysis of the impact of recruitment patterns on RDS estimates among a socially ordered population of female sex workers. Sociological Methods and Research. In Press. * Jake Fisher, M. Giovanna Merli. 2014. Stickiness of Respondent-Driven Sampling Recruitment Chains. Network Science. 2(2):298-301.

Last updated on January 10, 2018