An Empirical Analysis of the Impact of Recruitment Patterns on RDS Estimates among a Socially Ordered Population of Female Sex Workers in China
Bibliographic Data
| ID | 2330686 |
|---|---|
| Authors | Thespina J Yamanis (0000-0001-9611-1714, School of International Service, American University, Washington, DC, USA), M Giovanna Merli (0000-0001-8258-529X, School of International Service, American University, Washington, DC, USA), William Whipple Neely (Duke University), F F Tian (0000-0001-7903-7559), James Moody (0000-0003-2266-5348, Lake Forest Park, WA, USA), Xiaowen Tu (0009-0002-4122-4951, Duke University), Ersheng Gao (0000-0001-5772-2117, Shanghai Institute of Planned Parenthood Research) |
| Year | 2013 |
| Volume | 42 |
| Issue | 3 |
| Pages | 392-425 |
| Publication date | 2013-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0049124113494576 |
| PMID | 24288418 |
| PMCID | PMC3840895 |
| OpenAlex | W2057556029 |
| Language | EN |
| Citations received | 17 |
| References cited | 41 |
Respondent-driven sampling (RDS) is a method for recruiting 'hidden' populations through a network-based, chain and peer referral process. RDS recruits hidden populations more effectively than other sampling methods and promises to generate unbiased estimates of their characteristics. RDS's faithful representation of hidden populations relies on the validity of core assumptions regarding the unobserved referral process. With empirical recruitment data from an RDS study of female sex workers (FSWs) in Shanghai, we assess the RDS assumption that participants recruit nonpreferentially from among their network alters. We also present a bootstrap method for constructing the confidence intervals around RDS estimates. This approach uniquely incorporates real-world features of the population under study (e.g., the sample's observed branching structure). We then extend this approach to approximate the distribution of RDS estimates under various peer recruitment scenarios consistent with the data as a means to quantify the impact of recruitment bias and of rejection bias on the RDS estimates. We find that the hierarchical social organization of FSWs leads to recruitment biases by constraining RDS recruitment across social classes and introducing bias in the RDS estimates
Econometrics · Political science · Population · Respondent · Sample size determination · Sampling (signal processing) · Sampling bias · Sociology · Statistics · Computer Science · Demography · HIV, Drug Use, Sexual Risk · HIV/AIDS Research and Interventions · Mathematics · Psychology · Sex work and related issues
HIV Prevalence and Risk Behaviours Among Foreign Migrant Women Residing in Cape Town, South Africa
Diagnostics for Respondent-Driven Sampling
Sampling migrants from their social networks
Stickiness of respondent-driven sampling recruitment chains
HIV Prevalence and Risk Factors Among Male Foreign Migrants in Cape Town, South Africa
Implementing Respondent-Driven Sampling to Recruit Women Who Exchange Sex in New York City
New Survey Questions and Estimators for Network Clustering with Respondent-driven Sampling Data
Generalizing the Network Scale-up Method
Associations of Sex Work Modality Classifications with Condom and Substance Use Behaviors Among Women Who Exchange Sex for Drugs or Money – Four Metropolitan U.S. Cities, 2016
The Development and the Assessment of Sampling Methods for Hard-to-Reach Populations in HIV Surveillance
HIV Prevalence and Awareness of Positive Serostatus Among Men Who Have Sex With Men and Transgender Women in Bogotá, Colombia
Using Social Networks to Sample Migrants and Study the Complexity of Contemporary Immigration
Sexual Mixing in Shanghai
A comparison of network sampling designs for a hidden population of drug users
Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling
Model-Based and Design-Based Inference
Network Sampling
Power, community mobilization, and condom use practices among female sex workers in Andhra Pradesh, India
Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation
Evaluation of Respondent-driven Sampling
Assessing respondent-driven sampling
HIV/Aids Risk Among Brothel-Based Female Sex Workers in China
Dangerous Pleasures
A study on female sex workers in southern China (Shenzhen)
High risk of HIV in non-brothel based female sex workers in India
An Empirical Comparison of Respondent-driven Sampling, Time Location Sampling, and Snowball Sampling for Behavioral Surveillance in Men Who Have Sex with Men, Fortaleza, Brazil
Extensions of Respondent-Driven Sampling
Accessing Men Who have Sex with Men Through Long-Chain Referral Recruitment, Guangzhou, China
The Effectiveness of Respondent Driven Sampling for Recruiting Males Who have Sex with Males in Dhaka, Bangladesh
Variance Estimation, Design Effects, and Sample Size Calculations for Respondent-Driven Sampling
Below Replacement Fertility Preferences in Shanghai
Assessment of Respondent Driven Sampling for Recruiting Female Sex Workers in Two Vietnamese Cities
Simultaneous Recruitment of Drug Users and Men Who Have Sex with Men in the United States and Russia Using Respondent-Driven Sampling
Recruiting Injection Drug Users
Exploring Barriers to ‘Respondent Driven Sampling’ in Sex Worker and Drug-Injecting Sex Worker Populations in Eastern Europe
Respondent-Driven Sampling
An Empirical Test of Respondent-Driven Sampling
Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling
Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling
Prostitutes, prostitution and STD/HIV transmission in Mainland China
Modelling the spread of HIV/Aids in China
Respondent-Driven Sampling
Web-Based Network Sampling
| Unique citing works | 17 |
|---|---|
| Citations per year | 1,42 |
| Citation span | 2014 - 2026 (13) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 16 |