# Exploring Design Aspects of Web-Based Respondent-Driven Sampling for Racial/Ethnic Minorities

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $234,000

## Abstract

ABSTRACT
Increasing minority data availability is a priority of the HHS Action Plan to Reduce Racial and Ethnic Health
Disparities. Currently available research data for racial/ethnicity minorities are, however, inadequate in their
sample sizes and, hence, do not provide sufficient statistical power for analysis. Web-based respondent driven
sampling (Web-RDS) is an extension of RDS, which exploits existing social networks for recruiting research
participants, specifically, of rare, hidden and/or hard-to-reach groups. Rather than researchers recruiting
participants directly, in RDS, participants recruit other eligible persons from own social networks. This
recruitment process continues in waves and is mostly handled by incentivized recruitment coupons. Hence,
RDS has potential to capture those who, otherwise, are difficult for researchers to reach and is much less cost
intensive than traditional sampling methods. While RDS is typically administered in person, Web-RDS applies
RDS on the Web, which leads to eliminating the need for interviewers and the constraints associated with the
time and geography. Once the data collection system is established, marginal costs for Web-RDS is
considerably lower than RDS, further reducing cost burdens. At the same time, racial/ethnicity minorities are
reported to form tight ingroup social networks and to access the Web at a remarkably high rate, around 90%, a
figure virtually the same as non-minorities'. The strong social network combined with the high Web access rate
among minorities makes Web-RDS an attractive platform for minority data collection. However, there is a
notable void in the literature on design aspects of RDS. Design issues are not only practical questions for
which any Web-RDS studies are likely to seek answers but also a critical factor influencing data quality.
This study attempts to fill this gap by providing practical design guidelines and tools for Web-RDS for a goal of
improving data quality through successful implementations, where success is measured with sample
composition and recruitment propensity. Specifically, we will explore two specific design elements (seed
selection and coupon design) through randomized experiments and develop a data collection monitoring
system. For doing so, Web-RDS will be applied to a national survey of Korean Americans, a rare minority
group that comprises less than 1% of the U.S. population. Despite being rare, basic socio-demographic
information is available for many racial/ethnic minority groups, such as Korean Americans, from the American
Community Survey. This is particularly advantageous as the quality of the Web-RDS data under different
experimental conditions can be verified against the minority-specific ACS data. By empirically demonstrating Web-
RDS as a method for minority-specific data collection and its data quality through design experiments, the
proposed study will bring these practical RDS-specific design issues to the literature and increase...

## Key facts

- **NIH application ID:** 9924497
- **Project number:** 5R21AG062844-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Sung-Hee Lee
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $234,000
- **Award type:** 5
- **Project period:** 2019-05-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9924497

## Citation

> US National Institutes of Health, RePORTER application 9924497, Exploring Design Aspects of Web-Based Respondent-Driven Sampling for Racial/Ethnic Minorities (5R21AG062844-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9924497. Licensed CC0.

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