Respondent-Driven Sampling: An Overview in the Context of Human Trafficking

Abstract

Respondent-driven sampling (RDS) is both a sampling strategy and an estimation method. It is commonly used to study individuals that are difficult to access with standard sampling techniques. As with any sampling strategy, RDS has advantages and challenges. This article examines recent work using RDS in the context of human trafficking. We begin with an overview of the RDS process and methodology, then discuss RDS in the particular context of trafficking. We end with a description of recent work and potential future directions.

Acknowledgment

The authors are supported by the National Institute of Mental Health of the NIH under Award Number DP2MH122405 and by the Center for Statistics and the Social Sciences at the University of Washington. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Additional information

Notes on contributors

Jessica P. Kunke

Jessica P. Kunke is a PhD student in statistics at the University of Washington. Her research interests include survey methodology, network models, and spatiotemporal statistics with environmental and social applications. She has an MS in statistics from the University of Chicago, for which she conducted her master’s thesis at the Argonne National Laboratory, and teaches coding, math, and data management workshops for researchers in and outside of academia.

Adam Visokay

Adam Visokay is a PhD student in the Department of Sociology at the University of Washington, focused on networks, survey methods, NLP, and computational social science. He spent two years as a researcher at the Center for Statistics and the Social Sciences. Visokay has a BA in history and economics from the University of Virginia and an MA in economics from Syracuse University.

Tyler H. McCormick

Tyler McCormick is a professor of statistics and sociology at the University of Washington, core faculty member in the Center for Statistics and the Social Sciences, and senior data science fellow at the eScience Institute who develops statistical models for inference and prediction in scientific settings where data are sparsely observed or measured with error. He holds a PhD in statistics with distinction from Columbia University and has received the NIH Director’s New Innovator Award, NIH Career Development (K01) Award, Army Research Office Young Investigator Program Award, and a Google Faculty Research Award. During the 2019–2020 academic year, he was a visiting faculty researcher at Google People + AI Research (PAIR). He is the former editor of the Journal of Computational and Graphical Statistics (JCGS) and a Fellow of the ASA.