Work

Respondent-Driven Sampling in Online Social Networks

Year: 2013

Type: preprint

Abstract: Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their mem- bers unwilling to identify themselves. Obtaining accurate statistical data about such communities is important because, for instance, they often have different health burdens from the greater population, and without good statistics it is hard and expensive to effectively reach them for pre- vention or treatment interventions. Online social networks (OSN) have the potential to transform RDS for the better. We present a new RDS recruitment protocol for (OSNs) and show via simulation that it out- performs the standard RDS protocol in terms of sampling accuracy and approaches the accuracy of Markov chain Monte Carlo random walks.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1308.6356

Authors: Homan, Christopher M., Silenzio, Vincent, Sell, Randall

Institutions: Rochester Institute of Technology, University of Rochester Medicine, Drexel University

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, National Institute of Mental Health