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Methods for Inference from Respondent-Driven Sampling Data

Year: 2018
Type: review
Abstract: Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to join the sample. Although the method is often effective at attaining large and varied samples, its reliance on convenience samples, social network contacts, and participant decisions makes it subject to a large number of statistical concerns. This article reviews inferential methods available for data collected by respondent-driven sampling.
DOI: 10.1146/annurev-statistics-031017-100704
Language: English
Open Access status: bronze
Funders: National Science Foundation, National Institutes of Health, National Agricultural Statistics Service, Office of Naval Research, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G2998923865, G4154686922, Innovations in Network Modeling for HIV Prevention Studies, Scalable Model-Based Inference for Social Networks from Complex Sampling Designs, BIGDATA: Collaborative Research: IA: Population Reproduction of Poverty at Birth from Surveys, Censuses, and Birth Registrations, Diagnosis and Reduction of Bias in Respondent-driven Sampling, G6298873131, California Center for Population Research (CCPR) at UCLA, Statistical Methods for Respondent Driven Sampling Data, G8876996369, Valid Inference for Respondent Driven Sampling of Hidden Networked Populations
OpenAlex ID: W2620449362
FWCI: 0.657
Cites: 91
Cited by: 53
Related to: 10