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Network Model-Assisted Inference from Respondent-Driven Sampling Data

Year: 2015
Type: article
Abstract: Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.
DOI: 10.1111/rssa.12091
Language: English
Open Access status: green
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, Innovations in Network Modeling for HIV Prevention Studies, Scalable Model-Based Inference for Social Networks from Complex Sampling Designs, Diagnosis and Reduction of Bias in Respondent-driven Sampling, California Center for Population Research (CCPR) at UCLA, Statistical Methods for Respondent Driven Sampling Data, Center for Studies in Demography and Ecology, Valid Inference for Respondent Driven Sampling of Hidden Networked Populations
OpenAlex ID: W2063127585
FWCI: 8.604
Cites: 45
Cited by: 73
Related to: 10