Network model-assisted inference from respondent-driven sampling data

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 impl...

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Vydáno v:	Journal of the Royal Statistical Society. Series A, Statistics in society Ročník 178; číslo 3; s. 619 - 639
Hlavní autoři:	Gile, Krista J., Handcock, Mark S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Blackwell Publishing Ltd 01.06.2015 John Wiley & Sons Ltd Oxford University Press
Témata:	Alternative approaches Bootstrap method Demonstrations Estimating techniques Estimation Estimation bias Estimators Exponential family random graph model Hard-to-reach population sampling High risk HIV Human immunodeficiency virus Infections Inference Link tracing Network analysis Network sampling Population Population estimates Population size Respondents Sampling Sampling bias Sampling studies Simulations Social networks Statistical inference Network sampling Exponential-family random graph model Social networks Link-tracing Hard-to-reach population sampling
ISSN:	0964-1998, 1467-985X
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Popis
Shrnutí:	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 compute the sampling weights for traditional design-based inference directly, and likelihood inference requires modelling 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 with existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of the prevalence of human immunodeficiency virus in a high-risk population.
Bibliografie:	ark:/67375/WNG-MVT6S95N-7 istex:82FA99A51B2EBCE3ACF8C6375A32CEDA8E1C9F1D ArticleID:RSSA12091 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0964-1998 1467-985X
DOI:	10.1111/rssa.12091