B-graph sampling to estimate the size of a hidden population

M. Spreen, S. Bogaerts

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Abstract

Link-tracing designs are often used to estimate the size of hidden populations by utilizing the relational links between their members. A major problem in studies of hidden populations is the lack of a convenient sampling frame. The most frequently applied design in studies of hidden populations is respondent-driven sampling in which no sampling frame is used. However, in some studies multiple but incomplete sampling frames are available. In this article, we introduce the B-graph design that can be used in such situations. In this design, all available incomplete sampling frames are joined and turned into one sampling frame, from which a random sample is drawn and selected respondents are asked to mention their contacts. By considering the population as a bipartite graph of a two-mode network (those from the sampling frame and those who are not on the frame), the number of respondents who are directly linked to the sampling frame members can be estimated using Chao’s and Zelterman’s estimators for sparse data. The B-graph sampling design is illustrated using the data of a social network study from Utrecht, the Netherlands.
Keywords: Network sampling; capture recapture; hidden populations
Original languageEnglish
Pages (from-to)723-736
JournalJournal of Official Statistics
Volume31
Issue number4
DOIs
Publication statusPublished - 2015

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Graph in graph theory
Estimate
Graph Design
Capture-recapture
Sparse Data
Sampling Design
Tracing
Bipartite Graph
Social Networks
Chaos
Contact
Estimator
Design

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B-graph sampling to estimate the size of a hidden population. / Spreen, M.; Bogaerts, S.

In: Journal of Official Statistics, Vol. 31, No. 4, 2015, p. 723-736.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Bogaerts, S.

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AB - Link-tracing designs are often used to estimate the size of hidden populations by utilizing the relational links between their members. A major problem in studies of hidden populations is the lack of a convenient sampling frame. The most frequently applied design in studies of hidden populations is respondent-driven sampling in which no sampling frame is used. However, in some studies multiple but incomplete sampling frames are available. In this article, we introduce the B-graph design that can be used in such situations. In this design, all available incomplete sampling frames are joined and turned into one sampling frame, from which a random sample is drawn and selected respondents are asked to mention their contacts. By considering the population as a bipartite graph of a two-mode network (those from the sampling frame and those who are not on the frame), the number of respondents who are directly linked to the sampling frame members can be estimated using Chao’s and Zelterman’s estimators for sparse data. The B-graph sampling design is illustrated using the data of a social network study from Utrecht, the Netherlands.Keywords: Network sampling; capture recapture; hidden populations

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