<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

B-Graph Sampling to Estimate the Size of a Hidden Population

Stenden University of Applied Sciences-School of Social Work and Art Therapies, Rengerslaan 8 Leeuwarden 8917 DD, The Netherlands.

Tillburg University, Department of Developmental Psychology, Warandelaan 2, 5037 AB Tilburg, The Netherlands.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"B-Graph Sampling to Estimate the Size of a Hidden Population"}

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...

B-Graph Sampling to Estimate the Size of a Hidden Population

Publié en ligne: 16 déc. 2015

Pages: 723 - 736

Reçu: 01 sept. 2013

Accepté: 01 janv. 2015

DOI: https://doi.org/10.1515/jos-2015-0042

Mots clés
Network sampling, capture recapture, hidden populations

© 2015 Marinus Spreen et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

B-Graph Sampling to Estimate the Size of a Hidden Population

Publié en ligne: 16 déc. 2015

Pages: 723 - 736

Reçu: 01 sept. 2013

Accepté: 01 janv. 2015

DOI: https://doi.org/10.1515/jos-2015-0042

Mots clésNetwork sampling, capture recapture, hidden populations

© 2015 Marinus Spreen et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Network sampling, capture recapture, hidden populations