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Respondent-driven sampling on directed networks
Stockholm University, Faculty of Social Sciences, Department of Sociology. Karolinska Institutet, Sweden; National University of Defense Technology, China.
Stockholm University, Faculty of Science, Department of Mathematics.
Stockholm University, Faculty of Social Sciences, Department of Sociology.
Stockholm University, Faculty of Science, Department of Mathematics.
2013 (English)In: Electronic Journal of Statistics, ISSN 1935-7524, E-ISSN 1935-7524, Vol. 7, 292-322 p.Article in journal (Refereed) Published
Abstract [en]

Respondent-driven sampling (RDS) is a widely used method for generating chain-referral samples from hidden populations. It is an extension of the snowball sampling method and can, given that some assumptions are met, generate unbiased population estimates. One key assumption, not likely to be met, is that the acquaintance network in which the recruitment process takes place is undirected, meaning that all recruiters should have the potential to be recruited by the person they recruit. Using a mean-field approach, we develop an estimator which is based on prior information about the average indegrees of estimated variables. When the indegree is known, such as for RDS studies over internet social networks, the estimator can greatly reduce estimate error and bias as compared with current methods; when the indegree is not known, which is most common for interview-based RDS studies, the estimator can through sensitivity analysis be used as a tool to account for uncertainties of network directedness and error in self-reported degree data. The performance of the new estimator, together with previous RDS estimators, is investigated thoroughly by simulations on networks with varying structures. We have applied the new estimator on an empirical RDS study for injecting drug users in New York City.

Place, publisher, year, edition, pages
2013. Vol. 7, 292-322 p.
Keyword [en]
Respondent-driven sampling, directed networks, degree correlation, attractivity ratio, HIV
National Category
Mathematics Sociology
Identifiers
URN: urn:nbn:se:su:diva-92524DOI: 10.1214/13-EJS772ISI: 000321052800001OAI: oai:DiVA.org:su-92524DiVA: diva2:639534
Note

AuthorCount:4;

Available from: 2013-08-08 Created: 2013-08-07 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Some advances in Respondent-driven sampling on directed social networks
Open this publication in new window or tab >>Some advances in Respondent-driven sampling on directed social networks
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Respondent-driven sampling (RDS) is one of the most commonly used methods when sampling from hidden or hard-to-reach populations. The RDS methodology combines an improved snowball sampling scheme with a mathematical model that is able to produce unbiased population estimates given that some assumptions about the actual recruitment process are fulfilled. One critical assumption, which is not likely to hold in most cases, is that the underlying social network of the population is undirected. The papers in this thesis provide extensions of RDS theory to populations with partially directed social networks.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2013. 80 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-94704 (URN)
Presentation
2013-11-01, Rum 306, Matematiska institutionen, Hus 6, Kräftriket, Stockholm, 10:00
Opponent
Supervisors
Funder
Swedish Research Council, 2009-5759
Available from: 2013-10-09 Created: 2013-10-09 Last updated: 2016-05-11Bibliographically approved
2. Studies in respondent-driven sampling: Directed networks, epidemics, and random walks
Open this publication in new window or tab >>Studies in respondent-driven sampling: Directed networks, epidemics, and random walks
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Respondent-driven sampling (RDS) is a link-tracing sampling methodology especially suitable for sampling hidden populations. A clever sampling mechanism and inferential procedures that facilitate asymptotically unbiased population estimates has contributed to the rising popularity of the method. The papers in this thesis extend RDS estimation theory to some population structures to which the classical RDS estimation framework is not applicable and analyse the behaviour of the RDS recruitment process. 

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2016. 46 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-129287 (URN)978-91-7649-430-1 (ISBN)
Public defence
2016-06-15, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: In press. Paper 3: Accepted. Paper 4: Manuscript.

Available from: 2016-05-23 Created: 2016-04-20 Last updated: 2017-02-23Bibliographically approved

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