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Some advances in Respondent-driven sampling on directed social networks
Stockholm University, Faculty of Science, Department of Mathematics.
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: urn:nbn:se:su:diva-94704OAI: oai:DiVA.org:su-94704DiVA: diva2:654902
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
List of papers
1. Respondent-driven sampling on directed networks
Open this publication in new window or tab >>Respondent-driven sampling on directed networks
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.

Keyword
Respondent-driven sampling, directed networks, degree correlation, attractivity ratio, HIV
National Category
Mathematics Sociology
Identifiers
urn:nbn:se:su:diva-92524 (URN)10.1214/13-EJS772 (DOI)000321052800001 ()
Note

AuthorCount:4;

Available from: 2013-08-08 Created: 2013-08-07 Last updated: 2017-12-06Bibliographically approved
2. Random walks on directed networks: Inference and respondent-driven sampling
Open this publication in new window or tab >>Random walks on directed networks: Inference and respondent-driven sampling
2016 (English)In: Journal of Official Statistics, ISSN 0282-423X, E-ISSN 2001-7367, Vol. 32, no 2, 433-459 p.Article in journal (Refereed) Published
Abstract [en]

Respondent-driven sampling (RDS) is often used to estimate population properties (e.g., sexual risk behavior) in hard-to-reach populations. In RDS, already sampled individuals recruit population members to the sample from their social contacts in an efficient snowball-like sampling procedure. By assuming a Markov model for the recruitment of individuals, asymptotically unbiased estimates of population characteristics can be obtained. Current RDS estimation methodology assumes that the social network is undirected, that is, all edges are reciprocal. However, empirical social networks in general also include a substantial number of nonreciprocal edges. In this article, we develop an estimation method for RDS in populations connected by social networks that include reciprocal and nonreciprocal edges. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. The proposed estimators are evaluated on artificial and empirical networks and are shown to generally perform better than existing estimators. This is the case in particular when the fraction of directed edges in the network is large.

Keyword
Hidden population, social network, nonreciprocal relationship, Markov model
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-129251 (URN)10.1515/JOS-2016-0023 (DOI)000377566800011 ()
Funder
Swedish Research Council, 2009-5759Riksbankens Jubileumsfond, P12-0705:1
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2017-11-30Bibliographically approved

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