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

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.

Random Walks on Directed Networks: Inference and Respondent-Driven Sampling

Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden.

Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.

Department of Engineering Mathematics, University of Bristol, Merchant Venturers Building, Woodland Road, Clifton, Bristol BS8 1UB, United Kingdom.

Journal of Official Statistics

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

{"article-title":"Random Walks on Directed Networks: Inference and Respondent-Driven Sampling"}

Respondent-driven sampling (RDS) is often used to estimate population properties (e.g., sexual risk behavior) in hard-to-reach populations. In RDS, already...

Random Walks on Directed Networks: Inference and Respondent-Driven Sampling

Published Online: May 28, 2016

Page range: 433 - 459

Received: Feb 01, 2014

Accepted: Oct 01, 2015

DOI: https://doi.org/10.1515/jos-2016-0023

Keywords
Hidden population, social network, nonreciprocal relationship, Markov model

© 2016 Jens Malmros et al., published by De Gruyter Open

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

Random Walks on Directed Networks: Inference and Respondent-Driven Sampling

Published Online: May 28, 2016

Page range: 433 - 459

Received: Feb 01, 2014

Accepted: Oct 01, 2015

DOI: https://doi.org/10.1515/jos-2016-0023

KeywordsHidden population, social network, nonreciprocal relationship, Markov model

© 2016 Jens Malmros et al., published by De Gruyter Open

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

Keywords
Hidden population, social network, nonreciprocal relationship, Markov model