1. bookVolume 32 (2016): Issue 2 (June 2016)
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01 Oct 2013
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access type Open Access

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

Published Online: 28 May 2016
Page range: 433 - 459
Received: 01 Feb 2014
Accepted: 01 Oct 2015
Journal Details
License
Format
Journal
First Published
01 Oct 2013
Publication timeframe
4 times per year
Languages
English

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.

Keywords

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