Abstract
Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section.
Highlights
Given the growing interest in telefunken and Privatized Network Sampling (PNS)-like techniques [26, 34, 54], this paper aims to provide a systematic exposition of its strategy for one-step, anonymity preserving, network-based population size estimation
The results shown here indicate that size estimates for hidden and hard-to-reach populations can be derived from respondent-driven sampling (RDS) samples across a range of topologies, and in the presence of significant network clustering
This is accomplished under conditions of anonymity by way of identity hashing, e.g. using telefunken codes [50] or a Privatized Network Sampling (PNS) design [53]
Summary
While Berchenko and Frost have developed techniques that combine capture-recapture methods with RDS, their approach requires an initial degreebiased random sample and a second (independent) respondent driven sample [36] Their hybrid schemes have been validated through simulations, and applied in the context of several field studies [37, 38]. The telefunken method was so named because its application entailed asking each RDS respondent to report on others in the population known to them by providing an encoding of their associates’ telephone number and demographic features (note that the technique is in no way related to the German apparatus company, Telefunken) In taking this approach, the method avoided reliance on official statistics (as needed in scale-up methods), and the requirement of drawing two independent samples (as needed by capturerecapture methods). Limitations and steps toward validation/extension are discussed at the end of the paper
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.