Electronic Journal of Statistics

Estimating hidden population size using Respondent-Driven Sampling data

Mark S. Handcock, Krista J. Gile, and Corinne M. Mar

Full-text: Open access

Abstract

Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female sex workers. Most analysis of RDS data has focused on estimating aggregate characteristics, such as disease prevalence. However, RDS is often conducted in settings where the population size is unknown and of great independent interest. This paper presents an approach to estimating the size of a target population based on data collected through RDS.

The proposed approach uses a successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes. The inference uses the Bayesian framework, allowing for the incorporation of prior knowledge. A flexible class of priors for the population size is used that aids elicitation. An extensive simulation study provides insight into the performance of the method for estimating population size under a broad range of conditions. A further study shows the approach also improves estimation of aggregate characteristics. Finally, the method demonstrates sensible results when used to estimate the size of known networked populations from the National Longitudinal Study of Adolescent Health, and when used to estimate the size of a hard-to-reach population at high risk for HIV.

Article information

Source
Electron. J. Statist., Volume 8, Number 1 (2014), 1491-1521.

Dates
First available in Project Euclid: 2 September 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1409619420

Digital Object Identifier
doi:10.1214/14-EJS923

Mathematical Reviews number (MathSciNet)
MR3263129

Zentralblatt MATH identifier
1295.62011

Subjects
Primary: 91D30: Social networks 62D05: Sampling theory, sample surveys
Secondary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]

Keywords
Hard-to-reach population sampling network sampling social networks successive sampling model-based survey sampling

Citation

Handcock, Mark S.; Gile, Krista J.; Mar, Corinne M. Estimating hidden population size using Respondent-Driven Sampling data. Electron. J. Statist. 8 (2014), no. 1, 1491--1521. doi:10.1214/14-EJS923. https://projecteuclid.org/euclid.ejs/1409619420


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