Publication Date
2020
Journal or Book Title
Electronic Journal of Statistics
Abstract
Respondent-Driven sampling (RDS) is a sampling method devised to overcome challenges with sampling hard-to-reach human populations. The sampling starts with a limited number of individuals who are asked to recruit a small number of their contacts. Every surveyed individual is subsequently given the same opportunity to recruit additional members of the target population until a pre-established sample size is achieved. The recruitment process consequently implies that the survey respondents are responsible for deciding who enters the study. Most RDS prevalence estimators assume that participants select among their contacts completely at random. The main objective of this work is to correct the inference for departure from this assumption, such as systematic recruitment based on the characteristics of the individuals or based on the nature of relationships. To accomplish this, we introduce three forms of non-random recruitment, provide estimators for these recruitment behaviors and extend three estimators and their associated variance procedures. The proposed methodology is assessed through a simulation study capturing various sampling and network features. Finally, the proposed methods are applied to a public health setting.
ISSN
1935-7524
DOI
https://doi.org/10.1214/20-EJS1718
Pages
2678-2713
Volume
14
License
UMass Amherst Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Beaudry, Isabelle S. and Gile, Krista J., "Correcting for differential recruitment in respondent-driven sampling data using ego-network information" (2020). Electronic Journal of Statistics. 1304.
https://doi.org/10.1214/20-EJS1718