Document Type
Article
Publication Date
2016
Publication Title
Electronic Journal of Statistics
Volume
10
Abstract
Respondent-Driven Sampling (RDS) is a widely adopted linktracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling frame. RDS estimators rely upon the assumption that each edge (representing a relationship between two individuals) in the underlying network has an equal probability of being sampled. We show that this assumption is violated in even the simplest cases, and that RDS estimators are sensitive to the violation of this assumption.
First Page
1109
Last Page
1132
Recommended Citation
Ott, Miles Q. and Gile, Krista J., "Unequal Edge Inclusion Probabilities in Link-Tracing Network Sampling With Implications for Respondent-Driven Sampling" (2016). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/9
Digital Object Identifier (DOI)
DOI: 10.1214/16-EJS1138
Rights
EJS is sponsored by the Institute of Mathematical Statistics and by the Bernoulli Society. EJS is an open access journal. Voluntary fees or donations to the Open Access Fund are accepted. Copyright for all articles in EJS is CC BY 4.0.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
Archived as published.