Inferring bivariate association from respondent‐driven sampling data

Dongah Kim, Krista J. Gile, Honoria Guarino and Pedro Mateu‐Gelabert

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 2, 415-433

Abstract: Respondent‐driven sampling (RDS) is an effective method of collecting data from many hard‐to‐reach populations. Valid statistical inference for these data relies on many strong assumptions. In standard samples, we assume observations from pairs of individuals are independent. In RDS, this assumption is violated by the sampling dependence between individuals. We propose a method to semi‐parametrically estimate the null distributions of standard test statistics in the presence of sampling dependence, allowing for more valid statistical testing for dependence between pairs of variables within the sample. We apply our method to study characteristics of young adult illicit opioid users in New York City.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12465

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:70:y:2021:i:2:p:415-433

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().