Respondent-driven sampling
Matthias Schonlau () and
Elisabeth Liebau ()
Additional contact information
Matthias Schonlau: University of Waterloo
Elisabeth Liebau: DIW Berlin
Stata Journal, 2012, vol. 12, issue 1, 72-93
Abstract:
Respondent-driven sampling is a network sampling technique typi- cally employed for hard-to-reach populations (for example, drug users, men who have sex with men, people with HIV). Similarly to snowball sampling, initial seed respondents recruit additional respondents from their network of friends. The re- cruiting process repeats iteratively, thereby forming long referral chains. Unlike in snowball sampling, it is crucial to obtain estimates of respondents’ personal network sizes (that is, number of acquaintances in the target population) and information about who recruited whom. Markov chain theory makes it possible to derive population estimates and sampling weights. We introduce a new Stata command for respondent-driven sampling and illustrate its use. Copyright 2012 by StataCorp LP.
Keywords: ds; rds_network; respondent-driven sampling (search for similar items in EconPapers)
Date: 2012
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj12-1/st0247/
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0247 link to article purchase
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:tsj:stataj:v:12:y:2012:i:1:p:72-93
Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html
Access Statistics for this article
Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins
More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().