Hi All --
Our speaker this Wednesday at Applied Stats will be Krista Gile who is a Professor in the
Department of Mathematics and Statistics at the University of Massachusetts. She will be
giving a talk entitled New methods for inference from Respondent-Driven Sampling Data. The
abstract is included below.
Professor Gile's research focuses on developing statistical methodology for social
and behavioral science research, particularly related to making inference from
partially-observed social network structures. Most of her current work is focused on
understanding the strengths and limitations of data sampled with link-tracing designs such
as snowball sampling, contact tracing, and respondent-driven sampling.
As per usual, the talk will be held at 12 noon in CGIS K354. Lunch will be served.
I hope to see you all there!
Tess
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Title:
New methods for inference from Respondent-Driven Sampling Data
Abstract:
Respondent-Driven Sampling is type of link-tracing network sampling used to study
hard-to-reach populations. Beginning with a convenience sample, each person sampled is
given 2-3 uniquely identified coupons to distribute to other members of the target
population, making them eligible for enrollment in the study. This is effective at
collecting large diverse samples from many populations.
Current estimation relies on sampling weights estimated by treating the sampling process
as a random walk on the underlying network of social relations. These estimates are based
on strong assumptions allowing the data to be treated as a probability sample. In
particular, existing estimators assume a with-replacement sample with an ideal initial
sample. We introduce two new estimators, the first based on a without-replacement
approximation to the sampling process, and the second based on fitting a social network
model (ERGM), and demonstrate their ability to adjust for biases due to the finite
population and initial convenience sample. Our estimators are based on a model-assisted
design-based approach, using standard errors based on a parametric bootstrap. We conclude
with an application to data collected among injecting drug users, including extension to
observable features of the sampling process.
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