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Krista J. Gile, PhD

Assistant Professor, Department of Mathematics and Statistics, University of Massachusetts

Inference and Diagnostics for Respondent-Driven Sampling Data

ALL ARE WELCOME

Abstract:

Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations.听 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.听

Unfortunately, sampling is affected by many features of the network and sampling process, which complicate inference.听 In this talk, I highlight key methodological challenges arising from data collected in this manner.听 I then introduce key methods for diagnostics and inference in these settings, and describe new methods under development.

Bio:

Krista J. 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.

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