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Gathering Every Voice

NSF Award:

Statistical Methods for Respondent Driven Sampling Data  (University of Washington)

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Communities and businesses use information collected through surveys as part of their decision making process. For this reason, it is important that everyone in a community is recognized. Traditional survey methods based on a sampling frame do not work well and may be expensive when people under study are hard to find. 

Respondent-driven sampling (RDS) is a survey approach that uses social ties to friends and coworkers to help ensure hard-to-reach people are included in surveys. As an alternative survey method, RDS can reach these groups, but such social network approaches are problematic in practice because of their complexity.

Researchers from the University of Washington have developed mathematical models and rigorous methods to better understand when these social network methods can be used with confidence and which situations may call for another approach. Improved understanding of RDS methods will make it easier and more cost effective for communities and businesses to acquire the information necessary to plan ahead and make decisions that best serve the community.

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  • graph depicts social network of people surveyed in a community
The social network of people surveyed in a community.
Mark S. Handcock, UCLA and Krista J. Gile, UMass, Amherst

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