Sampling a hidden population without a sampling frame: A practical application of Network Sampling with Memory.

Ted Mouw, UNC - Chapel Hill
Mouw and Verdery (2012) show that it is possible to increase the efficiency of sampling from a hidden population by collecting network information as part of the survey. They propose a new method, “Network Sampling with Memory” (NSM) that information on network members from the survey instrument to uncover the sampling frame for the target population. In this paper, we present a practical application of NSM that reduces the cost of data collection by collecting contact information on up to three referrals from the current respondent, which eliminates the need to re-contact prior respondents to ask for referrals. We test the accuracy of this modified method using simulated sampling on 215 school and university social networks, and we test the use of bootstrap methods to calculate the sampling variance. In addition, we report results from a pilot study using NSM, the 2013 Chinese African Health Study (CAHS) which sampled Chinese immigrants living in Tanzania, and we provide a step-by-step description of how to conduct an NSM-based survey in the field.
December, 3 2013 | 12:30 - 2:00 | 230E Gross Hall

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