Sampling a Hidden Population without a Sampling Frame: A Practical Application of Network Sampling with Memory

Ted Mouw, University of North Carolina at Chapel Hill
Ashton M. Verdery, University of North Carolina at Chapel Hill
Giovanna Merli, Duke University
Jing Li, Duke University
Jennifer Shen, Duke University

Mouw and Verdery (2012) propose a new method for sampling hidden populations, “Network Sampling with Memory” (NSM), which collects information on network members from the survey instrument to uncover the sampling frame for the target population. They show that NSM yields statistical estimates that are on average 98.5% more efficient than other popular approaches. Here, 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 this modification using simulated sampling on 215 school and university social networks. 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.