Testing Multiple Modes of Data Collection with Network Sampling with Memory
Many immigrants groups are either too small, requiring a large number of screening interviews to recruit a sufficiently large sample, or are reluctant to respond to conventional surveys for fear of repatriation if they are undocumented, resulting in incomplete or biased samples. This study extends work on an innovative approach. Network Sampling with Memory (NSM), to efficiently and cost-effectively sample from a rare population of immigrants in the US. This project takes three important steps designed to make NSM a viable alternative for researchers in the field. The project will: (1) use NSM to collect data on a rare population, Chinese immigrants in a well-defined geographic area (the Raleigh- Durham, N.C. metro area), which will allow a comparison of the results from our network-based samples to population estimates from the American Community Survey; (2) experimentally field three different survey modes: telephone, face-to-face, and online modes of data collection to identify the factors that enable NSM to generate large-scale samples with minimum cost and maximum response rates; and (3) develop and disseminate software for the NSM sampling algorithm and the management of the network data, which will facilitate the extension of NSM to additional immigrant groups, on a larger geographic scale, and promote the use of NSM by other researchers. The network data collected as part of NSM will allow modeling of social network dynamics of immigrants' social incorporation, job search, and peer effects on health behaviors to better understand the role of networks in immigrant assimilation and health outcomes.
Principal Investigators: Ted Mouw, Maria-Giovanna Merli
Funding Source: Duke University NIA
Grant Number: R21HD086738
Funding Period: 7/22/2016 - 6/30/2017
Primary Research Area: Demography