Giovanna Merli , Duke University
Theodore Mouw, University of North Carolina at Chapel Hill
Allison Stolte, Duke University
Claire Le Barbenchon, Duke University
Francesca Florey-Eischen, Duke University
Many immigrants groups are either too small, resulting in 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. Here we use an innovative sampling approach, Network Sampling with Memory (NSM), to efficiently and cost-effectively sample from a rare population of immigrants: Chinese in the Raleigh Durham Area of North Carolina. To evaluate the performance and population representativeness of our sample consisting of about 600 respondents interviewed with in-person, telephone and web interviews (200 per survey mode), we will compare sample characteristics with population estimates from the American Community Survey. To identify factors that enable NSM to generate large-scale samples with minimum cost and maximum response rates, we will evaluate the impact of administering each mode on multiple measures of data quality and response rates.
Presented in Session 79. Collecting Data on Migrant Populations