Using Social Networks to Sample Migrants and Study the Complexity of Contemporary Immigration: An Evaluation Study

Area of Science:

• Social Sciences
• Sociology
• Demography

Background:

• Recruiting rare immigrant populations for research presents significant challenges.
• Traditional sampling methods often struggle with efficiency and accuracy for these groups.
• Understanding social networks is crucial for migration and incorporation studies.

Purpose of the Study:

• To evaluate the effectiveness of network sampling with memory (NSM) for sampling rare immigrant groups.
• To demonstrate the feasibility of collecting novel network data beyond kinship ties.
• To assess the accuracy and cost-effectiveness of NSM compared to existing surveys.

Main Methods:

• Applied network sampling with memory (NSM), a link-tracing sampling approach.
• Utilized survey data to map population networks and enhance sampling efficiency.
• Compared NSM sampling results with data from the American Community Survey.

Main Results:

• Successfully recruited a probability sample of Chinese immigrants with high response rates.
• Demonstrated the feasibility and benefits of collecting comprehensive network data.
• NSM proved accurate, cost-effective, and highly performant for immigrant sampling.

Conclusions:

• Network sampling with memory (NSM) is a feasible and effective method for sampling rare immigrant populations.
• NSM facilitates the collection of valuable network data for migration and social incorporation research.
• The NSM approach can be extended for multisite sampling of immigrants in both origin and destination locations.