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The main aims of this research are to extend work on an innovative approach, Network Sampling with Memory (NSM), to efficiently and cost-effectively sample from rare and hidden populations of immigrants to the U.S. by fielding multiple data collection modes. This will lead to the identification of the best way to generate samples for representation of immigrants and to understand the role of immigrant social networks in labor market outcomes, health behaviors and outcomes and knowledge transfer. This sampling methodology can be easily extended to other hidden populations, especially those at risk of HIV and other sexually transmitted diseases.

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