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Data Collection for Network Sampling Approaches for Rare and Hard to Reach Populations: Lessons Learned
November 3, 2017 @ 12:00 pm - 1:00 pm
Dr. Giovanna Merli (Professor of Public Policy and Global Heath, Duke University)
M. Giovanna Merli is Professor of Public Policy, Sociology and Global Health in the Sanford School of Public Policy, Duke University. She is also the Director of the Duke Population Research Center (DPRC). She holds a PhD in demography from the University of Pennsylvania. Before going to Duke, she was on the faculty of the Department of Sociology at the University of Wisconsin, Madison. Merli has a strong background in the design, conduct and analyses of surveys fielded among Chinese populations in China and Chinese immigrant destinations. Her NIH-funded data collection efforts have relied on conventional probability sampling designs as well as venue-based and link-tracing sampling approaches to recruit samples of rare and hidden populations. She has also designed ego-centric network modules for the Chinese general population, which she has used to estimate the behavioral, social and relational determinants of prevalence of HIV and other STIs in China. In recent work, she has evaluated the performance of Respondent-Driven Sampling among populations at risk of HIV/STIs. Currently, in collaboration with Ted Mouw, she is fielding, testing and evaluating an innovative sampling approach for rare populations, Network Sampling with Memory, among Chinese immigrant populations in the U.S., Tanzania and France.
Dr. Ted Mouw (Associate Professor of Sociology; CPC Faculty Fellow)
Mouw’s current research on social mobility focuses on factors that affect the upward mobility of low wage workers. In his paper with Arne Kalleberg, “Stepping Stone versus Dead End Jobs: Occupational Pathways out of Working Poverty in the United States, 1996-2012”, he uses data from the Survey of Income and Program Participation (SIPP) to test whether the accumulation of task-specific skills increases the rate of upward mobility for low-wage workers. In a grant from the Russell Sage Foundation, he is linking the SIPP data to county-level data on labor demand shocks in order to analyze the role that structural factors play in the upward mobility of low-wage workers. This research builds on previous work that analyzed trends in between-occupation inequality and the impact of job mobility on changes in inequality. In the next five years, he plans to extend this work into a book-length project on working poverty and the social mobility of low-wage workers.