Presented By: Michigan Program in Survey and Data Science
MPSDS JPSM Seminar Series - Measuring Network Sizes in the Context of Respondent Driven Sampling: Evidence from Two Independent Surveys
Sunghee Lee
MPSDS JPSM Seminar Series
Onsite and via Zoom
Institute for Social Research (ISR), Room 1070
Measuring Network Sizes in the Context of Respondent Driven Sampling: Evidence from Two Independent Surveys
Social networks termed as “degrees” are the fundamental premise of respondent driven sampling (RDS). Extant RDS point estimators incorporate degrees as an adjustment factor approximating the selection probabilities. For this reason, degrees relevant to RDS should consider recruitability. However, the standard practice focuses on the state of connectedness when measuring degrees.
This study examines standard degrees along with degrees with priming recruitment requests and degrees using naming stimuli that tap into specifics of social relationships in two independent RDS surveys: one targeting people who inject drugs (n=410); and the other targeting Korean immigrants (n=637) with interview language randomly assigned for bilingual English-Korean speakers. The analysis focuses on 1) the distribution of different degrees, 2) the relationship among degrees, 3) the relationship between degrees and recruitment success, and 4) the contribution of degrees to population inference.
Sunghee Lee is a Research Associate Professor and the Director of Program in Survey and Data Science at the Institute for Social Research, University of Michigan. She is a survey methodologist whose research interest evolves around improving data quality through inclusivity, which has profound implications for equity in social programs and policy decisions.
Onsite and via Zoom
Institute for Social Research (ISR), Room 1070
Measuring Network Sizes in the Context of Respondent Driven Sampling: Evidence from Two Independent Surveys
Social networks termed as “degrees” are the fundamental premise of respondent driven sampling (RDS). Extant RDS point estimators incorporate degrees as an adjustment factor approximating the selection probabilities. For this reason, degrees relevant to RDS should consider recruitability. However, the standard practice focuses on the state of connectedness when measuring degrees.
This study examines standard degrees along with degrees with priming recruitment requests and degrees using naming stimuli that tap into specifics of social relationships in two independent RDS surveys: one targeting people who inject drugs (n=410); and the other targeting Korean immigrants (n=637) with interview language randomly assigned for bilingual English-Korean speakers. The analysis focuses on 1) the distribution of different degrees, 2) the relationship among degrees, 3) the relationship between degrees and recruitment success, and 4) the contribution of degrees to population inference.
Sunghee Lee is a Research Associate Professor and the Director of Program in Survey and Data Science at the Institute for Social Research, University of Michigan. She is a survey methodologist whose research interest evolves around improving data quality through inclusivity, which has profound implications for equity in social programs and policy decisions.
Livestream Information
ZoomSeptember 4, 2024 (Wednesday) 12:00pm
Meeting ID: 92461111899
Meeting Password: 2425
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