Evaluating and Validating How Social Network Ties Affect Respondent Driven Sampling’s Success

• In Event: Community and Urban Sociology Section Roundtables
  In Refereed Roundtable (60 minute): Table 16: Sociospatial Boundary-Making

Abstract

High-quality population data is vital for generating and implementing informed and equitable policies, however, the most common sampling method (i.e., probability sampling) only reflects populations with accessible sampling frames. An alternative approach is respondent-driven sampling (RDS) which generates representative population samples instead by relying on participants recruiting others through social network ties and post-recruitment weight-adjustments. College student populations offer an opportunity to validate RDS because social networks and population characteristics are well-known, but federal law constrains access to their sampling frames. Previous research gaps include: limited testing of initial participant (or seed) selection strategies; outdated comparison of recruitment criteria; and limited comparison of RDS data to known-population characteristics. To address these gaps, this study aims to compare sample and aggregate data, seed selection strategies, and recruitment criteria. First, I conduct focus groups to gain an understanding of college student network composition and their primary modes of communication by investigating the following questions: (a) How do college students form and maintain friendships? (b) How do shared characteristics (i.e., gender, geographical background, or academic standing) determine students’ immediate networks? (c) How might qualities of student friendships shape peer recruitment? Following this, I utilize RDS surveying amongst the same population to address: (1) Where and how should students be encountered to initiate representative peer-recruitment chains? (2) How can recruitment processes be made feasible and successful? This study aims to closely document the performance of network context-informed strategies and provide recommendations for future sampling of well connected, hard-to-reach populations.

Author

• Caroline C Teague, University of Washington