Measuring Network Sizes in the Context of Respondent Driven Sampling: Evidence from Two Independent Surveys

• Published in: Survey research methods Vol. 19; no. 1
• Main Authors: Sunghee Lee, Jacob Fisher, Ai Rene Ong, Michael Elliott, Kaidar Nurumov, Jinseok Kim
• Format: Journal Article
• Language: English
• Published: European Survey Research Association 10.04.2025
• Subjects: measurement error; network size measurement; question wording; respondent driven sampling
• DOI: 10.18148/srm/2025.v19i1.8336

Social networks are the fundamental premise of respondent driven sampling (RDS). Personal network sizes termed as “degrees” play an important role in the RDS literature, as RDS-specific point estimators incorporate degrees as an adjustment factor approximating the selection probabilities. For this reason, degrees relevant to RDS should consider recruitability rather than the state of connectedness. This study examines various measures of degrees (standard degrees; 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 (PWID, n=410) and the other targeting Korean immigrants (n=637). The latter randomized interview language for bilingual English-Korean speakers. There was greater noise in the standard degree measure compared to other degree measures. With a subtle hint about the recruitment request, respondents reported knowing fewer people, compared to the standard degree question, implying a mismatch between the standard degree question and recruitability. Degree reports were sensitive to interview language: reported degrees were smaller in Korean than English interviews and better explained the recruitment. Degrees measured in the contexts of close social relationships were shown to improve inference, while this was not true for the standard degree. This warrants scrutiny of network measures that reflect the RDS recruitment mechanism.