Total Survey Error and Respondent Driven Sampling: Focus on Nonresponse and Measurement Errors in the Recruitment Process and the Network Size Reports and Implications for Inferences

• Main Authors: Lee Sunghee, Suzer-Gurtekin Tuba, Wagner James, Valliant Richard
• Format: Article
• Language: English
• Published: Sciendo 2017-06-01
• Series: Journal of Official Statistics
• Subjects: sampling hard-to-reach populations; chain referral; network-based sampling; measurement error; nonresponse error
• Online Access: https://doi.org/10.1515/jos-2017-0017

This study attempted to integrate key assumptions in Respondent-Driven Sampling (RDS) into the Total Survey Error (TSE) perspectives and examine TSE as a new framework for a systematic assessment of RDS errors. Using two publicly available data sets on HIV-at-risk persons, nonresponse error in the RDS recruitment process and measurement error in network size reports were examined. On nonresponse, the ascertained partial nonresponse rate was high, and a substantial proportion of recruitment chains died early. Moreover, nonresponse occurred systematically: recruiters with lower income and higher health risks generated more recruits; and peers of closer relationships were more likely to accept recruitment coupons. This suggests a lack of randomness in the recruitment process, also shown through sizable intra-chain correlation. Self-reported network sizes suggested measurement error, given their wide dispersion and unreasonable reports. This measurement error has further implications for the current RDS estimators, which use network sizes as an adjustment factor on the assumption of a positive relationship between network sizes and selection probabilities in recruitment. The adjustment resulted in nontrivial unequal weighting effects and changed estimates in directions that were difficult to explain and, at times, illogical. Moreover, recruiters’ network size played no role in actual recruitment. TSE may serve as a tool for evaluating errors in RDS, which further informs study design decisions and inference approaches.