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Respondent-driven sampling (RDS) is currently widely used for the study ofHIV/AIDS-related high risk populations. However, recent studies have shown thattraditional RDS methods are likely to generate large variances and may beseverely biased since the assumptions behind RDS are seldom fully met in reallife. To improve estimation in RDS studies, we propose a new method to generateestimates with ego network data, which is collected by asking RDS respondentsabout the composition of their personal networks, such as "what proportion ofyour friends are married?". By simulations on an extracted real-world socialnetwork of gay men as well as on artificial networks with varying structuralproperties, we show that the new estimator, RDSI^{ego} shows superiorperformance over traditional RDS estimators. Importantly, RDSI^{ego} exhibitsstrong robustness to the preference of peer recruitment and variations innetwork structural properties, such as homophily, activity ratio, and communitystructure. While the biases of traditional RDS estimators can sometimes be aslarge as 10%~20%, biases of all RDSI^{ego} estimates are well restrained to beless than 2%. The positive results henceforth encourage researchers to collectego network data for variables of interests by RDS, for both hard-to-accesspopulations and general populations when random sampling is not applicable. Thelimitation of RDSI^{ego} is evaluated by simulating RDS assuming differentlevel of reporting error.

Linked Ego Networks: Improving Estimate Reliability and Validity with Respondent-driven Sampling