Linked Ego Networks: Improving Estimate Reliability and Validity with   Respondent-driven Sampling
  Respondent-driven sampling (RDS) is currently widely used for the study of HIV/AIDS-related high risk populations. However, recent studies have shown that traditional RDS methods are likely to generate large variances and may be severely biased since the assumptions behind RDS are seldom fully met in real life. To improve estimation in RDS studies, we propose a new method to generate estimates with ego network data, which is collected by asking RDS respondents about the composition of their personal networks, such as "what proportion of your friends are married?". By simulations on an extracted real-world social network of gay men as well as on artificial networks with varying structural properties, we show that the new estimator, RDSI^{ego} shows superior performance over traditional RDS estimators. Importantly, RDSI^{ego} exhibits strong robustness to the preference of peer recruitment and variations in network structural properties, such as homophily, activity ratio, and community structure. While the biases of traditional RDS estimators can sometimes be as large as 10%~20%, biases of all RDSI^{ego} estimates are well restrained to be less than 2%. The positive results henceforth encourage researchers to collect ego network data for variables of interests by RDS, for both hard-to-access populations and general populations when random sampling is not applicable. The limitation of RDSI^{ego} is evaluated by simulating RDS assuming different level of reporting error. 
Xin Lu
stat.ME physics.data-an