Respondent-Driven Sampling (RDS) is a method of network sampling that is used to sample hard-to-reach populations. The resultant sample is non-random, but different weighting methods can account for the over-sampling of (1) high-degree individuals and (2) homophilous groups that recruit members more effectively. While accounting for degree-bias is almost universally agreed upon, accounting for recruitment-bias has been debated as it can further increase estimate variance without substantially reducing bias. Simulation-based research has examined which weighting procedures perform best given underlying population network structures, group recruitment differences, and sampling processes. Yet, in the field, analysts do not have a priori knowledge of the network they are sampling. We show that the RDS sample data itself can determine whether a degree-based estimator is sufficient. Formulas derived from the decomposition of a ‘dual-component’ estimator can approximate the ‘recruitment component’ (RC) and ‘degree component’ (DC) of a sample’s bias. Simulations show that RC and DC values can predict the performance of different classes of estimators. Samples with extreme ‘RC’ values, a consequence of network homophily and differential recruitment, are better served by a classical estimator. The use of sample data to improve estimator selection is a promising innovation for RDS, as the population network features that should guide estimator selection are typically unknown.

响应者驱动采样（RDS）是一种网络采样方法，用于采样难以访问的人群。所得样本是非随机样本，但是不同的加权方法可以解释（1）高级个体和（2）更有效地招募成员的同质群体的过度抽样。尽管几乎普遍同意对度数偏差进行会计处理，但对征兵性偏差进行会计处理一直存在争议，因为它可以在不大幅减少偏差的情况下进一步增加估计差异。基于模拟的研究已经检查了哪种加权程序在给定的基本人口网络结构，群体招聘差异和抽样过程下表现最佳。但是，在该领域，分析师没有先验的能力他们正在采样的网络知识。我们表明，RDS样本数据本身可以确定基于程度的估计量是否足够。从“双成分”估计量的分解中得出的公式可以近似于样本偏差的“招聘成分”（RC）和“程度成分”（DC）。仿真表明，RC和DC值可以预测不同类别的估计器的性能。网络均质和差分补充的结果，具有极高“ RC”值的样本最好由经典估计器提供。对于RDS而言，使用样本数据来改善估计量选择是一项有前途的创新，因为通常不应该指导估计量选择的总体网络特征。