Wejnert Cyprian
Cornell University.
Sociol Methodol. 2009 Aug 1;39(1):73-116. doi: 10.1111/j.1467-9531.2009.01216.x.
This paper, which is the first large scale application of Respondent-Driven Sampling (RDS) to non-hidden populations, tests three factors related to RDS estimation against institutional data using two WebRDS samples of university undergraduates. First, two methods of calculating RDS point estimates are compared. RDS estimates calculated using both methods coincide closely, but variance estimation, especially for small groups, is problematic for both methods. In one method, the bootstrap algorithm used to generate confidence intervals is found to underestimate variance. In the other method, where analytical variance estimation is possible, confidence intervals tend to overestimate variance. Second, RDS estimates are found to be robust against varying measures of individual degree. Results suggest the standard degree measure currently employed in most RDS studies is among the best performing degree measures. Finally, RDS is found to be robust against the inclusion of out-of-equilibrium data. The results show that valid point estimates can be generated with RDS analysis using real data, however further research is needed to improve variance estimation techniques.
本文首次将应答驱动抽样（RDS）大规模应用于非隐性人群，利用两个大学生网络RDS样本，对照机构数据检验与RDS估计相关的三个因素。首先，比较了两种计算RDS点估计值的方法。用这两种方法计算出的RDS估计值非常接近，但两种方法的方差估计都存在问题，尤其是对于小群体。在一种方法中，用于生成置信区间的自助算法被发现低估了方差。在另一种可以进行分析方差估计的方法中，置信区间往往高估了方差。其次，发现RDS估计值对于不同的个体度测量具有稳健性。结果表明，目前大多数RDS研究中使用的标准度测量是表现最佳的度测量之一。最后，发现RDS对于纳入失衡数据具有稳健性。结果表明，使用真实数据进行RDS分析可以生成有效的点估计值，然而，需要进一步研究以改进方差估计技术。
