Burt V L, Cohen S B
Rev Public Data Use. 1984 Oct;12(3):159-68.
Complex survey designs are characterized by multistage selections with stratification and clustering. The departure from simple random sampling assumptions requires special consideration with regard to variance estimation. Specially designed software packages exist to generate variance estimates for statistics from complex survey data. The variance estimation techniques used include balanced repeated replication, jackknife, and Taylor series linearization. Many complex surveys generate thousands of tables. The computational and publishing costs soar if estimates of standard error are required for all statistics. To decrease these costs, several alternative techniques to approximate the standard errors of estimates are available. These include the widely used relative variance curve, a method based on the average relative standard error, and the average design effect model. In this paper these three methods are compared with respect to accuracy, computational and publishing costs, and ease of implementation.
复杂的调查设计具有分层和聚类的多阶段选择特征。与简单随机抽样假设的背离需要在方差估计方面进行特殊考虑。存在专门设计的软件包来生成复杂调查数据统计量的方差估计值。所使用的方差估计技术包括平衡重复复制、刀切法和泰勒级数线性化。许多复杂调查会生成数千个表格。如果所有统计量都需要标准误差估计值，那么计算和发布成本会飙升。为了降低这些成本，可以采用几种替代技术来近似估计值的标准误差。这些技术包括广泛使用的相对方差曲线、基于平均相对标准误差的方法以及平均设计效应模型。本文就准确性、计算和发布成本以及实施的简易程度对这三种方法进行了比较。
