论文标题
具有空间自相关的地理定义群集的有效设计
Efficient design of geographically-defined clusters with spatial autocorrelation
论文作者
论文摘要
集群构成了许多研究设计的基础，包括调查和实验研究。由于同一集群中的个体之间的相关性，基于群集的设计的成本较低，但效率也较低。他们的设计通常依赖于\ textit {ad hoc}相关参数的选择，并且对群集设计的变化不敏感。本文探讨了如何通过划定包含个人和家庭或其他单位的区域来有效设计它们在地理上定义的群集。使用对空间自相关的地理模型，我们在群集平均值之间生成近似值，以估算给定特定群集设计参数的有效样本量。我们展示了枚举位置的数量，集群区域，采样比例以及采样方法如何影响设计的效率，并考虑选择最有效的设计的优化问题，但要根据预算限制。我们还考虑如何简单地用“现实世界”数量来解释这些近似值的参数并用于设计分析。
Clusters form the basis of a number of research study designs including survey and experimental studies. Cluster-based designs can be less costly but also less efficient than individual-based designs due to correlation between individuals within the same cluster. Their design typically relies on \textit{ad hoc} choices of correlation parameters, and is insensitive to variations in cluster design. This article examines how to efficiently design clusters where they are geographically defined by demarcating areas incorporating individuals and households or other units. Using geostatistical models for spatial autocorrelation we generate approximations to within cluster average covariance in order to estimate the effective sample size given particular cluster design parameters. We show how the number of enumerated locations, cluster area, proportion sampled, and sampling method affect the efficiency of the design and consider the optimization problem of choosing the most efficient design subject to budgetary constraints. We also consider how the parameters from these approximations can be interpreted simply in terms of `real-world' quantities and used in design analysis.
