Efficient design of geographically-defined clusters with spatial autocorrelation.
Efficient design of geographically-defined clusters with spatial autocorrelation.
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DOI：
10.1080/02664763.2021.1941807
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发表时间：
2022
影响因子：
1.5
通讯作者：
Watson, Samuel, I
中科院分区：
文献类型：
--
作者：
Watson, Samuel, I
集群形成了许多研究设计的基础，包括调查和实验研究。基于集群的设计成本较低，但由于同一集群内个体之间的相关性，其效率也低于基于个体的设计。它们的设计通常依赖于相关参数的特别选择，并且对集群设计中的变化不敏感。本文探讨了如何有效地设计集群，它们是通过划分区域，包括个人和家庭或其他单位的地理定义。使用空间自相关的地统计模型，我们生成近似的集群内的平均协方差，以估计特定的集群设计参数的有效样本量。我们将展示如何枚举的位置，聚类区域，抽样比例，抽样方法的数量影响设计的效率，并考虑选择最有效的设计预算约束的优化问题。我们还考虑如何从这些近似的参数可以简单地解释在“现实世界”的数量和设计分析中使用。
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 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.
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