Oversampling of Minority Populations Through Dual-Frame Surveys.
Oversampling of Minority Populations Through Dual-Frame Surveys.
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DOI：
10.1093/jssam/smz054
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发表时间：
2020-01
影响因子：
2.1
通讯作者：
Sixia Chen;Alexander Stubblefield;J. Stoner
中科院分区：
文献类型：
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作者：
Sixia Chen;Alexander Stubblefield;J. Stoner
先前的研究表明，不同种族群体的健康状况和行为存在差异。抽样设计不考虑对某些少数民族人口（如美洲印第安人或非洲裔美国人）进行过抽样，可能无法产生足够的样本量来估计少数民族人口的健康参数。过采样是研究人员用于实现小域估计所需精度水平的最常用方法之一。然而，它尚未在双框架调查设置中进行严格的调查。为了利用Marketing Systems Group数据库中少数群体的额外信息，我们提出了一种新的最优过采样策略，该策略可以使受总成本限制的域方差最小化，反之亦然。我们进一步将该方法扩展到同时对多个少数群体进行过抽样。一项基于人口的社区调查的实证研究表明，与传统方法相比，我们提出的方法在统计效率和成本平衡方面具有优势。
Previous studies have shown disparities in health conditions and behaviors among different ethnic groups. Sampling designs that do not consider oversampling certain minority populations, such as American Indians or African Americans, may not produce sufficient sample sizes for estimating health parameters for minority populations. Oversampling is one of the most common approaches that researchers use to achieve required precision levels for small domain estimation. However, it has not been rigorously investigated in dual-frame survey settings. To take advantage of extra information for minority populations in the Marketing Systems Group database, we propose a novel optimal oversampling strategy that minimizes the domain variance subject to total cost restriction or vice versa. We further extend the method to oversample multiple minorities simultaneously. Empirical study using a population-based community survey shows the benefits of our proposed methods compared with traditional methods in terms of statistical efficiency and cost balance.