Constructing socio-economic status indices: how to use principal components analysis
Constructing socio-economic status indices: how to use principal components analysis
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DOI：
10.1093/heapol/czl029
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发表时间：
2006-11-01
影响因子：
3.2
通讯作者：
Kumaranayake, Lilani
中科院分区：
文献类型：
--
作者：
Vyas, Seema;Kumaranayake, Lilani
从理论上讲，家庭财富的衡量可以通过收入、消费或支出信息来反映。然而，收集准确的收入和消费数据需要大量资源用于住户调查。鉴于越来越多的常规应用的主成分分析（PCA）使用资产数据在创建社会经济地位（SES）指数，我们回顾PCA为基础的指数是如何构建的，如何使用，以及它们的有效性和局限性。具体而言，有关变量的选择，数据准备和问题，如数据聚类的问题得到解决。还讨论了结果的解释和将家庭划分为SES组的方法。主成分分析已被验证为一种方法来描述SES分化的人口。与基础数据有关的问题将影响PCA，在生成和解释结果时应考虑到这一点。
Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.