SOCIOECONOMIC STATUS MEASUREMENT WITH DISCRETE PROXY VARIABLES: IS PRINCIPAL COMPONENT ANALYSIS A RELIABLE ANSWER?
SOCIOECONOMIC STATUS MEASUREMENT WITH DISCRETE PROXY VARIABLES: IS PRINCIPAL COMPONENT ANALYSIS A RELIABLE ANSWER?
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DOI：
10.1111/j.1475-4991.2008.00309.x
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发表时间：
2009-03-01
影响因子：
2
通讯作者：
Angeles, Gustavo
中科院分区：
文献类型：
--
作者：
Kolenikov, Stanislav;Angeles, Gustavo
在过去几年中，在福利研究领域使用主成分分析的经济学出版物数量有所增加。本文探讨了离散数据可以被纳入PCA的方式。分析了观测变量的离散性对主成分分析的影响。分析了流行的Filmer和Pritchett（2001）方法的统计特性。多区和polyserial相关的概念，介绍了适当的参考现有的文献证明其统计特性。一个大的模拟研究进行比较各种实现的离散数据PCA。模拟结果表明，目前使用的方法运行PCA的一组虚拟变量，提出了由Filmer和Pritchett（2001），可以通过使用程序适当的离散数据，如保留有序变量，而不打破他们成一组虚拟变量或使用多元相关性进行改进。一个使用孟加拉国2000年人口和健康调查数据的实证例子有助于解释程序之间的差异。
The last several years have seen a growth in the number of publications in economics that use principal component analysis (PCA) in the area of welfare studies. This paper explores the ways discrete data can be incorporated into PCA. The effects of discreteness of the observed variables on the PCA are reviewed. The statistical properties of the popular Filmer and Pritchett (2001) procedure are analyzed. The concepts of polychoric and polyserial correlations are introduced with appropriate references to the existing literature demonstrating their statistical properties. A large simulation study is carried out to compare various implementations of discrete data PCA. The simulation results show that the currently used method of running PCA on a set of dummy variables as proposed by Filmer and Pritchett (2001) can be improved upon by using procedures appropriate for discrete data, such as retaining the ordinal variables without breaking them into a set of dummy variables or using polychoric correlations. An empirical example using Bangladesh 2000 Demographic and Health Survey data helps in explaining the differences between procedures.