Measuring Household Wealth with Latent Trait Modelling: An Application to Malawian DHS Data
Measuring Household Wealth with Latent Trait Modelling: An Application to Malawian DHS Data
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DOI：
10.1007/s11205-013-0447-z
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发表时间：
2014-09-01
影响因子：
3.1
通讯作者：
Vandemoortele, Milo
中科院分区：
文献类型：
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作者：
Vandemoortele, Milo
文献中越来越多的人意识到，更常用的衡量资产财富的方法是有问题的。总和分数的理论基础薄弱，主成分分析 (PCA) 是一种不适合应用于分类数据的统计方法，类似于对分类数据使用线性 OLS 回归。潜在特质模型（LTM）提供了一种统计上优越的方法来衡量家庭财富。使用 LTM 有强有力的论据：它考虑了资产数据的分类性质；它明确地说明了支撑模型的假设，并且允许对更广泛的人群做出推论。本文将 LTM 应用于马拉维的三项人口统计和健康调查，并将结果与 PCA 方法的结果进行比较。虽然相关性较高，表明正在衡量类似的概念，但 LTM 的结果反映了资产数据的特征，因此代表了统计上优越的家庭财富衡量标准。鼓励利用 LTM 方法来计算财富指数的进一步研究。
There is increasing awareness in the literature that the more commonly utilised approaches to measure wealth from assets are problematic. The theoretical foundations of sum-scores are weak and principal component analysis (PCA) is an inappropriate statistical method to apply to categorical data, akin to using a linear OLS regression with categorical data. Latent trait modelling (LTM) offers a statistically superior approach to measure household wealth. There are powerful arguments for using LTM: it takes into account the categorical nature of asset data; it is explicit about the assumptions underpinning the model, and it allows for inferences to be made about the broader population. This article applies LTM to three Malawian Demographic and Health Surveys, and compares results to those of a PCA approach. While the correlation is moderately high, indicating that a similar concept is being measured, results from LTM reflect the characteristics of the asset data and therefore represents a statistically superior measure of household wealth. Further research that draws on LTM methods to calculate wealth indices is to be encouraged.