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Using Propensity Score Analysis of Survey Data to Estimate Population Average Treatment Effects: A Case Study Comparing Different Methods.
Evaluation Review ( IF 3.0 ) Pub Date : 2020-07-16 , DOI: 10.1177/0193841x20938497
Nianbo Dong 1 , Elizabeth A Stuart 2, 3, 4 , David Lenis 5 , Trang Quynh Nguyen 2, 3
Affiliation  

Background:

Many studies in psychological and educational research aim to estimate population average treatment effects (PATE) using data from large complex survey samples, and many of these studies use propensity score methods. Recent advances have investigated how to incorporate survey weights with propensity score methods. However, to this point, that work had not been well summarized, and it was not clear how much difference the different PATE estimation methods would make empirically.

Purpose:

The purpose of this study is to systematically summarize the appropriate use of survey weights in propensity score analysis of complex survey data and use a case study to empirically compare the PATE estimates using multiple analysis methods that include ordinary least squares regression, weighted least squares regression, and various propensity score applications.

Methods:

We first summarize various propensity score methods that handle survey weights. We then demonstrate the performance of various analysis methods using a nationally representative data set, the Early Childhood Longitudinal Study–Kindergarten to estimate the effects of preschool on children’s academic achievement. The correspondence of the results was evaluated using multiple criteria.

Results and Conclusions:

It is important for researchers to think carefully about their estimand of interest and use methods appropriate for that estimand. If interest is in drawing inferences to the survey target population, it is important to take the survey weights into account, particularly in the outcome analysis stage for estimating the PATE. The case study shows, however, not much difference among various analysis methods in one applied example.



中文翻译:

使用调查数据的倾向评分分析来估计人口平均治疗效果:比较不同方法的案例研究。

背景:

许多心理学和教育研究旨在使用来自大型复杂调查样本的数据来估计人口平均治疗效果 (PATE),其中许多研究使用倾向评分方法。最近的进展研究了如何将调查权重与倾向评分方法结合起来。然而,到目前为止,这项工作还没有得到很好的总结,也不清楚不同的 PATE 估计方法在经验上会有多大差异。

目的:

本研究的目的是系统地总结调查权重在复杂调查数据的倾向得分分析中的适当使用,并通过案例研究使用多种分析方法(包括普通最小二乘回归、加权最小二乘回归、加权最小二乘回归、以及各种倾向评分应用程序。

方法:

我们首先总结了处理调查权重的各种倾向评分方法。然后,我们使用具有全国代表性的数据集“幼儿纵向研究-幼儿园”来展示各种分析方法的性能,以估计学前班对儿童学业成绩的影响。使用多个标准评估结果的对应性。

结果和结论:

研究人员仔细考虑他们感兴趣的估计量并使用适合该估计量的方法很重要。如果感兴趣的是对调查目标人群进行推断,那么考虑调查权重很重要,特别是在估计 PATE 的结果分析阶段。然而,案例研究表明,在一个应用示例中,各种分析方法之间没有太大区别。

更新日期:2020-07-16
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