Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia
Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia
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DOI：
10.1186/s12963-017-0157-x
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发表时间：
2017-12-21
影响因子：
3.3
通讯作者：
Vittinghoff, Eric
中科院分区：
文献类型：
--
作者：
Moseson, Heidi;Gerdts, Caitlin;Vittinghoff, Eric
背景：清单实验是一种很有前途的测量工具，可以激发对污名化或敏感健康行为的真实反应。然而，由于先前无法验证的假设和无法进行多变量分析，研究人员可能会对采用该方法犹豫不决。我们最近开发了一种统计检验，可以检测设计效应的存在- -不存在设计效应是清单实验方法的一个中心假设- -我们试图检验在利比里亚对自我报告的堕胎进行的清单实验的有效性。我们还旨在介绍最近开发的多变量回归估计器，用于分析列表实验数据，以探索受访者特征与堕胎之间的关系-这是了解堕胎妇女经历的重要组成部分。方法：为了检验利比里亚列表实验数据中没有设计效应的原假设，我们计算了以对控制项目的反应为特征的每种被试“类型”的百分比，并使用bonferroni调整的alpha标准比较了这些百分比在治疗组和对照组之间的差异。然后，我们实施了两个最小二乘和两个最大似然模型（共四个），每个模型代表不同的偏差-方差权衡，以估计受访者特征与堕胎之间的关联。结果：我们在利比里亚的列表实验数据中没有发现明显的设计效应的证据（p = 0.18），证实了该方法的第一个关键假设。多变量分析表明教育程度与流产史呈负相关。然而，由于调查对象堕胎的时间和安全性可能影响到她接受教育的能力，因此，测量堕胎终生经历的回顾性性质使结果的解释复杂化。结论：我们的工作表明，多变量分析以及关键设计假设的统计检验是可能的，尽管在考虑寿命测量时存在重要的局限性。我们概述了如何在未来的研究中使用列表实验数据来实现该方法。
Background: The list experiment is a promising measurement tool for eliciting truthful responses to stigmatized or sensitive health behaviors. However, investigators may be hesitant to adopt the method due to previously untestable assumptions and the perceived inability to conduct multivariable analysis. With a recently developed statistical test that can detect the presence of a design effect - the absence of which is a central assumption of the list experiment method - we sought to test the validity of a list experiment conducted on self-reported abortion in Liberia. We also aim to introduce recently developed multivariable regression estimators for the analysis of list experiment data, to explore relationships between respondent characteristics and having had an abortion - an important component of understanding the experiences of women who have abortions.Methods: To test the null hypothesis of no design effect in the Liberian list experiment data, we calculated the percentage of each respondent "type," characterized by response to the control items, and compared these percentages across treatment and control groups with a Bonferroni-adjusted alpha criterion. We then implemented two least squares and two maximum likelihood models (four total), each representing different bias-variance trade-offs, to estimate the association between respondent characteristics and abortion.Results: We find no clear evidence of a design effect in list experiment data from Liberia (p = 0.18), affirming the first key assumption of the method. Multivariable analyses suggest a negative association between education and history of abortion. The retrospective nature of measuring lifetime experience of abortion, however, complicates interpretation of results, as the timing and safety of a respondent's abortion may have influenced her ability to pursue an education.Conclusion: Our work demonstrates that multivariable analyses, as well as statistical testing of a key design assumption, are possible with list experiment data, although with important limitations when considering lifetime measures. We outline how to implement this methodology with list experiment data in future research.