Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component Effect (AMCE)
Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component Effect (AMCE)
复制标题
使用联合实验分析选举：平均边际成分效应 (AMCE) 的重要作用
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Teppei Yamamoto
中科院分区：
文献类型：
--
作者：
Kirk Bansak;Jens Hainmueller;D. Hopkins;Teppei Yamamoto
政治科学家越来越多地部署联合调查实验，以了解各种环境中的多维选择。我们开始与一个通用的框架，分析选民的偏好，在多属性的选举使用连接。在这个框架中，我们证明了平均边际成分效应（AMCE）是定义良好的个人偏好，并代表了一个中心的数量感兴趣的实证学者的选举：在一个属性的变化对候选人或政党的预期投票份额的影响。无论选民偏好的异质性、强度或互动性如何，也无论选票如何聚集成席位，这一属性都成立。总的来说，我们的研究结果表明，AMCEs的理解选举的重要作用，相应的文献综述支持的结论。我们还提供了解释AMCE的实用建议，并讨论了如何结合数据可以用来估计其他数量的利益，选举研究。
Political scientists have increasingly deployed conjoint survey experiments to understand multi-dimensional choices in various settings. We begin with a general framework for analyzing voter preferences in multi-attribute elections using conjoints. With this framework, we demonstrate that the Average Marginal Component Effect (AMCE) is well-defined in terms of individual preferences and represents a central quantity of interest to empirical scholars of elections: the effect of a change in an attribute on a candidate or party's expected vote share. This property holds irrespective of the heterogeneity, strength, or interactivity of voters' preferences and regardless of how votes are aggregated into seats. Overall, our results indicate the essential role of AMCEs for understanding elections, a conclusion buttressed by a corresponding literature review. We also provide practical advice on interpreting AMCEs and discuss how conjoint data can be used to estimate other quantities of interest to electoral studies.
条件 Logistic 回归的正则化路径：clogitL1 包。
影响因子：
5.8
作者：
Reid,Stephen;Tibshirani,Rob
通讯作者：
Tibshirani,Rob