Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model

被引:55
作者
Caughey, Devin [1 ]
Warshaw, Christopher [1 ]
机构
[1] MIT, Dept Polit Sci, Cambridge, MA 02139 USA
关键词
PUBLIC-OPINION; STATES; POSTSTRATIFICATION;
D O I
10.1093/pan/mpu021
中图分类号
D0 [政治学、政治理论];
学科分类号
030201 [政治学理论];
摘要
Over the past eight decades, millions of people have been surveyed on their political opinions. Until recently, however, polls rarely included enough questions in a given domain to apply scaling techniques such as IRT models at the individual level, preventing scholars from taking full advantage of historical survey data. To address this problem, we develop a Bayesian group-level IRT approach that models latent traits at the level of demographic and/or geographic groups rather than individuals. We use a hierarchical model to borrow strength cross-sectionally and dynamic linear models to do so across time. The group-level estimates can be weighted to generate estimates for geographic units. This framework opens up vast new areas of research on historical public opinion, especially at the subnational level. We illustrate this potential by estimating the average policy liberalism of citizens in each U.S. state in each year between 1972 and 2012.
引用
收藏
页码:197 / 211
页数:15
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