Scaling Politically Meaningful Dimensions Using Texts and Votes

Benjamin E. Lauderdale and Tom S. Clark

American Journal of Political Science, 2014, vol. 58, issue 3, 754-771

Abstract: Item response theory models for roll‐call voting data provide political scientists with parsimonious descriptions of political actors' relative preferences. However, models using only voting data tend to obscure variation in preferences across different issues due to identification and labeling problems that arise in multidimensional scaling models. We propose a new approach to using sources of metadata about votes to estimate the degree to which those votes are about common issues. We demonstrate our approach with votes and opinion texts from the U.S. Supreme Court, using latent Dirichlet allocation to discover the extent to which different issues were at stake in different cases and estimating justice preferences within each of those issues. This approach can be applied using a variety of unsupervised and supervised topic models for text, community detection models for networks, or any other tool capable of generating discrete or mixture categorization of subject matter from relevant vote‐specific metadata.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1111/ajps.12085

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:amposc:v:58:y:2014:i:3:p:754-771

Access Statistics for this article

More articles in American Journal of Political Science from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().