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Scaling politically meaningful dimensions using texts and votes

Lauderdale, Benjamin E. and Clark, Tom S. (2014) Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science, 58 (3). 754 - 771. ISSN 0092-5853

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Identification Number: 10.1111/ajps.12085


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.

Item Type: Article
Official URL:
Additional Information: © 2014 Midwest Political Science Association
Divisions: Methodology
Subjects: H Social Sciences > HA Statistics
J Political Science > JA Political science (General)
Date Deposited: 28 Feb 2014 15:33
Last Modified: 20 Oct 2021 02:09
Projects: SES-0909235
Funders: National Science Foundation

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