Scaling Meaningful Political Dimensions

35 Pages Posted: 8 Jun 2012

See all articles by Benjamin E. Lauderdale

Benjamin E. Lauderdale

London School of Economics & Political Science (LSE)

Tom S. Clark

Emory University - Department of Political Science

Date Written: June 7, 2012

Abstract

Item response theory (IRT) 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. Latent Dirichlet Allocation (LDA) models are an increasingly applied approach to using relative term frequencies to estimate the degree to which each text in a corpus discusses a set of issues. However, while models based on relative term frequencies are powerful for discovering which issues are being discussed in which texts, they have proven less useful for discovering variation in political positions within corpuses that cover a range of issues. We combine these two models into a new model for discovering preference variation within issues, using voting data augmented with texts describing each vote. We demonstrate our approach using data from the U.S. Supreme Court.

Suggested Citation

Lauderdale, Benjamin E. and Clark, Tom S., Scaling Meaningful Political Dimensions (June 7, 2012). 7th Annual Conference on Empirical Legal Studies Paper, Available at SSRN: https://ssrn.com/abstract=2079561 or http://dx.doi.org/10.2139/ssrn.2079561

Benjamin E. Lauderdale

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

Tom S. Clark (Contact Author)

Emory University - Department of Political Science ( email )

Atlanta, GA 30322
United States
404-727-6615 (Phone)

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