A sparse factor analytic probit model for congressional voting patterns

P. Richard Hahn, Carlos M. Carvalho, James G. Scott

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

The paper adapts sparse factor models for exploring covariation in multivariate binary data, with an application to measuring latent factors in US Congressional roll-call voting patterns. This straightforward modification provides two advantages over traditional factor analysis of binary data. First, a sparsity prior can be used to assess the evidence that a given factor loading may be exactly 0, realizing a principled unification of exploratory and confirmatory factory analysis. Second, incorporating sparsity into existing factor analytic probit models effects a favourable bias-variance trade-off in estimating the covariance matrix of the multivariate Gaussian latent variables. Posterior summaries from this model applied to the roll-call data provide novel metrics of partisanship of a given Senate.

Original languageEnglish (US)
Pages (from-to)619-635
Number of pages17
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume61
Issue number4
DOIs
StatePublished - Aug 1 2012
Externally publishedYes

Keywords

  • Covariance estimation
  • Factor models
  • Multivariate probit models
  • Voting patterns

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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