A sparse factor analytic probit model for congressional voting patterns

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

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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
Issue number4
StatePublished - Aug 2012
Externally publishedYes


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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