Understanding complex legislative and judicial behaviour via hierarchical ideal point estimation

Ying Lu, Xiaohui Wang

Research output: Contribution to journalArticlepeer-review


Ideal point estimation is an important tool to study legislative and judicial voting behaviours. We propose a hierarchical ideal point estimation framework that directly models complex voting behaviours on the basis of the characteristics of the political actors and the votes that they cast. Through simulations and empirical examples we show that this framework holds good promise for resolving many unsettled issues, such as the multi-dimensional aspects of ideology, and the effects of political parties. As a companion to this paper, we offer an easy-to-use R package that implements the methods discussed.

Original languageEnglish (US)
Pages (from-to)93-107
Number of pages15
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Issue number1
StatePublished - Jan 2011


  • Bayesian estimation
  • Item response theory
  • Random- and fixed effect models
  • Roll call data
  • Vote cast data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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