Analyses & Alternatives (분석과 대안)

Korea Consensus Institute(KCI) (사단법인 코리아컨센서스연구원)
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A Unifying Model for Hypothesis Testing Using Legislative Voting Data: A Multilevel Item-Response-Theory Model

  • Received : 2021.01.15
  • Accepted : 2021.03.15
  • Published : 2021.03.30
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Abstract

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

Keywords

References

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