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A Unifying Model for Hypothesis Testing Using Legislative Voting Data: A Multilevel Item-Response-Theory Model

  • Analyses & Alternatives
  • Abbr : A&A
  • 2021, 5(1), pp.3~24
  • DOI : 10.22931/aanda.2021.5.1.001
  • Publisher : Korea Consensus Institute
  • Research Area : Social Science > Social Science in general
  • Received : January 15, 2021
  • Accepted : March 15, 2021
  • Published : March 30, 2021

Gyung-Ho Jeong 1

1The University of British Columbia

Accredited

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.

Citation status

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