분석과 대안 (Analyses & Alternatives)

  • 제5권1호
  • /
  • Pages.3-24
  • /
  • 2021
  • /
  • 2508-822X(pISSN)
  • /
  • 2508 - 8238(eISSN)
사단법인 코리아컨센서스연구원 (Korea Consensus Institute(KCI))
권호 표지

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

  • 투고 : 2021.01.15
  • 심사 : 2021.03.15
  • 발행 : 2021.03.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. Albert, James. (1992). Bayesian Estimation of Normal Ogive Item Response Curves Using Gibbs Sampling. Journal of Educational Statistics, 17: 251-269. https://doi.org/10.3102/10769986017003251
  2. Bailey, Michael. (2001). Ideal Point Estimation with a Small Number of Votes: A Random-Effects Approach. Political Analysis, 9: 192-210. https://doi.org/10.1093/polana/9.3.192
  3. Clinton, Joshua, Simon Jackman, and Douglas Rivers. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2): 355-370. https://doi.org/10.1017/s0003055404001194
  4. Deering, Christopher J. (1993). Decision Making in the Armed Services Committees. In Congress Resurgent: Foreign and Defense Policy on Capitol Hill, Ann Arbor, MI: The University of Michigan Press.
  5. Kriner, Douglas L. (2010). After the Rubicon: Congress, Presidents, and the Politics of Waging War. Chicago: University of Chicago Press.
  6. Martin, Andrew D., Kevin M. Quinn, and Jong Hee Park. (2011). MCMCpack: Markov Chain Monte Carlo in R. Journal of Statistical Software, 42: 1-21.
  7. Zorn, Christopher. (2005). A Solution to Separation in Binary Response Models. Political Analysis, 13(2): 157-170. https://doi.org/10.1093/pan/mpi009