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To Simulate or NOMINATE?
Author(s) -
CLINTON JOSHUA D.,
JACKMAN SIMON
Publication year - 2009
Publication title -
legislative studies quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.728
H-Index - 54
eISSN - 1939-9162
pISSN - 0362-9805
DOI - 10.3162/036298009789869691
Subject(s) - nominate , ideal point , ideal (ethics) , bayesian probability , quadratic equation , computer science , voting , binary number , class (philosophy) , point (geometry) , mathematical economics , econometrics , mathematics , artificial intelligence , machine learning , law , geometry , arithmetic , politics , political science
Carroll et al. (2009) summarize the similarities and differences between the NOMINATE and IDEAL methods of fitting spatial voting models to binary roll‐call data. As those authors note, for the class of problems with which either NOMINATE and the Bayesian quadratic‐normal model can be used, the ideal point estimates almost always coincide, and when they do not, the discrepancy is due to the somewhat arbitrary identification and computational constraints imposed by each method. There are, however, many problems for which the Bayesian quadratic‐normal model can be easily generalized, so as to address a broad array of questions and take advantage of additional data. Given the nature and source of the differences between NOMINATE and the Bayesian approach—as well as the fact that both approaches are approximations of the decision‐making processes being modeled—we believe that it is preferable to choose the more flexible Bayesian approach.