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A Multinomial Framework for Ideal Point Estimation

Published online by Cambridge University Press:  09 October 2018

Max Goplerud*
Affiliation:
Harvard University, Department of Government, 1737 Cambridge Street, Cambridge, MA 02138, USA. Email: goplerud@g.harvard.edu

Abstract

This paper creates a multinomial framework for ideal point estimation (mIRT) using recent developments in Bayesian statistics. The core model relies on a flexible multinomial specification that includes most common models in political science as “special cases.” I show that popular extensions (e.g., dynamic smoothing, inclusion of covariates, and network models) can be easily incorporated whilst maintaining the ability to estimate a model using a Gibbs Sampler or exact EM algorithm. By showing that these models can be written and estimated using a shared framework, the paper aims to reduce the proliferation of bespoke ideal point models as well as extend the ability of applied researchers to estimate models quickly using the EM algorithm. I apply this framework to a thorny question in scaling survey responses—the treatment of nonresponse. Focusing on the American National Election Study (ANES), I suggest that a simple but principled solution is to treat questions as multinomial where nonresponse is a distinct (modeled) category. The exploratory results suggest that certain questions tend to attract many more invalid answers and that many of these questions (particularly when signaling out particular social groups for evaluation) are masking noncentrist (typically conservative) beliefs.

Type
Articles
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Author’s note: I thank Kosuke Imai, Gary King, Michael Peress, Marc Ratkovic, Dustin Tingley, Christopher Warshaw, Xiang Zhou and discussants at MPSA 2017 for helpful comments on earlier versions of this paper. All errors remaining are my own. Code to implement the models in the paper, and the mIRT more generally, can be found at http://dx.doi.org/10.7910/DVN/LD0ITE.

Contributing Editor: Jonathan N. Katz

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