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Large-Scale Ideal Point Estimation

Published online by Cambridge University Press:  31 March 2021

Michael Peress Open the ORCID record for Michael Peress [Opens in a new window]
Michael Peress*
Affiliation:
The State University of New York, Stony Brook, NY, USA. E-mail: michael.peress@stonybrook.edu
*
Corresponding author Michael Peress
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Abstract

Recent advances in the study of voting behavior and the study of legislatures have relied on ideal point estimation for measuring the preferences of political actors, and increasingly, these applications have involved very large data matrices. This has proved challenging for the widely available approaches. Limitations of existing methods include excessive computation time and excessive memory requirements on large datasets, the inability to efficiently deal with sparse data matrices, inefficient computation of standard errors, and ineffective methods for generating starting values. I develop an approach for estimating multidimensional ideal points in large-scale applications, which overcomes these limitations. I demonstrate my approach by applying it to a number of challenging problems. The methods I develop are implemented in an r package (ipe).

Keywords

ideal point estimationitem response theory
Type
Article
Information
Political Analysis , Volume 30 , Issue 3 , July 2022 , pp. 346 - 363
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

References

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Supplementary material: Link

Peress Dataset

Dataset

https://doi.org/10.7910/DVN/P5SIO1
Link
Supplementary material: PDF

Peress supplementary material

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