Towards a General Methodology of Bridging Ideological Spaces

28 Pages Posted: 22 Sep 2020 Last revised: 29 Oct 2020

See all articles by Tzu-Ping Liu

Tzu-Ping Liu

University of California, Davis, Department of Political Science

Gento Kato

Meiji University - School of Political Science and Economics

Sam Fuller

University of California, Davis - Department of Political Science

Date Written: August 8, 2020

Abstract

Bridging ideological spaces is an important, but relatively troubled branch of the scaling literature. The most common bridging procedure, joint-scaling, ignores structural differences between groups resulting in uninformative results. Alternatively, dimensional-mapping addresses this issue by using transformation rather than merging. However, current implementations cannot bridge multi-dimensional spaces nor estimate ideal points non-parametrically. Furthermore, these methods require shared individuals between the two groups. To address these major issues, we introduce a generalized methodology for dimensional-mapping that enables both non-parametric and multi-dimensional ideal point estimation using either real or ''synthetic anchors.'' Synthetic anchors remove the stringent anchor assumption and are generated by transferring a small number of individuals from one group to the other and, when used appropriately, do not distort the ideological space. We demonstrate the utility of our methodology on two sets of voter-politician data from the United States and Japan by comparing its performance with existing approaches. Our results suggest that not only does our method make less stringent assumptions and is more widely applicable than existing techniques, but our approach also generates bridged ideal point estimates comparable to those generated by other methods.

Keywords: Ideal-Point Estimation, Bridging, Procrustes Transformation, Non- Parametric

Suggested Citation

Liu, Tzu-Ping and Kato, Gento and Fuller, Sam, Towards a General Methodology of Bridging Ideological Spaces (August 8, 2020). Available at SSRN: https://ssrn.com/abstract=3669591 or http://dx.doi.org/10.2139/ssrn.3669591

Tzu-Ping Liu (Contact Author)

University of California, Davis, Department of Political Science ( email )

United States

Gento Kato

Meiji University - School of Political Science and Economics ( email )

1-1 Kanda-Surugadai
Chiyoda-ku, Tokyo 101-8301
Japan

Sam Fuller

University of California, Davis - Department of Political Science ( email )

Davis, CA 95616
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
24
Abstract Views
412
PlumX Metrics