Publication Type
Journal Article
Version
publishedVersion
Publication Date
6-2013
Abstract
Automated and statistical methods for estimating latent political traits and classes from textual data hold great promise, because virtually every political act involves the production of text. Statistical models of natural language features, however, are heavily laden with unrealistic assumptions about the process that generates these data, including the stochastic process of text generation, the functional link between political variables and observed text, and the nature of the variables (and dimensions) on which observed text should be conditioned. While acknowledging statistical models of latent traits to be "wrong," political scientists nonetheless treat their results as sufficiently valid to be useful. In this article, we address the issue of substantive validity in the face of potential model failure, in the context of unsupervised scaling methods of latent traits. We critically examine one popular parametric measurement model of latent traits for text and then compare its results to systematic human judgments of the texts as a benchmark for validity.
Discipline
Models and Methods | Political Science
Research Areas
Political Science
Publication
Political Analysis
Volume
21
Issue
3
First Page
298
Last Page
313
ISSN
1047-1987
Identifier
10.1093/pan/mpt002
Publisher
Political Methodology Section
Citation
LOWE, Will, & BENOIT, Kenneth.(2013). Validating estimates of latent traits from textual data using human judgment as a benchmark. Political Analysis, 21(3), 298-313.
Available at: https://ink.library.smu.edu.sg/soss_research/3982
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1093/pan/mpt002