Validating estimates of latent traits from textual data using human judgment as a benchmark


Lowe, Will ; Benoit, Kenneth



DOI: https://doi.org/10.1093/pan/mpt002
URL: https://www.cambridge.org/core/journals/political-...
Additional URL: http://pan.oxfordjournals.org/content/21/3/298.ful...
Document Type: Article
Year of publication: 2013
The title of a journal, publication series: Political Analysis
Volume: 21
Issue number: 3
Page range: 298-313
Place of publication: Oxford [u.a.]
Publishing house: Oxford Univ. Press
ISSN: 1047-1987
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department B
Subject: 320 Political science
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.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Lowe, Will ; Benoit, Kenneth (2013) Validating estimates of latent traits from textual data using human judgment as a benchmark. Political Analysis Oxford [u.a.] 21 3 298-313 [Article]


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