Hostname: page-component-745bb68f8f-l4dxg Total loading time: 0 Render date: 2025-01-07T15:46:31.078Z Has data issue: false hasContentIssue false
  • English
  • Français

Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark

Published online by Cambridge University Press:  04 January 2017

Will Lowe  and
Kenneth Benoit
Will Lowe
Affiliation:
MZES, University of Mannheim e-mail: [email protected]
Kenneth Benoit*
Affiliation:
Department of Methodology, London School of Economics and the Department of Political Science, Trinity College, Dublin
*
e-mail: [email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Type
Research Article
Information
Political Analysis , Volume 21 , Issue 3 , Summer 2013 , pp. 298 - 313
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: Replication materials for this article are available from the Political Analysis dataverse at http://hdl.handle.net/1902.1/20387. Supplementary materials for this article are available on the Political Analysis Web site.

References

Benoit, K., and Laver, M. 2006. Party policy in modern democracies. London: Routledge.CrossRefGoogle Scholar
Benoit, K., and Laver, M. 2012. The dimensionality of political space: Epistemological and methodological considerations. European Union Politics 13: 194–218.CrossRefGoogle Scholar
Benoit, K., Laver, M., and Mikhaylov, S. 2009. Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science 53(2): 495–513.CrossRefGoogle Scholar
Cameron, A. C., and Trivedi, P. K. 1998. Regression analysis of count data. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Carlstein, E. 1986. The use of subseries methods for estimating the variance of a general statistic from a stationary time series. Annals of Statistics 14: 1171–79.CrossRefGoogle Scholar
Church, K., and Gale, W. 1995. Poisson mixtures. Natural Language Engineering 1: 163–90.CrossRefGoogle Scholar
Davison, A. C., and Hinkley, D. V. 1997. Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Firth, D. 2005. Bradley-Terry models in R. Journal of Statistical Software 12: 1–12.CrossRefGoogle Scholar
Goodman, L. A. 1979. Simple models for the analysis of association in cross-classifications having ordered categories. Journal of the American Statistical Association 74(367): 537–52.CrossRefGoogle Scholar
Greenacre, M. 2007. Correspondence analysis in practice, 2nd ed. London: Chapman and Hall.CrossRefGoogle Scholar
Grimmer, J., and King, G. 2011. General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences 108(7): 2643–50.CrossRefGoogle ScholarPubMed
Grimmer, J., and Stewart, B. M. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21(3): 267–97.CrossRefGoogle Scholar
Jordan, M. I. 1995. Why the logistic function? A tutorial discussion on probabilities and neural networks. Computational Cognitive Science Report 9503, MIT.Google Scholar
Künsch, H. R. 1989. The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17: 1217–41.CrossRefGoogle Scholar
Laver, M., Benoit, K., and Garry, J. 2003. Estimating the policy positions of political actors using words as data. American Political Science Review 97: 311–31.CrossRefGoogle Scholar
Lo, J., Proksch, S.-O., and Slapin, J. B. 2011. Party ideology and intra-party cohesion: A theory and measure of election manifestos. Paper presented at MPSA 2011.Google Scholar
Lowe, W., and Benoit, K. R. 2011. Practical issues in text scaling models: Estimating legislator ideal points in multi-party systems using speeches. Paper presented at MPSA 2011.Google Scholar
Manning, C. D., Raghavan, P., and Schütze, H. 2008. Introduction to information retrieval. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Mikhaylov, S., Laver, M., and Benoit, K. 2012. Coder reliability and misclassification in the human coding of party manifestos. Political Analysis 20: 78–91.CrossRefGoogle Scholar
Monroe, B., and Maeda, K. 2004. Talk's cheap: Text-based estimation of rhetorical ideal-points. Working paper, Michigan State University.Google Scholar
Monroe, B. L., Quinn, K. M., and Colaresi, M. P. 2008. Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis 16: 372–403.CrossRefGoogle Scholar
Proksch, S.-O., and Slapin, J. B. 2010. Position taking in the European Parliament speeches. British Journal of Political Science 40(3): 587–611.CrossRefGoogle Scholar
Slapin, J. B., and Proksch, S.-O. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3): 705–22.CrossRefGoogle Scholar
Wallach, H. M., Murray, I., Salakhutdinov, R., and Mimno, D. 2009. Evaluation methods for topic models. Proceedings of the 26th International Workshop on Machine Learning, New York, NY.CrossRefGoogle Scholar
Zhang, H. 2004. The optimality of Naïve Bayes. In FLAIRS Conference, eds. Barr, V. and Markov, Z. Menlo Park, CA: AAAI Press.Google Scholar
Supplementary material: PDF

Lowe and Benoit supplementary material

Supplementary Material

Download Lowe and Benoit supplementary material(PDF)
PDF 315.9 KB