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

Political Analysis

Volumn 21, Issue 3, 2013, Pages 298-313

  Lowe, Will ^a   Benoit, Kenneth ^b  

^a UNIVERSITY OF MANNHEIM   (Germany)

^b LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84880686370     PISSN: 10471987     EISSN: 14764989     Source Type: Journal    
DOI: 10.1093/pan/mpt002     Document Type: Article

References (25)

1. Benoit, K., and M. Laver. 2006. Party policy in modern democracies. London: Routledge.
2. Benoit, K. 2012. The dimensionality of political space: Epistemological and methodological considerations. European Union Politics 13:194-218.
3. Benoit, K., M. Laver, and S. Mikhaylov. 2009. Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science 53(2):495-513.
4. Cameron, A. C., and P. K. Trivedi. 1998. Regression analysis of count data. Cambridge, UK: Cambridge University Press.
5. 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.
6. Church, K., and W. Gale. 1995. Poisson mixtures. Natural Language Engineering 1:163-90.
7. Davison, A. C., and D. V. Hinkley. 1997. Bootstrap methods and their application. Cambridge, UK: Cambridge University Press.
8. Firth, D. 2005. Bradley-Terry models in R. Journal of Statistical Software 12:1-12.
9. 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.
10. Greenacre, M. 2007. Correspondence analysis in practice, 2nd ed. London: Chapman and Hall.
11. Grimmer, J., and G. King. 2011. General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences 108(7):2643-50.
12. Grimmer, J., and B. M. Stewart. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21(3):267-97.
13. Jordan, M. I. 1995. Why the logistic function? A tutorial discussion on probabilities and neural networks. Computational Cognitive Science Report 9503, MIT.
14. Künsch, H. R. 1989. The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17:1217-41.
15. Laver, M., K. Benoit, and J. Garry. 2003. Estimating the policy positions of political actors using words as data. American Political Science Review 97:311-31.
16. Lo, J., S.-O. Proksch, and J. B. Slapin. 2011. Party ideology and intra-party cohesion: A theory and measure of election manifestos. Paper presented at MPSA 2011.
17. Lowe, W., and K. R. Benoit. 2011. Practical issues in text scaling models: Estimating legislator ideal points in multi-party systems using speeches. Paper presented at MPSA 2011.
18. Manning, C. D., P. Raghavan, and H. Schütze. 2008. Introduction to information retrieval. Cambridge, UK: Cambridge University Press.
19. Mikhaylov, S., M. Laver, and K. Benoit. 2012. Coder reliability and misclassification in the human coding of party manifestos. Political Analysis 20:78-91.
20. Monroe, B., and K. Maeda. 2004. Talk's cheap: Text-based estimation of rhetorical ideal-points. Working paper, Michigan State University.
21. Monroe, B. L., K. M. Quinn, and M. P. Colaresi. 2008. Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis 16:372-403.
22. Proksch, S.-O., and J. B. Slapin. 2010. Position taking in the European Parliament speeches. British Journal of Political Science 40(3):587-611.
23. Slapin, J. B., and S.-O. Proksch. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3):705-22.
24. Wallach, H. M., I. Murray, R. Salakhutdinov, and D. Mimno. 2009. Evaluation methods for topic models. Proceedings of the 26th International Workshop on Machine Learning, New York, NY.
25. Zhang, H. 2004. The optimality of Naïve Bayes. In FLAIRS Conference, eds. V. Barr and Z. Markov. Menlo Park, CA: AAAI Press.