Exploratory Data Analysis
Copyright © 2011 SAGE Publications. Not for sale, reproduction, or distribution. 530 Data Analysis, Exploratory The ultimate quantitative extreme in textual data Klingemann, H.-D., Volkens, A., Bara, J., Budge, I., & analysis uses scaling procedures borrowed from McDonald, M. (2006). Mapping policy preferences II: item response theory methods developed originally Estimates for parties, electors, and governments in in psychometrics. Both Jon Slapin and Sven-Oliver Eastern Europe, European Union and OECD Proksch’s Poisson scaling model and Burt Monroe 1990–2003. Oxford, UK: Oxford University Press. and Ko Maeda’s similar scaling method assume Laver, M., Benoit, K., & Garry, J. (2003). Extracting that word frequencies are generated by a probabi- policy positions from political texts using words as listic function driven by the author’s position on data. American Political Science Review, 97(2), some latent scale of interest and can be used to 311–331. estimate those latent positions relative to the posi- Leites, N., Bernaut, E., & Garthoff, R. L. (1951). Politburo images of Stalin. World Politics, 3, 317–339. tions of other texts. Such methods may be applied Monroe, B., & Maeda, K. (2004). Talk’s cheap: Text- to word frequency matrixes constructed from texts based estimation of rhetorical ideal-points (Working with no human decision making of any kind. The Paper). Lansing: Michigan State University. disadvantage is that while the scaled estimates Slapin, J. B., & Proksch, S.-O. (2008). A scaling model resulting from the procedure represent relative dif- for estimating time-series party positions from texts. ferences between texts, they must be interpreted if a American Journal of Political Science, 52(3), 705–722.
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