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Compared to What? A Comment on “A Robust Transformation Procedure for Interpreting Political Text” by Martin and Vanberg

  • Kenneth Benoit (a1) and Michael Laver (a2)
Extract

In “A Robust Transformation Procedure,” Martin and Vanberg (2007, hereafter MV) propose a new method for rescaling the raw virgin text scores produced by the “Wordscores” procedure of Laver, Benoit, and Garry (2003, hereafter LBG). Their alternative method addresses two deficiencies they argue exist with the transformation of virgin text scores proposed by LBG: First, that the LBG transformation is sensitive to the selection of virgin texts, and second, that it distorts the reference metric by failing to recover the original reference scores when reference texts are scored and transformed as if they were virgin texts. Their proposed alternative is “robust” in the sense that it avoids both shortcomings. Not only is MV's transformation a welcome contribution to the Wordscores project but also the critical analysis on which it is based brings to light a number of assumptions and choices that face the analyst seeking to estimate actors' policy positions using statistical analyses of the texts they generate. When first describing the possibility of rescaling the raw virgin text estimates, we emphasized that our

particular approach to rescaling is not fundamental to our word-scoring technique but, rather, is a matter of substantive research design unrelated to the validity of the raw virgin text scores… Other transformations are of course possible. (LBG, 316)

To explore more fully into the assumptions and choices behind alternative transformations and the research designs which motivate them, we offer the following comments.

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Corresponding author
e-mail: kbenoit@tcd.ie (corresponding author)
Footnotes
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Authors' note: We thank Georg Vanberg and Lanny Martin for comments and discussions during the drafting of this Comment.

Footnotes
References
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Benoit, Kenneth, Laver, Michael, Arnold, Christine, Hosli, Madeleine O., and Pennings, Paul. 2005. Measuring national delegate positions at the convention on the future of Europe using computerized wordscoring. European Union Politics 6: 291313.
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. The statistical analysis of roll call voting: A unified approach. American Political Science Review 98: 355–70.
Gabel, Matthew, and Huber, John. 2000. Putting parties in their place: Inferring party left-right ideological positions from party manifesto data. American Journal of Political Science 44: 94103.
Laver, Michael, and Garry, John. 2000. Estimating policy positions from political texts. American Journal of Political Science 44: 619–34.
Laver, Michael, and Benoit, Kenneth. 2002. Locating TDs in policy spaces: Wordscoring Dáil Speeches. Irish Political Studies 17(1): 5973.
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97: 311–31.
Laver, Michael, Benoit, Kenneth, and Sauger, Nicholas. 2006. Policy competition in the 2002 French legislative and presidential elections. European Journal of Political Research 45: 667–97.
Martin, Lanny W., and Vanberg, Georg. 2007. A robust transformation procedure for interpreting political text. Political Analysis (forthcoming).
Monroe, Burt, and Maeda, Ko. 2004. Talk's cheap: Text-based estimation of rhetorical ideal-points. Working paper, Michigan State University.
Poole, Keith, and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.
Slapin, Jonathan, and Proksch, Sven-Oliver. 2007. A scaling model for estimating time-series policy positions from texts. Paper presented at the annual meeting of the Midwest Political Science Association, Palmer House Hilton and Towers, Chicago, IL, April 12, 2007.
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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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