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A Robust Transformation Procedure for Interpreting Political Text

Author

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  • Martin, Lanny W.
  • Vanberg, Georg

Abstract

In a recent article in the American Political Science Review, Laver, Benoit, and Garry (2003, “Extracting policy positions from political texts using words as data,†97:311—331) propose a new method for conducting content analysis. Their Wordscores approach, by automating text-coding procedures, represents an advance in content analysis that will potentially have a large long-term impact on research across the discipline. To allow substantive interpretation, the scores produced by the Wordscores procedure require transformation. In this note, we address several shortcomings in the transformation procedure introduced in the original program. We demonstrate that the original transformation distorts the metric on which content scores are placed—hindering the ability of scholars to make meaningful comparisons across texts—and that it is very sensitive to the texts that are scored—opening up the possibility that researchers may generate, inadvertently or not, results that depend on the texts they choose to include in their analyses. We propose a transformation procedure that solves these problems.

Suggested Citation

  • Martin, Lanny W. & Vanberg, Georg, 2008. "A Robust Transformation Procedure for Interpreting Political Text," Political Analysis, Cambridge University Press, vol. 16(1), pages 93-100, January.
  • Handle: RePEc:cup:polals:v:16:y:2008:i:01:p:93-100_00
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    Cited by:

    1. Auffenberg, Jennie & Marcinkiewicz, Kamil, 2013. "Wer gestaltet, wer verwaltet Reformen im öffentlichen Dienst? Ein Methodenvergleich zur Analyse von Arbeitsbeziehungen in Reformprozessen anhand der Polizei Brandenburg," TranState Working Papers 170, University of Bremen, Collaborative Research Center 597: Transformations of the State.
    2. Pierre-Marc Daigneault & Dominic Duval & Louis M. Imbeau, 2018. "Supervised scaling of semi-structured interview transcripts to characterize the ideology of a social policy reform," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2151-2162, September.
    3. Marzagão, Thiago, 2017. "Automated Democracy Scores," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 37(1), May.
    4. Jonathan B. Slapin & Sven‐Oliver Proksch, 2008. "A Scaling Model for Estimating Time‐Series Party Positions from Texts," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 705-722, July.
    5. Anna Calissano & Simone Vantini & Marika Arena, 2020. "Monitoring rare categories in sentiment and opinion analysis: a Milan mega event on Twitter platform," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 787-812, December.
    6. Protte, Benjamin, 2012. "Does Fleet Street shape politics? Estimating the Effect of Newspaper Coverage about Globalization on the Support for Unemployment Insurance," Working Papers 12-19, University of Mannheim, Department of Economics.
    7. Marcinkiewicz, Kamil & Auffenberg, Jennie & Kittel, Bernhard, 2012. "Politikpositionen im Reformprozess des öffentlichen Dienstes: Zur Übertragbarkeit der quantitativen Textanalyse," TranState Working Papers 162, University of Bremen, Collaborative Research Center 597: Transformations of the State.
    8. Hanna Bäck & Marc Debus & Wolfgang C. Müller, 2016. "Intra-party diversity and ministerial selection in coalition governments," Public Choice, Springer, vol. 166(3), pages 355-378, March.
    9. Marc Debus, 2009. "Pre-electoral commitments and government formation," Public Choice, Springer, vol. 138(1), pages 45-64, January.

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