Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

A Scaling Model for Estimating Time-Series Party Positions from Texts

Jonathan B. Slapin and Sven-Oliver Proksch
American Journal of Political Science
Vol. 52, No. 3 (Jul., 2008), pp. 705-722
Stable URL: http://www.jstor.org/stable/25193842
Page Count: 18
  • Read Online (Free)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Scaling Model for Estimating Time-Series Party Positions from Texts
Preview not available

Abstract

Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing text-based methods face challenges in producing valid and reliable time-series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time-series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.

Page Thumbnails

  • Thumbnail: Page 
705
    705
  • Thumbnail: Page 
706
    706
  • Thumbnail: Page 
707
    707
  • Thumbnail: Page 
708
    708
  • Thumbnail: Page 
709
    709
  • Thumbnail: Page 
710
    710
  • Thumbnail: Page 
711
    711
  • Thumbnail: Page 
712
    712
  • Thumbnail: Page 
713
    713
  • Thumbnail: Page 
714
    714
  • Thumbnail: Page 
715
    715
  • Thumbnail: Page 
716
    716
  • Thumbnail: Page 
717
    717
  • Thumbnail: Page 
718
    718
  • Thumbnail: Page 
719
    719
  • Thumbnail: Page 
720
    720
  • Thumbnail: Page 
721
    721
  • Thumbnail: Page 
722
    722