IDEAS home Printed from https://ideas.repec.org/a/wly/amposc/v58y2014i4p1064-1082.html
   My bibliography  Save this article

Structural Topic Models for Open‐Ended Survey Responses

Author

Listed:
  • Margaret E. Roberts
  • Brandon M. Stewart
  • Dustin Tingley
  • Christopher Lucas
  • Jetson Leder‐Luis
  • Shana Kushner Gadarian
  • Bethany Albertson
  • David G. Rand

Abstract

Collection and especially analysis of open‐ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open‐ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.

Suggested Citation

  • Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
  • Handle: RePEc:wly:amposc:v:58:y:2014:i:4:p:1064-1082
    DOI: 10.1111/ajps.12103
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/ajps.12103
    Download Restriction: no

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:amposc:v:58:y:2014:i:4:p:1064-1082. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: https://doi.org/10.1111/(ISSN)1540-5907 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.