IDEAS home Printed from https://ideas.repec.org/a/cup/apsrev/v111y2017i04p835-843_00.html
   My bibliography  Save this article

A Pairwise Comparison Framework for Fast, Flexible, and Reliable Human Coding of Political Texts

Author

Listed:
  • CARLSON, DAVID
  • MONTGOMERY, JACOB M.

Abstract

Scholars are increasingly utilizing online workforces to encode latent political concepts embedded in written or spoken records. In this letter, we build on past efforts by developing and validating a crowdsourced pairwise comparison framework for encoding political texts that combines the human ability to understand natural language with the ability of computers to aggregate data into reliable measures while ameliorating concerns about the biases and unreliability of non-expert human coders. We validate the method with advertisements for U.S. Senate candidates and with State Department reports on human rights. The framework we present is very general, and we provide free software to help applied researchers interact easily with online workforces to extract meaningful measures from texts.

Suggested Citation

  • Carlson, David & Montgomery, Jacob M., 2017. "A Pairwise Comparison Framework for Fast, Flexible, and Reliable Human Coding of Political Texts," American Political Science Review, Cambridge University Press, vol. 111(4), pages 835-843, November.
    • Handle: RePEc:cup:apsrev:v:111:y:2017:i:04:p:835-843_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0003055417000302/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qiushi Yu & Kevin M. Quinn, 2022. "A multidimensional pairwise comparison model for heterogeneous perceptions with an application to modelling the perceived truthfulness of public statements on COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1049-1073, July.
    2. Ginevra Floridi & Benjamin E. Lauderdale, 2022. "Pairwise comparisons as a scale development tool for composite measures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 519-542, April.
    3. Keren Weinshall & Lee Epstein, 2020. "Developing High‐Quality Data Infrastructure for Legal Analytics: Introducing the Israeli Supreme Court Database," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(2), pages 416-434, June.

    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:cup:apsrev:v:111:y:2017:i:04:p:835-843_00. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/psr .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.