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

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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