Treating words as data with error: Uncertainty in text statements of policy positions

Kenneth Benoit, Michael Laver, Slava Mikhaylov

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Political text offers extraordinary potential as a source of information about the policy positions of political actors. Despite recent advances in computational text analysis, human interpretative coding of text remains an important source of text-based data, ultimately required to validate more automatic techniques. The profession's main source of cross-national, time-series data on party policy positions comes from the human interpretative coding of party manifestos by the Comparative Manifesto Project (CMP). Despite widespread use of these data, the uncertainty associated with each point estimate has never been available, undermining the value of the dataset as a scientific resource. We propose a remedy. First, we characterize processes by which CMP data are generated. These include inherently stochastic processes of text authorship, as well as of the parsing and coding of observed text by humans. Second, we simulate these error-generating processes by bootstrapping analyses of coded quasi-sentences. This allows us to estimate precise levels of nonsystematic error for every category and scale reported by the CMP for its entire set of 3,000-plus manifestos. Using our estimates of these errors, we show how to correct biased inferences, in recent prominently published work, derived from statistical analyses of error-contaminated CMP data.

    Original languageEnglish (US)
    Pages (from-to)495-513
    Number of pages19
    JournalAmerican Journal of Political Science
    Volume53
    Issue number2
    DOIs
    StatePublished - Apr 2009

    ASJC Scopus subject areas

    • Sociology and Political Science
    • Political Science and International Relations

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