Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions.
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 au...
| Publicado en: | American Journal of Political Science Vol. 53; no. 2; pp. 495 - 514 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
April 2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511409014&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511409014 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00925853 APS jtl: American Journal of Political Science issn: 00925853 maglogo: N pubinfo: dt: April 2009 vid: 53 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 511409014 10.1111/j.1540-5907.2009.00383.x ppf: 495 ppct: 19 formats: tig: atl: Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions. aug: au: Benoit, Kenneth Laver, Michael Mikhaylov, Slava su: Decision making Government policy sug: subj: Decision making Government policy ab: 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. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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