Tracking opinion over time: a method for reducing sampling error.
Across a wide range of applications, the Kalman filtering and smoothing algorithm provides survey researchers with a single, systematic technique by which to generate four kinds of useful information. First, it enables survey analysts to differentiate between random sampling error and true opinion...
| Publicado en: | Public Opinion Quarterly Vol. 63; no. 2; pp. 178 - 193 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / UK
Summer 1999
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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=512858331&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512858331 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033362X POQ jtl: Public Opinion Quarterly issn: 0033362X maglogo: N pubinfo: dt: Summer 1999 vid: 63 iid: 2 pid: 622 pub: Oxford University Press / UK artinfo: ui: 512858331 10.1086/297710 ppf: 178 ppct: 15 formats: tig: atl: Tracking opinion over time: a method for reducing sampling error. aug: au: Green, Donald P. Gerber, Alan S. De Boef, Suzanna su: Statistical sampling Error analysis in mathematics Kalman filtering Statistical smoothing Social surveys -- Methodology Public opinion polls United States sug: subj: United States Statistical sampling Error analysis in mathematics Kalman filtering Statistical smoothing Social surveys -- Methodology Public opinion polls ab: Across a wide range of applications, the Kalman filtering and smoothing algorithm provides survey researchers with a single, systematic technique by which to generate four kinds of useful information. First, it enables survey analysts to differentiate between random sampling error and true opinion change. Second, Kalman smoothing provides a means by which to accumulate information across surveys, greatly increasing the precision with which public opinion is gauged at any given point in time. Third, this technique provides a rigorous means by which to interpolate missing observations and calculate the uncertainty associated with these interpolations. Finally, the Kalman algorithm improves the accuracy with which public opinion may be forecasted. Our empirical examples, which focus on party identification, show that the Kalman algorithm can dramatically reduce sampling error in survey data. Since software implementing this technique is readily available, survey analysts are encouraged to use it to make more efficient use of the data at their disposal. 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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