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The total survey error paradigm and pre-election polls: the case of the 2006 Italian general elections

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  • Fumagalli, Laura
  • Sala, Emanuela

Abstract

Pre-election polls sometimes fail to reach the purpose for which they are carried out: to provide accurate predictions of electoral out-comes. By looking at the 2006 Italian General Elections, this paper aims to assess the role that different factors play in determining the accuracy of the pre-election polls. We find strong evidence that the quality of the sampling frame and non-respondents may contribute to biasing the polls results. This paper also aims to show how to over-come some of the limitations of the survey data by using statistical matching techniques and weighing procedures.

Suggested Citation

  • Fumagalli, Laura & Sala, Emanuela, 2011. "The total survey error paradigm and pre-election polls: the case of the 2006 Italian general elections," ISER Working Paper Series 2011-29, Institute for Social and Economic Research.
    • Handle: RePEc:ese:iserwp:2011-29
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    1. Paola Profeta, 2007. "Political support and tax reforms with an application to Italy," Public Choice, Springer, vol. 131(1), pages 141-155, April.
    2. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    3. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    4. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    5. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
    6. Seligson, Mitchell A., 1980. "Trust, Efficacy and Modes of Political Participation: A Study of Costa Rican Peasants," British Journal of Political Science, Cambridge University Press, vol. 10(1), pages 75-98, January.
    7. Colm O'Muircheartaigh & Peter Lynn, 1997. "Editorial: The 1997 UK Pre‐election Polls," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 381-385, September.
    8. T. M. F. Smith, 1996. "Public Opinion Polls: The Uk General Election, 1992," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 535-545, May.
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    Cited by:

    1. Sala, Emanuela & Lillini, Roberto, 2014. "The impact of unlisted and no-landline respondents on non-coverage bias. The Italian case," ISER Working Paper Series 2014-16, Institute for Social and Economic Research.

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