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Estimation of Underrepresented Strata in Preelection Polls: A Comparative Study

  • João Figueiredo
  • Pedro Campos
Chapter
Part of the Studies in Theoretical and Applied Statistics book series (STAS)

Abstract

In this work we aim at increasing the utility of a preelection poll, by improving the quality of the vote share estimates, both at macro and micro level. Three different methodologies are applied with that purpose: (1) polls aggregation, using existing auxiliary polling; (2) application of multilevel regression methods, using the multilevel structure of the data; and (3) methods of small area estimation, making use of auxiliary information through the application of the Empirical Best Linear Unbiased Prediction (EBLUP). These methods are applied to real data collected from a survey with the aim of estimating the vote share in the Portuguese legislative elections. When auxiliary information is required, we concluded that polls aggregations and EBLUP have to be applied with caution, since this information is extremely important for a good application of these models to the data set and to obtain good reliable forecasts. On the other hand, if auxiliary information is not available or if it is not of good quality, then multilevel regression can and should be seen as a safe alternative to obtain more precise estimates, either at the micro or macro level. Besides, this is the method which further improves the precision of the estimates. In the presence of good auxiliary information, EBLUP proved to be the method with greater proximity with real values.

Keywords

District Level Markov Chain Monte Carlo Method Vote Share Auxiliary Information Best Linear Unbiased Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Coelho, P.: Estimadores combinados para pequenos domínios. Revista de Estatística 2, 23–43 (1996)Google Scholar
  2. 2.
    Erikson, R.S., Wright, G.C., McIver, J.P.: Statehouse Democracy: Public Opinion and Policy in the American States. Cambridge University Press, Cambridge (1993)Google Scholar
  3. 3.
    Fay, R.E., Herriot, R.A.: Estimate of income for small places: an application of James-Stein procedures to census data. J. Am. Stat. Assoc. 74, 269–277 (1979)Google Scholar
  4. 4.
    Figueiredo, J.: Estimação em Estratos sub-representados no contexto das Sondagens Eleitoriais – Uma comparação de métodos. Msc Thesis, Faculdade de Economia da Universidade do Porto (2010)Google Scholar
  5. 5.
    Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel Hierarchical Models. Cambridge University Press, Cambridge (2007)Google Scholar
  6. 6.
    Gelman, A., Little, T.C.: Poststratication into many categories using hierarchical logistic regression. Surv. Methodol. 23, 127–135 (1997)Google Scholar
  7. 7.
    INE - Instituto Nacional de Estatítica: Censos 2001 - XIV Recenseamento Geral da População e IV Recenseamento Geral da Habitação, Resultados Definitivos, Lisboa (2002)Google Scholar
  8. 8.
    Lax, P.: How should we estimate public opinion in the states? Am. J. Polit. Sci. 53, 107–121 (2009)Google Scholar
  9. 9.
    Leeuw, J., Meijer, E. (eds.): Handbook of Multilevel Analysis. Springer, New York (2008)Google Scholar
  10. 10.
    Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D.: WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10, 325–337 (2000)Google Scholar
  11. 11.
    Park, D., Gelman, A., Bafumi, J.: Bayesian multilevel estimation with poststratification: state-level estimates from national polls. Polit. Anal. 12, 375–385 (2004)Google Scholar
  12. 12.
    Pratesi, M., Salvati, N.: Small area estimation: the EBLUP estimator based on spatially correlated random area effects. Stat. Methods Appl. 17, 113–141 (2008)Google Scholar
  13. 13.
    R Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing (2010)Google Scholar
  14. 14.
    Rao, C.R.: Small Area Estimation. Wiley, New Jersey (2003)Google Scholar
  15. 15.
    Snijders, T., Bosker, R.: An Introduction to Basic and Advanced Multilevel Modeling. Sage, Beverly Hills (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculdade de Economia do PortoPortoPortugal
  2. 2.LIAAD – INESC Porto, L.A and Faculdade de Economia da Universidade do PortoPortoPortugal

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