Poststratification without population level information on the poststratifying variable with application to political polling

Cavan Reilly, Andrew Gelman, Jonathan Katz

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

We investigate the construction of more precise estimates of a collection of population means using information about a related variable in the context of repeated sample surveys. The method is illustrated using poll results concerning presidential approval rating (our related variable is political party identification). We use poststratification to construct these improved estimates, but because we do not have population level information on the poststratifying variable, we construct a model for the manner in which the poststratifier develops over time. In this manner, we obtain more precise estimates without making possibly untenable assumptions about the dynamics of our variable of interest, the presidential approval rating.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalJournal of the American Statistical Association
Volume96
Issue number453
DOIs
StatePublished - Mar 1 2001

Keywords

  • Bayesian inference
  • Poststratification
  • Sample surveys
  • State-space models

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