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Post-Stratification without Population Level Information on the Post-Stratifying Variable, with Application to Political Polling

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Author Info
Reilly, Cavan
Gelman, Andrew
Katz, Jonathan N.

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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 post-stratification to construct these improved estimates, but since we don't have population level information on the post-stratifying variable, we construct a model for the manner in which the post-stratifier 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.

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File URL: http://www.hss.caltech.edu/SSPapers/wp1091.pdf
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Publisher Info
Paper provided by California Institute of Technology, Division of the Humanities and Social Sciences in its series Working Papers with number 1091.

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Length: 21 pages
Date of creation: May 2000
Date of revision:
Publication status: Published:
Handle: RePEc:clt:sswopa:1091

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Postal: Working Paper Assistant, Division of the Humanities and Social Sciences, 228-77, Caltech, Pasadena CA 91125
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Postal: Working Paper Assistant, Division of the Humanities and Social Sciences, 228-77, Caltech, Pasadena CA 91125
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Related research
Keywords: Bayesian inference; post-stratification; sample surveys; State-space models.;

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This page was last updated on 2009-11-19.


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