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How Does Multilevel Regression and Poststratification Perform with Conventional National Surveys?

Published online by Cambridge University Press:  04 January 2017

Matthew K. Buttice
Affiliation:
California Research Bureau, California State Library, Sacramento, CA 94237-0001 e-mail: matthew.buttice@library.ca.gov
Benjamin Highton*
Affiliation:
Department of Political Science, University of California, Davis, CA 95616-8682
*
e-mail: bhighton@ucdavis.edu (corresponding author)
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Abstract

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Multilevel regression and poststratification (MRP) is a method to estimate public opinion across geographic units from individual-level survey data. If it works with samples the size of typical national surveys, then MRP offers the possibility of analyzing many political phenomena previously believed to be outside the bounds of systematic empirical inquiry. Initial investigations of its performance with conventional national samples produce generally optimistic assessments. This article examines a larger number of cases and a greater range of opinions than in previous studies and finds substantial variation in MRP performance. Through empirical and Monte Carlo analyses, we develop an explanation for this variation. The findings suggest that the conditions necessary for MRP to perform well will not always be met. Thus, we draw a less optimistic conclusion than previous studies do regarding the use of MRP with samples of the size found in typical national surveys.

Information

Type
Research Article
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology