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

  • Matthew K. Buttice (a1) and Benjamin Highton (a2)

Abstract

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.

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e-mail: bhighton@ucdavis.edu (corresponding author)

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Authors' note: We appreciate helpful advice from Kyle Joyce, Eric McGhee, Matt Pietryka, and Walt Stone on this article. Buttice began work on this project while at UC Davis and finished while at the California Research Bureau. The research results and conclusions expressed in this article do not necessarily reflect the views of the California Research Bureau or California State Library. The replication archive for this article is available at the Political Analysis Dataverse as Buttice and Highton (2013). Supplementary materials for this article are available on the Political Analysis Web site.

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References

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

  • Matthew K. Buttice (a1) and Benjamin Highton (a2)

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