Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-18T21:13:36.655Z Has data issue: false hasContentIssue false
  • English
  • Français

BARP: Improving Mister P Using Bayesian Additive Regression Trees

Published online by Cambridge University Press:  06 August 2019

JAMES BISBEE Open the ORCID record for JAMES BISBEE [Opens in a new window]
JAMES BISBEE*
Affiliation:
New York University
*
*James Bisbee, PhD Candidate, NYU Wilf Family Department of Politics, New York University, james.bisbee@nyu.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units. However, innovations in nonparametric regularization methods can further improve the researcher’s ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more flexible methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a nonparametric approach called Bayesian additive regression trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regularization methods to extrapolate opinion data to target geographical units. I provide an R package that implements the BARP method.

Type
Letter
Information
American Political Science Review , Volume 113 , Issue 4 , November 2019 , pp. 1060 - 1065
Copyright
Copyright © American Political Science Association 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

I am grateful to Neal Beck, Patrick Egan, Shane Mahon, Keith McCart, Kevin Munger, Thiago Moreira da Silva, and Drew Dimmery for their helpful feedback. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/LMW871.

References

REFERENCES

Bartels, Larry M. 1991. “Constituency Opinion and Congressional Policy Making: The Reagan Defense Buildup.” American Political Science Review 85 (2): 457–74.CrossRefGoogle Scholar
Buttice, Matthew K., and Highton, Benjamin. 2013. “How Does Multilevel Regression and Poststratification Perform with Conventional National Surveys?” Political Analysis 21 (4): 449–67.CrossRefGoogle Scholar
Caughey, Devin, and Warshaw, Christopher. 2019. “Public Opinion in Subnational Politics.” The Journal of Politics 81 (1): 352–63. URL: https://doi.org/10.1086/700723.CrossRefGoogle Scholar
Chipman, Hugh A., George, Edward I., and McCulloch, Robert E.. 2010. “BART: Bayesian Additive Regression Trees.” The Annals of Applied Statistics 4 (1): 266–98.CrossRefGoogle Scholar
Gelman, Andrew. 2013. “Last Word on Mister P (For Now).” URL: https://statmodeling.stat.columbia.edu/2013/10/15/last-word-on-mister-p-for-now/.Google Scholar
Gelman, Andrew. 2018. “Regularized Prediction and Poststratification (The Generalization of Mister P).” URL: https://statmodeling.stat.columbia.edu/2018/05/19/regularized-prediction-poststratification-generalization-mister-p/.Google Scholar
Gelman, Andrew, and Little, Thomas C.. 1997. “Poststratification into many Categories Using Hierarchical Logistic Regression.” Survey Methodology 23 (2): 127–35.Google Scholar
Ghitza, Yair, and Gelman, Andrew. 2013. “Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups.” American Journal of Political Science 57 (3): 762–76.CrossRefGoogle Scholar
Hanretty, Chris, Lauderdale, Benjamin E., and Vivyan, Nick. 2018. “Comparing Strategies for Estimating Constituency Opinion from National Survey Samples.” Political Science Research and Methods 6 (3): 571–91.CrossRefGoogle Scholar
Hare, Christopher, and Monogan, James E.. 2018. “The Democratic Deficit on Salient Issues: Immigration and Healthcare in the States.” Journal of Public Policy: 1–28. Published online 22 October 2018.CrossRefGoogle Scholar
Kapelner, Adam, and Bleich, Justin. 2013. “bartMachine: Machine Learning with Bayesian Additive Regression Trees.” arXiv preprint arXiv:1312.2171.Google Scholar
Lax, Jeffrey R., and Phillips, Justin H.. 2009. “How Should We Estimate Public Opinion in the States?” American Journal of Political Science 53 (1): 107–21.CrossRefGoogle Scholar
Lax, Jeffrey R., and Phillips, Justin H.. 2012. “The Democratic Deficit in the States.” American Journal of Political Science 56 (1): 148–66.CrossRefGoogle Scholar
Linero, Antonio R. 2017. “A Review of Tree-Based Bayesian Methods.” Communications for Statistical Applications and Methods 24 (6): 543–59.CrossRefGoogle Scholar
Polley, Eric C., and Van der Laan, Mark J.. 2015. SuperLearner: Super Learner Prediction. (Package Version 2.0-15). Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Trangucci, Rob, Ali, Imad, Gelman, Andrew, and Rivers, Doug. 2018. “Voting Patterns in 2016: Exploration Using Multilevel Regression and Poststratification (MRP) on Pre-election Polls.” arXiv preprint arXiv:1802.00842.Google Scholar
Warshaw, Christopher, and Rodden, Jonathan. 2012. “How Should We Measure District-Level Public Opinion on Individual Issues?” The Journal of Politics 74 (1): 203–19.CrossRefGoogle Scholar
Supplementary material: Link

Bisbee Dataset

Dataset

https://doi.org/10.7910/DVN/LMW871
Link
Supplementary material: PDF

Bisbee supplementary material

Bisbee supplementary material 1

Download Bisbee supplementary material(PDF)
PDF 960.4 KB