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Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups

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  • Yair Ghitza
  • Andrew Gelman

Abstract

Using multilevel regression and poststratification (MRP), we estimate voter turnout and vote choice within deeply interacted subgroups: subsets of the population that are defined by multiple demographic and geographic characteristics. This article lays out the models and statistical procedures we use, along with the steps required to fit the model for the 2004 and 2008 presidential elections. Though MRP is an increasingly popular method, we improve upon it in numerous ways: deeper levels of covariate interaction, allowing for nonlinearity and nonmonotonicity, accounting for unequal inclusion probabilities that are conveyed in survey weights, postestimation adjustments to turnout and voting levels, and informative multidimensional graphical displays as a form of model checking. We use a series of examples to demonstrate the flexibility of our method, including an illustration of turnout and vote choice as subgroups become increasingly detailed, and an analysis of both vote choice changes and turnout changes from 2004 to 2008.

Suggested Citation

  • Yair Ghitza & Andrew Gelman, 2013. "Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups," American Journal of Political Science, John Wiley & Sons, vol. 57(3), pages 762-776, July.
    • Handle: RePEc:wly:amposc:v:57:y:2013:i:3:p:762-776
    DOI: 10.1111/ajps.12004
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    1. Abbie Turiansky & Erin Lipman & Arif Mamun & Cullen Seaton & Jonathan Gellar & Sarah Hughes, "undated". "Financial Inclusion and Resilience to COVID-19 Economic Shocks: Evidence from Kenya, Nigeria, and Uganda," Mathematica Policy Research Reports a7cc410219f848528f77e294d, Mathematica Policy Research.
    2. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    3. Christopher Claassen & Richard Traunmüller, 2020. "Improving and Validating Survey Estimates of Religious Demography Using Bayesian Multilevel Models and Poststratification," Sociological Methods & Research, , vol. 49(3), pages 603-636, August.
    4. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
    5. Nicholas Beauchamp, 2017. "Predicting and Interpolating State‐Level Polls Using Twitter Textual Data," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 490-503, April.
    6. Andrew Gelman & Bob Carpenter, 2020. "Bayesian analysis of tests with unknown specificity and sensitivity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1269-1283, November.
    7. Margaret Weden & Christine Peterson & Jeremy Miles & Regina Shih, 2015. "Evaluating Linearly Interpolated Intercensal Estimates of Demographic and Socioeconomic Characteristics of U.S. Counties and Census Tracts 2001–2009," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(4), pages 541-559, August.
    8. Rob Trangucci & Imad Ali & Andrew Gelman & Doug Rivers, 2018. "Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on pre-election polls," Papers 1802.00842, arXiv.org, revised Mar 2018.
    9. Marina Christofoletti & Tânia R. B. Benedetti & Felipe G. Mendes & Humberto M. Carvalho, 2021. "Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    10. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
    11. Yonatan Ben-Shalom & Ignacio Martinez & Mariel Finucane, "undated". "Risk of Workforce Exit Due to Disability: State Differences in 2003–2016," Mathematica Policy Research Reports 8aed03744a06419dbda68be8c, Mathematica Policy Research.
    12. Kevin Dayaratna & Jesse Crosson & Chandler Hubbard, 2022. "Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout," Stats, MDPI, vol. 5(4), pages 1-21, November.

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