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

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  • Buttice, Matthew K.
  • Highton, Benjamin

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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  • Buttice, Matthew K. & Highton, Benjamin, 2013. "How Does Multilevel Regression and Poststratification Perform with Conventional National Surveys?," Political Analysis, Cambridge University Press, vol. 21(4), pages 449-467.
    • Handle: RePEc:cup:polals:v:21:y:2013:i:04:p:449-467_01
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    Cited by:

    1. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    2. Matto Mildenberger & Peter Howe & Erick Lachapelle & Leah Stokes & Jennifer Marlon & Timothy Gravelle, 2016. "The Distribution of Climate Change Public Opinion in Canada," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
    3. Jinan N. Allan & Joseph T. Ripberger & Wesley Wehde & Makenzie Krocak & Carol L. Silva & Hank C. Jenkins‐Smith, 2020. "Geographic Distributions of Extreme Weather Risk Perceptions in the United States," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2498-2508, December.
    4. Matto Mildenberger & Jennifer R. Marlon & Peter D. Howe & Anthony Leiserowitz, 2017. "The spatial distribution of Republican and Democratic climate opinions at state and local scales," Climatic Change, Springer, vol. 145(3), pages 539-548, December.
    5. 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.
    6. Bullinger, Lindsey Rose & Wing, Coady, 2019. "How many children live with adults with opioid use disorder?," Children and Youth Services Review, Elsevier, vol. 104(C), pages 1-1.
    7. 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.
    8. Roberto Cerina & Raymond Duch, 2021. "Polling India via regression and post-stratification of non-probability online samples," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-34, November.

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