IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/u3ekq_v1.html
   My bibliography  Save this paper

Improving Small-Area Estimates of Public Opinion by Calibrating to Known Population Quantities

Author

Listed:
  • Marble, William
  • Clinton, Joshua

Abstract

Multilevel regression and poststratification is widely used to estimate opinion in small geographies and to adjust unrepresentative surveys — and has become a mainstay in the study of dyadic representation. Yet, errors generated by nonignorable non-response and modeling uncertainty make discrepancies between public opinion and policy outcomes difficult to interpret. We propose a principled, data-driven method to leverage auxiliary quantities with known marginal distributions — e.g., election outcomes — to improve estimates of policy attitudes. Our method estimates the geographic correlation between the auxiliary variables and the outcomes of interest, then adjusts the estimates using observed errors in the auxiliary variable estimate. We illustrate our approach using a pre-election poll measuring support for an abortion referendum, finding that the method reduces county-level error by two-thirds. We find similarly dramatic improvements estimating precinct-level opinion. Our method generates new possibilities for accurately estimating policy attitudes at previously unattainable levels of geographic resolution.

Suggested Citation

  • Marble, William & Clinton, Joshua, 2024. "Improving Small-Area Estimates of Public Opinion by Calibrating to Known Population Quantities," SocArXiv u3ekq_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:u3ekq_v1
    DOI: 10.31219/osf.io/u3ekq_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6658e0d20f8c8002aa3c92cc/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/u3ekq_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rosenman, Evan T. R. & McCartan, Cory & Olivella, Santiago, 2023. "Recalibration of Predicted Probabilities Using the “Logit Shift”: Why Does It Work, and When Can It Be Expected to Work Well?," Political Analysis, Cambridge University Press, vol. 31(4), pages 651-661, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:u3ekq_v1. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.