IDEAS home Printed from https://ideas.repec.org/a/cup/pscirm/v4y2016i01p97-121_00.html
   My bibliography  Save this article

Measuring State and District Ideology with Spatial Realignment

Author

Listed:
  • Monogan, James E.
  • Gill, Jeff

Abstract

We develop a new approach for modeling public sentiment by micro-level geographic region based on Bayesian hierarchical spatial modeling. Recent production of detailed geospatial political data means that modeling and measurement lag behind available information. The output of the models gives not only nuanced regional differences and relationships between states, but more robust state-level aggregations that update past research on measuring constituency opinion. We rely here on the spatial relationships among observations and units of measurement in order to extract measurements of ideology as geographically narrow as measured covariates. We present an application in which we measure state and district ideology in the United States in 2008.

Suggested Citation

  • Monogan, James E. & Gill, Jeff, 2016. "Measuring State and District Ideology with Spatial Realignment," Political Science Research and Methods, Cambridge University Press, vol. 4(1), pages 97-121, January.
    • Handle: RePEc:cup:pscirm:v:4:y:2016:i:01:p:97-121_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S2049847015000059/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jason S. Byers & Jeff Gill, 2022. "Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions," Mathematics, MDPI, vol. 10(21), pages 1-23, November.
    2. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.

    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:cup:pscirm:v:4:y:2016:i:01:p:97-121_00. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ram .

    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.