IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v159y1996i1p21-28.html
   My bibliography  Save this article

How Might Opinion Polls be Improved?: The Case for Probability Sampling

Author

Listed:
  • Peter Lynn
  • Roger Jowell

Abstract

A classification of sources of error in opinion polling is presented, and related to investigations of observed error in the polls at the time of the 1992 general election. This leads directly to implications for reducing the potential for error in future. It is argued that quota sampling bias, incorporating both selection bias and unit non‐response bias, is a major source of error. The adoption of probability sampling methods is proposed as a way to reduce this bias, and pollsters and their clients are encouraged to experiment with such methods. An example of a practical probability sampling design is described. Other sources of error which have been identified, and for which methodological improvement is suggested, are the treatment of item refusals, the treatment of those who say they ‘don't know’ for whom they will vote and the treatment of respondents' predictions of whether they will vote.

Suggested Citation

  • Peter Lynn & Roger Jowell, 1996. "How Might Opinion Polls be Improved?: The Case for Probability Sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(1), pages 21-28, January.
    • Handle: RePEc:bla:jorssa:v:159:y:1996:i:1:p:21-28
    DOI: 10.2307/2983465
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2983465
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2983465?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
    ---><---

    Citations

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


    Cited by:

    1. Shyamal Chowdhury & Lyn Squire, 2006. "Setting weights for aggregate indices: An application to the commitment to development index and human development index," Journal of Development Studies, Taylor & Francis Journals, vol. 42(5), pages 761-771.
    2. Wiśniowski, Arkadiusz & Bijak, Jakub & Forster, Jonathan J. & Smith, Peter W.F., 2019. "Hierarchical model for forecasting the outcomes of binary referenda," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 90-103.
    3. Paap, Richard & van Nierop, Erjen & van Heerde, Harald J. & Wedel, Michel & Franses, Philip Hans & Alsem, Karel Jan, 2005. "Consideration sets, intentions and the inclusion of "don't know" in a two-stage model for voter choice," International Journal of Forecasting, Elsevier, vol. 21(1), pages 53-71.

    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:bla:jorssa:v:159:y:1996:i:1:p:21-28. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.