IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v29y2004i3p317-331.html
   My bibliography  Save this article

The Superiority of Simple Alternatives to Regression for Social Science Predictions

Author

Listed:
  • Jason Dana
  • Robyn M. Dawes

Abstract

Some simple, nonoptimized coefficients (e.g., correlation weights, equal weights) were pitted against regression in extensive prediction competitions. After drawing calibration samples from large supersets of real and synthetic data, the researchers observed which set of sample-derived coefficients made the best predictions when applied back to the superset. When adjusted R from the calibration sample was

Suggested Citation

  • Jason Dana & Robyn M. Dawes, 2004. "The Superiority of Simple Alternatives to Regression for Social Science Predictions," Journal of Educational and Behavioral Statistics, , vol. 29(3), pages 317-331, September.
    • Handle: RePEc:sae:jedbes:v:29:y:2004:i:3:p:317-331
    DOI: 10.3102/10769986029003317
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986029003317
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986029003317?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. Edward E. Rigdon, 2013. "Lee, Cadogan, and Chamberlain: an excellent point . . . But what about that iceberg?," AMS Review, Springer;Academy of Marketing Science, vol. 3(1), pages 24-29, March.
    2. Graham Elliott, 2017. "Forecast combination when outcomes are difficult to predict," Empirical Economics, Springer, vol. 53(1), pages 7-20, August.
    3. Armstrong, J. Scott & Graefe, Andreas, 2011. "Predicting elections from biographical information about candidates: A test of the index method," Journal of Business Research, Elsevier, vol. 64(7), pages 699-706, July.
    4. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.
    5. David V. Budescu & Hsiu-Ting Yu, 2006. "To Bayes or Not to Bayes? A Comparison of Two Classes of Models of Information Aggregation," Decision Analysis, INFORMS, vol. 3(3), pages 145-162, September.
    6. Armstrong, J. Scott, 2011. "Illusions in Regression Analysis," MPRA Paper 81663, University Library of Munich, Germany.

    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:sae:jedbes:v:29:y:2004:i:3:p:317-331. 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: SAGE Publications (email available below). General contact details of provider: .

    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.