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The Superiority of Simple Alternatives to Regression for Social Science Predictions

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Graham Elliott, 2017. "Forecast combination when outcomes are difficult to predict," Empirical Economics, Springer, vol. 53(1), pages 7-20, August.
    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.

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