The superiority of simple alternatives to regression for social science predictions

Dana, Jason; Dawes, Robyn M.

doi:10.3102/10769986029003317

Cited by 131Forecasting Techniques and ApplicationsStatistical Methods and ApplicationsAdvanced Statistical Methods and Models

The superiority of simple alternatives to regression for social science predictions

Dana, Jason, Dawes, Robyn M.·2004·Journal of Educational and Behavioral Statistics

DOI 10.3102/10769986029003317

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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 < .6, correlation weights were typically superior to regression coefficients, even if the sample contained 100 observations per predictor; unit weights were likewise superior to all methods if adjusted R was < .4. Correlation weights were generally the best method. It was concluded that regression is rarely useful for prediction in most social science contexts.

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Regression estimates produce ex ante forecasts that are often less accurate than forecasts from 'unit weights' models.
“However, studies have shown that regression estimates produce ex ante forecasts that are often less accurate than forecasts from 'unit weights' models.”
From Illusions in Regression Analysis (2011) · cites this paper as Dana and Dawes (2004)

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Authors: Dana, J., & Dawes, R. M.
Affiliations: Carnegie Mellon University (US)
ORCID: Dana, Jason
Venue: Journal of Educational and Behavioral Statistics
Publisher: American Educational Research Association (AERA)
Published: 2004-09
Volume: 29
Issue: 3
Pages: 317-331
ISSN: 1076-9986 (ISSN-L)
1935-1054
DOI: 10.3102/10769986029003317
OpenAlex: W2165744193
Cited by: 131
References: 20
Citations by year: 2026: 1 · 2025: 2 · 2024: 4 · 2022: 6 · 2021: 4 · 2020: 5 · 2019: 5 · 2018: 11 · 2017: 9 · 2016: 10 · 2015: 13 · 2014: 8 · 2013: 11 · 2012: 5
FWCI: 4.22
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Educational and Behavioral Statistics (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: prediction, regression coefficients, correlation weights, equal weighting, unit weight, calibration samples, coefficient selection, synthetic data

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The superiority of simple alternatives to regression for social science predictions

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