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

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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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How many subjects does it take to do a regression analysis
Green, S. B. (1991) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr2603_7 · Cited by 3,916 · cited 4×
“Numerous rules of thumb abound, often of mysterious origin (listed in Green, 1991). The considerable variability of these rules is probably attributable in part to the ambiguity of the question; sample size prescriptions have been made for a variety of purposes. Green (1991) made suggestions based on power analyses fo…”
BackgroundInfluential
Practical Assessment, Research & Evaluation, 2000-2001.
Rudner, L. M., & Schafer, W. D. (2001) · Practical assessment, research & evaluation · Cited by 3,905
Simple Heuristics That Make Us Smart
Gigerenzer, G., & Todd, P. M. (1999) · Cited by 3,563
More Comments on C P
Mallows, C. L. (1995) · Technometrics · doi:10.1080/00401706.1995.10484370 · Cited by 2,622
Some Comments on C P
Mallows, C. L. (1973) · Technometrics · doi:10.2307/1267380 · Cited by 1,978
Linear models in decision making.
Dawes, R. M., & Corrigan, B. (1974) · Psychological Bulletin · doi:10.1037/h0037613 · Cited by 1,887
Self-citation
Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: the clinical -statistical controversy
Grove, W. M., & Meehl, P. E. (1996) · Psychology, Public Policy, and Law · doi:10.1037/1076-8971.2.2.293 · Cited by 1,457
Psychological Science Can Improve Diagnostic Decisions
Swets, J. A., Dawes, R. M., & Monahan, J. T. (2000) · Gothic.net · doi:10.1111/1529-1006.001 · Cited by 882
Self-citation
Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice
Hammond, K. R. (1996) · doi:10.1093/oso/9780195097344.001.0001 · Cited by 851
Estimating coefficients in linear models: It don't make no nevermind.
Wainer, H. (1976) · Psychological Bulletin · doi:10.1037/0033-2909.83.2.213 · Cited by 657

Show all 20 references

Research works printed but not linked: 8

Carroll B, 1999, Total football II: The official encyclopedia of the national football league
Goldberg LR, 1972, Multivariate Behavioral Research Monograph, 7
Guion RM, 1965, Personnel testing
Mallows CL, 1973, Technometrics, 15, 661
Marks MR, 1966, Paper presented at the meeting of the American Psychological Association
Miller WE, 1999, National Election Studies, 1988 Super Tuesday Study [dataset]
Nunnally JC, 1994, Psychometric theory
Swets JA, 2000, Psychological Sciences in the Public Interest, 1

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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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