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The robust beauty of improper linear models in decision making (1979)
| Venue: | American Psychologist |
| Citations: | 267 - 1 self |
BibTeX
@ARTICLE{Dawes79therobust,
author = {Robyn M. Dawes},
title = {The robust beauty of improper linear models in decision making},
journal = {American Psychologist},
year = {1979},
pages = {571--582}
}
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Abstract
ABSTRACT: Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in Paul Meehl's book on clinical versus statistical prediction—and a plethora of research stimulated in part by that book—all indicates that when a numerical criterion variable (e.g., graduate grade point average) is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method; for example, they may be obtained on the basis of intuition, derived from simulating a clinical judge's predictions, or set to be equal. This article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit (i.e., equal) weighting is quite robust for making such predictions. The article discusses, in some detail, the application of unit weights to decide what bullet the Denver Police Department should use. Finally, the article considers commonly raised technical, psychological, and ethical resistances to using linear models to make important social decisions and presents arguments that could weaken these resistances.
Keyphrases
improper linear model proper linear model decision making robust beauty predictor variable clinical intuition paul meehl regression analysis numerical criterion nonoptimal method important social decision clinical versus statistical prediction numerical criterion variable denver police department unit weight linear model point average present argument ethical resistance clinical judge numerical predictor numerical predictor variable discriminant function analysis standard regression analysis
