General Conditions for the Success of Bootstrapping Models

Abstract

Linear models which fit regression equations to clinical judgments. then use the fitted parts of judgments as "bootstrapped" judgments, have outperformed clinical judgments in many tasks. Empirically, the phenomenon has been pervasive, but general conditions for the success of bootstrapping models have never been explicitly linked to cross-study data. This link, combined with psychologically plausible evidence about the relationships between judgmental variables, shows that bootstrapping will improve judgments slightly under almost any realistic task conditions. This result allows one to apply bootstrapping blindly in cases where criterion information is missing or vague (precisely the cases where bootstrapping models are useful), and be confident that predictions are being improved. A simple comparison of bootstrapping models with equal weighting models is also made, but general conditions for relative success of those two models are not specified.

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