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Statistical models of judgment 449
involves following valid principles but following them poorly these valid
principles will be abstracted by the model.
Goldberg 40 reported an intensive study of clinical judgment, pitting
experienced and inexperienced clinicians against linear models and a
variety of non-linear or configural models in the psychotic/neurotic
prediction task. He was led to conclude that Meehl chose the wrong task
for testing the clinicians’ purported ability to utilize complex configural
relationships. The clinicians achieved a 62% rate, while the simple linear
composite achieved 70%. A 50% hit rate could have been achieved
by chance as the criterion base rate was approximately 50% neurotic,
50% psychotic.
Dawes and Corrigan 41 have called the replacement of the decision
maker by his model ‘bootstrapping’. Belief in the efficacy of bootstrapping
is based on a comparison of the validity of the linear model of the
judge with the validity of his or her holistic judgments. However, as
Dawes and Corrigan point out, that is only one of two logically possible
comparisons. The other is between the validity of the linear model or the
judge and the validity of linear models in general. That is, to demonstrate
that bootstrapping works because the linear model catches the essence
of a judge’s expertise and at the same time eliminates unreliability, it
is necessary to demonstrate that the weights obtained from an analysis
of the judge’s behavior are superior to those that might be obtained in
another way – for example, obtained randomly.
Dawes and Corrigan constructed semi-random linear models to predict
the criterion. The sign of each predictor variable was determined
on an aprioribasis so that it would have a positive relationship to
the criterion.
On average, correlations between the criterion and the output predicted
from the random models were higher than those obtained from
the judge’s models. Dawes and Corrigan also investigated equal weighting
and discovered that such weighting was even better than the models
of the judges or the random linear models. In all cases, equal weighting
was superior to the models based on judges’ behavior.
Dawes and Corrigan concluded that the human decision maker need
specify with very little precision the weightings to be used in the
decision – at least in the context studied. What must be specified is the
variables to be utilized in the linear additive model. It is precisely this
knowledge of ‘what to look for’ in reaching a decision that is the province
of the expert clinician.
The distinction between knowing what to look for and the ability
to integrate information is illustrated in a study by Einhorn. 42 Expert