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The aim of clinical assessment is to gather data that allow us to reduce uncertainty regarding the probabilities of events. This is a Bayesian view of assessment that is consistent with the well-known concept of incremental validity. Conventional approaches to evaluating the accuracy of assessment methods are confounded by the choice of cutting points, by the base rates of the events, and by the assessment goal (e.g. nomothetic vs idiographic predictions). Clinical assessors need a common metric for quantifying the information value of assessment data, independent of the cutting points, base rates, or particular application. Signal detection theory (SDT) provides such a metric. We review SDT's history, concepts, and methods and provide examples of its application to a variety of assessment problems.
McFall et al. (Mon,) studied this question.