Psychologists often have to make a diagnostic decision about a person’s group membership based upon test performance. For example, a clinical psychologist might need to determine if there is sufficient evidence to diagnose a patient as being depressed or a cognitive gerontologist might need to determine if an older adult is suffering from a natural age-related memory impairment or a more serious condition such as Alzheimer’s disease. These classification judgments can be difficult to make given natural variability within a population, as well as the inherent uncertainty of a person’s true group membership.
The classification applet is a web-based computer application that calculates cutting scores to maximize classification utility. The program takes into account population base rates and means and variances of the test score distributions, and the benefit of a correct classification and the cost of an incorrect classification. Finally, the program interactively displays the model’s underlying normal distributions, cutting scores which designate predicted group membership, and predicted classification accuracy.
The classification applet appears below. A further description of the applet is available here.
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