Pattern Analysis of an Actuarial Strategy for Computerized Diagnosis of Childhood Exceptionality

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Abstract
The construct and predictive validity of a computerized diagnostic decision-making system, known as multidimensional actuarial classification (MAC), is investigated through a unique pattern analysis procedure. The MAC diagnostic system applies a series of actuarial decision rules to differentially classify children's mental retardation, learning disabilities, academic achievement problems, social and behavioral maladjustment, and other related disorders. Using a selected set of the decision routines available through MAC, the computerized system was used to classify 200 children referred for psychological services. A cross-products agreement matrix was constructed from the resultant multidimensional classifications produced for each child and was subjected thereafter to factor pattern analysis. Seven distinct and recognizable patterns emerged, supporting the verity and utility of the systems-actuarial approach to classification of child exceptionality. Moreover, pattern scores indicating the extent of children's similarity to the classification pattern named learning disability were found to be significantly good predictors of the actual severity of learning impairment.