Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…

We examined how strongly motivation, metacognition, and psychopathology acted as predictors of learning disabilities (LD). The results from five studies suggested that level of motivation (as shown through self-efficacy, motivational force, task avoidance, goal commitment, or self-concept) was highly accurate in classifying students with or at…

Seven discriminant analyses were performed with significant group separation occurring for six of these. Classification analyses indicated that using the 10 major subtests produced accurate classification in approximately 71 percent of cases. Results were discussed in the light of previous research methodologies and the debate as to the…

Two discriminant function analyses were conducted to determine the cognitive/educational profile which differentiated 4 groups of 68 elementary/secondary level students: attention deficit disorder (ADD), with and without medication; learning disabilities (LD); and nonhandicapped. By treating the LD and nonmedicated ADD subjects as one group, a…

This paper examines cognitive processing problems associated with attention deficit disorders (ADD) and their relationship to learning disabilities in elementary and secondary students. Children with ADD, medicated (N=20) and unmedicated (N=21), were compared on the Raven test of Progressive Matrices and other tests with children who had been…

A recategorization system developed by Bannatyne to categorize subtest scatter from the Wechsler Intelligence Scale for Children-Revised (WISC-R) is used as an alternative to isolated analysis of each individual subtest. Reading disabled, learning disabled, and educable mentally handicapped students are categorized according to their performance…

This study found that a linear discriminant function was able, with 73 percent accuracy, to distinguish between learning-disabled dropouts (n=213) and learning-disabled graduates (n=92). The discriminant function was composed of six variables--student ethnicity, reading ability, family intactness, family socioeconomic status, school transfers, and…