The authors consider the problem of classifying an unknown observation into 1 of several populations by using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for classification procedures that can be used regardless of the group-conditional…

Tests for and identifies populations within a family of electronics industries. Data include 669 United States and 144 Japanese electronics firms. Demonstrates the relevance of a general organizational classification for explaining how different natural selection processes affect different populations. (75 references) (MLF)

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

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…

Autism Behavior Checklists were completed for 52 autistic individuals (ages 2-31) and 62 nonautistic individuals, and data were analyzed using signal detection methodology. A single item (looks "through" people) combined with one of five other items discriminated as well as the sum scores, and further combinations did not improve…

The Autism Behavior Checklist was completed for 67 autistic and 56 mentally retarded and learning-disabled children (ages 6-15). The checklist was effective in classifying children when using either the full scale or only the more highly weighted items. Results did not support a single unidimensional scale or subdividing the checklist into five…

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…