CLASSIFICATION OF DOCUMENTS INVOLVES THREE DISTINCT PROCESSES-- (1) DEFINING A STRUCTURE OF CATEGORIES, (2) DETERMINING A BASIS FOR A CLASSIFICATION DECISION, AND (3) CLASSIFYING DOCUMENTS INTO CATEGORIES. OF THE THREE COMPUTER TECHNIQUES ARE DISCUSSED FOR THE LAST TWO. A WORD SELECTION MEASURE IS USED TO DELETE THOSE TERMS IN THE DOCUMENT THAT…

Most successful vocational education programs are identified in 16 community colleges through the use of the Delphi method. The design provided for a reliability check on the Delphi technique through the use of two independent Delphi panels on each campus. Hard data on 36 "most successful" and 36 "other" programs on 12 campuses…

Presents a method for identifying indexing terms from word stems, using discriminant analysis to distinguish terms which refer to topics from terms which do not refer to topics. A test of the method on the Harris Survey Question database is discussed. Included are 11 data tables and a reference list. (Author/JL)

Employed a discriminant analysis with a number of instruments to determine their relative power in classifying hyperactive children. Results indicated that only nine of 27 measures used had the potential to discriminate hyperactive from normal children. Only three measures were needed in the discriminant equation for accurate classification.…

A study of the ability of different models--including the classification techniques of discriminant analysis, logistic regression, and neural networks--to predict the academic success of master's degree students in business administration suggests that prediction is difficult, but that classification and nonparametric techniques may be…

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…

Linear classification functions (LCFs) arise in a predictive discriminant analysis for the purpose of classifying experimental units into criterion groups. The relative contribution of the response variables to classification accuracy may be based on LCF-variable correlations for each group. It is proved that, if the raw response measures are…

Used discriminant analysis in an attempt to differentiate nonviolent and violent families as a function of variables said to be significant predictors of family violence in a decade review by Gelles. Results indicate that the predictor variables fail to account consistently for differences among the groups. (Author)

A linear classification rule (used with equal covariance matrices) was contrasted with a quadratic rule (used with unequal covariance matrices) for accuracy of internal and external classification. The comparisons were made for seven situations which resulted from combining three data conditions (equal and unequal covariance matrices, minimal and…

Discriminant analysis is reviewed in terms of: (1) formulations, (2) interpretations, (3) uses, (4) issues and problems in applications, (5) recent developments and conceptualizations, and (6) general references and computer programs. Four aspects of a discriminant analysis are considered. They are: (1) separation: determining intergroup…

Discusses ways to improve quality of retrieval systems that depend on use of truncated words or quasi-word stems as indexing vocabulary. Problems of generalizability and stability of discriminant function analysis for selecting good topical terms in database drawn from abstracts of Harris Survey press releases are addressed. References are cited.…

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…

A technique for classifying hospitals on a multidimensional basis was developed. Three major sets of attributes were examined: patient case mix, facility mix, and personnel mix. Using multivariate techniques (factor analysis, cluster analysis, and discriminant analysis), 83 variables were examined for 547 hospitals. A total of six factors were…