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Hanges, Paul J.; And Others – Educational and Psychological Measurement, 1991
Whether it is possible to develop a classification function that identifies the underlying range restriction from sample information alone was investigated in a simulation. Results indicate that such a function is possible. The procedure was found to be relatively accurate, robust, and powerful. (SLD)
Descriptors: Classification, Computer Simulation, Equations (Mathematics), Mathematical Models
Sadek, Ramses F.; Huberty, Carl J. – 1992
Using computer simulation data, the effect of a single global outlier in two-group classification analysis was explored in terms of the outcome variables of change in classification results (PCHNG), change in misclassification rate (MISDIF), and change in precision of misclassification rate estimation. The precision of misclassification rate…
Descriptors: Change, Classification, Computer Simulation, Estimation (Mathematics)

Schweizer, Karl – Multivariate Behavioral Research, 1991
A mathematical formula is introduced for the effect of integrating data. A method is then derived to eliminate the effect from correlations of variables, including mean composites, thus allowing for a clustering algorithm that requires allocation of variables according to the magnitude of their correlations. Examples illustrate the procedure. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Computer Simulation

Kloot, Willem A. van der; Herk, Hester van – Multivariate Behavioral Research, 1991
Two sets of real sorting data from 50 college students are used to compare results of multidimensional scaling of raw co-occurrence frequencies or dissimilarity measures (D) and profile distances (delta) to determine which yields a better representation of the underlying structure of 2 sets of stimuli. Slight differences are discussed. (SLD)
Descriptors: Classification, Cognitive Processes, College Students, Comparative Analysis
Owston, Ronald D. – 1979
The development of a probabilistic model for validating Gange's learning hierarchies is described. Learning hierarchies are defined as paired networks of intellectual tasks arranged so that a substantial amount of positive transfer occurs from tasks in a lower position to connected ones in a higher position. This probabilistic validation technique…
Descriptors: Associative Learning, Classification, Difficulty Level, Mathematical Models