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| Multivariate Behavioral… | 7 |
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| Journal Articles | 5 |
| Reports - Evaluative | 5 |
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Peer reviewedvan Buuren, Stef; de Leeuw, Jan – Multivariate Behavioral Research, 1992
Application of equality constraints on the categories of a variable is a simple and useful extension of multiple correspondence analysis. Equality is an easy way to incorporate prior knowledge. A procedure to deal with unequal category numbers and with subsets of variables is outlined and illustrated. (SLD)
Descriptors: Classification, Knowledge Level, Mathematical Models, Multivariate Analysis
Peer reviewedMillsap, Roger E.; Meredith, William – Multivariate Behavioral Research, 1991
Mathematical relationships between three-mode component analysis and stationary component analysis are explored. Theorems are presented giving constraints that must be satisfied for equivalency between component representations provided by the methods. In general, the two approaches give mathematically distinct representations. (SLD)
Descriptors: Classification, Equations (Mathematics), Longitudinal Studies, Mathematical Models
Peer reviewedHuberty, Carl J.; Curry, Allen R. – Multivariate Behavioral Research, 1978
Classification is a procedure through which individuals are classified as being members of a particular group based on a variety of independent variables. Two methods of makin such classifications are discussed; the quadratic method is seen to be superior to the linear under certain constraints. (JKS)
Descriptors: Analysis of Covariance, Classification, Discriminant Analysis, Groups
Peer reviewedLevin, Joseph – Multivariate Behavioral Research, 1974
Descriptors: Classification, Correlation, Factor Analysis, Mathematical Models
Peer reviewedJoachimsthaler, Erich A.; Stam, Antonie – Multivariate Behavioral Research, 1990
Mathematical programing formulas are introduced as new approaches to solve the classification problem in discriminant analysis. The research literature is reviewed, and an illustration using a real-world classification problem is provided. Issues relevant to potential uses of these formulations are discussed. (TJH)
Descriptors: Classification, Discriminant Analysis, Equations (Mathematics), Literature Reviews
Peer reviewedSchweizer, 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
Peer reviewedKloot, 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


