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Simmen, Martin W. – Multivariate Behavioral Research, 1996
Several methodological issues in the multidimensional scaling of coarse dissimilarities were studied, examining whether it was better to scale dissimilarity data directly or to scale a new matrix derived from the original by row comparisons. Findings support an alternative row-comparison measure based on the Jacard coefficient. (SLD)
Descriptors: Comparative Analysis, Matrices, Multidimensional Scaling, Research Methodology
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Trendafilov, Nickolay T. – Multivariate Behavioral Research, 1994
An alternative to the PROMAX exploratory method is presented for constructing a target matrix in Procrustean rotation in factor analysis. A technique is proposed based on vector majorization. The approach is illustrated with several standard numerical examples. (SLD)
Descriptors: Equations (Mathematics), Factor Analysis, Factor Structure, Matrices
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Schaffer, Catherine M.; Green, Paul E. – Multivariate Behavioral Research, 1996
The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…
Descriptors: Cluster Analysis, Comparative Analysis, Marketing, Matrices
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Jackson, Douglas N. – Multivariate Behavioral Research, 1975
A method is proposed for the evaluation of the degree to which trait measures show stability across diverse methods of measurement. The technique is illustrated using multitrait-multimethod matrices from personality assessment, which yield trait-specific factors. (Author/BJG)
Descriptors: Factor Analysis, Matrices, Measurement Techniques, Orthogonal Rotation
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Harrop, John W.; Velicer, Wayne F. – Multivariate Behavioral Research, 1985
Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)
Descriptors: Computer Simulation, Data Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Velicer, Wayne F.; McDonald, Roderick P. – Multivariate Behavioral Research, 1991
The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)
Descriptors: Cross Sectional Studies, Data Analysis, Generalizability Theory, Mathematical Models
Peer reviewed Peer reviewed
Spiegel, Douglas K. – Multivariate Behavioral Research, 1986
Tau, Lambda, and Kappa are measures developed for the analysis of discrete multivariate data of the type represented by stimulus response confusion matrices. The accuracy with which they may be estimated from small sample confusion matrices is investigated by Monte Carlo methods. (Author/LMO)
Descriptors: Mathematical Models, Matrices, Monte Carlo Methods, Multivariate Analysis
Peer reviewed Peer reviewed
Stelzl, Ingeborg – Multivariate Behavioral Research, 1986
Since computer programs have been available for estimating and testing linear causal models, these models have been used increasingly in the behavioral sciences. This paper discusses the problem that very different causal structures may fit the same set of data equally well. (Author/LMO)
Descriptors: Computer Software, Correlation, Goodness of Fit, Mathematical Models