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Price, Lewis C. – Psychometrika, 1980
Two algorithms based on a latent class model are presented for discovering hierarchical relations that exist among a set of dichotomous items. The algorithms presented, and three competing deterministic algorithms are compared using computer-generated data. (Author/JKS)
Descriptors: Algorithms, Mathematical Models, Statistical Analysis
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Olsson, Ulf; And Others – Psychometrika, 1982
The polyserial and point polyserial correlations are discussed as generalizations of the biserial and point biserial correlations. The relationship between the polyserial and point polyserial correlation is derived. Some practical applications of the polyserial correlation are described. (Author/JKS)
Descriptors: Algorithms, Correlation, Item Analysis, Statistical Analysis
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Kiers, Henk A. L.; ten Berge, Jos M. F. – Psychometrika, 1989
Two alternating least squares algorithms are presented for the simultaneous components analysis method of R. E. Millsap and W. Meredith (1988). These methods, one for small data sets and one for large data sets, can indicate whether or not a global optimum for the problem has been attained. (SLD)
Descriptors: Algorithms, Factor Analysis, Least Squares Statistics, Statistical Analysis
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Borg, Ingiver; Lingoes, James C. – Psychometrika, 1980
A method for externally constraining certain distances in multidimensional scaling configurations is introduced and illustrated. The method is described in detail and several examples are presented. (Author/JKS)
Descriptors: Algorithms, Hypothesis Testing, Mathematical Models, Multidimensional Scaling
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Lehner, Paul E.; Norma, Elliot – Psychometrika, 1980
A new algorithm is used to test and describe the set of all possible solutions for any linear model of an empirical ordering derived from techniques such as additive conjoint measurement, unfolding theory, general Fechnerian scaling, and ordinal multiple regression. The algorithm is computationally faster and numerically superior to previous…
Descriptors: Algorithms, Mathematical Models, Measurement, Multiple Regression Analysis
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McClelland, Gary; Coombs, Clyde H. – Psychometrika, 1975
ORDMET is applicable to structures obtained from additive conjoint measurement designs, unfolding theory, general Fechnerian scaling, types of multidimensional scaling, and ordinal multiple regression. A description is obtained of the space containing all possible numerical representations which can satisfy the structure, size, and shape of which…
Descriptors: Algorithms, Computer Programs, Data Analysis, Matrices
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Goldberger, Arthur S.; Joreskog, Karl G. – Psychometrika, 1972
Descriptors: Algorithms, Factor Analysis, Least Squares Statistics, Mathematical Models
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Kiers, Henk A. L.; ten Berge, Jos M. F. – Psychometrika, 1992
A procedure is described for minimizing a class of matrix trace functions, which is a refinement of an earlier procedure for minimizing the class of matrix trace functions using majorization. Several trial analyses demonstrate that the revised procedure is more efficient than the earlier majorization-based procedure. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Least Squares Statistics, Mathematical Models
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Lingoes, James C.; Schonemann, Peter H. – Psychometrika, 1974
Descriptors: Algorithms, Goodness of Fit, Matrices, Orthogonal Rotation
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Schonemann, Peter H.; Wang, Ming Mei – Psychometrika, 1972
A model for the analysis of paired comparison data is presented which is metric, mathematically tractable, and has an exact algebraic solution. (Authors/MB)
Descriptors: Algorithms, Individual Differences, Mathematical Models, Multidimensional Scaling
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Rubin, Donald B.; Thayer, Dorothy T. – Psychometrika, 1982
The details of EM algorithms for maximum likelihood factor analysis are presented for both the exploratory and confirmatory models. An example is presented to demonstrate potential problems in other approaches to maximum likelihood factor analysis. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Matrices, Maximum Likelihood Statistics
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Hubert, Lawrence – Psychometrika, 1973
The present paper discusses two rather different types of partitioning techniques that still have the same property of monotone invariance. (Author)
Descriptors: Algorithms, Cluster Analysis, Cluster Grouping, Goodness of Fit
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Hubert, Lawrence – Psychometrika, 1972
Paper is an attempt to extend the hierarchical partitioning algorithms and to emphasize a general connection between these clustering procedures and the mathematical theory of lattices. (Author)
Descriptors: Algorithms, Cluster Grouping, Goodness of Fit, Mathematical Applications
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DeLeeuw, Jan; Kroonenberg, Peter M. – Psychometrika, 1980
A new method to estimate the parameters of Tucker's three mode principal component model is discussed, and the convergence properties of the alternating least squares algorithm to solve the estimation problem are considered. An example is presented. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Least Squares Statistics, Measurement
Peer reviewed Peer reviewed
Polson, Peter G.; Huizinga, David – Psychometrika, 1974
Descriptors: Algorithms, Computer Programs, Goodness of Fit, Learning Processes
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