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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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Clarkson, Douglas B.; Gonzalez, Richard – Psychometrika, 2001
Defines a random effects diagonal metric multidimensional scaling model, gives its computational algorithms, describes researchers' experiences with these algorithms, and provides an illustration of the use of the model and algorithms. (Author/SLD)
Descriptors: Algorithms, Mathematical Models, Multidimensional Scaling
Peer reviewed Peer reviewed
Milligan, Glenn W. – Psychometrika, 1979
Johnson has shown that the single linkage and complete linkage hierarchical clustering algorithms induce a metric on the data known as the ultrametric. Johnson's proof is extended to four other common clustering algorithms. Two additional methods also produce hierarchical structures which can violate the ultrametric inequality. (Author/CTM)
Descriptors: Algorithms, Cluster Analysis, Mathematical Models, Organization
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Cudeck, Robert; And Others – Psychometrika, 1993
An implementation of the Gauss-Newton algorithm for the analysis of covariance structure that is specifically adapted for high-level computer languages is reviewed. This simple method for estimating structural equation models is useful for a variety of standard models, as is illustrated. (SLD)
Descriptors: Algorithms, Analysis of Covariance, Computer Software, Equations (Mathematics)
Peer reviewed Peer reviewed
Mooijaart, Ab; van der Heijden, Peter G. M. – Psychometrika, 1992
It is shown that it is not easy to apply the EM algorithm to latent class models in the general case with equality constraints because a nonlinear equation has to be solved. A simpler condition is given in which the EM algorithm can be easily applied. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Estimation (Mathematics), Mathematical Models
Peer reviewed Peer reviewed
Leenen, Iwin; Van Mechelen, Iven; De Boeck, Paul; Rosenberg, Seymour – Psychometrika, 1999
Presents a three-way, three-mode extension of the two-way, two-mode hierarchical classes model of P. De Boeck and S. Rosenberg (1998) for the analysis of individual differences in binary object x attribute arrays. Illustrates the model with data on psychiatric diagnosis and discusses the relation between the model and other extant models. (SLD)
Descriptors: Algorithms, Individual Differences, Models, Set Theory
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Nishisato, Shizuhiko; Arri, P. S. – Psychometrika, 1975
A modified technique of separable programming was used to maximize the squared correlation ratio of weighted responses to partially ordered categories. The technique employs a polygonal approximation to each single-variable function by choosing mesh points around the initial approximation supplied by Nishisato's method. Numerical examples were…
Descriptors: Algorithms, Linear Programing, Mathematical Models, Matrices
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Cohen, Harvey S.; Jones, Lawrence E. – Psychometrika, 1974
Descriptors: Algorithms, Correlation, Models, Multidimensional Scaling
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Okamoto, Masashi; Ihara, Masamori – Psychometrika, 1983
A new algorithm to obtain the least squares solution in common factor analysis is presented. It is based on the up-and-down Marquadt algorithm developed by the present authors. Experiments in the use of the algorithm under various conditions are discussed. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Least Squares Statistics, Mathematical Models
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Arminger, Gerhard; Muthen, Bengt O. – Psychometrika, 1998
Nonlinear latent variable models are specified that include quadratic forms and interactions of latent regressor variable as special cases. To estimate the parameters, the models are put in a Bayesian framework with conjugate priors for the parameters. The proposed estimation methods are illustrated by two simulation studies. (SLD)
Descriptors: Algorithms, Bayesian Statistics, Estimation (Mathematics), Mathematical Models
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Ferligoj, Anuska; Batagelj, Vladimir – Psychometrika, 1982
Using constraints with cluster analysis limits the possible number of clusters. This paper deals with clustering problems where grouping is constrained by a symmetric and reflexive relation. Two approaches, along with illustrations, are presented. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models
Peer reviewed Peer reviewed
Desarbo, Wayne S. – Psychometrika, 1982
A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)
Descriptors: Algorithms, Cluster Analysis, Data Analysis, Mathematical Models
Peer reviewed Peer reviewed
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
Peer reviewed Peer reviewed
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
Peer reviewed Peer reviewed
Rindskopf, David – Psychometrika, 1992
A general approach is described for the analysis of categorical data when there are missing values on one or more observed variables. The method is based on generalized linear models with composite links. Situations in which the model can be used are described. (SLD)
Descriptors: Algorithms, Classification, Data Analysis, Estimation (Mathematics)
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