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Hwang, Heungsun; Suk, Hye Won; Lee, Jang-Han; Moskowitz, D. S.; Lim, Jooseop – Psychometrika, 2012

We propose a functional version of extended redundancy analysis that examines directional relationships among several sets of multivariate variables. As in extended redundancy analysis, the proposed method posits that a weighed composite of each set of exogenous variables influences a set of endogenous variables. It further considers endogenous…

Descriptors: Redundancy, Psychometrics, Computation, Least Squares Statistics

Hwang, Heungsun; Jung, Kwanghee; Takane, Yoshio; Woodward, Todd S. – Psychometrika, 2012

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the…

Descriptors: Multivariate Analysis, Computation, Data Analysis, Short Term Memory

Hsieh, Fushing; Ferrer, Emilio; Chen, Shuchun; Mauss, Iris B.; John, Oliver; Gross, James J. – Psychometrika, 2011

We present an approach for evaluating coherence in multivariate systems that considers all the variables simultaneously. We operationalize the multivariate system as a network and define coherence as the efficiency with which a signal is transmitted throughout the network. We illustrate this approach with time series data from 15…

Descriptors: Multivariate Analysis, Emotional Response, Networks, Efficiency

Steinley, Douglas; Brusco, Michael J. – Psychometrika, 2008

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the…

Descriptors: Models, Comparative Analysis, Multivariate Analysis, Evaluation Methods

Hwang, Heungsun; Montreal, Hec; Dillon, William R.; Takane, Yoshio – Psychometrika, 2006

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

Descriptors: Heterogeneous Grouping, Multivariate Analysis, Models

van de Velden, Michel; Bijmolt, Tammo H. A. – Psychometrika, 2006

A method is presented for generalized canonical correlation analysis of two or more matrices with missing rows. The method is a combination of Carroll's (1968) method and the missing data approach of the OVERALS technique (Van der Burg, 1988). In a simulation study we assess the performance of the method and compare it to an existing procedure…

Descriptors: Multivariate Analysis, Matrices, Simulation, Comparative Testing

Brusco, Michael J. – Psychometrika, 2006

Minimization of the within-cluster sums of squares (WCSS) is one of the most important optimization criteria in cluster analysis. Although cluster analysis modules in commercial software packages typically use heuristic methods for this criterion, optimal approaches can be computationally feasible for problems of modest size. This paper presents a…

Descriptors: Multivariate Analysis, Evaluation Criteria, Heuristics, Problem Solving

Shieh, Gwowen – Psychometrika, 2005

This article considers the problem of power and sample size calculations for normal outcomes within the framework of multivariate linear models. The emphasis is placed on the practical situation that not only the values of response variables for each subject are just available after the observations are made, but also the levels of explanatory…

Descriptors: Sample Size, Multivariate Analysis, Monte Carlo Methods, Intellectual Development

Kiers, Henk A. L.; Vicari, Donatella; Vichi, Maurizio – Psychometrika, 2005

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are…

Descriptors: Classification, Multidimensional Scaling, Multivariate Analysis, Models

Takane, Yoshio; Hwang, Heungsun – Psychometrika, 2005

Lazraq and Cleroux (Psychometrika, 2002, 411-419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare…

Descriptors: Redundancy, Monte Carlo Methods, Predictor Variables, Psychometrics

Yuan, Ke-Hai; Bentler, Peter M. – Psychometrika, 2004

Since data in social and behavioral sciences are often hierarchically organized, special statistical procedures for covariance structure models have been developed to reflect such hierarchical structures. Most of these developments are based on a multivariate normality distribution assumption, which may not be realistic for practical data. It is…

Descriptors: Statistical Analysis, Statistical Inference, Statistical Distributions, Multivariate Analysis

Peer reviewed

Romanazzi, Mario – Psychometrika, 1992

The perturbation theory of the generalized eigenproblem is used to derive influence functions of each squared canonical correlation coefficient and the corresponding canonical vector pair. Three sample versions of these functions are described, and some properties are noted. Two obvious applications, multiple correlation and correspondence…

Descriptors: Equations (Mathematics), Estimation (Mathematics), Mathematical Models, Multivariate Analysis

Peer reviewed

Lee, Sik-Yum; And Others – Psychometrika, 1992

A two-stage approach based on the rationale of maximum likelihood and generalized least-squares methods is developed to analyze the general structural equation model for continuous and polytomous variables. Some illustrative examples and a small simulation study (50 replications) are reported. (SLD)

Descriptors: Equations (Mathematics), Estimation (Mathematics), Least Squares Statistics, Mathematical Models

Peer reviewed

Dolan, Conor V.; van der Maas, Han L. J. – Psychometrika, 1998

Discusses fitting multivariate normal mixture distributions to structural equation modeling. The model used is a LISREL submodel that includes confirmatory factor and structural equation models. Two approaches to maximum likelihood estimation are used. A simulation study compares confidence intervals based on the observed information and…

Descriptors: Goodness of Fit, Maximum Likelihood Statistics, Multivariate Analysis, Simulation

Peer reviewed

Preuss, Lucien; Vorkauf, Helmut – Psychometrika, 1997

An information-theoretic framework is used to analyze the knowledge content in multivariate cross-classified data. Proposes measures based on the information concept, including the knowledge content of a cross classification, its terseness, and the separability of one variable. Presents applications for situations when classical analysis is…

Descriptors: Data Analysis, Information Theory, Knowledge Level, Multivariate Analysis

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