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Schepers, Jan; Van Mechelen, Iven – Psychological Methods, 2011
Profile data abound in a broad range of research settings. Often it is of considerable theoretical importance to address specific structural questions with regard to the major pattern as included in such data. A key challenge in this regard pertains to identifying which type of interaction (double ordinal, mixed ordinal/disordinal, double…
Descriptors: Matrices, Profiles, Multivariate Analysis, Models
Radhakrishnan, R.; Choudhury, Askar – International Journal of Mathematical Education in Science and Technology, 2009
Computing the mean and covariance matrix of some multivariate distributions, in particular, multivariate normal distribution and Wishart distribution are considered in this article. It involves a matrix transformation of the normal random vector into a random vector whose components are independent normal random variables, and then integrating…
Descriptors: Computers, Multivariate Analysis, Matrices, Mathematics Instruction
A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis
Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu – Multivariate Behavioral Research, 2007
Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…
Descriptors: Multivariate Analysis, Statistical Analysis, Statistical Inference, Matrices

Hwang, Heungsun; Takane, Yoshio – Psychometrika, 2002
Proposes a comprehensive approach, generalized constrained multiple correspondence analysis, for imposing both row and column constraints on multivariate discrete data. Each set of discrete data is decomposed into several submatrices and then multiple correspondence analysis is applied to explore relationships among the decomposed submatrices.…
Descriptors: Equations (Mathematics), Matrices, Multivariate Analysis
Brusco, Michael J.; Steinley, Douglas – Psychological Methods, 2006
The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although…
Descriptors: Stimuli, Multivariate Analysis, Psychology, Data
Hwang, Heungsun; Takane, Yoshio – Psychometrika, 2004
A multivariate reduced-rank growth curve model is proposed that extends the univariate reduced rank growth curve model to the multivariate case, in which several response variables are measured over multiple time points. The proposed model allows us to investigate the relationships among a number of response variables in a more parsimonious way…
Descriptors: Multivariate Analysis, Mathematical Models, Psychometrics, Matrices

Santa Ana, A. Otto – Language Variation and Change, 1996
Three analyses of /-t,d/ deletion are undertaken to investigate whether convergence with the matrix regional dialect has taken place in Los Angeles Chicano English. Two superficial analyses mistakenly find convergence. A third emic multivariate analysis finds no phonological convergence. (33 references) (Author/CK)
Descriptors: Dialect Studies, English, Matrices, Mexican Americans