Conventional canonical methods distinguish between the two variable sets being analyzed, but the methods do not attempt to optimize the variance from a given variable set that will be contained in the final solution. In this respect canonical methods are said the be "symmetric." This paper proposes two non-symmetric, canonical-like…

In their Monte Carlo study of canonical analysis, Barcikowski and Stevens evaluated the relative stability of canonical weights and loadings. This paper identifies some weaknesses in their study, suggests directions for future research in this area, and discusses interpretation of canonical analysis both in development and in cross-validation. For…

This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)

This paper presents a discussion of various statistical concepts and techniques in light of two propositions. The first is that researchers need to select analytical techniques that prevent them from committing Type VI errors, which are inconsistencies between the research question and the statistical analysis. The second is that many statistical…

The available methods for analyzing and interpreting data with multivariate analysis of variance are reviewed, and guidelines for their use are presented. Causal models that underlie the various methods are presented to facilitate the use and understanding of the methods. (Author/PN)

A particular strategy for investigating effects from a multivariate analysis of variance (MANOVA) is proposed. The strategy involves multiple two-group multivariate analyses. The analysis strategy is described in detail and illustrated with real data sets. (Author/JKS)

A distinction is drawn between redundancy measurement and the measurement of multivariate association between two sets of variables. Several measures of multivariate association between two sets of variables are examined. (Author/JKS)

The source of an event-related brain potential (ERP) is estimated from multivariate measures of ERP on the head under several mathematical and physical constraints on the parameters of the source model. Statistical aspects of estimation are discussed, and new tests are proposed. (SLD)

A number of important applications require the clustering of binary data sets. Traditional nonhierarchical cluster analysis techniques, such as the popular K-means algorithm, can often be successfully applied to these data sets. However, the presence of masking variables in a data set can impede the ability of the K-means algorithm to recover the…

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…

This study analyzed the degree to which state education funding systems supported social justice for the 1998-99 school year, where social justice was operationalized using the theory of vertical equity and research-based factors that placed students at risk of academic failure. The results of the study combined content analysis and statistical…

With the 2004 reauthorization of the Individuals with Disabilities Education Improvement Act (IDEIA), the definition of a specific learning disability was significantly altered. No longer is it required that a student demonstrate a discrepancy between ability and performance to receive educational support (Horowitz, 1999). With this in mind,…

Virtually all parametric statistical procedures have been shown to be special cases of canonical correlation analysis, which is a useful research methodology particularly when augmented by the calculation of canonical structure, index, and invariance coefficients. A logic for conducting stepwise canonical correlation analysis based upon evaluation…

Describes the method of discriminant analysis, including the concept of discriminant function, discriminant score, group centroid, and discriminant weights and loadings. Discusses methods for testing the statistical significance of a function, methods of using the function in classification, and the concept of rotating functions. Illustrates the…

Provides an example to illustrate the clustering approach. Discusses the variety of approaches in clustering; choice of cluster analytic techniques; the steps in cluster analysis; the data features such as level, shape, and scatter, that affect cluster results; alternate clustering methods and their relative effectiveness; and applications of…