Univariate procedures proposed by M. Brown and A. Forsythe (1974) and the multivariate procedures from D. Nel and C. van der Merwe (1986) were generalized to form five new multivariate alternatives to one-way multivariate analysis of variance (MANOVA) for use when dispersion matrices are heteroscedastic. These alternatives are evaluated for Type I…

An explicit statement of a statistic which is a nonparametric analog to one-way MANOVA is presented. The statistic is a multivariate extension of the nonparametric Kruskal-Wallis test (1952). In addition two post hoc procedures are developed and compared. (Author/JKS)

A hypothesized data set is used to illustrate that canonical correlation analysis is a general linear model, subsuming other parametric procedures as special cases. Specific techniques included in analyses are t tests, Pearson correlation, multiple regression, analysis of variance, multivariate analysis of variance, and discriminant analysis. (SLD)

The equivalence of one-way multivariate analysis of variance and canonical correlation is well known. The usefulness of canonical correlation analysis is performing factorial multivariate analysis of variance is demonstrated. (Author)

The Shine-Bower single subject ANOVA is extended to a multivariate case, with one example assuming between-variate dependencies among within-subject errors and the second assuming no between-variate dependencies among within-subject errors. Standard and simplified multivariate ANOVA procedures are used, respectively. (Author/CM)

The method of unweighted means (MUM) was studied to determine Type I error rates associated with various degrees of imbalance among the cell frequencies. Conditions under which the MUM is a reasonable alternative to the least squares method are discussed, and the issue of power is considered. (SLD)

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)

In response to Webb and Shavelson (EJ 241 567), Nussbaum questions the relevance of the universe score variance as a meaning reliability index and whether it is useful to determine the weights which enter into a composite by means of maximum generalizability. (CM)

Answering Nussbaum's (TM 507 069) criticism, the generalizability coefficient for absolute decisions, the use of the error variance formula, composites of maximum generalizability, and covariance components are discussed as yardsticks of measurement precision with arguments for the use of each procedure to interpret data. (CM)

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)

Interpretations of relative variable importance in multivariate analysis of variance are discussed, with attention to (1) latent construct definition; (2) linear discriminant function scores; and (3) grouping variable effects. Two numerical ranking methods are proposed and compared by the bootstrap approach using two real data sets. (SLD)

This study examined whether the density of noncontingent outcomes limits instructor expressiveness as an effective teaching behavior in different lecture content conditions. Results indicated that for high-content lectures, instructor expressiveness facilitated achievement and confidence in students who received contingent and low noncontingent…

Repeated measures designs have traditionally been analyzed by the univariate mixed model approach, in which the repeated measures effect is tested against an error term based on the subject by treatment interaction. This paper considers an extension of this analysis to designs in which the individual repeated measures are multivariate. (Author/JKS)

This book presents a comprehensive overview of univariate and multivariate generalizability theory, a psychometric model that provides a powerful approach to the analysis of errors of measurement through the use of random-effects and mixed-model analysis of variance. (SLD)

Provides an expository presentation of multivariate analysis (MANOVA). Addresses several questions relating to why one should use MANOVA, its structure, how MANOVA test statistics and follow-up tests are obtained and interpreted, presentation of results, and alternatives to MANOVA. Uses an example data set to illustrate these points. (Author/KS)