Multivariate techniques have been implemented with greater and greater frequency. In order to use multivariate techniques researchers must understand the fundamental assumptions. The purpose of this paper is to evaluate one of the assumptions of multivariate analysis, normality. Overall, normal distributions are unimodal and symmetrical, and they…

The use of generalized discriminant analysis as a descriptive technique which can be employed outside of the traditional analysis of variance studies is discussed. Examples based on real data are provided. (Author/JKS)

Three potential applications of stepwise procedures in canonical analysis and several alternative stepping decision rules are described. Results of an empirical investigation of the procedures indicated that more parsimonious approaches to maintaining variables held up better under cross-validation. (Author/JKS)

The triangular decomposition method is suggested as a general technique for obtaining the various measures of an ill-conditioned matrix. The advantages of using triangular decomposition are computing nicety, cost, and parsimony. (Author/PN)

The index of redundancy is a measure of association between a set of independent variables and a set of dependent variables. Properties and interpretations of redundancy variables, in a particular subset of the original variables, are discussed. (JKS)

Recent work (EJ 208 813) showing that generalized eigenvalue problems in which both matrices are singular can be solved by reducing them to similar problems of smaller order is discussed. Possible extensions of the work are indicated. (Author/JKS)

This paper discusses considerations in the unwary use of packaged discriminant analysis procedures including: the differences between the "group classification function" and the textbook classification function in both form and use, classification table confusions and their alleviation, and the hazards of stepping procedures. (Author/BW)

To investigate whether research studies on the mildly handicapped have univariate or multivariate research designs, 105 empirical research articles from 1975 to 1979 were reviewed. Factors considered were sample size, type of study, and type of inferential statistic. (SB)

Examples are given to show how the semistrandardized (SS) regression coefficient provides information not given by the conventional standardized regression coefficients used in factor, canonical, and path analysis. (Author/RL)

The purpose of this paper is to outline the role of orthogonal rotation in canonical analysis, including the evaluative measures that need be reported and scrutinized upon application. (Author)

An extension of Wollenberg's redundancy analysis (an alternative to canonical correlation) is proposed to derive Y-variates corresponding to the optimal X-variates. These variates are maximally correlated with the given X-variates, and depending upon the standardization chosen they also have certain properties of orthogonality. (Author/JKS)

Means of identifying sources of rejection of hypotheses regarding linear multivariate statistical models are discussed. Problems with the use of a global test using Roy's largest root criterion and means of solving them are presented, along with a practical application of the techniques. (TJH)

A summary and a unified treatment of fully general computational solutions for two criteria for transforming two or more matrices to maximal agreement are provided. The two criteria--Maxdiff and Maxbet--have applications in the rotation of factor loading or configuration matrices to maximal agreement and the canonical correlation problem. (SLD)

Contends that multivariate statistical analyses should be studied and practiced more extensively. Gives several reasons for doing multivariate analysis and discusses two common errors in statistical analysis. Presents examples of how single multivariate analysis can produce different results than do separate univariate analyses and illustrates…

The finite intersection test (FIT) developed by P. K. Krishnaiah (1964, 1965) is discussed and compared with more familiar methods for simultaneous inference. How the FIT can be used to analyze differences among all means for both univariate and multivariate experimental designs is explained. (SLD)