Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R[squared] contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria…

The use of repeated measures research designs is explored. Repeated measures designs are often advantageous and can be implemented in a variety of research settings. One of the main advantages in repeated measures designs is the control of subject variability. Other advantages are the reduction of error variance and economy in subject recruitment.…

Research in the area of multivariate outliers is reviewed, emphasizing the problems associated with definition and identification. Treatment of the problem can be traced to 1777 and the work of D. Bernoulli. Most of the many procedures developed for identifying outliers proceed sequentially starting with the most aberrant observation, or proceed…

A simulation study was conducted to identify the best hot-deck variation to impute missing values. The three variations included in the study were the hot-deck random, the hot-deck sequential, and the hot-deck distance. The properties of these methods were investigated under three levels of the proportion of incomplete records and four levels…

The use of multivariate statistics in behavioral research is investigated, with emphasis on the reasons why multivariate methods can be so important. The concepts of testwise and experimentwise error are explained, and it is noted that multivariate methods can be used to control the inflation of experimentwise Type I error. It is also noted that…

This note described a FORTRAN IV, CDC, computer program for the canonical analysis of a two-way contingency table. (Author)

A component method is presented for maximizing estimates of a statistical procedure called redundancy analysis. Relationships of redundancy analysis to multiple correlation and principal component analysis are pointed out. An elaborate example comparing canonical correlation analysis and redundancy analysis on artificial data is presented.…

Explains factor analysis, discussing its relation to other multivariate techniques and describing characteristics of the data to consider in determining the appropriateness of factor analysis. Reviews considerations in making decisions about communality estimates, factor extraction, the number of factors to rotate, methods of factor rotation,…

Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher's tanh(sup minus 1) variance stabilizing transformation of the correlation coefficient. (Author/TJH)

Fleiss' popular multirater kappa is known to be influenced by prevalence and bias, which can lead to the paradox of high agreement but low kappa. It also assumes that raters are restricted in how they can distribute cases across categories, which is not a typical feature of many agreement studies. In this article, a free-marginal, multirater…

D. Wood and J. Erskine (1976) and B. Thompson (1989) provided bibliographies of roughly 130 applications of canonical correlation analysis, but the features of such reports have not been widely studied. This report examines the features of recent canonical reports, including substantive inquiries, but also measurement applications examining…

Simple modifications of principal component methods are described that have distinct advantages for structural analysis of relations among educational and psychological variables. The methods are contrasted theoretically and empirically with conventional principal component methods and with maximum likelihood factor analysis. (Author/GK)

Canonical correlation and redundancy analysis are two approaches to analyzing the interrelationships between two sets of measurements made on the same variables. A component method is presented which uses aspects of both approaches. An empirical example is also presented. (Author/JKS)

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)