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)

Two major and two minor principles are shown to serve to generate a large number of multivariate models, including canonical analysis, factor analysis, and latent trait test theory. The statistical underpinnings of the theory are discussed. (Author/JKS)

Sets of randomly generated numbers are used to produce simulated data sets to illustrate that high heritability of a property within each of two populations may be consistent with a vanishingly small heritability in the combined population. (A response by Jensen appears in the next issue of Journal of Educational Statistics, p.385). (CTM)

The nature and appropriate application of the technique of multivariate analysis are discussed. More specifically, the intent of the paper is to demystify and explain the use of multivariate analysis as well as provide guidelines for selection of the most effective statistics for use in specific situations. For the purpose of this paper, the term…

The many methodological contributions of Raymond B. Cattell to multivariate analysis are discussed in terms of contemporary issues in structural equation modeling. His factor analytic approach is compared with current modeling practices. A critical evaluation finds much of Cattell's work still innovative, technically advanced, and valuable to…

A broad spectrum of methodologies pertinent to studies of schooling effects are reviewed. Methodological issues and problems are addressed according to a three-dimensional conceptual framework: (1) indicators of schooling effects; (2) study approaches; and (3) units of analysis. Problems and uses of status attainment and difference scores as…