Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Although several antecedents of statistics anxiety have been identified, many of these factors are relatively immutable (e.g., gender) and, at best, identify students who are at risk for debilitative levels of statistics anxiety, thereby having only minimal implications for intervention. Furthermore, the few interventions that have been designed…

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

Issues the author has explored in his work on the philosophy of statistics are reviewed. Indeterminacy, the place of empiricism, questions of causation and causality, and explorations of language have preceded the study of objectivity. The relationship between objectivity and multivariate statistics is examined. (SLD)

It is argued that, given the importance and the increased use of multivariate techniques such as factor analysis and canonical correlation, students need to be made aware of multivariate methods and the appropriate ways in which they can be applied. As a general linear model that subsumes all other parametric measures, canonical correlation…