Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis.
An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is pointing out the analogy between a common univariate statistical technique and the corresponding multivariate method. Many computer examples--drawing on SAS software --are used as demonstrations.
Throughout the book, the computer is used as an adjunct to the presentation of a multivariate statistical method in an empirically oriented approach. Basically, the model adopted in this book is to first present the theory of a multivariate statistical method along with the basic mathematical computations necessary for the analysis of data. Subsequently, a real world problem is discussed and an example data set is provided for analysis. Throughout the presentation and discussion of a method, many references are made to the computer, output are explained, and exercises and examples with real data are included.
"…the text presents a good introduction to multivariate analysis with well-written examples and explanations."
—Journal of Quality Technology
"This book is a good introduction to multivariate statistical methods for someone who wants to understand the basic underpinnings of…techniques and try them out on some data….[The authors] provide a great deal of guidance for those people who, with the advent of readily available and very sophisticated statistical software, will be applying these methods to their own data."
"It is my opinion that Marcoulides and Hershberger have expertly summarized the complexities associated with multivariate statistics in a fashion that will be readily coherent to the introductory graduate student. They provide the right amount of mathematics so as not to overwhelm the introductory student, but just enough to whet their appetite….Again, I found this text to be well-organized and coherent in its treatment of such complex techniques as PCA, DA, and canonical correlation. Especially for a multivariate class that plans to have some emphasis on model development, the FA section and the CFA and SEM chapter will provide an excellent foundation for the introductory student."
—Structural Equation Modeling
Contents: Introduction. Basic Matrix Algebra. The Multivariate Normal Distribution and Tests of Significance. Factorial Multivariate Analysis of Variance. Discriminant Analysis. Canonical Correlation. Principal Components and Factor Analysis. Confirmatory Factor Analysis and Structural Equation Modeling. Appendix.