Multivariate Statistics

The book provides a clear understanding of multivariate statistics using R, SPSS, and SAS data sets and program code. The book content provides the depth of coverage needed to conduct research using multivariate statistics. The book covers multivariate methods that test mean differences and use correlation methods. The programs and data sets are provided in each chapter with a practical and applied interpretation of the analysis. This includes understanding assumptions for each method, editing data, interpretation of results, and an example write-up of how to report the results. A unique feature of the book is the biographies of important scholars that shaped the field of multivariate statistics. This provides an important reference to why the multivariate statistic was created and how it was initially used to solve an important practical problem. Photographs of the eminent statisticians are provided via a web link. An important feature of the book is the coverage of multiple regression methods that provide a basic understanding for the subsequent multivariate methods. The multiple regression methods presented are ordinary least squares regression, repeated measures, non-linear regression, logistic regression, and log-linear regression. The statistical issues, assumptions, data analysis, and interpretation of results for each multiple regression method is discussed. This book is ideal for an advanced graduate-level course in education, psychology, or other social sciences. The R program code and output is included with the SPSS and SAS syntax and computer output. This provides the student or applied researcher a choice in which software to use. Each chapter provides a working knowledge of the software package. Each chapter also includes a list of web resources and references. The book comes with a list of data sets and corresponding R, SPSS, and SAS program code for each chapter, which is listed in a table for easy reference. It also includes exercises and answers to the exercises should an instructor wish to use the book in a multivariate statistics class. The chapter topics include multiple regression models, hierarchical linear models, Hotelling T-squared, multivariate analysis of variance, multivariate analysis of covariance, multivariate repeated measures, discriminant analysis, canonical correlation, exploratory factor analysis, principal component analysis, and multidimensional scaling. The appendices include coverage of multivariate statistics issues and assumptions, a reference table for chapter data sets and programs, and statistical tables.