Overview

Using R with Multivariate Statistics is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis. Designed to serve as a companion to a more comprehensive text on multivariate statistics, this book helps students and researchers in the social and behavioral sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.

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9781483377964

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Reviews

This book is not only an excellent introductory resource of multivariate statistics using R, but also provides a complete coverage of multivariate statistics. I really love this book and look forward to using it for my stats courses. -- Jianmin Guan, University of Texas at San Antonio The use of the programming language R in a meaningful way is a great strength of this book, as is the associated emphasis on matrix algebra. Also, the addition of brief biographies of key statisticians makes this book more interesting. Finally, the range and scope of techniques that are presented is impressive. -- David E. Drew, Claremont Graduate University The text is down-to-earth and practical, with a straightforward approach to communicating a set of procedures for analyzing data. -- Darrell Rudmann, Shawnee State University [...]I found the directions very clear and was able to run the syntax and get the output very easily. -- Camille L. Bryant, Columbus State University

Author Information

Dr. Randall E. Schumacker is professor of educational research at the University of Alabama. He has written and co-edited several books, including A Beginner’s Guide to Structural Equation Modeling, Third Edition, Advanced Structural Equation Modeling: Issues and Techniques, Interaction and Non-Linear Effects in Structural Equation Modeling, New Developments and Techniques in Structural Equation Modeling, Understanding Statistical Concepts Using S-PLUS, Understanding Statistics Using R, and Learning Statistics Using R.  Dr. Schumacker was the founder and is now emeritus editor of Structural Equation Modeling: A Multidisciplinary Journal, and has established the Structural Equation Modeling Special Interest Group within the American Educational Research Association (AERA). He is also the emeritus editor of Multiple Linear Regression Viewpoints, the oldest journal sponsored by AERA (Multiple Linear Regression: General Linear Model Special Interest Group). Dr. Schumacker has conducted international and national workshops, has served on the editorial board of several journals, and currently pursues his research interests in statistics and structural equation modeling. He was the 1996 recipient of the Outstanding Scholar Award and the 1998 recipient of the Charn Oswachoke International Award. In 2010, he launched the DecisionKit App for the iPhone, iPad, and iTouch, which can assist researchers in making decisions about which measurement, research design, or statistic to use in their research projects. In 2011, he received the Apple iPad Award. In, 2012, he received the CIT Faculty Technology Award. In 2013, he received the McCrory Faculty Excellence in Research Award from the College of Education at the University of Alabama. In 2014, Dr. Schumacker was the recipient of the Structural Equation Modeling Service Award at AERA.

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