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Using R With Multivariate Statistics
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Using R With Multivariate Statistics

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August 2015 | 408 pages | SAGE Publications, Inc
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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Preface
 
Acknowledgments
 
About the Author
 
1. Introduction and Overview
Background

 
Persons of Interest

 
Factors Affecting Statistics

 
R Software

 
Web Resources

 
References

 
 
2. Multivariate Statistics: Issues and Assumptions
Issues

 
Assumptions

 
SPSS Check

 
Summary

 
Web Resources

 
References

 
 
3. Hotelling’s T2 : A Two-Group Multivariate Analysis
Overview

 
Assumptions

 
Univariate Versus Multivariate Hypothesis

 
Practical Examples Using R

 
Power and Effect Size

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
4. Multivariate Analysis of Variance
MANOVA Assumptions

 
MANOVA Example: One-Way Design

 
MANOVA Example: Factorial Design

 
Effect Size

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
5. Multivariate Analysis of Covariance
Assumptions

 
Multivariate Analysis of Covariance

 
Reporting and Interpreting

 
Propensity Score Matching

 
Summary

 
Web Resources

 
References

 
 
6. Multivariate Repeated Measures
Assumptions

 
Advantages of Repeated Measure Design

 
Multivariate Repeated Measure Examples

 
Reporting and Interpreting Results

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
7. Discriminant Analysis
Overview

 
Assumptions

 
Dichotomous Dependent Variable

 
Polytomous Dependent Variable

 
Effect Size

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
8. Canonical Correlation
Overview

 
Assumptions

 
R Packages

 
Canonical Correlation Example

 
Effect Size

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
9. Exploratory Factor Analysis
Overview

 
Types of Factor Analysis

 
Assumptions

 
Factor Analysis Versus Principal Components Analysis

 
EFA Example

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
Appendix: Attitudes Toward Educational Research Scale

 
 
10. Principal Components Analysis
Overview

 
Assumptions

 
Basics of Principal Components Analysis

 
Principal Component Example

 
Reporting and Interpreting

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
11. Multidimensional Scaling
Overview

 
Assumptions

 
R Packages

 
Goodness-of-Fit Index

 
MDS Metric Example

 
MDS Nonmetric Example

 
Reporting and Interpreting Results

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
12. Structural Equation Modeling
Overview

 
Assumptions

 
Equal Variance-Covariance Matrices

 
Correlation Versus Covariance Matrix

 
R Packages

 
CFA Models

 
Structural Equation Models

 
Reporting and Interpreting Results

 
Summary

 
Exercises

 
Web Resources

 
References

 
 
Statistical Tables
Table 1: Areas Under the Normal Curve (z Scores)

 
Table 2: Distribution of t for Given Probability Levels

 
Table 3: Distribution of r for Given Probability Levels

 
Table 4: Distribution of Chi-Square for Given Probability Levels

 
Table 5: The F Distribution for Given Probability Levels (.05 Level)

 
Table 6: The Distribution of F for Given Probability Levels (.01 Level)

 
Table 7: Distribution of Hartley F for Given Probability Levels

 
 
Chapter Answers
 
R Installation and Usage
 
R Packages, Functions, Data Sets, and Script Files
 
Index

Supplements

Student Resource Site

Download the data files you’ll need to follow-along in the book!

“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

I adopted this book as the supplementary book to my course. This book, in my opinion, has the advantage of being technical (compared to Filed's books) which makes it more attractive to stronger students who want to have deeper understanding.

Dr Amin Mousavi
Educational Psychology , University Of Saskatchewan
October 11, 2016

Sample Materials & Chapters

Chapter 3

Chapter 5


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