# Using R With Multivariate Statistics

- Randall E. Schumacker - University of Alabama, Tuscaloosa, USA

**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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Background |

Persons of Interest |

Factors Affecting Statistics |

R Software |

Web Resources |

References |

Issues |

Assumptions |

SPSS Check |

Summary |

Web Resources |

References |

Overview |

Assumptions |

Univariate Versus Multivariate Hypothesis |

Practical Examples Using R |

Power and Effect Size |

Reporting and Interpreting |

Summary |

Exercises |

Web Resources |

References |

MANOVA Assumptions |

MANOVA Example: One-Way Design |

MANOVA Example: Factorial Design |

Effect Size |

Reporting and Interpreting |

Summary |

Exercises |

Web Resources |

References |

Assumptions |

Multivariate Analysis of Covariance |

Reporting and Interpreting |

Propensity Score Matching |

Summary |

Web Resources |

References |

Assumptions |

Advantages of Repeated Measure Design |

Multivariate Repeated Measure Examples |

Reporting and Interpreting Results |

Summary |

Exercises |

Web Resources |

References |

Overview |

Assumptions |

Dichotomous Dependent Variable |

Polytomous Dependent Variable |

Effect Size |

Reporting and Interpreting |

Summary |

Exercises |

Web Resources |

References |

Overview |

Assumptions |

R Packages |

Canonical Correlation Example |

Effect Size |

Reporting and Interpreting |

Summary |

Exercises |

Web Resources |

References |

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 |

Overview |

Assumptions |

Basics of Principal Components Analysis |

Principal Component Example |

Reporting and Interpreting |

Summary |

Exercises |

Web Resources |

References |

Overview |

Assumptions |

R Packages |

Goodness-of-Fit Index |

MDS Metric Example |

MDS Nonmetric Example |

Reporting and Interpreting Results |

Summary |

Exercises |

Web Resources |

References |

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 |

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 |

### Supplements

“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.”

“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.”

“The text is down-to-earth and practical, with a straightforward approach to communicating a set of procedures for analyzing data.”

“[…]I found the directions very clear and was able to run the syntax and get the output very easily.”

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.

**Educational Psychology , University Of Saskatchewan**