Using R With Multivariate Statistics

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Using R With Multivariate Statistics by Randall E. Schumacker

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The book Using R With Multivariate Statistics was written to supplement existing full textbooks on the various multivariate statistical methods. The multivariate statistics books provide a more in-depth coverage of the methods presented in this book, but without the use of R software. The R code is provided for some of the data set examples in the multivariate statistics books listed below. It is hoped that students can run the examples in R and compare results in the books that used SAS, IBM® SPSS® Statistics*, or STATA statistics packages. The advantage of R is that it is free and runs on Windows, Mac, and LINUX operating systems.

The full textbooks also provide a more in-depth discussion of the assumptions and issues, as well as provide data analysis and interpretation of the results using SPSS, SAS, and/or STATA. The several multivariate statistics books I consulted and referenced are as follows:

- Afifi, A., Clark, V., & May, S. (2004). Computer-aided multivariate analysis (4th ed.). Boca Raton, FL: Chapman & Hall/CRC Press.

- Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

- Meyers, L. S., Gamst, G., & Guarino, A. J. (2013). Applied multivariate research: Design and interpretation (2nd ed.). Thousand Oaks, CA: Sage.

- Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York, NY: Routledge (Taylor & Francis Group).

- Stevens, S. S. (2009). Applied multivariate statistics for the social sciences (5th ed.). New York, NY: Routledge (Taylor & Francis Group).

- Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Allyn & Bacon.

This book was written to provide researchers with access to the free R software when conducting multivariate statistical analysis. There are many packages and functions available, which can be overwhelming, so I have collected some of the widely used packages and functions for the multivariate methods in the book. Many of the popular multivariate statistics books will provide a more complete treatment of the topics covered in this book along with SAS and/or SPSS solutions. I am hopeful that this book will provide a good supplemental coverage of topics in multivariate books and permit faculty and students to run R software analyses. The R software permits the end users to customize programs to provide the type of analysis and output they desire. The R commands can be saved in a script file for future use, can be readily shared, and can provide the user control over the analytic steps and algorithms used. The advantages of using R software are many, including the following:

Free software

- The ability to customize statistical analysis

- Control over analytic steps and algorithms used

- Available on Window, Mac, and Linux operating systems

- Multitude of packages and functions to conduct analytics

- Documentation and reference guides available



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  • The author's reference is not required

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