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Using R With Multivariate Statistics
A practical guide for researchers and students on how to perform a wide range of common multivariate statistical analyses using the free and powerful R software.
A profile of this book is on the way.
What it’s about
This book is a practical supplement to traditional multivariate statistics textbooks, offering hands-on guidance for implementing common multivariate methods using the free R software. Instead of focusing on deep theory, it provides the necessary R code and step-by-step examples for techniques like Hotelling's T², MANOVA, MANCOVA, discriminant analysis, canonical correlation, factor analysis, and structural equation modeling. Each chapter introduces the key concepts and assumptions for a specific method, then walks the reader through the analysis using clear datasets. This book empowers students and researchers to move from theoretical understanding to practical application, making sophisticated statistical analysis accessible without the cost of commercial software packages.
The through-line
- Who it’s for
- A student, researcher, or analyst who understands the theory behind multivariate statistics but struggles to apply these methods to their own data, often due to a lack of access to or familiarity with the right software tools. They want to conduct sophisticated analyses competently and independently.
- The problem
- The reader needs to perform multivariate statistical analyses for their research, but commercial software like SPSS or SAS is expensive and may not be available. They are unsure how to implement these techniques in an accessible platform. The reader feels intimidated by programming-based statistical software and is frustrated by the gap between their theoretical knowledge and their practical ability to analyze data. They may feel stuck or limited in their research capabilities.
- The plan
- Learn the key issues and assumptions underlying multivariate statistics and how to test them in R.
- Follow chapter-by-chapter tutorials for specific multivariate methods like MANOVA, Factor Analysis, and SEM.
- Apply the provided R code to example datasets to understand the process and interpret the output.
- Adapt the R scripts and techniques to analyze your own research data.
- The payoff
- The reader becomes a competent and confident analyst, capable of performing a wide range of multivariate statistical techniques using R. · They can independently manage their entire data analysis workflow, from assumption checking to final interpretation and reporting. · They save money on software and gain a valuable, transferable skill in R programming for statistical analysis.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.