An R Companion for Applied Statistics II
Multivariable and Multivariate Techniques
- Danney Rasco - West Texas A&M University, USA
Other Titles in:
Quantitative Methods | Research Methods in Psychology | Statistical Computing Environments
Quantitative Methods | Research Methods in Psychology | Statistical Computing Environments
July 2020 | 288 pages | SAGE Publications, Inc
An R Companion for Applied Statistics II: Multivariable and Multivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use R to analyze multivariate data. The book focuses on the statistics generally covered in an intermediate or multivariate statistics course and provides one or two ways to run each analysis in R. The book has been designed to be an R companion to Rebecca M. Warner's Applied Statistics II: Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide for a multivariate statistics course, without reference to the Warner text. Datasets and scripts to run the examples are provided on an accompanying website.
Preface
Acknowledgments
About the Author
CHAPTER 1 • Beyond Two Variables and Null Hypothesis Significance Testing
CHAPTER 2 • Advanced Data Screening, Outliers, and Missing Values
CHAPTER 3 • Statistical Control
CHAPTER 4 • Statistical Control With Regression Analysis
CHAPTER 5 • Beyond Three Variables: Regression With Multiple Predictors
CHAPTER 6 • Regression With Dummy Variables
CHAPTER 7 • Moderation
CHAPTER 8 • Analysis of Covariance
CHAPTER 9 • Mediation
CHAPTER 10 • Discriminant Analysis
CHAPTER 11 • Multivariate Analysis of Variance
CHAPTER 12 • Exploratory Factor Analysis
CHAPTER 13 • Reliability and Validity for Multiple-Item Scales
CHAPTER 14 • Repeated-Measures Tests: Further Exploration
CHAPTER 15 • Brief Introduction to Latent-Variable Structural Equation Modeling
CHAPTER 16 • Binary Logistic Regression
CHAPTER 17 • Additional Statistical Techniques
References
Supplements
Student Study Site
Open-access Student Resources include R code and data sets provided by the author for student download for completing in-chapter exercises.
Open-access Student Resources include R code and data sets provided by the author for student download for completing in-chapter exercises.
Sample Materials & Chapters
CHAPTER 1 • Beyond Two Variables and Null Hypothesis...
CHAPTER 2 • Advanced Data Screening, Outliers...