Using R With Multivariate Statistics
1st Edition
Published on 8. September 2016
Book
Paperback/Softback
408 pages
978-1-4833-7796-4 (ISBN)
Description
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.
Reviews / Votes
"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 UniversityMore details
Edition
First Edition
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 232 mm
Width: 187 mm
Weight
707 gr
ISBN-13
978-1-4833-7796-4 (9781483377964)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Dr. Randall E. Schumacker is professor of educational research at the University of Alabama. He has written and co-edited several books, including A Beginner's Guide to Structural Equation Modeling, Third Edition, Advanced Structural Equation Modeling: Issues and Techniques, Interaction and Non-Linear Effects in Structural Equation Modeling, New Developments and Techniques in Structural Equation Modeling, Understanding Statistical Concepts Using S-PLUS, Understanding Statistics Using R, and Learning Statistics Using R. Dr. Schumacker was the founder and is now emeritus editor of Structural Equation Modeling: A Multidisciplinary Journal, and has established the Structural Equation Modeling Special Interest Group within the American Educational Research Association (AERA). He is also the emeritus editor of Multiple Linear Regression Viewpoints, the oldest journal sponsored by AERA (Multiple Linear Regression: General Linear Model Special Interest Group). Dr. Schumacker has conducted international and national workshops, has served on the editorial board of several journals, and currently pursues his research interests in statistics and structural equation modeling. He was the 1996 recipient of the Outstanding Scholar Award and the 1998 recipient of the Charn Oswachoke International Award. In 2010, he launched the DecisionKit App for the iPhone, iPad, and iTouch, which can assist researchers in making decisions about which measurement, research design, or statistic to use in their research projects. In 2011, he received the Apple iPad Award. In, 2012, he received the CIT Faculty Technology Award. In 2013, he received the McCrory Faculty Excellence in Research Award from the College of Education at the University of Alabama. In 2014, Dr. Schumacker was the recipient of the Structural Equation Modeling Service Award at AERA.
Content
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
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