Using R with multivariate statistics
著者
書誌事項
Using R with multivariate statistics
Sage, c2016
- : pbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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
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