This book helps students and researchers in the social and behavioural 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.
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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