Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.
New to the Second Edition
- Includes new chapters on Moderation (Chapter 15) and Mediation (Chapter 16), extending the range of statistical tools covered
- Updates all SPSS screen shots and output to ISBM SPSS version 19
- Provides many new references, including for the analysis of data with many zeroes, and problems that can arise when standardized regression coefficients are compared across groups
- Offers an expanded glossary of helpful key terms.
Rebecca M. Warner received a B.A. from Carnegie-Mellon University in Social Relations in 1973 and a Ph.D. in Social Psychology from Harvard in 1978. She has taught statistics for more than 25 years: from Introductory and Intermediate Statistics to advanced topics seminars in Multivariate Statistics, Structural Equation Modeling, and Time Series Analysis. She is currently a Full Professor in the Department of Psychology at the University of New Hampshire. She is a Fellow in the Association for Psychological Science and a member of the American Psychological Association, the International Association for Relationships Research, the Society of Experimental Social Psychology, and the Society for Personality and Social Psychology. She has consulted on statistics and data management for the World Health Organization in Geneva and served as a visiting faculty member at Shandong Medical University in China.
Preface Acknowledgments About the Author Chapter 1. Review of Basic Concepts Chapter 2. Basic Statistics, Sampling Error, and Confidence Intervals Chapter 3. Statistical Significance Testing Chapter 4. Preliminary Data Screening Chapter 5. Comparing Group Means Using the Independent Samples t Test Chapter 6. One-Way Between-Subjects Analysis of Variance Chapter 7. Bivariate Pearson Correlation Chapter 8. Alternative Correlation Coefficients Chapter 9. Bivariate Regression Chapter 10. Adding a Third Variable: Preliminary Exploratory Analyses Chapter 11. Multiple Regression With Two Predictor Variables Chapter 12. Dummy Predictor Variables in Multiple Regression Chapter 13. Factorial Analysis of Variance Chapter 14. Multiple Regression With More Than Two Predictors Chapter 15. Moderation: Tests for Interaction in Multiple Regression Chapter 16. Mediation Chapter 17. Analysis of Covariance Chapter 18. Discriminant Analysis Chapter 19. Multivariate Analysis of Variance Chapter 20. Principal Components and Factor Analysis Chapter 21. Reliability, Validity, and Multiple-Item Scales Chapter 22. Analysis of Repeated Measures Chapter 23. Binary Logistic Regression Appendix A: Proportions of Area Under a Standard Normal Curve Appendix B: Critical Values for t Distribution Appendix C: Critical Values of F Appendix D: Critical Values of Chi-Square Appendix E: Critical Values of the Pearson Correlation Coefficient Appendix F: Critical Values of the Studentized Range Statistic Appendix G: Transformation of r (Pearson Correlation) to Fisher Z Glossary References Index