“This is an excellent treatment of a complex subject. [Warner] has done a great job of making the ideas as clear and accessible as possible.”A?
- W. James Potter, University of California at Santa Barbara

“I very much like the author's style of writing-she explains complex concepts in simple and accessible language.”
-Ruth Childs, University of Toronto, Canada

“The book is easy to read. The author provides excellent practical advice, including the benefits and consequences of different statistical methods, as well as useful APA guidelines for research reports.”
-Patrick Leung, University of Houston

Applied Statistics: From Bivariate Through Multivariate TechniquesA?provides a clear introduction to widely used topics in bivariate andA?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. For example, "How do researchers' decisions about treatment dosage levels and sample size tend to influence the magnitude ofA?t and F ratios?" Each chapter presents a complete empirical research example to illustrate the application of a specific method, such as multiple regression. 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.

The robust Instructor's Resource CD ROMA?includeA?datasets in SPSS andA?Excel; answers to all comprehension questions; Microsoft® PowerPointA® slides for each chapter; a listing of useful Web sites; and more.A?Qualified adopters of this text shouldA?visit www.sagepub.com/warnerstudyA?for more information and to request a copy.

Key Features:

• Begins with a clear review and a fresh perspective on concepts including effect size, variance partitioning, and statistical control. Depending on student background and the level of the course, instructors can begin with chapters that review basic material, or they can begin with more advanced topics and use earlier chapters as supplemental review material.
• Examines three-variable research situations in detail and teaches students how to think about statistical control, which is essential for comprehension of multivariate analyses.
• Includes a chapter on reliability, validity, and multiple item scales, and draws extensively on path models to illustrate theories about possible causal and noncausal associations among variables, beginning with simple three-variable research situations.
• Utilizes graphics to explain concepts such as variance partitioning, statistical control, and factor rotation.
• Contains a glossary and extensive practice exercises to help readers digest the material presented.

A?

Contents Chapter 1. Review of Basic Concepts Chapter 2. Introdution to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals Chapter 3. Satistial 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 Preditor Variables Chapter 12. Dummy Predictor Variables and Interaction Terms in Multiple Regression Chapter 13. Factorial Analysis of Variance Chapter 14. Multiple Regression With More Than Two Predictors Chapter 15. Analysis of Covariance Chapter 16. Discriminant Analysis Chapter 17. Multivariate Analysis of Variance Chapter 18. Principal Components and Factor Analysis Chapter 19. Reliability, Validity, and Multiple-Item Scales Chapter 20. Analysis of Repeated Measures Chapter 21. Binary Logistic Regression