This book 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. For example, "How do researchers' decisions about treatment dosage levels and sample size tend to influence the magnitude of 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-ROM include datasets in SPSS and Excel; answers to all comprehension questions; Microsoft[R] PowerPoint[R] slides for each chapter; a listing of useful Web sites; and more. The book 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. The book examines three-variable research situations in detail and teaches students how to think about statistical control, which is essential for comprehension of multivariate analyses. It 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. Finally, the book utilizes graphics to explain concepts such as variance partitioning, statistical control, and factor rotation. The 13 chapters comprising this book are: (1) Review of Basic Concepts; (2)Introduction to SPSS: Basic Statistics, Sampling Error, and Confidence Intervals; (3) Statistical Significance Testing; (4) Preliminary Data Screening; (5) Comparing Group Means Using the Independent Samples t Test; (6) One-Way Between-Subjects Analysis of Variance; (7) Bivariate Pearson Correlation; (8) Alternative Correlation Coefficients; (9) Bivariate Regression; (10) Adding a Third Variable: Preliminary Exploratory Analyses; (11) Multiple Regression with Two Predictor Variables; (12) Dummy Predictor Variables and Interaction Terms in Multiple Regression; and (13) Factorial Analysis of Variance.