Bridging an understanding of Statistics and SPSS.
This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students' understanding of theoretical concepts by teaching them in a practical context.

1. Introduction
2. Descriptive Statistics
3. Chi-Squared Test
4. Linear Correlation
5. One- and Two Sample T-Tests
6. One-way ANOVA
7. Two- and Higher-way ANOVA
8. Within-subject ANOVA
9. Mixed-model ANOVA
10. MANOVA
11. Regression
12. ANCOVA
13. Factor and Components Analysis
14. Psychometrics
15. Non-parametric Tests
16. Matrix Algebra
17. Appendix on the General Formulation of Custom Contrasts using Syntax

Elliot T. Berkman is Assistant Professor of Psychology and director of the Social and Affective Neuroscience Laboratory at the University of Oregon. He has been teaching statistics to graduate students using SPSS for the past six years. In that time, he has been awarded the UCLA Distinguished Teaching Award and the Arthur J. Woodward Peer Mentoring Award. He has published numerous papers on the social psychological and neural processes involved in goal pursuit. His research on smoking cessation was recognized with the Joseph A. Gengerelli Distinguished Dissertation Award. He received his PhD in 2010 from the University of California, Los Angeles.