Fundamental Statistics for the Behavioral Sciences,
9th Edition

1. Introduction.
2. Basic Concepts.
3. Displaying Data.
4. Measures of Central Tendency.
5. Measures of Variability.
6. The Normal Distribution.
7. Basic Concepts of Probability.
8. Sampling Distributions and Hypothesis Testing.
9. Correlation.
10. Regression.
11. Multiple Regression.
12. Hypothesis Tests Applied to Means: One Sample.
13. Hypothesis Tests Applied to Means: Two Related Samples.
14. Hypothesis Tests Applied to Means: Two Independent Samples.
15. Power.
16. One-Way Analysis of Variance.
17. Factorial Analysis of Variance.
18. Repeated-Measures Analysis of Variance.
19. Chi-Square.
20. Nonparametric and Resampling Statistical Tests.
21. Meta-Analysis.
Appendix A: Arithmetic Review.
Appendix B: Symbols and Notation.
Appendix C: Basic Statistical Formulae.
Appendix D: Data Set.
Appendix E: Statistical Tables.
Glossary.
References.
Answers to Exercises.
Index.

• David C. Howell

  David C. Howell is a professor emeritus and former chair of the psychology department at the University of Vermont. Professor Howell's primary area of research is in statistics and experimental methods. He is also the author of STATISTICAL METHODS FOR PSYCHOLOGY, currently in an Eighth Edition (Wadsworth Cengage Learning, 2013), and the ENCYCLOPEDIA OF STATISTICS IN BEHAVIOR SCIENCE (2005) with Brian Everitt. Before retiring, he frequently served as consultant to other faculty, both in the psychology department and in departments as disparate as Geology and Animal Sciences, and brings those experiences to this endeavor. Professor Howell's other interests include computing and the World Wide Web, and how technology affects communication, teaching, and research.

• The emphasis on effect sizes and meta-analysis is in line with current trends in statistics in the behavioral sciences.

• The frequent demonstration of computer analyses through SPSS and R moves students toward procedures that are becoming critical in a field where hand calculation is no longer a reasonable approach. While the use of computer analyses amplifies the content, students can effectively work with this book even if they avoid all computer-based material.

• Graphical displays clarify the presentation.

• Simple resampling (simulation) studies are used to put various kinds of analyses in a different perspective and to illustrate the underlying nature of how hypotheses are tested.

• Complete answers are available for odd numbered exercises, and answers for all exercises are available to the instructor.

• The text presents material on computer analysis, but less as a way to compute the answer and more as a way to help you understand the material and see how analyses fit in a larger context.

• The frequent demonstration of computer analyses through SPSS and R introduces and moves you toward procedures that are becoming critical in a field where hand calculation is no longer a reasonable approach. However, you can effectively work with this book even if you avoid all computer-based material.

• Graphical displays clarify the presentation throughout the book. Each chapter ends with a summary and a set of review questions that focus your attention on the most important points in the chapter. Additional material on the author's website expands on what you learn in the text.

• Each chapter begins with a listing of terms to be used in the chapter that were introduced earlier. The author includes terms that students have the hardest time understanding, and which consequently warrant repeating. Especially in the early chapters, terms are redefined not only when first introduced, but also when used a second, third, or even a fourth time.

• The book's clear writing, clear definition of terms, and use of worked out examples help students understand some of the more difficult concepts in statistics.

• The text is full of applications, with real data used in all examples. This places a real life perspective on the material and allows for deeper student understanding. The applications are listed on the inside front covers.

• Based on the author's belief that formulas are most helpful in helping students learn concepts, rather than for computation, formulas used in the book are definitional rather than for purposes of calculation.

• Hundreds of exercises, most of which are based on data from published research, promote student interest and provide the context of statistics in behavioral research.

• Recent editions emphasize the importance of measures of effect size and, while covering statistical hypothesis tests in detail, downplay the idea that the goal of a statistical analysis is simply to reject, or not reject, some null hypothesis. This approach is very much in line with current discussions of hypothesis testing. The discussion of meta-analysis emphasizes the importance of seeing one experiment in the context of others.

• Each chapter begins with a listing of terms to be used in the chapter that were introduced earlier. The author includes terms that students often have the hardest time understanding, and which consequently warrant repeating. Especially in the early chapters, terms are redefined not only when first introduced, but also when used a second, third, or even a fourth time to ensure that you learn and retain their meaning.

• The book's clear writing, clear definition of terms, and use of worked out examples help you grasp and understand concepts. Student users have found the informal writing style to be particularly appealing.

• The text is full of applications, with real data used in all examples. This places a real life perspective on the material and allows for deeper understanding. The applications are listed on the inside front covers.

• Hundreds of exercises, most of which are based on data from published research, capture your interest and provide a meaningful context of statistics in behavioral research.

• Based on the author's belief that formulas are most helpful in helping students learn concepts, formulas used in the book are definitional rather than for purposes of calculation.

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