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STATISTICAL METHODS FOR PSYCHOLOGY surveys the statistical techniques commonly used in the behavioral and social sciences, particularly psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasizes conceptual understanding. This Eighth Edition continues to focus students on two key themes that are the cornerstones of this book's success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment. New and expanded topics--reflecting the evolving realm of statistical methods--include effect size, meta-analysis, and treatment of missing data.
- This extensively revised edition has been refocused in certain areas to cover topics that have become more important in the evolving realm of statistical methods. For instance, the author includes more material on effect size and the magnitude of effect, providing a thorough introduction. To make room for important new material while still keeping the text at a manageable length, coverage of some older topics has been moved to the author's website, where it is available for download in its entirety.
- A chapter on meta-analysis and single subject designs, addressing the fact that meta-analyses underlie the new emphasis in the behavioral sciences on "evidence-based medicine," has replaced Chapter 17 on log-linear models.
- Because the introduction of linear mixed models and methods of data imputation have significantly changed the treatment of missing data, expanded coverage of mixed models allows for better treatment of missing data and relaxes unreasonable assumptions about compound symmetry.
- Pared coverage of the wide range of multiple comparison techniques has been replaced by a discussion of Benjamini and Hochberg's False Discovery Rate.
- Other topics receiving expanded and/or updated treatment include the concept of resampling to illustrate what the more traditional approaches are attempting to do on the basis of underlying assumptions, and coverage of Cochran-Mantel-Haenszel analysis of contingency tables.
- The use of graphical displays has been expanded even further in this edition to help students better understand their data and evaluate the reasonableness of their assumptions.
- In addition to including material no longer contained in the text, the author's website presents a large number of programs and selections of "R" code, serving as a starting point for those interested in using the "R" computing environment and allowing for demonstrations of points made in the text.
- Each chapter begins with objectives, detailed content outlines, and a brief introduction.
- Key terms appear in the margin of the text and in bold within the text narrative, making them easy to find.
- End-of-chapter resources include key terms, exercises, and discussion questions. Students can find brief answers to the odd-numbered exercises at the end of the text, and worked-out answers in the Student Manual.
- Accessible to adopting instructors, the author's website includes a wealth of information, including topics formerly covered in the text, additional examples, additional data, demonstrations, and more.
- This text integrates two underlying themes. The first is the importance of looking at the data before formulating a hypothesis, and the second is the importance of the relationship between the statistical test to be employed and the theoretical questions being posed by the experiment.
- Howell explains the material at an intuitive level to give students a sense of how tests work and how they interrelate. He illustrates concepts with real-life examples from published literature.
2. Describing and Exploring Data.
3. The Normal Distribution.
4. Sampling Distributions and Hypothesis Testing.
5. Basic Concepts of Probability.
6. Categorical Data and Chi-Square.
7. Hypothesis Tests Applied to Means.
9. Correlation and Regression.
10. Alternative Correlational Techniques.
11. Simple Analysis of Variance.
12. Multiple Comparisons Among Treatment Means.
13. Factorial Analysis of Variance.
14. Repeated-Measures Designs.
15. Multiple Regression.
16. Analyses Of Variance and Covariance as General Linear Models.
17. Meta-Analysis and Single-Case Designs.
18. Resampling and Nonparametric Approaches to Data.
Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.
Instructor's Solutions Manual
Accessible online, this manual includes worked-out solutions to all book problems.
Student Solutions Manual
Contains fully worked-out solutions to all of the odd-numbered exercises in the text, giving students a way to check their answers and ensure that they took the correct steps to arrive at an answer.
Student Solutions Manual