Statistical Methods for Psychology

Auteur
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.

Texte du rabat

STATISTICAL METHODS FOR PSYCHOLOGY surveys the statistical techniques commonly used in the behavioral and social sciences, especially 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 emphasize conceptual understanding. Along with significantly updated discussions of effect size and meta-analysis, this Eighth Edition continues to focus 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.

Contenu

1. Basic Concepts. 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. 8. Power. 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.