Synopses & Reviews
This author team is committed to making statistics a highlight for psychology students! Now, in a 5th edition, Statistics for Psychology, continues to be an accessible, current, and interesting approach to statistics. With each revision, the authors have maintain those things about the book that have been especially appreciated, while reworking the text to take into account the feedback, their our own experiences, and advances and changes in the field.
The fifth edition of this popular text uses definitional formulas to emphasize concepts of statistics, rather than rote memorization. This approach constantly reminds students of the logic behind what they are learning, and each procedure is taught both verbally and numerically, which helps to emphasize the concepts. Thoroughly revised, with new content and many new practice examples, this text takes the reader from basic procedures through analysis of variance (ANOVA). While learning statistics, students also learn how to read and interpret current research.
This book brings to life the compelling underlying logic of statistical methods so that readers can not only do the computations, but also truly understand what they are doing and remember what they have learned for years to come. This book covers the basic introductory statistics and methods including central tendency and variability, inferential statistics (Z scores, the normal curve, sample versus population, and probability), the t test, the analysis of variance, correlation, prediction, and more. For research analysts and reporters in the field of psychology.
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Table of Contents
1. Displaying the order in a group of numbers.
2. Central tendency and variability.
3. Some key ingredients for inferential statistics: Z scores, the normal curve, sample versus population, and probability.
4. Introduction to hypothesis testing.
5. Hypothesis testing with means of samples.
6. Making sense of statistical significance: Effect size and statistical power.
7. Introduction to the t test: Single sample and dependent means.
8. The t test for independent means.
9. Introduction to the analysis of variance.
10. Factorial analysis of variance.
13. Chi-square tests.
14. Strategies when population distributions are not normal: Data transformations and rank-order tests.
15. Integration and the general linear model.
16. Making sense of advanced statistical procedures in research articles.