Description
Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course.
Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated.
Features:
- Presents an in-depth description of both classic and modern methods
- Explains and illustrates why recent advances can provide more power and a deeper understanding of data
- Provides numerous illustrations using the software R
- Includes an R package with over 1300 functions
- Includes a solution manual giving detailed answers to all of the exercises
This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described.
Rand Wilcox is a professor of psychology at the University of Southern California. He is the author of 13 other statistics books and the creator of the R package WRS. He currently serves as an associate editor for five statistics journals. He is a fellow of the Association for Psychological Science and an elected member of the International Statistical Institute.
Table of Contents
Introduction. Numerical and Graphical Summaries of Data. Probability and Related Concepts. Sampling Distributions and Confidence Intervals. Hypothesis Testing. Regression and Correlation. Bootstrap Methods. Comparing Two Independent Groups. Comparing Two Dependent Groups. One-Way ANOVA. Two-Way and Three-Way Designs. Comparing More than Two Dependent Groups. Multiple Comparisons. Some Multivariate Methods. Robust Regression and Measures of Association. Basic Methods for Analyzing Categorical Data. Answers to Selected Exercises. Tables. Basic Matrix Algebra. References. Index.
