This completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. The authors show how all analysis of variance and multiple regression can be accomplished within this framework. The model-comparison approach provides several benefits:
- It strengthens the intuitive understanding of the material thereby increasing the ability to successfully analyze data in the future
- It provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of questions
- It reduces the number of statistical techniques that must be memorized
- It teaches readers how to become data analysts instead of statisticians.
The book opens with an overview of data analysis. All the necessary concepts for statistical inference used throughout the book are introduced in Chapters 2 through 4. The remainder of the book builds on these models. Chapters 5 - 7 focus on regression analysis, followed by analysis of variance (ANOVA), mediational analyses, non-independent or correlated errors, including multilevel modeling, and outliers and error violations. The book is appreciated by all for its detailed treatment of ANOVA, multiple regression, nonindependent observations, interactive and nonlinear models of data, and its guidance for treating outliers and other problematic aspects of data analysis.
Intended for advanced undergraduate or graduate courses on data analysis, statistics, and/or quantitative methods taught in psychology, education, or other behavioral and social science departments, this book also appeals to researchers who analyze data. A protected website featuring additional examples and problems with data sets, lecture notes, PowerPoint presentations, and class-tested exam questions is available to adopters. This material uses SAS but can easily be adapted to other programs. A working knowledge of basic algebra and any multiple regression program is assumed.
Industry Reviews
'Data Analysis is a rare graduate statistics book that combines truth (scholarly strength) and beauty (a clear approach that builds from one chapter to the next). The book's model comparisons give students a systematic way to think deeply about their hypotheses as well as the flexibility to answer meaningful questions about their own data that most textbooks address only briefly if at all.' - Deborah Clawson, Catholic University of America, USA
'Most introductory statistics texts teach students how to apply specific tests in specific circumstances, with little room for generalizing knowledge to new settings. Data Analysis instead teaches students how to think like scientists, always framing hypothesis tests as formal comparisons between competing explanations. The first two editions were ahead of their time in their philosophical approach to data analysis, and this new edition retains and expands their unifying framework.' - Kristopher J. Preacher, Vanderbilt University, USA
'I am delighted that both logistic regression and multilevel modeling are now included. Both topics are introduced using the authors' clear, useful, and integrative approach. Not only does the new material help me to teach this to my students better, it also helps me to understand the topics better!' - J. Michael Bailey, Northwestern University, USA
