Data Analysis: A Model Comparison Approach, Second Edition

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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.

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Data Analysis: A Model Comparison Approach, Second Edition Review

This book is a fantastic graduate-level data analysis (statistics) text. The writing style is clear and compelling. It is ideal for the scientist/researcher who want to ask practical questions of their data without being forced into standard statistical recipes. I have been searching for a statistics text like this for nearly 10 years and am very pleased to have found it.
The book focuses you, the reader, on composing powerful, but simply-formed models of the questions you wish to ask of your data. It builds a unified approach to data analysis from first principles rather than presenting the typical hodge-podge of cookbook techniques found in many traditional statistics texts. Instead of trying to fit your data into a recipe (t-test, ANOVA, regression, ANCOVA, etc.) you specify a pair (or multiple pairs) of model equations and then use them to ask quantitative questions of your data. Thus, you spend your time asking questions of your data not forcing it into some pre-determined recipe which may be an ill fit. In addition to answering standard "recipe" questions, with this approach you can easily generate and answer questions that fall between the standard recipes, but that are quite valid to ask.
The text presents exactly as much math as necessary to understand the tools, but not more: it does not require a heavy mathematical background. The authors use the text in a graduate level psychology class, but it the book is applicable to those with a more or less quantitative background.

I highly recommend this book to anyone interested in a deep analysis of whatever data might cross your desk.

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