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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.
- Sales Rank: #294747 in Books
- Brand: Brand: Routledge
- Published on: 2008-08-29
- Format: Complete
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x .81" w x 7.01" l, 1.70 pounds
- Binding: Hardcover
- 328 pages
- Used Book in Good Condition
Review
"This text’s focus on a unifying framework and nonthreatening style sets it apart. I would certainly recommend this book to a student or colleague struggling to gain an intuitive understanding of the concepts. The original edition was popular and influential. I expect the second edition will be as well." - Kristopher J. Preacher, University of Kansas
"I am absolutely delighted to report that in virtually every way I could hope for, the new edition is better. The scholarship is top-notch, but in a very accessible way. The authors write beautifully … I will absolutely adopt and recommend the second edition. I love it." - J. Michael Bailey, Northwestern University
"After reviewing eight potential textbooks for my … graduate statistics course, I chose this one … The thoroughness with which the book teaches the model comparison approach allows students to understand and develop meaningful analyses of their own data." -Deborah M. Clawson, Catholic University, USA
"This second edition is the perfect book for today's data analysts." -David Kenny, University of Connecticut, USA
About the Author
Charles "Chick" Judd is Professor of Distinction in the College of Arts and Sciences at the University of Colorado at Boulder. He received his Ph.D. in 1976 from Columbia University. A Fellow of the American Psychological Association, the Society of Personality and Social Psychology, and the Society for the Psychological Study of Social Issues, Dr. Judd’s research focuses on social cognition and attitudes, intergroup relations and stereotypes, judgment and decision making, and methods of behavioral science research and data analysis.
Gary McClelland is Professor of Psychology at the University of Colorado at Boulder. He received his Ph.D. in 1974 from The University of Michigan. A Faculty Fellow at the Institute of Cognitive Science, Dr. McClelland’s research interests include judgment and decision making, psychological models of economic behavior, statistics and data analysis, measurement and scaling, and mathematical psychology.
Carey S. Ryan is a Professor in the Department of Psychology at the University of Nebraska at Omaha. After earning her Ph.D. at the University of Colorado at Boulder, Professor Ryan taught at the University of Pittsburgh and joined the University of Nebraska faculty in 2001. She has research interests in stereotyping and prejudice, group processes, and program evaluation.
Most helpful customer reviews
8 of 8 people found the following review helpful.
A Fantastic Text for Anyone Interested in Deeply Analyzing Data
By C. Long
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.
3 of 3 people found the following review helpful.
From someone who "ate the dogfood"... WOOF!!!
By Brad Aisa
I'm not going to write a long review of the book itself because C. Long expressed the immense virtues of this book perfectly--I just wanted to share my experience with it.
I was a student of Drs. Judd and McClelland in a year-long graduate level stats (oops! "Data analysis") course. Although this was nominally a Psychology course, its qualities were so famous that people came from departments all over the University to take it, such as Education, Science, Business, and others. I've taken stats a number of times over the years, and never really "got it"--it was all so mysterious with all these seemingly different test types. This was the first course in which I actually completely understood the underlying concepts! Believe it or not, it made statistics and data analysis exciting! I haven't actually used these techniques now in a couple of years, but have recently had need to brush up and start using them again. All the basic concepts are as fresh in my mind as the year I took this course, and I'm confident I'll be up to speed very quickly with just a quick refresh browse of the book.
I can't recommend this text highly enough!
P.S. Oh, yeah... yes, I got an "A"... ;)
1 of 1 people found the following review helpful.
Excellent book with a buggy kindle edition
By James Endicott
I loved this book. I've struggled with stats in a number of classes before but this one finally clicked. The focus on model comparisons as a unifying framework means that everything builds neatly on everything else and ties together in predictable ways. Other stats classes have thrown grab bags of ideas and formulae at you but this book does a great job of showing how there is really only one true formula and everything is either an estimate of that formula or trying to make it apply only to a specific situation instead of a more general form.
My two qualms with it are that the language used is different from what many other stats books use so it can be tough to find other resources online and problems with the kindle edition. Sometimes the wrong formulae is present, sometimes it can't correctly render special characters, and other little problems like that.
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