Data Analysis: A Model Comparison Approach, Second Edition

by Charles M. Judd, Gary H. McClelland, Carey S. Ryan

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: 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.

Genres: Textbooks, Nonfiction

328 pages, Hardcover

First published January 1, 1989

Reviews

[Wej · Rating 4 out of 5]

Experimental scientists, as least in my days, were taught a recipe-style approach to statistics. For instance, if your study design is X then use test Y etc. This book introduces a more sensible, in my view, way of teaching inferential statistics. The authors focus on comparing models, which all work like linear regression. Once the basics of linear regression are taught, it is easy to see how the same approach can be used with multivariate statistical tests, and how looking for the best metric between models (say R2 or RMSE) is the best way to go. This approach won’t be new to economists, but in behavioural science (the authors work in psychology and use examples from this discipline) it is an epiphany.

Admittedly, there are other ways of analysing data than fitting and comparing models. Fitting data to a model can often yield significant results if the number of parameters is increased. However, outside of academic research simpler approaches (e.g. fast and frugal heuristics) or ML-based predictions can be even more useful.

I never took so long to read a book as with this one. One one hand, it is full of detailed analysis and the readers are given examples and step-by-step calculations of key metrics. But then the examples seemed dry and after a while reading this book felt like a chore. It did not help that this was a compulsory reading for my statistics class. This meant that each time I opened it, it felt like homework.

[Pauline · Rating 5 out of 5]

Super clear, very interesting, and did what it's supposed to do: taught stats in an easy a way as possible.