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Multiple Regression in Behavioral Research, by Elazar J. Pedhazur
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Multiple Regression in Behavioral Research adopts a data-analysis approach to multiple regression. Pedhazur integrates design and analysis, and emphasizes learning by example and critiques of published research. Features: * Pedhazur places strong emphasis on the application of regression analysis to various research problems. He discusses major research studies and journal articles in conjunction with specific methods presented. This emphasis gives students a critical appreciation of how methods are used and abused in various research areas. * Chapters 11-15 discuss various coding methods for categorical variables. This detailed explanation teaches students the properties of different coding methods and their suitability for specific tasks. * Multiple Regression introduces path analysis and structural equation models, including indices of fit and decomposition of effects. This information, in Part 3, exposes students to recent developments in the field. New to this edition: * Pedhazur demonstrates applications of all analytic approaches with detailed analyses of simple numerical examples. He uses the most recent versions of BMDP, MINITAB, SAS, and SPSS, provides examples of the computer outputs, and comments on them in detail. * The latest versions of EQS and LISREL programs for the structural equation modeling are also introduced and used for analyzing causal models with single indicators (path analysis) and multiple indicators or unobserved variables. * New chapters include Chapter 3, Regression Diagnostics, Chapter 16, Elements of Multilevel Analysis and Chapter 17, Categorical Dependent Variable: Logistic Regression.
- Sales Rank: #545240 in Books
- Published on: 1997-01-24
- Original language: English
- Number of items: 1
- Dimensions: 1.53" h x 7.79" w x 9.59" l, 3.98 pounds
- Binding: Hardcover
- 1058 pages
Most helpful customer reviews
18 of 20 people found the following review helpful.
An excellent intorduction to linear regression
By A Customer
Pedhazur's third edition of this text that he began with Kerlinger is an excellent introduction to linear regression. I use this text in a statistics course that I teach to first-year graduate students and they have responded favourably to me about the book. Pedhazur is a stickler for 'getting it right,' which is critically important for the serious student to learn at this stage. The book is replete with examples of both doing it right and doing it wrong (the latter culled from the literature). If you want to learn how to do linear regression correctly, this text is excellent within its scope as a primer. The introductory chapters on multilevel modelling and logistic regression are nice touches. All in all a very useful text.
9 of 9 people found the following review helpful.
Quirky but a landmark achievement
By Roland B. Stark
If you're serious about learning quantitative research, well, this man takes his work very, very seriously, much to our benefit. He has an extraordinary intellect. I would call the book "magisterial" if his self-importance weren't taken to such hilarious heights. But that comic relief is welcome, too.
The book plumbs the depths of the general linear model, providing extensive, rigorous, extremely well-considered treatment of statistical control, linear regression, analysis of covariance, logistic regression, and structural equation modeling. Includes important sections on topics such as nonlinear relationships and interactions. Has sections that I reread every few months to a year because the ideas are so important to my understanding of analytic issues and so hard to come by anywhere else.
It's not for beginners, but don't wait too long! You'll kick yourself for all the insights you could have developed, and all the conceptual and technical mistakes you could have avoided.
Two drawbacks. There is too much attention to SPSS, SAS, and other code for many people's taste. Also, it is weighted down by unnecessarily precise calculation, overemphasizing hypothesis testing and statistical significance at the expense of the size of effects. One of the more insidious statistical mistakes (see [...]).
10 of 11 people found the following review helpful.
A Great Regression Book for a Class Devoted to Regression
By B. S. Lebow
This is a wonderful text for a class primarily dedicated to multiple regression. I own and have read many regression textbooks, and this continues to be one of the very best! The reader reviews below appear to mostly be expressing frustration with instructors, and not this text. If you are taking an introductory course in which regression is one of many topics, this book may be too advanced for your needs (and may be a poor choice by your instructor!). However, if you are taking a course dedicated to regression (linear and logistic; or general linear models in general), this is one of the very best text available!
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