Multivariate Data Analysis (6th Edition) [Hardcover]

Joseph F. Hair (Author), Bill Black (Author), Barry Babin (Author), Rolph E. Anderson (Author), Ronald L. Tatham (Author)

Book Description

Publication Date: November 7, 2005 | ISBN-10: 0130329290 | ISBN-13: 978-0130329295 | Edition: 6

For graduate-level courses in Marketing Research, Research Design and Data Analysis. Multivariate Data Analysis provides an applications-oriented introduction to multivariate data analysis for the nonstatistician by focusing on the fundamental concepts that affect the use of specific techniques.

Editorial Reviews

From the Back Cover

Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today — e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. --This text refers to an out of print or unavailable edition of this title.

Product Details

• Hardcover: 928 pages
• Publisher: Prentice Hall; 6 edition (November 7, 2005)
• Language: English
• ISBN-10: 0130329290
• ISBN-13: 978-0130329295
• Product Dimensions: 10.1 x 8.1 x 1.5 inches
• Shipping Weight: 4.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (20 customer reviews)
• Amazon Best Sellers Rank: #534,916 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

63 of 65 people found the following review helpful:

5.0 out of 5 stars A GREAT reference, July 4, 2000

By 

A. Maxham "Just trying to help..." - See all my reviews
(VINE VOICE)    (REAL NAME)   

This review is from: Multivariate Data Analysis (5th Edition) (Hardcover)

I took multivariate in Ph.D. school. Our professor didn't have a book (just his notes), so I had no book to be loyal to when I got out. I was given this book once I graduated and consider it to be a fabulous reference. I can't speak to *learning* multivariate techniques out of it because that's not been my experience with this book.

I use it as a reference- to refresh myself on a technique, or to consult when I run into a problem- this book has yet to let me down and has been able to answer any question or solve any problem that I've had.

You see this book cited in academic behavioral research, but the book does a great job of explaining things in a managerial way as well.

Other of these reviews have criticized it for going on too long on an example or a technique- for that I PRAISE this book- I WANT that extra information. I'm reminded of that quote from "Amadeus"- "Too many notes." I WANT as many notes as I can get- that's what makes it so much more helpful.

If you are looking for a great reference book for multivariate techniques, look no further.

44 of 44 people found the following review helpful:

3.0 out of 5 stars A good, practical introduction to data-analysis, February 27, 1999

By A Customer

This review is from: Multivariate Data Analysis (5th Edition) (Hardcover)

The Hair et al textbook has been one of my favorite recommendations to graduate students who are wrestling with their data analysis. I also use this book in a methodology course I give over here at Vrije University Amsterdam. The following points, in my opinion make this book the best choice for this purpose: 1. This book, contrary to many more statistically oriented texts, discusses analysis methods that are really used. Not the techniques that are necessarily most elegant, but the techniques that are most practical 2. The approach taken in this textbook is not formula-oriented. The authors simply discuss the prerequisites of a certain techniques and the interpretation of the outcomes. 3. The authors make a nice attempt to help the reader in choosing an appropriate technique. Although the treatment of the technique sometimes is too superficial, this is not a problem. A textbook cannot treat all details and good references are given in each chapter.

One the negative side, a number of caveats has to be mentioned however: 1. This most recent edition, unfortunately is worse than previous one. I tend to recommend older editions. The authors apparently have been trying to include a lot of material that users (i.e. instructors) of the book thought was missing. The structure of the book suffers from this added material. Another problem is that really high-level material gets mixed up with elementary statistics. A textbook simply cannot be everything for everyone. 2. The book is too big. Too much material has been added to this text. Furthermore, often too much time is spend discussing the examples. E.g., the illustration of discriminant and logit analysis alone takes over 40 pages.

Overall my rating of this book is not negative. If this edition of the book had been my first encounter with this text, I probably had been more enthousiastic. Now I tend to compare it with earlier editions, and must conclude the book has degraded. A next edition, in which a good editor goes through the text to enhance structure and get rid of superfluous material might easily solve the problems mentioned.

47 of 48 people found the following review helpful:

5.0 out of 5 stars Great book, June 28, 2001

By 

Wayne Parker (Scottsdale, AZ United States) - See all my reviews

This review is from: Multivariate Data Analysis (5th Edition) (Hardcover)

This is the best applied book on multivariate analysis I know. It clearly explains how to do statistical analyses and how to interpret the output. Clear examples throughout. Syntax supplied for each type of multivariate analysis in both SPSS and SAS, with LISREL notation for CFA. In addition to specific techniques (factor analysis, multiple regression, multiple discriminant analysis, MANOVA, conjoint analysis, canonical correlation, cluster analysis, multidimensional scaling, structural equation modeling/CFA) excellent sections on structuring data, cleaning data, and handling missing data. While mathematical sophistication always helps in stat, this book doesn't require it. No knowledge of matrix algebra needed to understand this book. Few if any formulas. Emphasis is on logic rather than math.