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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.
- Sales Rank: #954775 in Books
- Published on: 2005-11-07
- Original language: English
- Number of items: 1
- Dimensions: 10.10" h x 1.53" w x 8.60" l,
- Binding: Hardcover
- 928 pages
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.
Most helpful customer reviews
46 of 46 people found the following review helpful.
A good, practical introduction to data-analysis
By A Customer
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.
0 of 0 people found the following review helpful.
Good content, but not reader friendly
By Amazon Customer
Bought this book because my marketing teacher was full of praise for it. Though complete, I personally find it very hard to read through. Even though the content is very thoroughly researched, well written and structured, the book is not reader friendly - huge pages filled with tiny characters. Learning can be fun too! Then again, maybe I have not taken enough marketing research classes yet.
5 of 5 people found the following review helpful.
Sure it's good, and a good price by the pund too!
By Tom Anderson
A pretty good overview and a lot of in-depth material on Multivariate data analysis. Not quite a bed time read though.
I recommend this book as part of your analytical library.
If you liked this book, another good book on multivariate data analysis you may want to check out as well is Sharma, S.; Applied Multivariate Techniques, New York: John Wiley & Sons, Inc., 1996.
If you want something easier to read/more practical, and you prefer SPSS over SAS you may want to check out either `Discovering Statistics using SPSS for Windows' by Andy Field, or probably even better/simpler `SPSS Survival Manual' by Pallant.
Tom Anderson
Anderson Analytics, LLC
(...)
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