Multivariate Data Analysis Hardcover – 28 Oct 2005
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Top Customer Reviews
Immensely useful are the basic chapters and the sections about assumptions in your data that have to be met for each analysis to be valid. No one told me about testing all these basic assumptions before doing more advanced tests, and in most scientific articles these issues are not reported. I had a pretty good idea what each method was about and when I should use it after reading this book. But more importantly, I was able to do most of the described analyses in SPSS and interpret the output correctly (at least our stats guru said so).
So this is a definite recommendation to read, and I think it's useful for you if you're into research in social science, management, applied psychology, or business.
2. You almost don't find a single formula in the book.
3. You are supposed to remember given stages and steps and opitions to process a MVA.
4. You are not told the theoretical basis of any method.
5. You won't be able to develop statistical methods by your own after reading the book.
6. I feel incredible frustrations in reading it.
Most Helpful Customer Reviews on Amazon.com (beta)
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.
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.
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