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Using Multivariate Statistics (5th Edition), by Barbara G. Tabachnick, Linda S. Fidell
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Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS, SPSS, and SYSTAT, some not available in software manuals. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set — when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.
- Sales Rank: #616315 in Books
- Published on: 2006-03-03
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 1.70" w x 7.50" l, 3.65 pounds
- Binding: Paperback
- 1008 pages
Most helpful customer reviews
5 of 5 people found the following review helpful.
Marred by Errata
By Amazon Customer
The 5th edition of this popular book is, in my opinion, marred by a large number of errata (see the book's web page.) While it is common for technical books to have errata, I must raise some doubts about how carefully the book was "proofed." Needless to say, this is not exactly confidence inspiring.
4 of 4 people found the following review helpful.
Valued Resource
By Greg V.
I've bought a lot of stats books over the years - and this is one I keep going back to. It fills a nice gap between introductory stats books and advanced texts that address one particular method/topic.
I like that each chapter has, more or less, three parts: 1) a conceptual part, 2) a math part, and 3) a stats program part. Some parts may be more or less useful to you. When reviewing, I usually like to stare at, and mentally work through, the math. I also like the stats program parts as syntax for software is often provided.
This being said, if I get really into a topic (e.g., using mixed models to examine change over time), I will go to a more in-depth text. However, reading the T&F summary first does help give me a broader conceptual framework on which to hang the details of more in-depth materials.
Lastly, I also like the chapter on data cleaning and checking basic (univariate) stats before continuing on with more complex analyses. I work with other people who take a very point-and-click, magic button, get the stat approach to data analysis. You know, just looking for that p-value for a very specific term. I like having something to show that gives steps for getting to know your data better than that. It leads to better practice, IMHO.
2 of 2 people found the following review helpful.
A Lifesaver!!
By Kristen
This book has been at my side throughout my entire dissertation process. It clearly explains different statistical methods, analysis assumptions, and interpretive issues. It also provides sample write-ups, which have been helpful. It covers basic data screening techniques and simpler analyses such as factor analysis and multiple regression to more advanced topics such as multi-level linear modeling. Most importantly, it explains the major topics in plain English. It is advanced enough to not be insulting to users who have some familiarity in statistics. However, the writing is not so technical as to be incomprehensible to those without advanced statistics degrees. I highly recommend this book to graduate students and advanced researchers and will continue to use it as a valuable resource throughout my research career.
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