Using Multivariate Statistics

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For advanced undergraduate and graduate courses in Social Statistics. An in-depth introduction to today's most commonly used statistical and multivariate techniques Using Multivariate Statistics, 7th Edition presents complex statistical procedures in a way that is maximally useful and accessible to researchers who may not be statisticians. The authors focus on the benefits and limitations of applying a technique to a data set - when, why, and how to do it. Only a limited knowledge of higher-level mathematics is ...

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Alan O

Oct 4, 2012

Unexpectedly readable

It's a statistics textbook. It's not going to be made into a compelling movie. But within the realm of math texts, I've found this book to be better than most at describing techniques sufficiently to a non-quant that they can be implemented.

Psychologist

Jan 15, 2009

A must have for the researcher

This well written book is classic! Every researcher should have the latest version although all editions are quite good. It is easy to read for those new to multivariate statistics yet the experienced researcher will continue to use it as a reference.

GonzoPlatonicTypewriter2

Feb 28, 2008

This is a book-- as the authors state-- that give the reader an understanding of multivariate statistics in a way analogous to the (usual, mainstream) driver of automobiles-- KNOWING HOW TO USE THE EQUIPAGE WITHOUT BY NECESSITY BEING SUPER-TECH!!! In other words, one can by thoughtful reading of this book get the "gist" of multivariate statistics without the ponderous math lying beneath this type of software-- such as SPSS (all versions) SAS (all versions) etc. etc. Understanding of this sort will help one drive-through-the-maze of software applications-- all of which will do SOME multivariate computation-- but some much more fittingly for one's purpose.

I find it to be true that many in social science research will simply resort to guess-ology when it comes to this sort of quantitative work: they will go from data-input to a supposed multivariate computation without such understanding as Tabachnick and Fidell could provide-- and then blindly just-read the output. This may come close to exactly being garbage-in-garbage-out (GIGO).

What I am positing therefore is that one needs to have a sense of the logic of software in any event before unquestioningly inputting data, and "reading." If one does not have such an ground-up understanding, the mistakes that can be made-- and assumed to be correct-- abound. But if one has a solid understanding of the presentation in Tabachnick and Fidell, this is the-less-likely to occur.

The authoresses begin with an overview-- in a few paragraphs per type of program deriving from a decision-tree-diagram for all multivariate software. Then separate chapters for each type of software are provided, so that one starts from the general and goes to the very-specific-indeed. By a careful analysis of these chapters and overview, one can avoid the illusory/mistake-rife tendency to "just put in the data and see what comes out."

In short, this is just about the best overview-book on multivariate stats on the market, and has been so for some time (decades, really.) All that is pre-required would be a good grasp of univariate (STATS 101) statistics, and a good head for sensible-research-questioning. If one's design is good/valid-- and if one has done the fact-finding homework-- the product will augment the result of univariate work by several orders of magnitide.

So: by all means: get this book, empirically-minded researchers! Since all things are headed in a multivariate cybernetic direction, this acquisition will never be mistaken!

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