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Reading and Understanding Multivariate Statistics | 
 enlarge | Author: Laurence G. Grimm
Creator: Paul R. Yarnold
Publisher: American Psychological Association (APA)
Category: Book
List Price: $29.95
Buy New: $19.77
You Save: $10.18 (34%)
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Rating:  8 reviews
Sales Rank: 24338
Media: Paperback
Edition: 1
Pages: 373
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.9 x 6.9 x 0.9
ISBN: 1557982732
Dewey Decimal Number: 001.4225
EAN: 9781557982735
Publication Date: January 1995
Availability: Usually ships in 24 hours
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Product Description
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for -- and the types of empirical questions that can best be addressed by -- each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful. The most commonly used multivariate techniques are examined in detail: multiple regression and correlation, path analysis, principal-components analysis, exploratory and confirmatory factor analysis, multidimensional scaling, analysis of cross-classified data, logistic regression, multivariate an alysis of variance (MANOVA), discriminant analysis, and meta-analysis. Statistical notations are explained, underlying assumptions are described, and terms are defined clearly and understandably. Concepts and symbols are presented with minimal use of formulas and a generous use of real-world research examples. Each chapter also includes suggestions for additional reading and a glossary of statistical and related terms.
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| Customer Reviews: Read 3 more reviews...
 I read it - and I understood it! November 1, 2002
debvh (New Jersey)
38 out of 39 found this review helpful
"Reading and Understanding Multivariate Statistics" achieves exactly what its title implies. Geared toward non-statisticians in behavioral and social science fields, this book provides clear and reasonably simple explanations of some of the most common multivariate analyses. Each chapter focuses on a different analysis and presents its conceptual underpinnings, underlying assumptions, and basic procedures with a minimum of equations and many concrete examples. It does not teach you how to perform the analyses but does provide references for those who wish to get more detailed information. As a research scientist who doesn't always remember everything I learned in graduate statistics class, I find this book an invaluable aid keeping up with the current literature in my field and in making the most of statistical consultations. This book is ideal for anyone whose job requires them to be a "consumer" of research; for researchers who wish to further their understanding of data analysis; and as a companion text for graduate statistics classes.
Finally! A statistics book in English instead of statistese! March 12, 1999
68 out of 71 found this review helpful
The book is an overview of a variety of multivariate statistical techniques. The book is geared as a companion to a heavy duty stats textbook, with the purpose of providing conceptual discussions of each of the techniques. And it succeeds brilliantly. The authors of each chapter treat the topic statistic with very little, if any Greek, plenty of concrete examples, and clearly delineate the assumptions of the statistical procedure, when it is appropriate and inappropriate to use the procedure, and how to interpret the results of the analysis. If what you want is simply to get a good, practical, conceptual understanding of a statistical procedure and its uses, without any of the theoretical, mathematical derivations, this is the book for you!! This book literally saved my grade in my multivariate stats class. No grad student should be without this.
Great Resource for Statistics August 7, 2003
Chava79 (USA)
14 out of 15 found this review helpful
In many introductory statistics courses you usually do not cover multivariate statistics. This book and its companion volume are useful for anyone in upper level undergraduate or graduate programs. It is a great reference to have when planning research.You can read it all at once to get a general understanding of this area or you can look at it as you need it as a reference. It was much better than the statistics books I have had as required reading in courses. It's a great resource overall!
Fantastic Treatment of Sophisticated Mathemeatical Concepts November 5, 2004
G. A. Dawson (Chicago, IL United States)
12 out of 15 found this review helpful
I've long wanted a better explanation of Eigenvectors and Eigenvalues than I recieved in a econometrics or statistics textbook. This book gives me an incredibly clear understanding of what they are. Now when I look back at the mathematical interpretation again it means so much more. This is a fantastic book that would highly recommend to anyone wanting a clear conceptual understanding of these sophisticated topics. 5 stars, no questions about it!
 Excellent Introductory Text for Non-Statisticians June 11, 2001
J. Swartz (Chicago, IL USA)
62 out of 62 found this review helpful
As someone who has tried to teach multivariate statistics to non-statistician graduate students for the past 5 years, I have found this to be a very valuable and clearly-written text. As advertised and as the previous reviewer noted, the text is largely free of complex statistical equations and instead has clear descriptions of each type of test as well as common applications of that test. It is a perfect introduction for students who are intimidated by numbers and equations yet need to know about multivariate statistics for their graduate studies. The book has several weaknesses that I found require supplementing with other texts. For one, there is no tie-in with major computerized statistical applications like SPSS and SAS nor are there example exercises for students to run and interpret statistical tests for themselves. I have found such exercises to be invaluable in teaching the meaning and uses of multivariate tests. There also should have been a discussion of general issues that cut across the different multivariate tests such as data cleaning, data transformation, the role of correlation matrices and the like and so on. For coverage of these issues, I have found it helpful to use chapters from Tabachnik and Fidel's Using Multivariate Statistics text. Finally, a number of tests, such as survival analysis are not covered in this text, though a second volume by the same authors does cover survival analysis as well as other techniques and should be considered as a companion volume as well. In sum, this is an excellent and unusually clearly written text that is ideal for non-statistician graduate students in the social sciences. More in-depth analysis of important issues related to multivariate statistics and classroom exercises using statistical computer applications requires augmenting this text with additional readings.
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