Customer Reviews:
 Over-simplification March 2, 2009
Kyra
0 out of 1 found this review helpful
I give 2 stars to balance other 5-star reviews to what I think is a major shortcoming: the idea that multivariate statistics can be grasped without an intuitive knowledge of the background engine (i.e. basic statistics). It is a myth that one can understand academic papers on multivariate analyses just by reading this book. You need to understand the background statistics, at least intuitively. Otherwise, you are like an electrician who doesn't know basic theories about electricity. Hence, if you want to learn something about multivariate statistics, you are better off getting a book that gives an intuitive grasp of the techniques, rather than just texts, and more texts, without ever explaining "how it works." A plus point of this book is its low cost, but you get what you pay for. Decide for yourself.
 These guys are great! January 4, 2007
Katie (New Jersey)
1 out of 3 found this review helpful
Grimm and Yarnold really offer great, user-friendly explanations of stats. I've used these for 3 years now, and they were great for getting through coursework and dissertation.
I highly recommend these books!
Great Statistics Resource August 6, 2003
Chava79 (USA)
11 out of 14 found this review helpful
This book and its companion volume are invaluable resources if you need to understand or work with multivariate statistics. It is easy to read and I would recommend it over any of the required texts that I have had for statistics courses.
I read more - and I understood more! March 21, 2003
debvh (New Jersey)
28 out of 28 found this review helpful
Like its predecessor, "Reading and Understanding MORE 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 common multivariate analyses. This book includes special attention to scales of measurement, reliability and generalizability theory, item response theory, and assessing the validity of measurement. In addition, it covers cluster analysis, Q-technique factor analysis, structural equation modeling, canonical correlation analysis, repeated measures analysis, and survival analysis. The authors present the conceptual underpinnings, underlying assumptions, and basic procedures for each analysis with a minimum of equations and many concrete examples. The book 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.
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