Analyzing Multivariate Data

Most books on the subject are too theoretical to be of any use (a surplus of PhD's in the subject perhaps). This book is rigorous in its treatment of the subject but easy to follow.

"Analyzing Multivariate Data" is not just one the of the best multivariate data analysis texts available, but also one of the best structured text books that I have ever seen. After providing an introductory discussion of the prerequisite materials (namely matrices and regression analysis), Lattin, Carroll and Green cover all of the expected areas of multivariate analysis, specifically: principal components analysis, factor analysis, multidimensional scaling, cluster analysis, canonical correlation, structural equation models, analysis of variance, discriminant analysis and logit choice models. What makes this book stand out from other, similar, text books is how the material is structured. In each chapter, the authors explain all of the relevant mathematics behind the techniques, but also explain the concepts in laymans terms. Important questions are posed and answered, such as "when should this technique be used?" and worked examples are given. Furthermore, at the end of each chapter, the main points are summarized in point form and a large number of exercises are provided. The only drawback I can see is that the solutions to the exercises are not provided. Even though this text covers material at a reasonably high level (this book would be suitable for later year undergraduates or graduate students), this is still an extremely user-friendly textbook. I just wish more texts were this well written.

I bought this book as a cheap international edition because, er, it was cheap. It turned out to be an excellent reference for understanding many useful concepts in multivariate analysis, and as another reviewer said, it clarifies many of the ideas with real examples. Because the style is so accessible, I ended up reading it as much out of interest as the need to learn about all the techniques. It lacks the rigor of some other texts, but there are plenty of them out there to refer to for more concrete proofs if you need them. Read this to understand the techniques...

Analyzing Multivariate Data by Lattin, Carroll, and Green fills an important niche in practical statistics for the applied researcher. For a purely conceptual introduction, Sam Kash Kachigan (Multivariate Statistical Analysis) or Joseph Hair et al (Multivariate Data Analysis) are excellent references; for more rigorously mathematical approaches, Johnson & Wichern's and Donald Morrison's texts are excellent. Analyzing Multivariate Data bridges those two poles. The rigorous texts teach how to twirl matrices around like a flaming baton, but unless the researcher has insight into the art and science of the applications of the multivariate methods little will be understood. This is particularly important if the researcher uses point-and-click stat packages. For the applied researcher and grad student who want to know how and when to use the multivariate methods but want accessible supporting mathematics as well, this book is exceptional. I use it to explain the applications of the mathematically intensive methods I am learning in a grad multivariate class.

I had the great pleasure of learning from this book when it was still in manuscript form. For the first time in my long history as a student I found that I understood the text at first reading! The chapters generally present two approaches: the technical (scary!) and the commonsense -- with the latter truly written in a commonsensical fashion. In fact, it is so well written that it is possible to go over the material on one's own. The book is pricey, but it delivers on its promise.