Reading and Understanding More Multivariate Statistics

Lawrence Grimm and Paul Yarnold's edited collection of chapters about multivariate methods used in developing measurement scales is a companion volume to their previously-published volume,  Reading and Understanding Multivariate Statistics , that introduces multivariate statistical methods more generally.The editors have a clear vision for their book. "Like its predecessor, this volume does not portend to teach readers how to actually perform the statistical procedures discussed." Its aim is to `...present the fundamental conceptual aspects of common multivariate techniques and... the assumptions, statistical notations, and research contexts within which the analyses are often used." Their target audience is the research consumer rather than the practitioner.There are two types of chapters. One type introduces and explores basic measurement concepts, including levels of measurement, scale reliability, and establishing validity using the most appropriate evidence. Most chapters are of the second type, showing readers how to interpret the results of a specific method. The methods covered are cluster analysis, Q-technique factor analysis, structural equation modeling, canonical correlation, multivariate analysis of variance, and survival analysis.The chapters are clearly written and each contain helpful instructional features such as thoroughly-elaborated examples, glossaries, and recommended readings. There is a clear advantage to focusing the resources at the chapter, rather than the book level. It is easy to use it as a reference for a single technique without searching through the rest of the book to string together related information.Grimm and Yarnold's book is recommended for anyone who needs to understand research using these multivariate methods.

Like the first book, this book simply and concisely teaches you statistics. This book and the one that comes before it are two of my favorite statistic books. It makes statistics so easy and makes me more confident in my statistics knowledge. I am really math-phobic so this book was very helpful for me.

The treatment of newer statistical tools (multivariate analyses) is covered in this book, with different authors writing the different parts. This can help to keep the material fresh, but it also gives each section a disjointed feel, as some sections are very long and comprehensive and others are quite short. The book makes a case for using newer tools such as cluster analysis in place of some other typically-used tools (e.g., discriminant analysis, factor analysis) as a fresher approach; This is fine because many dissertations and published studies are using more and more qualitative methods (or mixed methods) these days, but it should put them into context - no cluster analysis will ever be as robust as a factor analysis.

This books explains all the necessary concepts to understand publications and dissertations with statistics. I wish I would have had this at the first MA stats class instead of the last PhD stats class.... perhaps universities would consider giving the harder work at an earlier time?

Fundamental book regarding multivariate stats

It is a very good book

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!

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.

Reading and Understanding MORE Multivariate Statistics this is a very unusual book it expleian how the calculation are carried and what they mean, a very interesting book, its companion books is equally good.

A useful guide to these techniques, and reasonably easy to read. It helps fit a gap between the novice and the very technical texts.