Reading and Understanding More Multivariate Statistics

Rating:
Summary: I read more - and I understood more!
Review: 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.

Rating:
Summary: I read more - and I understood more!
Review: 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.

Rating:
Summary: Great Statistics Resource
Review: 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.