The Essence of Multivariate Thinking: Basic Themes and Methods (Multivariate Applications Series) Review

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(More customer reviews)There exists a growing library of multivariate texts that include considerable depth and cover of a dazzling array of applications. The Essence of Multivariate Thinkingis a different kind of textbook. This is a short text that reads more like a series of handouts from an excellent course than a multivariate reference. The author makes clear that the goals of the book are not to present complete details of each method, rather the primary goal is to present a context for understanding multivariate analyses.
Most readers with an interest in MV statistics are familiar with the "gold standard" of the field, Tabachnick and Fidell's (2001) Using Multivariate Statistics (T&F for short). Perhaps the most relevant question for instructors and researchers is how Essence compares to T&F. T&F is a user-friendly, detailed and expansive statistical handbook that is a reference for research and teaching. Essence focuses on general strategies for understanding and thinking about MV statistics and learning the basics of several applications. This is not a text directed at researchers applying the procedures - for whom I strongly recommend T&F - but it is very well suited for instruction.
In reading the text, I was struck by how easy it would be to build a course around the book and supporting materials. Assignments, data, and computer programming code supplement the highly readable chapters. I would strongly recommend this text to first-time instructors, as the accompanying assignments and SAS materials are mostly ready-to-use and would reduce course preparation time considerably.
Perhaps the best thing I can say about the book is that after reading it I decided to adopt it for use in my own MV stats course.

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The Essence of Multivariate Thinking is intended to make multivariate statistics more accessible to a wide audience. To encourage a more thorough understanding of multivariate methods, author Lisa Harlow suggests basic themes that run through most statistical methodology. The most pervasive theme is multiplicity. The author argues that the use of multivariate methods encourages multiple ways of investigating phenomena. She explains that widening our lens to identify multiple theories, constructs, measures, samples, methods, and time points provide greater reliability and validity in our research. Dr. Harlow then shows how these themes are applied to several multivariate methods, with the hope that this will ease understanding in the basic concepts of multivariate thinking. Formulas are kept at a minimum.The first three chapters review the core themes that run through multivariate methods. Seven different multivariate methods are then described using 10 questions that illuminate the main features, uses, multiplicity, themes, interpretations, and applications. The seven methods covered are multiple regression, analysis of covariance, multivariate analysis of variance, discriminant function analysis, logistic regression, canonical correlation, and principal components/factor analysis. The final chapter pulls together the principal themes and features charts that list common themes and how they pertain to each of the methods discussed.The Essence of Multivariate Thinking, features:A unique focus on the underlying themes that run through most multivariate methods.A dual focus on significance tests and effect sizes to encourage readers to adopt a thorough approach to assessing the significance and magnitude of their findings.A detailed example for each method to delineate how the multivariate themes apply.Tabular results from statistical analysis programs that mirror sections of the output files.A common dataset throughout the chapters to provide continuity with the variables and research questions.A CD with data, SAS program setup and output, homework exercises, and chapter lectures.This book is useful to advanced students, professionals, and researchers interested in applying multivariate methods in such fields as behavioral medicine, social, health, personality, developmental, cognitive, and industrial-organizational psychology, as well as in education and evaluation. A preliminary knowledge of basic statistics, research methods, basic algebra, and finite mathematics is recommended.

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