The Essence of Multivariate Thinking
Basic Themes and Methods
Psychology Press – 2005 – 264 pages
Series: Multivariate Applications Series
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:
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.
"The most unique aspect of Essence is a focus on a series of themes that are later applied to individual statistical procedures. This approach is very effective as it gives readers a sense that they are learning about how different applications fit a general model of multivariate statistics rather than focusing on applications in isolation….it is a very good text for teaching….how easy it would be to build a course around the book and supporting materials. The text's strengths are its clear and explicit focus on thinking about topics and its highly readable presentation." - PsycCRITIQUES
"In view of the ever expanding applications of multivariate statistics in various disciplines…books that make the topic of multivariate statistics more accessible and comprehensible to a wide audience are welcome additions….This book fits this need well and does not require knowledge of advanced mathematical methods beyond basic algebra and finite mathematics. The book presumes knowledge of basic statistics and methods as taught at the undergraduate level in most social science fields….could be covered in a statistics course for first-year graduate students or advanced undergraduate. One noteworthy feature of this book is its organization. The author suggests basic themes that run through most statistical methodology and then show how these themes are applied to several multivariate methods." - Technometrics
"In this publication, Lisa Harlow's stated objective is to "make the topic of multivariate statistics more accessible to and comprehensible to a wide audience." I believe she easily exceeds her objective […] the book is a useful review for experienced researchers and a good learning tool and introduction to multivariate statistics for students." - Charles F. Seifert, Siena College, USA
Contents: Preface. Part I: Overview. Introduction. Multivariate Themes. Background Themes. Part II: Intermediate Multivariate Methods With One Continuous Outcome. Multiple Regression. Analysis of Covariance. Part III: Matrices. Matrices and Multivariate Methods. Part IV: Multivariate Group Methods. Multivariate Analysis of Variance. Discriminant Function Analysis. Logistic Regression. Part V: Multivariate Correlational Methods With Continuous Variables. Canonical Correlation.