An insightful guide to understanding and visualizing multivariate
statistics using SAS®, STATA®, and SPSS®
Multivariate Analysis for the Biobehavioral and Social Sciences:
A Graphical Approach outlines the essential multivariate methods
for understanding data in the social and biobehavioral sciences.
Using real-world data and the latest software applications, the
book addresses the topic in a comprehensible and hands-on manner,
making complex mathematical concepts accessible to readers.
The authors promote the importance of clear, well-designed
graphics in the scientific process, with visual representations
accompanying the presented classical multivariate statistical
methods . The book begins with a preparatory review of univariate
statistical methods recast in matrix notation, followed by an
accessible introduction to matrix algebra. Subsequent chapters
explore fundamental multivariate methods and related key concepts,
including:
* Factor analysis and related methods
* Multivariate graphics
* Canonical correlation
* Hotelling’s T-squared
* Multivariate analysis of variance (MANOVA)
* Multiple regression and the general linear model (GLM)
Each topic is introduced with a research-publication case study
that demonstrates its real-world value. Next, the question ‚how do
you do that?‘ is addressed with a complete, yet simplified,
demonstration of the mathematics and concepts of the method.
Finally, the authors show how the analysis of the data is performed
using Stata®, SAS®, and SPSS®. The discussed
approaches are also applicable to a wide variety of modern
extensions of multivariate methods as well as modern univariate
regression methods. Chapters conclude with conceptual questions
about the meaning of each method; computational questions that test
the reader’s ability to carry out the procedures on simple
datasets; and data analysis questions for the use of the discussed
software packages.
Multivariate Analysis for the Biobehavioral and Social Sciences
is an excellent book for behavioral, health, and social science
courses on multivariate statistics at the graduate level. The book
also serves as a valuable reference for professionals and
researchers in the social, behavioral, and health sciences who
would like to learn more about multivariate analysis and its
relevant applications.
Inhaltsverzeichnis
PREFACE xiii
1 OVERVIEW OF MULTIVARIATE AND REGRESSION METHODS 1
2 THE SEVEN HABITS OF HIGHLY EFFECTIVE QUANTS: A REVIEW OF
ELEMENTARY STATISTICS USING MATRIX ALGEBRA 20
3 FUNDAMENTALS OF MATRIX ALGEBRA 88
4 FACTOR ANALYSIS AND RELATED METHODS: QUINTESSENTIALLY
MULTIVARIATE 139
5 MULTIVARIATE GRAPHICS 227
6 CANONICAL CORRELATION: THE UNDERUSED METHOD 283
7 HOTELLING’S T 2 AS THE SIMPLEST CASE OF
MULTIVARIATE INFERENCE 333
8 MULTIVARIATE ANALYSIS OF VARIANCE 351
9 MULTIPLE REGRESSION AND THE GENERAL LINEAR MODEL
373
APPENDICES: STATISTICAL TABLES 443
NAME INDEX 456
SUBJECT INDEX 460
Über den Autor
Bruce L. Brown, Ph D, is Professor of Psychology at Brigham Young
University. He has over thirty years of academic experience working
with large scale data analysis methods, and he currently focuses
his research on applying these methods to cognitive neuroscience
and vocal emotion.
Suzanne B. Hendrix, Ph D, is President of Pentara Corporation and
an independent consultant to the pharmaceutical industry, with
extensive experience in clinical biostatistics. Dr. Hendrix’s
current areas of research interest include clinical trial design
and reporting in neurodegenerative diseases such as Alzheimer’s and
Parkinson’s.
Dawson W. Hedges, MD, is Professor of Psychology at Brigham
Young University. He has published extensively in his areas of
research interest, which include neuroimaging, posttraumatic stress
disorder, psychopharmacology, and psychopathology.
Timothy B. Smith, Ph D, is Chair of the Department of Counseling
Psychology and Special Education at Brigham Young University. He
currently focuses his research on multiculturalism and diversity as
well as spirituality and religion in education or counseling.
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