Approaching Multivariate Analysis, 2nd Edition PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Approaching Multivariate Analysis, 2nd Edition PDF full book. Access full book title Approaching Multivariate Analysis, 2nd Edition by Pat Dugard. Download full books in PDF and EPUB format.
Author: Pat Dugard
Publisher:
ISBN: 9780415645911
Category : Multivariate analysis
Languages : en
Pages : 0
Get Book Here
Book Description
This edition has been expanded to include new chapters describing methods and examples of particular interest to medical researchers It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems.
Author: Pat Dugard
Publisher: Taylor & Francis
ISBN: 1000787257
Category : Psychology
Languages : en
Pages : 440
Get Book Here
Book Description
This fully updated new edition not only provides an introduction to a range of advanced statistical techniques that are used in psychology, but has been expanded to include new chapters describing methods and examples of particular interest to medical researchers. It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems. This book provides a non-mathematical introduction to multivariate methods, with an emphasis on helping the reader gain an intuitive understanding of what each method is for, what it does and how it does it. The first chapter briefly reviews the main concepts of univariate and bivariate methods and provides an overview of the multivariate methods that will be discussed, bringing out the relationships among them, and summarising how to recognise what types of problem each of them may be appropriate for tackling. In the remaining chapters, introductions to the methods and important conceptual points are followed by the presentation of typical applications from psychology and medicine, using examples with fabricated data. Instructions on how to do the analyses and how to make sense of the results are fully illustrated with dialogue boxes and output tables from SPSS, as well as details of how to interpret and report the output, and extracts of SPSS syntax and code from relevant SAS procedures. This book gets students started, and prepares them to approach more comprehensive treatments with confidence. This makes it an ideal text for psychology students, medical students and students or academics in any discipline that uses multivariate methods.
Author: Pat Dugard
Publisher:
ISBN: 9780415645911
Category : Multivariate analysis
Languages : en
Pages : 0
Get Book Here
Book Description
This edition has been expanded to include new chapters describing methods and examples of particular interest to medical researchers It takes a very practical approach, aimed at enabling readers to begin using the methods to tackle their own problems.
Author: Parimal Mukhopadhyay
Publisher: World Scientific Publishing Company
ISBN: 9813107111
Category : Mathematics
Languages : en
Pages : 568
Get Book Here
Book Description
This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each. With its roots in matrix algebra, for which a separate chapter has been added as an appendix, the book includes both data-oriented techniques and a reasonable coverage of classical methods supplemented by comments about robustness and general practical applicability. It also illustrates the methods of numerical calculations at various stages.This self-contained book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians.The author is a former Professor of the Indian Statistical Institute, India.
Author: Jane E. Miller
Publisher: University of Chicago Press
ISBN: 022603819X
Category : Mathematics
Languages : en
Pages : 576
Get Book Here
Book Description
Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results. For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses. This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.
Author: Alvin C. Rencher
Publisher: John Wiley & Sons
ISBN: 0471461725
Category : Mathematics
Languages : en
Pages : 739
Get Book Here
Book Description
Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on: Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.
Author: Kim H. Esbensen
Publisher: Multivariate Data Analysis
ISBN: 9788299333030
Category : Experimental design
Languages : en
Pages : 622
Get Book Here
Book Description
"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.
Author: Ravindra Khattree
Publisher: Sas Inst
ISBN: 9781555442392
Category : Mathematics
Languages : en
Pages : 396
Get Book Here
Book Description
Author: Kimmo Vehkalahti
Publisher: CRC Press
ISBN: 135120226X
Category : Mathematics
Languages : en
Pages : 439
Get Book Here
Book Description
Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics
Author: Lisa L. Harlow
Publisher: Psychology Press
ISBN: 1135652988
Category : Education
Languages : en
Pages : 265
Get Book Here
Book Description
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.
Author: Brian Everitt
Publisher: Springer Science & Business Media
ISBN: 1441996508
Category : Mathematics
Languages : en
Pages : 284
Get Book Here
Book Description
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
