Applied Multivariate Statistical Concepts PDF Download
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Author: Debbie L. Hahs-Vaughn
Publisher: Taylor & Francis
ISBN: 1040128459
Category : Psychology
Languages : en
Pages : 877
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Book Description
This second edition of Applied Multivariate Statistical Concepts covers the classic and cutting-edge multivariate techniques used in today’s research. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps readers master key concepts so they can implement and interpret results generated by today’s sophisticated software. Additional features include examples using real data from the social sciences; templates for writing research questions and results that provide manuscript-ready models; step-by-step instructions on using R and SPSS statistical software with screenshots and annotated output; clear coverage of assumptions, including how to test them and the effects of their violation; and conceptual, computational, and interpretative example problems that mirror the real-world problems students encounter in their studies and careers. This edition features expanded coverage of topics, such as propensity score analysis, path analysis and confirmatory factor analysis, and centering, moderation effects, and power as related to multilevel modelling. New topics are introduced, such as addressing missing data and latent class analysis, while each chapter features an introduction to using R statistical software. This textbook is ideal for courses on multivariate statistics/analysis/design, advanced statistics, and quantitative techniques, as well as for graduate students broadly in social sciences, education, and behavioral sciences. It also appeals to researchers with no training in multivariate methods.
Author: Debbie L. Hahs-Vaughn
Publisher: Taylor & Francis
ISBN: 1040128459
Category : Psychology
Languages : en
Pages : 877
Get Book Here
Book Description
This second edition of Applied Multivariate Statistical Concepts covers the classic and cutting-edge multivariate techniques used in today’s research. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps readers master key concepts so they can implement and interpret results generated by today’s sophisticated software. Additional features include examples using real data from the social sciences; templates for writing research questions and results that provide manuscript-ready models; step-by-step instructions on using R and SPSS statistical software with screenshots and annotated output; clear coverage of assumptions, including how to test them and the effects of their violation; and conceptual, computational, and interpretative example problems that mirror the real-world problems students encounter in their studies and careers. This edition features expanded coverage of topics, such as propensity score analysis, path analysis and confirmatory factor analysis, and centering, moderation effects, and power as related to multilevel modelling. New topics are introduced, such as addressing missing data and latent class analysis, while each chapter features an introduction to using R statistical software. This textbook is ideal for courses on multivariate statistics/analysis/design, advanced statistics, and quantitative techniques, as well as for graduate students broadly in social sciences, education, and behavioral sciences. It also appeals to researchers with no training in multivariate methods.
Author: Richard A. Johnson
Publisher: Pearson
ISBN: 9780134995397
Category : Multivariate analysis
Languages : en
Pages : 808
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Book Description
This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. For courses in Multivariate Statistics, Marketing Research, Intermediate Business Statistics, Statistics in Education, and graduate-level courses in Experimental Design and Statistics. Appropriate for experimental scientists in a variety of disciplines, this market-leading text offers a readable introduction to the statistical analysis of multivariate observations. Its primary goal is to impart the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Ideal for a junior/senior or graduate level course that explores the statistical methods for describing and analyzing multivariate data, the text assumes two or more statistics courses as a prerequisite.
Author: Wolfgang Härdle
Publisher: Springer Science & Business Media
ISBN: 0387707840
Category : Computers
Languages : en
Pages : 367
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Book Description
The authors have cleverly used exercises and their solutions to explore the concepts of multivariate data analysis. Broken down into three sections, this book has been structured to allow students in economics and finance to work their way through a well formulated exploration of this core topic. The first part of this book is devoted to graphical techniques. The second deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The final section contains a wide variety of exercises in applied multivariate data analysis.
Author: Daniel Zelterman
Publisher: Springer Nature
ISBN: 3031130057
Category : Medical
Languages : en
Pages : 469
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Book Description
Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.
Author: Parimal Mukhopadhyay
Publisher: World Scientific Publishing Company
ISBN: 9813107111
Category : Mathematics
Languages : en
Pages : 568
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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: Lawrence S. Meyers
Publisher: SAGE Publications
ISBN: 1506329780
Category : Social Science
Languages : en
Pages : 938
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Book Description
Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.
Author: Richard Arnold Johnson
Publisher:
ISBN: 9780131219731
Category : Multivariate analysis
Languages : en
Pages : 767
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Book Description
For an undergraduate or graduate level course that explores the statistical methods for describing and analyzing multivariate data. Appropriate for experimental scientists in a variety of disciplines, this text offers a readable introduction to the statistical analysis of multivariate observations.
Author: Parimal Mukhopadhyay
Publisher: World Scientific
ISBN: 9812791752
Category : Mathematics
Languages : en
Pages : 568
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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." "This 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." --Book Jacket.
Author: Brian Everitt
Publisher: Springer Science & Business Media
ISBN: 1441996508
Category : Mathematics
Languages : en
Pages : 284
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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.
Author: Richard G Lomax
Publisher: Routledge
ISBN: 1136490124
Category : Psychology
Languages : en
Pages : 1004
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Book Description
This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book. The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed. Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.
