Applied Multivariate Statistical Concepts
1st Edition
Published on 30. November 2016
Book
Hardback
648 pages
978-0-415-84235-8 (ISBN)
Description
More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today's research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. 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 reader's master key concepts so they can implement and interpret results generated by today's sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a 'mathematical snapshot' that highlights the technical components of each procedure, so only the most crucial equations are included.
Highlights include:
-Outlines, key concepts, and vignettes related to key concepts preview what's to come in each chapter
-Examples using real data from education, psychology, and other social sciences illustrate key concepts
-Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique
-Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers
-A focus on data screening and power analysis with attention on the special needs of each particular method
-Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results
-Templates for writing research questions and APA-style write-ups of results which serve as models
-Propensity score analysis chapter that demonstrates the use of this increasingly popular technique
-A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed)
-www.routledge.com/9780415842365 provides the text's datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors
Highlights include:
-Outlines, key concepts, and vignettes related to key concepts preview what's to come in each chapter
-Examples using real data from education, psychology, and other social sciences illustrate key concepts
-Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique
-Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers
-A focus on data screening and power analysis with attention on the special needs of each particular method
-Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results
-Templates for writing research questions and APA-style write-ups of results which serve as models
-Propensity score analysis chapter that demonstrates the use of this increasingly popular technique
-A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed)
-www.routledge.com/9780415842365 provides the text's datasets preformatted for use in SPSS and other statistical packages for readers, as well as answers to all chapter problems, Power Points, and test items for instructors
Reviews / Votes
"Hah-Vaughn provides a strong foundation for learning advanced statistical techniques by first explaining 'why' each analysis is used and then supporting the 'how' each statistical application is conducted with a review of basic theoretical concepts and a summary of the mathematical background for each statistical analysis. Her approach provides exactly the right balance of theory to practice for understanding and applying multivariate statistical analyses." -- Robyn Cooper, Drake University"Ideal for students in a wide variety of social science disciplines, this book approaches multivariate statistics with an appealing mix of conceptual and technical content. Easy-to-follow and interesting demonstrations of applications to real-world problems make it an ideal teaching tool and will keep students engaged. The writing is clear, concise, and informative in a way that students at the advanced undergraduate and graduate levels will really appreciate. Unlike many other multivariate statistics textbooks, this book includes important concepts such as cluster analysis and propensity score analysis, which are very important areas that are too infrequently covered." -- W. Holmes Finch, Ball State University
"The text provides comprehensive coverage of multivariate statistical techniques, with explanations that are both concise and clear. The step-by-step instructions and annotated outputs will continue to serve as excellent resources for students even after completing the course." -- Sylvie Mrug, University of Alabama at Birmingham
"Applied Multivariate Statistical Concepts is a great addition to ... textbooks in the social and behavioral sciences for graduate students and researchers. The author took extreme care in selecting key pedagogical methods and statistical procedures in current use. Moreover, she makes sure that students will have the necessary tools to see their projects completed from start to finish by providing innovative instructional and learning strategies specific to relevant fields of study. Students will be challenged by the topics but also guided throughout the research enterprise with step-by-step instructions including the appropriate use of various statistical software applications on real data sets." -- Arturo Olivarez, Jr., University of Texas at El Paso. "Hah-Vaughn provides a strong foundation for learning advanced statistical techniques by first explaining 'why' each analysis is used and then supporting the 'how' each statistical application is conducted with a review of basic theoretical concepts and a summary of the mathematical background for each statistical analysis. Her approach provides exactly the right balance of theory to practice for understanding and applying multivariate statistical analyses." -- Robyn Cooper, Drake University
"Ideal for students in a wide variety of social science disciplines, this book approaches multivariate statistics with an appealing mix of conceptual and technical content. Easy-to-follow and interesting demonstrations of applications to real-world problems make it an ideal teaching tool and will keep students engaged. The writing is clear, concise, and informative in a way that students at the advanced undergraduate and graduate levels will really appreciate. Unlike many other multivariate statistics textbooks, this book includes important concepts such as cluster analysis and propensity score analysis, which are very important areas that are too infrequently covered." -- W. Holmes Finch, Ball State University
"The text provides comprehensive coverage of multivariate statistical techniques, with explanations that are both concise and clear. The step-by-step instructions and annotated outputs will continue to serve as excellent resources for students even after completing the course." -- Sylvie Mrug, University of Alabama at Birmingham
"Applied Multivariate Statistical Concepts is a great addition to ... textbooks in the social and behavioral sciences for graduate students and researchers. The author took extreme care in selecting key pedagogical methods and statistical procedures in current use. Moreover, she makes sure that students will have the necessary tools to see their projects completed from start to finish by providing innovative instructional and learning strategies specific to relevant fields of study. Students will be challenged by the topics but also guided throughout the research enterprise with step-by-step instructions including the appropriate use of various statistical software applications on real data sets." -- Arturo Olivarez, Jr., University of Texas at El Paso
"Unlike the competitors ... this text will REALLY be applied, including examples, SPSS screenshots, etc. ... The learning tools are stronger [and]... the ... table of contents is more thorough ... [which] allows instructors to pick and choose those topics most important for their particular course. ... The author writes quite clearly. ... I will certainly be using it in my multivariate courses. ... This book makes a significant contribution to the field." - Richard Lomax, The Ohio State University, USA
"This book provides quite a few advantages over the existing books that are commonly used in the field. ... It will help fill a void that is currently there for teaching graduate levels statistics. ... The [addition of] ... a few 'newer' techniques ... will make this a very popular book. ... The examples were well done and the information presented [was] accurate and easy to understand. ... Having the information on how to right results section in APA format is also very helpful. ... I would use this [book] in my classroom."- Pamela Davis - Kean, University of Michigan, USA
"The text and examples were very clear. ... One of the major strengths of the book is the inclusion of propensity and latent trait modeling. ... Due to the less 'mathematical' and more practical approach to the subject matter, the book will fill a needed niche. ... It is more complete than other similar texts and the SPSS examples make it easier to demonstrate many of the concepts." - Robert Triscari, Florida Gulf Coast University, USA
"A text like this would be used in an applied statistics course in many different social science disciplines. ... I like the inclusion of the screen shots and their annotation ... the power material ... [and] the APA-style write ups. ... I like the ... coverage and would certainly be willing to consider it as a text." - William B. Ware, University of North Carolina - Chapel Hill, USA
"The examples were relevant and easy to understand. ... I may make the book the text book for my Multivariate course ... [which] has MBA students. ...The author is technically ... very competent." -Rodney K. Schutz, Georgia State University, USA
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Edition type
Annotated edition
Product notice
sewn/stitched
Cloth over boards
Illustrations
38 s/w Tabellen
38 Tables, black and white
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 40 mm
Weight
1426 gr
ISBN-13
978-0-415-84235-8 (9780415842358)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Debbie L. Hahs-Vaughn
Applied Multivariate Statistical Concepts
Book
10/2024
2nd Edition
Routledge
€195.70
Shipment within 10-20 days
Additional editions
Debbie L. Hahs-Vaughn
Applied Multivariate Statistical Concepts
Book
11/2016
1st Edition
Routledge
€135.50
Article exhausted; check different version
Person
Debbie L. Hahs-Vaughn is a Professor in the University of Central Florida's Methodology, Measurement, & Analysis Program in the College of Education and Human Performance.
Content
1. Multivariate Statistics 2. Univariate and Bivariate Statistics Review 3. Data Screening 4. Multiple Linear Regression 5. Logistic Regression 6. Multivariate Analysis of Variance: Single Factor, Factorial, and Repeated Measures Designs 7. Discriminant Analysis 8. Cluster Analysis 9. Exploratory Factor Analysis 10. Path Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling 11. Multilevel Linear Modeling 12. Propensity Score Analysis