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 reader’s 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; 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.
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
Biography
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, USA.
Praise for the First Edition:
“Hahs-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, USA
“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, USA
“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, USA
“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. More-
over, she makes sure that students will have the necessary tools to see their projects com-
pleted 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 Olivárez, Jr., University of Texas at El Paso, USA
