- 876 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Applied Multivariate Statistical Concepts
About this book
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.
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Information
Table of contents
- Cover
- Endorsements
- Half Title
- Title
- Copyright
- Brief Contents
- Detailed Contents
- Preface
- Acknowledgments
- 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
- Appendix A: An Introduction to Matrix Algebra
- Appendix B: Distribution Tables
- Index