"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 withscreenshots 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"--
This second edition of Applied Multivariate Statistical Concepts covers the classic and cutting-edge multivariate techniques used in today’s research.
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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