Applied Multivariate Statistical Concepts

by Debbie L. Hahs-Vaughn (Author)



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)

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