By focusing on underlying themes, this book helps readers better understand the connections between multivariate methods. For each method the author highlights: the similarities and differences between the methods, when they are used and the questions they address, the key assumptions and equations, and how to interpret the results. The concepts take center stage while formulas are kept to a minimum. Examples using the same data set give readers continuity so they can more easily apply the concepts. Each method is also accompanied by a worked out example, SPSS and SAS input, and an example of how to write up the results. EQS code is used for the book’s SEM applications.
This extensively revised edition features:
The first two chapters review the core themes that run through most multivariate methods. The author shows how understanding multivariate methods is much more achievable when we notice the themes that underlie these statistical techniques. This multiple level approach also provides greater reliability and validity in our research. After providing insight into the core themes, the author illustrates them as they apply to the most popular multivariate methods used in the social, and behavioral sciences. First, two intermediate methods are explored – multiple regression and analysis of covariance. Next the multivariate grouping variable methods of multivariate analysis of variance, discriminant function analysis, and logistic regression are explored. Next the themes are applied to multivariate modeling methods including multilevel modeling, path analysis, confirmatory factor analysis, and latent variable models that include exploratory structural methods of principal component and factor analysis. The book concludes with a summary of the common themes and how they pertain to each method discussed in this book.
Intended for advanced undergraduate and/or graduate courses in multivariate statistics taught in psychology, education, human development, business, nursing, and other social and life sciences, researchers also appreciate this book‘s applied approach. Knowledge of basic statistics, research methods, basic algebra, and finite mathematics is recommended.
"Harlow writes with authority and great clarity. She provides numerous examples and uses shrewdly-chosen themes to make it all make sense. This greatly expanded second edition is a wonderful guide to the multivariate world." – Geoff Cumming, Professor Emeritus, La Trobe University, Australia
"This book is a virtual goldmine of information on a wide array of multivariate statistical procedures. Its uniqueness lies in the author’s ability to link these strategies to, and interpret results within particular multivariate themes. Definitely a must-have for graduate students and others whose analyses are multivariate-based." – Barbara Byrne, University of Ottawa, Canada
"Lisa Harlow empowers readers to master the art of multivariate analysis with her latest edition. The book guides readers from assumption testing to interpretation and write-up by providing us with pertinent examples and computer-generated output (SPSS, SAS) for the leading multivariate methods utilized in social science research." – Jennifer Ann Morrow, University of Tennessee, USA
"Harlow pairs a clear and explicit focus on thinking about topics with a highly readable presentation. …The consistent use and application of themes for each analysis is exceptional. This truly… teaches the "essence" of how to think about these topics. … Providing basic guidance on the use of [SPSS & SAS] is a major advantage. … The setup, output, and interpretation …goes a long way toward facilitating understanding." – Chris Aberson, Humboldt State University, USA
"The primary strengths of the text are the accuracy and … the comprehensive coverage of the topics identified with multivariate statistics. … The details of the statistical procedures (conceptually and mathematically) are excellent." – Carla Thompson, University of West Florida, USA
1. Introduction and Multivariate Themes 2. Background Themes 3. Multiple Regression 4. Analysis of Covariance 5. Multivariate Group Methods with Categorical Variables 6. Discriminant Function Analysis 7. Logistic Regression 8. Multi-level Modeling 9. Principal Components and Factor Analysis 10. Structural Equation Modeling 11. Path Analysis 12. Confirmatory Factor Analysis 13. Latent Variable Modeling 14. Integration of Multivariate Methods Appendix A Codebook for Data Used in Example Appendix B Matrices and Multivariate Methods
This series of books offers highly accessible and widely applicable methodological topics that have broad appeal and are written in easy-to understand language. Sponsored by the Society of Multivariate Experimental Psychology http://www.smep.org/, it welcomes methodological applications from a variety of disciplines, such as psychology, public health, sociology, education, and business. Authored or edited volumes should feature one of several approaches:
Interested persons should e-mail: Lisa L. Harlow at LHarlow@uri.edu.