Applied Multivariate Research: Design and Interpretation

This book provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-mathematical, approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are encouraged to focus on design and interpretation rather than the intricacies of specific computations.

About the Author :
Lawrence S. Meyers earned his doctorate in experimental psychology and has been a Professor in the Psychology Department at California State University, Sacramento, for a number of years. He supervises research students and teaches research design courses as well as history of psychology at both the undergraduate and graduate levels. His areas of expertise include test development and validation. Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus on conversation memory and discourse processing. He received his PhD in experimental psychology from the University of Arkansas. A. J. Guarino is a professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous National Institutes of Health grants and a reviewer on several research journals. He received his BA from the University of California, Berkeley, and a PhD in statistics and research methodologies from the Department of Educational Psychology, the University of Southern California.

Review :
For me the comprehensive nature of the text is most important even when I don t cover topics in class students gain value by being able to read about cluster analysis or ROC analysis in enough detail that they can conduct their own analyses. Students appreciate the integration with SPSS. There is an appropriate balance of practice and background so that students learn what they need to know about the techniques but also learn how to implement and interpret the analysis.--E. Kevin Kelloway, Saint Mary's University" My students think the book is well written and the language is easy for them to understand--Xiaofen Deng Keating, The University of Texas at Austin Organization is excellent.--Thomas J. Keil, Arizona State University The comprehensive nature of the topics presented and the numerous figures and charts.--Marie Kraska, Ph.D., Auburn University The key strengths are its clearly written explanations of OLS regression and logistic regression as well as its treatment of path analysis.--Andrew Jorgenson, University of Utah Well written and accessible. I find the additional readings at the end of the chapters to be valuable and have tracked down several of the sources for my own personal use.--Glenn J. Hansen, University of Oklahoma