Multivariate General Linear Models

Richard F. Haase

Nov 2011 · SAGE Publications

About this ebook

This book provides an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the univariate general linear model, this volume defines the key steps in analyzing linear model data and introduces multivariate linear model analysis as a generalization of the univariate model. Richard F. Haase focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy. The volume concludes with a discussion of canonical correlation analysis that is shown to subsume all the multivariate procedures discussed in previous chapters. The analyses are illustrated throughout the text with three running examples drawing from several disciples, including personnel psychology, anthropology, environmental epidemiology, and neuropsychology.

About the author

Richard F. Haase is Professor Emeritus and Research Professor in the Division of Counseling Psychology of the School of Education and Fellow of the Institute for Health and the Environment of the School of Public Health, both at the University at Albany of the State University of New York. After completing his Ph.D. in Psychology from Colorado State University he has taught research methods, statistics and data analysis at the University of Massachusetts at Amherst, Texas Tech University and the University at Albany. His interests are in the areas of research methods, univariate and multivariate statistics, and vocational psychology. His work on research methodology and data analysis has appeared in the Journal of Consulting and Clinical Psychology, Journal of Counseling Psychology, Educational and Psychological Measurement, Multivariate Behavioral Research, Applied Psychological Measurement, Environmental Research, and the Journal of Vocational Behavior.