Über den Autor
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.
1. Introduction and Review of Univariate General Linear Models
2. Specifying the Structure of the Multivariate General Linear Model
3. Estimating the Parameters of the Multivariate General Linear Model
4. Partitioning the SSCP, Measures of Strength of Association, and Test Statistics in the Multivariate General Linear Model
5. Testing Hypotheses in the Multivariate General Linear Model
6. Coding the Design Matrix and the Multivariate Analysis of Variance
7. The Eigenvalue Solution to the Multivariate General Linear Model: Canonical Correlation and Multivariate Test Statistics
This book provides a graduate level introduction to multivariate multiple regression analysis. The book can be used as a sole text for that topic, or as a supplemental text in a course that addresses a larger number of multivariate topics.
The text is divided into seven short chapters. Apart from the introductory chapter giving an overview of multivariate multiple regression models, the content outline follows the classic steps required to solve multivariate general linear model problems:
(a) specifying the model
(b)estimating the parameters of the model
(c) establishing measures of goodness of fit of the model
(d) establishing test statistics and testing hypotheses about the model
(e) diagnosing the adequacy of the model.