Multivariate general linear models

Bibliographic Information

Multivariate general linear models

Richard F. Haase

(Sage publications series, . Quantitative applications in the social sciences ; no. 09-170)

Sage, c2011

  • : pbk

Available at  / 33 libraries

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Note

Series no. on spine: 07-170

Bibliography: p. 207-210

Includes index

Description and Table of Contents

Description

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.

Table of Contents

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 References

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