Applied multivariate analysis
Springer-Verlag, 1988 - Business & Economics - 508 pages
The book is a basic graduate level textbook in multivariate analysis. It is designed to emphasize the problems of analyzed data as opposed to testing formal models. One of the most important is a discussion of the connection between mathematical techniques and substantial issues. Simulation is given a prominent role. Topical content is standard except for a chapter devoted to the analysis of scales, an important issue for clinical and social psychologists. Students can learn how to evaluate issues of interest to them. Emphasis is also placed on how not to become overwhelmed by the complexities of computer printouts. The single most important part of the book is that the author attempts to address the reader in clear language, not mathematics. Considerable care was devoted to presenting examples that readers will find meaningful.
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INTRODUCTION AND PREVIEW
Some Important Themes
The Role of Computers in Multivariate Analysis
40 other sections not shown
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ANOVA assume basic beta weights between-group canonical analysis canonical correlations canonical variates centroid Chapter clusters coefficient columns common factor model contains correlation matrix covariance criterion cross products cutoff defined denote derived diagonal discriminant analysis discriminant axis discriminant function discriminant scores discussed dummy codes effect eigenanalysis eigenvalues eigenvectors elements equal equation estimate example F ratio factor analysis factor scores given group means Hence high school GPA important individual interaction intercorrelations linear combinations LISREL Mahalanobis distance measures multiple correlation multiple regression multivariate analysis normally distributed Note observation obtained orthogonal pair parameters pooled within-group prediction predictors principal component problem procedure R2 values raw score relation reliability residual rotation rows scalar scale significant similar simple solution specific square root standard deviation statistical structure substantive model sum of squares Table tion validity variance-covariance matrix vector z-score zero