Applied Multivariate Data Analysis

About this book

This book is fully updated to include new sections on neural networks, graphical modelling, hierarchical or multilevel modelling, and latent class models. The sections on correspondence analysis and principal components analysis have been expanded. It also offers more exercises for students and an updated review of the available software suited to multivariate analysis. The text avoids irrelevant theoretical statistics and concentrates on enabling the students to understand the concepts behind the data analysis.

Contents

Multivariate data / Mathematical and statistical background / Exploring multivariate data graphically / Exploratory factor analysis / Correspondence analysis / Multidimensional scaling / Cluster analysis / Latent class analysis / Discriminant function analysis / Multivariate dependencies / Classical multivariate inference / Longitudinal data / Problems of measurement error / Confirmatory factor analysis and structural equation models.

Customer Reviews

Biography

Graham Dunn, School of Epidemiology and Health Sciences, University of Manchester, UK