Making Sense of Multivariate Data Analysis is a short introduction to multivariate data analysis (MDA) for students and practitioners in the behavioral and social sciences. It provides a conceptual overview of the foundations of MDA and of a range of specific techniques including multiple regression, logistic regression, discriminant analysis, multivariate analysis of variance, factor analysis, and log-linear analysis. As a conceptual introduction, the book assumes no prior statistical knowledge, and contains very few symbols or equations. Its primary objective is to expose the conceptual unity of MDA techniques both in their foundations and in the common analytic strategies that lie at the heart of all of the techniques. Although introductory, the book encourages the reader to reflect critically on the general strengths and limitations of MDA techniques. Each chapter includes references for further reading accessible to the beginner.
This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource.
Key Features
* Assumes no prior statistical knowledge
* Relies mainly on verbal exposition rather than symbols and formulae
* Carries the reader from basic to advanced ideas in a relatively short space to demonstrate their continuity
* Highlights the underlying strategies that unite all common multivariate techniques
* Encourages the reader to reflect critically on the limitations of multivariate analysis in the broad research context
* Uses examples from contemporary research on subjective well-being drawn from a variety of areas in the behavioral and social sciences