Exploring how to use key multivariate methods in the social sciences, this book contains three chapters on regression analysis, confirmatory factor analysis and structural equation models, and multilevel models. It presents various examples of real-world applications and establishes an approach to latent variable modeling.

" Written by some of the leaders in the field, the second edition expands the horizon of the first edition by three new chapters. The new edition enabled the authors to deal with two equally important types of methodsthose for data summarization and those that are model based. The book should provide a superb introduction to these methods for graduate students who are without substantial statistical or mathematical training Good examples abound [and] so do worked-out applications. I also like the authors' effort to compare related methods across the chapters The website is a treasure trove the book is essential to read ."Tim Futing Liao, University of Illinois, Journal of the Royal Statistical Society, Series A, 2010 "The strength of this book lies in the right mixture of simple mathematical expressions, comprehensive non-mathematical descriptions of various multivariate approaches, numerous interesting real-life data examples, and detailed interpretation of the results. The comprehensive web resource the authors provide is also commendable. Overall, this is an outstanding book on multivariate statistics in the field of social sciences, with a strong focus on categorical data. It can be recommended without reservations for quantitative graduate courses in psychology, sociology, education, and related areas. "Journal of Statistical Software, February 2009 "I am pleased that the authors emphasise that the book is in no sense a cookbook. the presentation is well matched to its intended audience, relying on only the minimal necessary mathematics and driving the development with examples, figures, and verbal descriptions. This is the sort of book from which I would have liked to have learnt multivariate statistics."International Statistical Review, 2008

Autorentext
David J. Bartholomew, Fiona Steele, Fiona Steele, Irini Moustaki

Klappentext

Multivariate analysis is an important tool in the social sciences, but it can be technical for those with a limited statistical background. Requiring minimal statistical knowledge, Analysis of Multivariate Social Science Data provides an accessible introduction to multivariate data analysis for the social sciences. This second edition features additional material on confirmatory factor analysis, structural equation modeling, and multilevel modeling. In addition to a wide range of worked examples, an abundance of end-of-chapter exercises, and a comprehensive appendix of solutions, the text includes an enhanced software section with data sets as well as code available on the web.

Inhalt
Preface. Setting the Scene.Cluster Analysis.Multidimensional Scaling.Correspondence Analysis.Principal Components Analysis.Regression Analysis.Factor Analysis.Factor Analysis for Binary Data. Factor Analysis for Ordered Categorical Variables.Latent Class Analysis for Binary Data. Confirmatory Factor Analysis and Structural Equation Models.Multilevel Modeling. References. Index.