Analysis of Multivariate Social Science Data
2nd Edition
Published on 4. June 2008
Book
Paperback/Softback
384 pages
978-1-58488-960-1 (ISBN)
Description
Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models.
After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.
Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research.
Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.
After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.
Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research.
Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.
Reviews / Votes
"... 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 methods-those 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
More details
Series
Edition
2nd edition
Language
English
Place of publication
Oxford
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Undergraduate
Illustrations
87 s/w Abbildungen, 172 s/w Tabellen
172 Tables, black and white; 87 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 21 mm
Weight
582 gr
ISBN-13
978-1-58488-960-1 (9781584889601)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Irini Moustaki | Fiona Steele | Yunxiao Chen
Analysis of Multivariate Social Science Data
Statistical Machine Learning Methods
Book
approx. 02/2026
3rd Edition
Chapman & Hall/CRC
€77.80
Not yet published
Additional editions
David J. Bartholomew | Fiona Steele | Irini Moustaki
Analysis of Multivariate Social Science Data
Book
08/2017
2nd Edition
CRC Press
€289.69
Shipment within 10-20 days
David J. Bartholomew | Fiona Steele | Irini Moustaki
Analysis of Multivariate Social Science Data
E-Book
06/2008
2nd Edition
Chapman & Hall/CRC
€78.99
Available for download
David J. Bartholomew | Fiona Steele | Irini Moustaki
Analysis of Multivariate Social Science Data
E-Book
06/2008
2nd Edition
Chapman and Hall
€78.99
Available for download
Previous edition
J.I. Galbraith | Irini Moustaki | David J. Bartholomew
The Analysis and Interpretation of Multivariate Data for Social Scientists
Book
02/2002
1st Edition
Chapman & Hall/CRC
€57.13
Article exhausted; check for reprint
Persons
David J. Bartholomew, Fiona Steele, Fiona Steele, Irini Moustaki
Content
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.