| Analysis of Multivariate Social Science Data Contributor(s): Bartholomew, David J. (Author), Steele, Fiona (Author), Galbraith, Jane (Author) |
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ISBN: 1584889608 ISBN-13: 9781584889601 Publisher: CRC Press OUR PRICE: $68.39 Product Type: Paperback - Other Formats Published: June 2008 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis |
| Dewey: 519.535 |
| LCCN: 2008005638 |
| Series: Statistics in the Social and Behavioral Sciences |
| Physical Information: 0.79" H x 5.78" W x 8.67" (1.20 lbs) 384 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |