Analysis of Multivariate Social Science Data
Analysis of Multivariate Social Science Data by David J. Bartholomew is published by CRC Press and released on August 21, 2017. This second edition spans 371 pages and is written in English. The book provides a foundational understanding of key multivariate methods applicable in the social sciences, drawing from the author’s extensive experience in teaching and research. It covers essential topics such as regression analysis, confirmatory factor analysis, structural equation models, and multilevel models, with updates in every chapter to reflect current methodologies.
Readers will find a focus on data summarization in the initial chapters, transitioning to regression analysis, which serves as a bridge between descriptive and inferential methods. The text emphasizes model-based approaches that infer processes generating data, supported by numerous numerical examples. These examples not only illustrate the methods but also highlight their interconnections. The book encourages exploration beyond conventional exercises, making it accessible to those with minimal mathematical and statistical knowledge while addressing substantive research questions in fields like psychology and social science.
Official synopsis Publisher
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.
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