Analysis of Multivariate Social Science Data, Second Edition (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

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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.

Analysis of Multivariate Social Science Data, Second Edition (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences) Review

I bought this book on another website for a class on multivariate analysis and highly recommend it for those interested in applied statistical methods. First off, I am a Ph.D. student in political science in a large Mid-western public university. Although one of my fields is political methodology my department does not require rigorous mathematical training in order to take advanced statistics classes; this book therefore, is primarily for students who have a firm grasp of linear regression models (meaning its assumptions etc.) and would like to use advanced methods such as principal component analysis, item response theory, cluster analysis etc. without having to first take calculus III, linear algebra and probability theory. The best part about this book is that the chapters are self-contained, meaning the first chapter I read in this book was chapter 7 and I was able to understand all the assumptions of latent factor analysis and how it compares to principal component analysis. Another plus of this book is that chapters end with examples, so that you can understand how to use each method in your own work. Finally, the book uses plain english to explain equations and principles and does not include tedious and unnecessary equations and matrices. This book is very accessible to those interested in advanced applied statistical models and you will not regret buying it.

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