Analysis of Multivariate Social Science Data (2nd ed.)

Analysis of Multivariate Social Science Data is written by four well-respected scholars in the social sciences and is designed “to give students and social researchers with limited mathematical and statistical knowledge a basic understanding of some of the main multivariate methods and the knowledge to carry them out” (p. ix). Using practical examples and minimizing the use of formal mathematics, the authors aim to provide practitioners with an accessible common-language introduction to what they consider the most important multivariate methods used in social scientific research. We reviewed the second edition of this book; for an alternative review of this edition see Mair (2009), and for a review of the first edition see Austin (2003). In this second edition, the authors have maintained the use of practical examples that were cited by reviewers as a clear strength of the book. This has resulted in an emphasis on example-driven interpretation of statistical results that serve to explain, illustrate, and even to compare and contrast the different methods. The first chapter is designed to orient the reader by providing a brief history of how the book was developed from course notes; it includes detailed explanations of the authors’ nonmathematical approach to instruction, provides a review of basic notation and terminology, and discusses the focal role of examples throughout the book. Each of the subsequent 11 chapters is devoted to exploring a particular analytic method following a progression that the authors describe as moving “from simpler to more complex” (p. x). The first four of these chapters are devoted to methods that the authors collect under the terms descriptive methods, data reduction methods, and methods for data summarization: cluster analysis (Chapter 2), multidimensional scaling (Chapter 3), correspondence analysis (Chapter 4), and principal components analysis (Chapter 5). Simple linear regression, multiple linear regression, logistic regression, and path analysis are all covered in Chapter 6 (new to this edition), which serves as a pivotal transitional chapter between the first and second main sections of the book. In these later chapters,