The book has eight chapters, of which the first one is an introduction and the last one a summary. The other six chapters discuss (1) theoretical aspects (chapters 2,3, and 4), (2) examples (chapters 5 and 6), and (3) computer programs of multidimensional scaling methods (chapter 7). In the theoretical chapters a classification of data types is presented (chapter 2 and section 4.1). The third chapter presents the main models for proximity data: Torgerson's classical metric model, Shepard and Kruskal's nonmetric model, Carroll and Chang's individual differences model, and Ramsay's power model. Ramsay's model differs from the other models in the respect that it is a stochastic model, which means, among others, that hypotheses on the number of dimensions underlying the proximities can be tested statistically. The fourth chapter discusses the simple unfolding and vector models for preference data. In the fifth and sixth chapter the methods are applied to empirical data. A group of 20 subjects judged proximities of nine means of transport, e.g., the similarity of train and airplane, train and city bus, and so on. The fifth chapter reports the analysis of these data, mainly using Ramsay's power model and his MULTISCALE-II program. Moreover, a relatively young group of 20 subjects and a relatively old group of 20 subjects ordered the vine means of transport according to preference. The data were analyzed using Carroll's vector model and his PREFMAP program; the results are reported in the sixth chapter. The seventh chapter is devoted to the description of computer programs for applying the methods, which were discussed in the theoretical chapters.
The theoretical part of the book (chapter 2,3 and 4) is well-written, but is also rather dense. The example part (chapters 5 and 6) is excellent. It does not only demonstrate the methods, but also nicely discusses practical issues, such as model choice, individual differences, and interpretation and reliability of solutions. The computer program part (chapter 7) is very useful because it contains detailed instructions for using the most common programs.
The book is recommned to investigators who want to apply multidimensional scaling methods in their research. The book is also recommended for teaching an introductory multidimensional scaling course. Because of its density the theoretical part of the book must be supplemented by lecture notes or other study materials. The example part is well suited for demonstrating the methods and for motivating students and investigators to apply them in their research. The computer program chapter will serve well for computer practices.