This guide to dealing with real-world data in tourism goes beyond the methods usually covered in introductory textbooks. The first part of the book considers key issues associated with using well-known methods to produce valid and reliable models of real-world phenomena, emphasizing issues in data selection, approaches to factor and cluster analysis, and mathematical modeling using regression methods and structural equation modeling. After a chapter on the nature of data in tourism, there is material on testing hypotheses, data analysis, model building, and time-dependent phenomena and forecasting. Part 2, on numerical methods, covers new approaches to modeling: maximum likelihood estimation, Monte Carlo methods, and agent-based modeling and simulation. Examples of applications to tourism research are given, and common pitfalls and solutions are discussed. An appendix summarizes the software introduced in the book. Baggio teaches computer science at Bocconi University, Italy. Klobas is a research fellow at Bocconi University. There is no subject index. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)