The second edition of this popular book brings students fully up to date with the latest methods and techniques in choice analysis. Comprehensive yet accessible, it offers a unique introduction to anyone interested in understanding how to model and forecast the range of choices made by individuals and groups. In addition to a complete rewrite of several chapters, new topics covered include ordered choice, scaled MNL, generalized mixed logit, latent class models, group decision making, heuristics and attribute processing strategies, expected utility theory, and prospect theoretic applications. Many additional case studies are used to illustrate the applications of choice analysis with extensive command syntax provided for all Nlogit applications and datasets available online. With its unique blend of theory, estimation, and application, this book has broad appeal to all those interested in choice modeling methods and will be a valuable resource for students as well as researchers, professionals, and consultants.

A fully updated second edition of this popular introduction to applied choice analysis, written for graduate students, researchers, professionals and consultants.

Preface Part I. Getting Started 1. In the beginning 2. Choosing 3. Choice and utility 4. Families of discrete choice models 5. Estimating discrete choice models 6. Experimental design and choice experiments 7. Statistical inference 8. Other matters that analysts often inquire about Part II. Software and Data 9. NLOGIT for applied choice analysis 10. Data issues in NLOGIT Part III. The Suite of Choice Models 11. Getting started modelling: the workhorse - MNL 12. Handling unlabelled discrete choice data 13. Getting more from your model 14. Nested logit estimation 15. Mixed logit estimation 16. Latent class models 17. Binary choice models 18. Ordered choices 19. Combining sources of data Part IV. Advanced Topics 20. Frontiers of choice analysis 21. Attribute processing, heuristics, and preference construction 22. Group decision making References Index