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Applied Choice Analysis
Abstract
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.
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The NL structure allows the choice set to be partitioned such that the constant variance assumption holds among alternatives in the same partition while allowing differential variance between partitions and correlation among alternatives within a partition.
“Essentially the NL structure allows the choice set to be partitioned in such a way that the constant variance assumption holds among alternatives in the same partition while allowing differential variance between partitions and correlation among alternatives within a partition (Hensher et al., 2015).”
From Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model (2019) · cites this paper as Hensher et al., 2015
The nested logit (NL) models greatly outperform the multinomial logit (MNL) models based on the likelihood ratio test.
“The NL models greatly outperform the MNL models in terms of this important metric. All of these values are many times the critical value.”
From Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model (2019) · cites this paper as Hensher et al., 2015
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Preferences for e-mental health interventions in Germany: a discrete choice experiment
Phillips, E. A., Himmler, S. F., & Schreyögg, J. (2021) · Value in Health · doi:10.1016/j.jval.2020.09.018 · Cited by 88
Asioli, D., & Grasso, S. (2021) · Food Quality and Preference · doi:10.1016/j.foodqual.2021.104194 · Cited by 86
Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model
University of Utah, Ewing, R., Sabouri, S., University of Utah, Park, K., University of Utah, Lyons, T., University of Utah, Tian, G., & University of Utah (2019) · doi:10.15760/trec.246 · Cited by 3
Metadata
- Authors
- Hensher, D. A., Rose, J. M., & Greene, W. H.
- Affiliations
- The University of Sydney (AU)
- University of South Australia (AU)
- New York University (US)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2015-04-30
- OpenAlex
- W1202833455
- Cited by
- 1,585
- References
- 0
- Citations by year
- 2026: 168 · 2025: 180 · 2024: 144 · 2023: 123 · 2022: 129 · 2021: 163 · 2020: 142 · 2019: 115 · 2018: 91 · 2017: 62 · 2016: 46 · 2015: 24
- FWCI
- 73.03
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Thammasat University Digital Collections (submittedVersion)
- TU Digital Collections (Thammasat University) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- discrete choice models, latent class models, group decision making, heuristics, expected utility theory, prospect theory
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