Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well-established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi-attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator-based models of decision making used in response time research can also provide process-level nstantiations for random utility models.

Item Type:	Refereed Article
Keywords:	Preference; Decision making; Best–worst scaling; Random utility model; Evidence accumulation; Linear ballistic accumulator
Research Division:	Psychology
Research Group:	Cognitive and computational psychology
Research Field:	Decision making
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in psychology
ID Code:	98935
Year Published:	2014
Web of Science® Times Cited:	33
Deposited By:	Medicine
Deposited On:	2015-03-10
Last Modified:	2017-10-20
Downloads:	0