Constrained stated choice experimental designs.

Collins, AT; Bliemer, MCJ; Rose, J

Constrained stated choice experimental designs.

Collins, AT Bliemer, MCJ Rose, J

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Publication Type:: Conference Proceeding
Citation:: 10th International Conference on Transport Survey Methods, 2014, pp. ? - ? (17)
Issue Date:: 2014-11-01

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Field	Value	Language
dc.contributor.author	Collins, AT	en_US
dc.contributor.author	Bliemer, MCJ	en_US
dc.contributor.author	Rose, J	en_US
dc.date	2014-11-16	en_US
dc.date.issued	2014-11-01	en_US
dc.identifier.citation	10th International Conference on Transport Survey Methods, 2014, pp. ? - ? (17)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119855
dc.description.abstract	While a significant literature exists on the generation of experimental designs for stated choice experiments, little work has been done to effectively accommodate constraints on these designs. Constraints may include the prevention of dominated alternatives, the exclusion of specific combinations of attribute levels on plausibility and realism grounds, and the imposition of attribute level balance. We argue that most such constraints are fundamental to the success of the experiment, and should always, at least where feasible, be respected. The notable exception is level balance, which can instead be imposed as a soft constraint, using a level balance measure introduced herein. Various rule structures for specifying the constraints are proposed, as are two new algorithms that can conform to any such rules, whilst allowing the analyst to control the level of importance placed on level balance. From the two case studies investigated, it is determined that the type of constraints specified may influence which algorithm performs best, or even if the algorithm finds a solution at all. Excluding specific combinations of attribute levels reduces the statistical efficiency of the design but is nonetheless warranted. By contrast, the reduction in efficiency resulting from level balance may be excessive, and the analyst should test the trade-offs between these two design properties, by varying the weight placed on level balance in the objective function. The proposed techniques allow researchers to focus not just on the statistical efficiency of these experiments, but behavioural realism and plausibility as well.	en_US
dc.relation.ispartof	10th International Conference on Transport Survey Methods	en_US
dc.relation.ispartof	International Conference on Transport Survey Methods	en_US
dc.title	Constrained stated choice experimental designs.	en_US
dc.type	Conference Proceeding
utslib.location.activity	Leura, Australia	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1507 Transportation and Freight Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Strength - BIDA - Business Intelligence and Data Analytics
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2014-11-21	en_US
pubs.start-date	2014-11-16	en_US

Abstract:

While a significant literature exists on the generation of experimental designs for stated choice experiments, little work has been done to effectively accommodate constraints on these designs. Constraints may include the prevention of dominated alternatives, the exclusion of specific combinations of attribute levels on plausibility and realism grounds, and the imposition of attribute level balance. We argue that most such constraints are fundamental to the success of the experiment, and should always, at least where feasible, be respected. The notable exception is level balance, which can instead be imposed as a soft constraint, using a level balance measure introduced herein. Various rule structures for specifying the constraints are proposed, as are two new algorithms that can conform to any such rules, whilst allowing the analyst to control the level of importance placed on level balance. From the two case studies investigated, it is determined that the type of constraints specified may influence which algorithm performs best, or even if the algorithm finds a solution at all. Excluding specific combinations of attribute levels reduces the statistical efficiency of the design but is nonetheless warranted. By contrast, the reduction in efficiency resulting from level balance may be excessive, and the analyst should test the trade-offs between these two design properties, by varying the weight placed on level balance in the objective function. The proposed techniques allow researchers to focus not just on the statistical efficiency of these experiments, but behavioural realism and plausibility as well.

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http://hdl.handle.net/10453/119855