Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?

Fifer, S; Rose, J; Greaves, S

Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?

Fifer, S Rose, J Greaves, S

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Publication Type:: Journal Article
Citation:: Transportation Research Part A: Policy and Practice, 2014, 61 pp. 164 - 177
Issue Date:: 2014-03-01

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Field	Value	Language
dc.contributor.author	Fifer, S	en_US
dc.contributor.author	Rose, J	en_US
dc.contributor.author	Greaves, S	en_US
dc.date.issued	2014-03-01	en_US
dc.identifier.citation	Transportation Research Part A: Policy and Practice, 2014, 61 pp. 164 - 177	en_US
dc.identifier.issn	0965-8564	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118841
dc.description.abstract	The extent to which Stated Choice (SC) experiments suffer from hypothetical bias continues to be a controversial topic in the literature. This research provides further evidence in this debate by examining the existence of hypothetical bias in a transport-related SC experiment. Data for this research were sourced from a University of Sydney study exploring the effect of exposure-based charging on motorist behaviour. The sample included 148 Sydney motorists who were recruited to take part in the 10-week GPS driving field study (Revealed Preference/RP data). In addition, participants were also required to complete an SC survey which was designed to mimic the RP decision context in order to capture what participants indicated they would do as opposed to what participants actually did in reaction to the charging regime.The current state of practice for measuring hypothetical bias in the literature is to compare aggregate differences in model outcomes using SC and RP data sources. Aggregate analysis is limited in its scope and does not allow for the calculation of the prevalence of hypothetical bias (i.e., how many participants are affected by hypothetical bias). This research is uniquely structured to allow for individual categorisation of hypothetical bias by comparing SC and RP data from the same sample for the direct purpose of investigating the prevalence of hypothetical bias. Furthermore, the extent to which mitigation techniques (cheap talk and certainty scales) influence hypothetical bias is also explored. The findings from this research show that the SC model estimates are prone to hypothetical bias and that the mitigation techniques have potential to compensate for this inherent bias. © 2014 Elsevier Ltd.	en_US
dc.relation.ispartof	Transportation Research Part A: Policy and Practice	en_US
dc.relation.isbasedon	10.1016/j.tra.2013.12.010	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?	en_US
dc.type	Journal Article
utslib.citation.volume	61	en_US
utslib.for	1507 Transportation and Freight Services	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1205 Urban and Regional Planning	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.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

The extent to which Stated Choice (SC) experiments suffer from hypothetical bias continues to be a controversial topic in the literature. This research provides further evidence in this debate by examining the existence of hypothetical bias in a transport-related SC experiment. Data for this research were sourced from a University of Sydney study exploring the effect of exposure-based charging on motorist behaviour. The sample included 148 Sydney motorists who were recruited to take part in the 10-week GPS driving field study (Revealed Preference/RP data). In addition, participants were also required to complete an SC survey which was designed to mimic the RP decision context in order to capture what participants indicated they would do as opposed to what participants actually did in reaction to the charging regime.The current state of practice for measuring hypothetical bias in the literature is to compare aggregate differences in model outcomes using SC and RP data sources. Aggregate analysis is limited in its scope and does not allow for the calculation of the prevalence of hypothetical bias (i.e., how many participants are affected by hypothetical bias). This research is uniquely structured to allow for individual categorisation of hypothetical bias by comparing SC and RP data from the same sample for the direct purpose of investigating the prevalence of hypothetical bias. Furthermore, the extent to which mitigation techniques (cheap talk and certainty scales) influence hypothetical bias is also explored. The findings from this research show that the SC model estimates are prone to hypothetical bias and that the mitigation techniques have potential to compensate for this inherent bias. © 2014 Elsevier Ltd.

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http://hdl.handle.net/10453/118841