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A platform for research: civil engineering, architecture and urbanism
Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?
https://orcid.org/0000-0002-3310-6704
Highlights We explore the responses of motorists to an exposure-based charging regime. We capture what motorists indicated they would do using a stated choice (SC) survey. We compare the SC results with what they actually did in a field study using GPS. The results demonstrate that hypothetical bias is a significant issue in SC surveys. Bias mitigation techniques have potential to compensate for this bias.
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.
Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?
https://orcid.org/0000-0002-3310-6704
Highlights We explore the responses of motorists to an exposure-based charging regime. We capture what motorists indicated they would do using a stated choice (SC) survey. We compare the SC results with what they actually did in a field study using GPS. The results demonstrate that hypothetical bias is a significant issue in SC surveys. Bias mitigation techniques have potential to compensate for this bias.
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.
Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?
https://orcid.org/0000-0002-3310-6704
Highlights We explore the responses of motorists to an exposure-based charging regime. We capture what motorists indicated they would do using a stated choice (SC) survey. We compare the SC results with what they actually did in a field study using GPS. The results demonstrate that hypothetical bias is a significant issue in SC surveys. Bias mitigation techniques have potential to compensate for this bias.
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.
Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?
Fifer, Simon (author) / Rose, John (author) / Greaves, Stephen (author)
Transportation Research Part A: Policy and Practice ; 61 ; 164-177
2013-12-16
14 pages
Article (Journal)
Electronic Resource
English
Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?
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