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Article: On the Use of Probit-Based Models for Ranking Data Analysis

TitleOn the Use of Probit-Based Models for Ranking Data Analysis
Authors
Issue Date2019
PublisherSage Publications, Inc. The Journal's web site is located at http://journals.sagepub.com/home/trr
Citation
Transportation Research Record, 2019, v. 2673 n. 4, p. 229-240 How to Cite?
AbstractIn consumer surveys, more information per response regarding preferences of alternatives may be obtained if individuals are asked to rank alternatives instead of being asked to select only the most-preferred alternative. However, the latter method continues to be the common method of preference elicitation. This is because of the belief that ranking of alternatives is cognitively burdensome. In addition, the limited research on modeling ranking data has been based on the rank ordered logit (ROL) model. In this paper, we show that a rank ordered probit (ROP) model can better utilize ranking data information, and that the prevalent view of ranking data as not being reliable (because of the attenuation of model coefficients with rank depth) may be traced to the use of a misspecified ROL model rather than to any cognitive burden considerations.
Persistent Identifierhttp://hdl.handle.net/10722/269479
ISSN
2020 Impact Factor: 1.56
2015 SCImago Journal Rankings: 0.474
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorNair, GS-
dc.contributor.authorBhat, CR-
dc.contributor.authorPendyala, RM-
dc.contributor.authorLoo, BPY-
dc.contributor.authorLam, WHK-
dc.date.accessioned2019-04-24T08:08:33Z-
dc.date.available2019-04-24T08:08:33Z-
dc.date.issued2019-
dc.identifier.citationTransportation Research Record, 2019, v. 2673 n. 4, p. 229-240-
dc.identifier.issn0361-1981-
dc.identifier.urihttp://hdl.handle.net/10722/269479-
dc.description.abstractIn consumer surveys, more information per response regarding preferences of alternatives may be obtained if individuals are asked to rank alternatives instead of being asked to select only the most-preferred alternative. However, the latter method continues to be the common method of preference elicitation. This is because of the belief that ranking of alternatives is cognitively burdensome. In addition, the limited research on modeling ranking data has been based on the rank ordered logit (ROL) model. In this paper, we show that a rank ordered probit (ROP) model can better utilize ranking data information, and that the prevalent view of ranking data as not being reliable (because of the attenuation of model coefficients with rank depth) may be traced to the use of a misspecified ROL model rather than to any cognitive burden considerations.-
dc.languageeng-
dc.publisherSage Publications, Inc. The Journal's web site is located at http://journals.sagepub.com/home/trr-
dc.relation.ispartofTransportation Research Record-
dc.titleOn the Use of Probit-Based Models for Ranking Data Analysis-
dc.typeArticle-
dc.identifier.emailLoo, BPY: bpyloo@hku.hk-
dc.identifier.authorityLoo, BPY=rp00608-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0361198119838987-
dc.identifier.scopuseid_2-s2.0-85064550470-
dc.identifier.hkuros297642-
dc.identifier.volume2673-
dc.identifier.issue4-
dc.identifier.spage229-
dc.identifier.epage240-
dc.identifier.isiWOS:000472914200021-
dc.publisher.placeUnited States-
dc.identifier.issnl0361-1981-

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