Reducing the randomness of latent variables using the evaluative space grid: Implementation in a hybrid choice model

Borriello, A; Scagnolari, S; Rose, JM

Reducing the randomness of latent variables using the evaluative space grid: Implementation in a hybrid choice model

Borriello, A

Scagnolari, S Rose, JM

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Publication Type:: Journal Article
Citation:: Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 62 pp. 192 - 211
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Borriello, A https://orcid.org/0000-0001-7709-041X	en_US
dc.contributor.author	Scagnolari, S	en_US
dc.contributor.author	Rose, JM	en_US
dc.date.available	2021-04-01T18:01:20Z
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 62 pp. 192 - 211	en_US
dc.identifier.issn	1369-8478	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129932
dc.description.abstract	© 2019 Elsevier Ltd The study of latent variables, and in particular of attitudes, contributes to a better understanding of individual preferences and behavior and it is now common practice within transportation literature. However, the procedure of attitude measurement is still not optimal. Two major issues are the misspecification of the attitude itself and the number of suitable items used for defining the psychological factor. The incorrect measurement entails a poor representation of individuals on the latent continuum and a less precise definition of the latent variable itself. These issues become even more relevant when a Likert scale is used. Indeed, the neutral point of this scale is selected by both individuals having an ambivalent and an indifferent attitude, and the poor representation makes impossible to distinguish these categories. Nevertheless, such a distinction can be very profitable for policy reasons. To overcome this issue and to suggest more effective policies, we propose using the Evaluative Space Grid (ESG), which is a single-item measure of positivity and negativity, to collect attitudinal variables. This tool can distinguish between individuals with indifferent and ambivalent attitudes, as well as those with positive and negative inclinations. This paper models the ESG using a pair of ordered logit regressions and suggests a procedure to include this approach in the framework of hybrid choice models. Furthermore, it endeavors to shed light on the preferences of individuals having indifferent and ambivalent inclinations in a transportation context, showing the hypothesis that their preferences are different for commuting trips.	en_US
dc.relation.ispartof	Transportation Research Part F: Traffic Psychology and Behaviour	en_US
dc.relation.isbasedon	10.1016/j.trf.2018.12.018	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Reducing the randomness of latent variables using the evaluative space grid: Implementation in a hybrid choice model	en_US
dc.type	Journal Article
utslib.citation.volume	62	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	1507 Transportation and Freight Services	en_US
utslib.for	1701 Psychology	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	62	en_US

Abstract:

© 2019 Elsevier Ltd The study of latent variables, and in particular of attitudes, contributes to a better understanding of individual preferences and behavior and it is now common practice within transportation literature. However, the procedure of attitude measurement is still not optimal. Two major issues are the misspecification of the attitude itself and the number of suitable items used for defining the psychological factor. The incorrect measurement entails a poor representation of individuals on the latent continuum and a less precise definition of the latent variable itself. These issues become even more relevant when a Likert scale is used. Indeed, the neutral point of this scale is selected by both individuals having an ambivalent and an indifferent attitude, and the poor representation makes impossible to distinguish these categories. Nevertheless, such a distinction can be very profitable for policy reasons. To overcome this issue and to suggest more effective policies, we propose using the Evaluative Space Grid (ESG), which is a single-item measure of positivity and negativity, to collect attitudinal variables. This tool can distinguish between individuals with indifferent and ambivalent attitudes, as well as those with positive and negative inclinations. This paper models the ESG using a pair of ordered logit regressions and suggests a procedure to include this approach in the framework of hybrid choice models. Furthermore, it endeavors to shed light on the preferences of individuals having indifferent and ambivalent inclinations in a transportation context, showing the hypothesis that their preferences are different for commuting trips.

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http://hdl.handle.net/10453/129932