Workshop report: working with repeated choice data

Daly, A; Hess, S; Eckert, C

Workshop report: working with repeated choice data

Daly, A Hess, S Eckert, C

Permalink

Publisher:: Edward Elgar Publishing
Publication Type:: Chapter
Citation:: Choice Modelling The State of the Art and the State of Practice, 2013, pp. 91 - 106
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	2014DaleyHessEckert.pdf	Published version	290.32 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Daly, A	en_US
dc.contributor.author	Hess, S	en_US
dc.contributor.author	Eckert, C https://orcid.org/0000-0002-8573-6978	en_US
dc.contributor.editor	Hess, S	en_US
dc.contributor.editor	Daly, A	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Choice Modelling The State of the Art and the State of Practice, 2013, pp. 91 - 106	en_US
dc.identifier.isbn	1781007276	en_US
dc.identifier.isbn	9781781007273	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119627
dc.description.abstract	In recent years the study of data containing multiple responses from each individual has become the approach of preference for choice modelling, mainly due to increasing reliance on data from stated choice surveys. Multiple responses offer analysts the opportunity to distinguish within-respondent from between-respondent heterogeneity, to investigate behaviour by the same respondent under a range of attribute level combinations and to study behaviours that may not exist in current markets; moreover there are often cost savings in this approach compared with alternative data capture procedures. Notwithstanding these advantages, the analysis of data containing multiple responses from individuals presents specific issues. It is necessary to make allowance for the correlation of individuals’ repeated responses arising from their specific unmeasured attributes in order to avoid biases in the inferences drawn from the data. The issues involved have been known for some years and methods have been developed to deal with them, but work continues and two schools of thought can be distinguished, each offering distinct insights. The first school takes the approach of modelling responses at the aggregate (sample) level while making allowances for the repeated choice nature of the data either explicitly or through correction approaches. The second approach is to develop a model for each individual respondent, thus bypassing the issue of separating out individual-specific effects	en_US
dc.publisher	Edward Elgar Publishing	en_US
dc.relation.ispartof	Choice Modelling The State of the Art and the State of Practice	en_US
dc.relation.isbasedon	10.4337/9781781007273.00009	en_US
dc.title	Workshop report: working with repeated choice data	en_US
dc.type	Chapter
utslib.location	Cheltenham, UK	en_US
utslib.for	1505 Marketing	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/Faculty of Business/Marketing Discipline
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Cheltenham, UK	en_US
pubs.publication-status	Published	en_US

Abstract:

In recent years the study of data containing multiple responses from each individual has become the approach of preference for choice modelling, mainly due to increasing reliance on data from stated choice surveys. Multiple responses offer analysts the opportunity to distinguish within-respondent from between-respondent heterogeneity, to investigate behaviour by the same respondent under a range of attribute level combinations and to study behaviours that may not exist in current markets; moreover there are often cost savings in this approach compared with alternative data capture procedures. Notwithstanding these advantages, the analysis of data containing multiple responses from individuals presents specific issues. It is necessary to make allowance for the correlation of individuals’ repeated responses arising from their specific unmeasured attributes in order to avoid biases in the inferences drawn from the data. The issues involved have been known for some years and methods have been developed to deal with them, but work continues and two schools of thought can be distinguished, each offering distinct insights. The first school takes the approach of modelling responses at the aggregate (sample) level while making allowances for the repeated choice nature of the data either explicitly or through correction approaches. The second approach is to develop a model for each individual respondent, thus bypassing the issue of separating out individual-specific effects

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119627