Best–worst scaling vs. discrete choice experiments: An empirical comparison using social care data

Potoglou, Dimitris; Burge, Peter; Flynn, Terry N.; Netten, Ann; Malley, Juliette; Forder, Julien E.; Brazier, John

doi:10.1016/j.socscimed.2011.03.027

Cited by 132 publications

(120 citation statements)

References 17 publications

(20 reference statements)

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“…Marley and Louviere (2005) and Marley et al (2008) develop alternative models to analyse the resulting data. In particular, they prove a number of choice theoretical properties of the maximum difference logit (max-diff) model, that has since been the workhorse model in empirical studies (Flynn et al, 2007;Lusk and Briggeman, 2009;Lusk and Natalie, 2009;Potoglou et al, 2011;Marti, 2012).…”

Section: Models For Single Profile Case Datamentioning

confidence: 88%

The use of alternative preference elicitation methods in complex discrete choice experiments

Yoo

Doiron

2013

Journal of Health Economics

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. We analyse stated preference data over nursing jobs collected from two different discrete choice experiments: a multi-profile case best-worst scaling experiment (BWS) prompting selection of the best and worst among alternative jobs, and a profile case BWS wherein the respondents choose the best and worst job attributes. The latter allows identification of additional utility parameters and is believed to be cognitively easier. Results suggest that respondents place greater value on pecuniary over non-pecuniary gains in the multi-profile case. There is little evidence that this discrepancy is induced by the extra cognitive burden of processing several profiles at once in the multi-profile case. We offer thoughts on other likely mechanisms.JEL classification: C23, C25, C81, J44 Key words: discrete choice experiment, preference elicitation, rank-ordered data, latent class logit, best-worst scaling, maximum-difference model Highlights:• We compare preferences on nursing jobs elicited by profile and multiprofile case DCEs.• The paper is the first to contrast the two types of DCEs using monetary and nonmonetary attributes.• Preferences are comparable across the DCEs but only for non-monetary attributes.• Respondents value salary gains relatively more in the multi-profile DCEs.• The evidence suggests that this discrepancy is not due to the variation in cognitive difficulty.

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Section: Models For Single Profile Case Datamentioning

confidence: 88%

The use of alternative preference elicitation methods in complex discrete choice experiments

Yoo

Doiron

2013

Journal of Health Economics

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“…102 Flynn 103 provides an overview and theoretical discussion of the different cases of BWS. Case 2 has particular advantages in valuation studies that seek to elicit general population preferences for important attributes of QoL (or whatever maximand is of relevance to policy-makers 104 ). In particular, it presents profiles one at a time, rather than in choice sets of size two or more as in a traditional DCE.…”

Section: Eliciting Preferencesmentioning

confidence: 99%

“…1,2 This reflects a wider emphasis on the importance of reflecting outcomes of publicly funded services, which has been evident for some time, for example in reflecting productivity of public services in National Accounts. 3 In times of financial stringency it is particularly important to estimate the impact of different interventions accurately if we are to identify cost-effective alternatives and make the best use of limited resources.…”

Section: Introductionmentioning

confidence: 99%

Outcomes of social care for adults: developing a preference-weighted measure.

Netten

Burge²,

Malley³

et al. 2012

Health Technol Assess

269

501

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“…These attribute-levels best-worst scaling tasks are thus an alternative way to DCEs to elicit preferences. Potoglou et al (2011), for example, give an empirical comparison of the two methods and conclude, "preference weights from best-worst scaling and discrete choice experiments do reveal similar patterns in preference and in the majority of cases preference weights -when normalised/rescaled -are not significantly different." The appeal of attribute-level best-worst (BW) (sometimes called profile-based BW or Case 2 BW) is that the task is cognitively less demanding for respondents, particularly if the area of application is unfamiliar.…”

Section: Introductionmentioning

confidence: 99%

“…In the absence of any prior estimates of the attribute-level parameters, these are assumed to be constant when designing a study (Potoglou et al (2011)) and in that case resolution 3 fractional factorial designs and the complete factorial have the same information matrix. By using a smaller set of profiles it is more likely that a set of profiles of consistent choice difficulty can be found.…”

Section: Introductionmentioning

confidence: 99%

Designing for Attribute-Level Best–Worst Choice Experiments

Street

Knox

2012

Journal of Statistical Theory and Practice

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In a traditional discrete choice experiment, respondents indicate which one of a set of products(services or profiles) they think is best, and they usually do this for a number of sets. Howeverthis does not allow the experimenter to gain any information about the relative contributionto the utility of each of the levels in a profile. To rectify this deficiency, researchers can showrespondents a profile, described by the levels of a number of attributes, and ask each respondentto choose the best and the worst feature (attribute-level) of the profile. Such a task is called anattribute-level best-worst choice task. Using the D-optimality criterion, resolution 3fractional factorial designs perform as well as the complete factorial design in attribute-level best-worst choice experiments, assuming both that all attribute levels are equally attractive and that only main effects of attribute levels are to be used to explain the results. We consider the performance of some small designs for various different prior assumptions about attribute levels.

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Best–worst scaling vs. discrete choice experiments: An empirical comparison using social care data

Cited by 132 publications

References 17 publications

The use of alternative preference elicitation methods in complex discrete choice experiments

The use of alternative preference elicitation methods in complex discrete choice experiments

Outcomes of social care for adults: developing a preference-weighted measure.

Designing for Attribute-Level Best–Worst Choice Experiments

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