Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data

Terry N Flynn*, Tim J Peters, Joanna Coast

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)

Abstract

Older people's valuation of health-related aspects of quality of life may be altered by response shift, where they lower expectations of aspects of well-being that are believed to naturally deteriorate with age. Policy-makers may wish to adjust estimated preferences if these reflect past inequities in health funding rather than the true production possibilities. Response shift might be quantified by changing the context of the choice task. The ICECAP-O valuation exercise achieved this by asking a binary choice holistic decision of respondents, in addition to the case 2 best-worst choice task among the five attributes. Answers to the former are more likely to be subject to response shift since they involve traditional trade-offs. Answers to the latter reflect only 'relative disutility' of various impairments. Individual level estimates for the latter were substituted into the design matrix in a series of latent class analyses of the binary choice data. The five attribute mean estimates from the conditional logit regressions are the attribute importance parameters and represent the (internal) scaling factors that respondents use in transforming their case 2 BWS utilities into ones relevant in multi-profile decision-making. The principal hypothesis was that there were classes of respondents who used identical attribute importance weights. Rejection of this hypothesis prompted testing of secondary hypotheses that respondents placed lower importance on control (independence) and role (doing thiHgs that make you feel valued), those attributes thought to be most vulHerable to response shift. Results showed that 17% of respondents never traded, in most cases illogically given their own ICECAP-O responses, and were dropped. Tests of parameter-equality suggested at least 30% and possibly as many as 53% of respondents for whom there is a single statistically significant attribute importance factor and 64 (21%) of respondents for whom there is a single statistically non-significant attribute importance factor. The remaining 9% of respondents had a moderate status-quo bias (preference for own life). These results do not provide strong support for response shift in the ICECAP-O valuation sample. There is only very limited support for differential weights for the five attributes when moving into a traditional DCE framework.

Original languageEnglish
Pages (from-to)34-43
Number of pages10
JournalJournal of Choice Modelling
Volume6
DOIs
Publication statusPublished - Mar 2013

Bibliographical note

E-pub ahead of print: 23/05/2013

Keywords

  • Attribute importance
  • Best-worst scaling
  • Choice modelling

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