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Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data

Author

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  • Zhang, Jing
  • Reed Johnson, F.
  • Mohamed, Ateesha F.
  • Hauber, A. Brett

Abstract

Best-practice guidelines for stated-preference methods suggest there is a limit to the number of attributes respondents can reliably evaluate. This study explores a cost-effective solution to combining elicitation formats from a single study to obtain more preference information from a given sample while limiting respondents’ cognitive burden.

Suggested Citation

  • Zhang, Jing & Reed Johnson, F. & Mohamed, Ateesha F. & Hauber, A. Brett, 2015. "Too many attributes: A test of the validity of combining discrete-choice and best–worst scaling data," Journal of choice modelling, Elsevier, vol. 15(C), pages 1-13.
    • Handle: RePEc:eee:eejocm:v:15:y:2015:i:c:p:1-13
    DOI: 10.1016/j.jocm.2014.12.001
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    2. Joseph F. Hair & Christian M. Ringle & Siegfried P. Gudergan & Andreas Fischer & Christian Nitzl & Con Menictas, 2019. "Partial least squares structural equation modeling-based discrete choice modeling: an illustration in modeling retailer choice," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 115-142, April.
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