IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v21y2016icp15-24.html
   My bibliography  Save this article

A formal and empirical comparison of two score measures for best–worst scaling

Author

Listed:
  • Marley, A.A.J.
  • Islam, T.
  • Hawkins, G.E.

Abstract

Best–worst scaling (BWS) is a method that asks individuals to choose the most and the least preferred option from a set of available options. There has been extensive discussion and evaluation of the use of scores (data summaries) in the analysis of such data. Here we motivate, summarize, and compare the usefulness of two such score measures: the analytical closed form solution (Lipovetsky and Conklin, 2014, Journal of Choice Modelling) and normalized best–worst scores (Louviere et al., 2015, Cambridge University Press). We conclude that both have underlying motivations in the maxdiff model of best–worst choice and that the analytical closed form solution provides better fits to the aggregate choices in several best–worst choice data sets.

Suggested Citation

  • Marley, A.A.J. & Islam, T. & Hawkins, G.E., 2016. "A formal and empirical comparison of two score measures for best–worst scaling," Journal of choice modelling, Elsevier, vol. 21(C), pages 15-24.
    • Handle: RePEc:eee:eejocm:v:21:y:2016:i:c:p:15-24
    DOI: 10.1016/j.jocm.2016.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534515300701
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2016.03.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    2. Lipovetsky, Stan & Conklin, Michael, 2014. "Best-Worst Scaling in analytical closed-form solution," Journal of choice modelling, Elsevier, vol. 10(C), pages 60-68.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chrzan, Keith & Peitz, Megan, 2019. "Best-Worst Scaling with many items," Journal of choice modelling, Elsevier, vol. 30(C), pages 61-72.
    2. Amanda Working & Mohammed Alqawba & Norou Diawara, 2020. "Dynamic Attribute-Level Best Worst Discrete Choice Experiments," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 11(2), pages 1-1, March.
    3. Alexandre Brouste & Christophe Dutang & Tom Rohmer, 2022. "A Closed-form Alternative Estimator for GLM with Categorical Explanatory Variables," Post-Print hal-03689206, HAL.
    4. White, Mark H., 2021. "bwsTools: An R package for case 1 best-worst scaling," Journal of choice modelling, Elsevier, vol. 39(C).
    5. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    6. Echaniz, Eneko & Ho, Chinh Q. & Rodriguez, Andres & dell'Olio, Luigi, 2019. "Comparing best-worst and ordered logit approaches for user satisfaction in transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 752-769.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Jianjun & Liu, Fang & Li, Li & Zhang, Jian, 2022. "More than innovativeness: Comparing residents’ motivations for participating renewable energy communities in different innovation segments," Renewable Energy, Elsevier, vol. 197(C), pages 552-563.
    2. Lan, Haifeng & Gou, Zhonghua & Yang, Linchuan, 2020. "House price premium associated with residential solar photovoltaics and the effect from feed-in tariffs: A case study of Southport in Queensland, Australia," Renewable Energy, Elsevier, vol. 161(C), pages 907-916.
    3. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    4. Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2022. "Roles of diffusion patterns, technological progress, and environmental benefits in determining optimal renewable subsidies in the US," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    5. Rode, Johannes & Müller, Sven, 2016. "Spatio-Temporal Variation in Peer Effects - The Case of Rooftop Photovoltaic Systems in Germany," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84765, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Woo, JongRoul & Moon, Sungho & Choi, Hyunhong, 2022. "Economic value and acceptability of advanced solar power systems for multi-unit residential buildings: The case of South Korea," Applied Energy, Elsevier, vol. 324(C).
    7. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    8. Lan, Haifeng & Gou, Zhonghua & Lu, Yi, 2021. "Machine learning approach to understand regional disparity of residential solar adoption in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    9. Sanghamitra Mukherjee, 2021. "A Framework to Measure Regional Disparities in Battery Electric Vehicle Diffusion in Ireland," Working Papers 202119, School of Economics, University College Dublin.
    10. Lim, Sesil & Huh, Sung-Yoon & Shin, Jungwoo & Lee, Jongsu & Lee, Yong-Gil, 2019. "Enhancing public acceptance of renewable heat obligation policies in South Korea: Consumer preferences and policy implications," Energy Economics, Elsevier, vol. 81(C), pages 1167-1177.
    11. Tibebu, Tiruwork B. & Hittinger, Eric & Miao, Qing & Williams, Eric, 2021. "What is the optimal subsidy for residential solar?," Energy Policy, Elsevier, vol. 155(C).
    12. Liu, Xueying & Madlener, Reinhard, 2019. "Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion," FCN Working Papers 15/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    13. Wilson, C. & Pettifor, H. & Chryssochoidis, G., 2018. "Quantitative modelling of why and how homeowners decide to renovate energy efficiently," Applied Energy, Elsevier, vol. 212(C), pages 1333-1344.
    14. Stefano Carattini & Simon Levin & Alessandro Tavoni, 2019. "Cooperation in the Climate Commons," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 13(2), pages 227-247.
    15. Hackbarth, André, 2018. "Attitudes, preferences, and intentions of German households concerning participation in peer-to-peer electricity trading," Reutlingen Working Papers on Marketing & Management 2019-2, Reutlingen University, ESB Business School.
    16. Echaniz, Eneko & Ho, Chinh Q. & Rodriguez, Andres & dell'Olio, Luigi, 2019. "Comparing best-worst and ordered logit approaches for user satisfaction in transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 752-769.
    17. dos Santos, L.L.C. & Canha, L.N. & Bernardon, D.P., 2018. "Projection of the diffusion of photovoltaic systems in residential low voltage consumers," Renewable Energy, Elsevier, vol. 116(PA), pages 384-401.
    18. Eslami, Hossein & Krishnan, Trichy, 2023. "New sustainable product adoption: The role of economic and social factors," Energy Policy, Elsevier, vol. 183(C).
    19. Braito, Michael & Flint, Courtney & Muhar, Andreas & Penker, Marianne & Vogel, Stefan, 2017. "Individual and collective socio-psychological patterns of photovoltaic investment under diverging policy regimes of Austria and Italy," Energy Policy, Elsevier, vol. 109(C), pages 141-153.
    20. Valentin Bertsch & Valeria Di Cosmo, 2018. "Are Renewables Profitable in 2030? A Comparison between Wind and Solar across Europe," Working Papers 2018.28, Fondazione Eni Enrico Mattei.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eejocm:v:21:y:2016:i:c:p:15-24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-choice-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.