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Students’ preferences for attributes of postgraduate economics modules: Evidence from a multi-profile best-worst scaling survey

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  • Meginnis, Keila
  • Campbell, Danny

Abstract

In this study, we investigate Scottish postgraduate economics students’ preferences for module design. Using a multi-profile best-worst scaling survey, we find that students have clear preferences on how they wish their modules to be delivered, taught and assessed. Furthermore, using a discrete mixtures modelling approach we explain the heterogeneous nature of preferences for the module attributes and the students’ lexicographic preference orderings. We show how failing to address this leads to erroneous results and limits the ability to derive reliable prediction. The findings in this study should appeal to university staff involved in the design of postgraduate (as well as undergraduate) courses as it should help them better establish a coherent learning experience for students, through which students can attain their full academic potential.

Suggested Citation

  • Meginnis, Keila & Campbell, Danny, 2017. "Students’ preferences for attributes of postgraduate economics modules: Evidence from a multi-profile best-worst scaling survey," International Review of Economics Education, Elsevier, vol. 24(C), pages 18-27.
    • Handle: RePEc:eee:ireced:v:24:y:2017:i:c:p:18-27
    DOI: 10.1016/j.iree.2016.11.001
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    References listed on IDEAS

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    1. Hess, S. & Bierlaire, Michel & Polak, J.W., 2007. "A systematic comparison of continuous and discrete mixture models," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 37, pages 35-61.
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    4. Thomas Lemieux, 2006. "Postsecondary Education and Increasing Wage Inequality," American Economic Review, American Economic Association, vol. 96(2), pages 195-199, May.
    5. Danny Campbell & W. Hutchinson & Riccardo Scarpa, 2008. "Incorporating Discontinuous Preferences into the Analysis of Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(3), pages 401-417, November.
    6. Danny Campbell & Edel Doherty, 2013. "Combining discrete and continuous mixing distributions to identify niche markets for food," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(2), pages 287-312, March.
    7. Stephane Hess & David Hensher, 2013. "Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach," Transportation, Springer, vol. 40(2), pages 397-412, February.
    8. David Hensher & John Rose & William Greene, 2012. "Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design," Transportation, Springer, vol. 39(2), pages 235-245, March.
    9. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    10. Campbell, Danny & Hensher, David A. & Scarpa, Riccardo, 2012. "Cost thresholds, cut-offs and sensitivities in stated choice analysis: Identification and implications," Resource and Energy Economics, Elsevier, vol. 34(3), pages 396-411.
    11. Flannery, Darragh & Kennelly, Brendan & Doherty, Edel & Hynes, Stephen & Considine, John, 2013. "Of mice and pens: A discrete choice experiment on student preferences for assignment systems in economics," International Review of Economics Education, Elsevier, vol. 14(C), pages 57-70.
    12. Riccardo Scarpa & Raffaele Zanoli & Viola Bruschi & Simona Naspetti, 2013. "Inferred and Stated Attribute Non-attendance in Food Choice Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(1), pages 165-180.
    13. Danny Campbell & David A. Hensher & Riccardo Scarpa, 2011. "Non-attendance to attributes in environmental choice analysis: a latent class specification," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 54(8), pages 1061-1076, December.
    14. Stephane Hess & Amanda Stathopoulos & Danny Campbell & Vikki O’Neill & Sebastian Caussade, 2013. "It’s not that I don’t care, I just don’t care very much: confounding between attribute non-attendance and taste heterogeneity," Transportation, Springer, vol. 40(3), pages 583-607, May.
    15. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    16. David Hensher & John Rose & William Greene, 2005. "The implications on willingness to pay of respondents ignoring specific attributes," Transportation, Springer, vol. 32(3), pages 203-222, May.
    17. Riccardo Scarpa & Timothy J. Gilbride & Danny Campbell & David A. Hensher, 2009. "Modelling attribute non-attendance in choice experiments for rural landscape valuation," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 36(2), pages 151-174, June.
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    More about this item

    Keywords

    Taught postgraduate; Module choice; Student's preferences; Multi-profile best-worst scaling; Discrete mixtures model; Attribute non-attendance;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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