Abstract
Choice experiments are widely used for measuring how the attributes of goods or services influence preference judgments. To this end, a suitable experimental design is used to combine attribute levels into options or profiles and to further arrange these into choice sets. Often incomplete descriptions of the options, which are known as partial profiles, are used in order to reduce the amount of information respondents need to process. For the situation where the choice sets are pairs, where only the main effects of the attributes are of interest and where the attributes fall into two groups such that all attributes within a group have the same number of levels, optimal designs which were obtained analytically are compared with algorithmically generated designs. For the situations considered, there are sometimes substantial differences between the efficiencies of the two types of design.
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References
Caussade, S., Ortúzar, J.de D., Rizzi, L.I., Hensher, D.A.: Assessing the influence of design dimensions on stated choice experiments. Transp. Res. 39B, 621–640 (2005)
Chrzan, K.: Using partial profile choice experiments to handle large numbers of attributes. Int. J. Mark. Res. 52, 827–840 (2010)
Graßhoff, U., Großmann, H., Holling, H., Schwabe, R.: Optimal designs for main effects in linear paired comparison models. J. Stat. Plan. Inference 126, 361–376 (2004)
Green, P.E.: On the design of choice experiments involving multifactor alternatives. J. Consum. Res. 1, 61–68 (1974)
Großmann, H., Holling, H., Graßhoff, U., Schwabe, R.: Optimal designs for asymmetric linear paired comparisons with a profile strength constraint. Metrika 64, 109–119 (2006)
Großmann, H., Graßhoff, U., Schwabe, R.: Approximate and exact optimal designs for paired comparisons of partial profiles when there are two groups of factors. J. Stat. Plan. Inference 139, 1171–1179 (2009)
JMP, Version 10. SAS Institute Inc., Cary, NC (1989–2012)
Kessels, R., Jones, B., Goos, P.: Bayesian optimal designs for discrete choice experiments with partial profiles. J. Choice Model. 4, 52–74 (2011)
Kessels, R., Jones, B., Goos, P.: A comparison of partial profile designs for discrete choice experiments with an application in software development. Research Paper 2012-004, University of Antwerp, Faculty of Applied Economics (2012)
Louviere, J.J., Hensher, D.A., Swait, J.D.: Stated Choice Methods: Analysis and Application. Cambridge University Press, Cambridge (2000)
Street, D.J., Burgess, L.: The Construction of Optimal Stated Choice Experiments: Theory and Methods. Wiley, Hoboken (2007)
Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge (2003)
Acknowledgements
The helpful comments of two referees are much appreciated. One reviewer noticed the systematic pattern in the results which had previously escaped my attention.
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Großmann, H. (2013). Differences between Analytic and Algorithmic Choice Designs for Pairs of Partial Profiles. In: Ucinski, D., Atkinson, A., Patan, M. (eds) mODa 10 – Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00218-7_15
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DOI: https://doi.org/10.1007/978-3-319-00218-7_15
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