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A comparison of partial profile designs for discrete choice experiments with an application in software development

  • KESSELS, Roselinde
  • BRADLEY, Jones
  • GOOS, Peter

In a discrete choice experiment, each respondent chooses the best product or service sequentially from many groups or choice sets of alternative goods. The alternatives, called profiles, are described by level combinations from a set of predefined attributes. Respondents sometimes make their choices on the basis of only one dominant attribute rather than making trade-offs among all the attributes. For example, in studies involving price as an attribute, respondents may always choose the profile with the lowest price. Also, a choice task including many attributes may encourage respondent decisions that are not fully compensatory. To thwart these behaviors, the investigator can hold the levels of some of the attributes constant in every choice set. The resulting designs are called partial profile designs. In this paper, we construct D-optimal partial profile designs for estimating main-effects models. We use a Bayesian design algorithm that integrates the D-optimality criterion over a prior distribution of likely parameter values. To determine the constant attributes in each choice set, we provide three alternative generalizations of an approach that makes use of balanced incomplete block designs. Each of our three generalizations constructs partial profile designs accommodating attributes with any number of levels and allowing flexibility in the numbers of choice sets and constant attributes. We show results from an actual experiment in software development performed using one of these algorithms. Finally, we compare the algorithms with respect to their statistical e±ciency and ability to avoid failures due to the presence of a dominant attribute.

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Paper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2012004.

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Length: 44 pages
Date of creation: Feb 2012
Date of revision:
Handle: RePEc:ant:wpaper:2012004
Contact details of provider: Postal: Prinsstraat 13, B-2000 Antwerpen
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/

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  1. Hensher, David A. & Rose, John M., 2009. "Simplifying choice through attribute preservation or non-attendance: Implications for willingness to pay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 583-590, July.
  2. Swait, Joffre & Adamowicz, Wiktor, 2001. "Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice," Organizational Behavior and Human Decision Processes, Elsevier, vol. 86(2), pages 141-167, November.
  3. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2010. "Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1268-1289, December.
  4. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
  5. Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
  6. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
  7. Kessels, Roselinde & Goos, Peter & Vandebroek, Martina, 2008. "Optimal designs for conjoint experiments," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2369-2387, January.
  8. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
  9. DeShazo, J. R. & Fermo, German, 2002. "Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency," Journal of Environmental Economics and Management, Elsevier, vol. 44(1), pages 123-143, July.
  10. Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
  11. Scott, Anthony, 2002. "Identifying and analysing dominant preferences in discrete choice experiments: An application in health care," Journal of Economic Psychology, Elsevier, vol. 23(3), pages 383-398, June.
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