Rank-order choice-based conjoint experiments: Efficiency and design
Section snippets
The rank-ordered conditional logit model
Any rank order can be regarded as a sequence of choices made by the respondent. This was used as the starting point for the extension of the conditional logit model to the rank-ordered conditional logit model by Beggs et al. (1981). In their approach, each ranking of a choice set is converted into a number of independent pseudo-choices. In this way, each ranking of alternatives in a choice set is considered as a sequential and conditional choice task. The alternative ranked first is perceived
Designs for the rank-ordered conditional logit model
This section is devoted to the derivation of the Bayesian D-optimality criterion for the rank-ordered conditional logit model and the corresponding D-error. In addition, we discuss how to create efficient designs using this D-optimality criterion. Finally, benchmark designs, with which the Bayesian D-optimal ranking design will be compared, are introduced.
Evaluating the Bayesian ranking design
In this section, we compare the different designs in terms of the Db-error and describe the findings of a simulation study by which the performance of the designs in terms of estimation and prediction accuracy was examined. In addition, we measure the improvement achieved by including extra ranking steps in the experiment. In the simulation study, we considered two cases: ranking nine choice sets of size four and ranking 12 choice sets of size three. In the following sections, we only report
Conclusion
In a rank-order choice-based conjoint experiment, the respondent is asked to rank a number of alternatives in each choice set. This way of performing a conjoint experiment offers the important advantage that extra information is extracted about the preferences of the respondent which results in better estimated part-worths and better predicted probabilities assuming constant error variances over the ranks in the rank-ordered conditional logit model.
Although rank-order choice-based conjoint
Acknowledgements
Bart Vermeulen was funded by project G.0611.05 of the Fund for Scientific Research Flanders. We are grateful to Qi Ding for his contribution to the development of the alternating-sample algorithm. He was funded by project NB/06/05 of the National Bank of Belgium.
References (21)
- et al.
Assessing the potential demand for electric cars
Journal of Econometrics
(1981) - et al.
Comparing ridership attraction of rail and bus
Transport Policy
(2002) - et al.
Analysis of the reliability of preference ranking data
Journal of Business Research
(1992) - et al.
Specifying and testing econometric models for rank-ordered data
Journal of Econometrics
(1987) - et al.
Valuing reductions in environmental pollution in a residential location context
Transportation Research Part D
(2002) Stated choice valuation of urban traffic air pollution and noise
Transportation Research Part D
(1999)- et al.
Logit models for sets of ranked items
Sociological Methodology
(1994) The valuation of the Ijmeer Nature Reserve using conjoint analysis
Environmental and Resource Economics
(2003)- et al.
Economic Valuation with Stated Preference Techniques. A Manual
(2002) - et al.
A comparison of conjoint analysis response formats
American Journal of Agricultural Economics
(2001)
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