AbstractThis paper is motivated by the increasing popularity of efficient designs for stated choice experiments'/> D-efficient or deficient? A robustness analysis of stated choice experimental designs
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D-efficient or deficient? A robustness analysis of stated choice experimental designs

机译：D有效或不足？陈述选择实验设计的鲁棒性分析

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AbstractThis paper is motivated by the increasing popularity of efficient designs for stated choice experiments. The objective in efficient designs is to create a stated choice experiment that minimizes the standard errors of the estimated parameters. In order to do so, such designs require specifying prior values for the parameters to be estimated. While there is significant literature demonstrating the efficiency improvements (and cost savings) of employing efficient designs, the bulk of the literature tests conditions where the priors used to generate the efficient design are assumed to be accurate. However, there is substantially less literature that compares how different design types perform under varying degree of error of the prior. The literature that does exist assumes small fractions are used (e.g., under 20 unique choice tasks generated), which is in contrast to computer-aided surveys that readily allow for large fractions. Further, the results in the literature are abstract in that there is no reference point (i.e., meaningful units) to provide clear insight on the magnitude of any issue. Our objective is to analyze the robustness of different designs within a typical stated choice experiment context of a trade-off between price and quality. We use as an example transportation mode choice, where the key parameter to estimate is the value of time (VOT). Within this context, we test many designs to examine how robust efficient designs are against a misspecification of the prior parameters. The simple mode choice setting allows for insightful visualizations of the designs themselves and also an interpretable reference point (VOT) for the range in which each design is robust. Not surprisingly, the D-efficient design is most efficient in the region where the true population VOT is near the prior used to generate the design: the prior is $20/h and the efficient range is $10–$30/h. However, the D-efficient design quickly becomes the most inefficient outside of this range (under $5/h and above $40/h), and the estimation significantly degrades above $50/h. The orthogonal and random designs are robust for a much larger range of VOT. The robustness of Bayesian efficient designs varies depending on the variance that the prior assumes. Implementing two-stage designs that first use a small sample to estimate priors are also not robust relative to uninformative designs. Arguably, the random design (which is the easiest to generate) performs as well as any design, and it (as well as any design) will perform even better if data cleaning is done to remove choice tasks where one alternative dominates the other.
机译： Abstract 本文的动机是，针对指定选择实验的有效设计越来越流行。有效设计的目的是创建一个陈述性的选择实验，以最大程度地减少估计参数的标准误差。为此，这样的设计要求为要估计的参数指定先验值。尽管有大量文献证明采用有效设计可以提高效率（并节省成本），但大量文献测试了用于生成有效设计的先验条件是准确的条件。但是，很少有文献比较在不同先验误差的情况下不同设计类型的表现。确实存在的文献假设使用的分数很小（例如，在生成的20个唯一选择任务下），这与容易允许使用较大分数的计算机辅助调查相反。此外，文献中的结果是抽象的，因为没有参考点（即有意义的单位）来提供对任何问题的严重程度的清晰见解。我们的目标是在价格和质量之间进行权衡的典型陈述选择实验环境下分析不同设计的鲁棒性。我们以交通方式选择为例，其中估算的关键参数是时间值（VOT）。在这种情况下，我们测试了许多设计，以检验鲁棒高效设计对现有参数的错误规范。简单的模式选择设置允许对设计本身进行深刻的可视化显示，并为每种设计的稳健范围提供一个可解释的参考点（VOT）。毫不奇怪，D效率设计在真实人口VOT接近用于生成设计的先验值的区域内最有效：先验价格为$ 20 / h，有效范围为$ 10– $ 30 / h。但是，D效率设计迅速成为此范围外（低于$ 5 / h且高于$ 40 / h）的最无效率的设计，并且估算值大大降低到$ 50 / h以上。正交和随机设计对于更大范围的VOT具有鲁棒性。贝叶斯有效设计的鲁棒性取决于先验假设的方差。相对于无信息的设计，实施首先使用小样本来估计先验值的两阶段设计也不可靠。可以说，随机设计（最容易生成）的性能要好于任何设计，如果完成数据清理以去除选择任务占主导的选择任务，那么随机设计（以及任何设计）的性能会更好。 / Para>

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