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Sample size requirements for stated choice experiments

机译：陈述选择实验的样本量要求

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摘要

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet in the past little has been known about the sample size requirements for models estimated from such data. Traditional orthogonal designs and existing sampling theories does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates, or are forced to make assumptions about the data that are unlikely to hold in practice. In this paper, we demonstrate how a recently proposed sample size computation can be used to generate so-called S-efficient designs using prior parameter values to estimate panel mixed multinomial logit models. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D-efficient and even more an S-efficient design require a (much) smaller sample size than a random orthogonal design in order to estimate all parameters at the level of statistical significance. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates.
机译：在个人，家庭和其他组织的行为响应研究中，状态选择（SC）实验代表了主要的数据范例，但是在过去，对于从此类数据估计的模型的样本大小要求知之甚少。传统的正交设计和现有的采样理论无法充分解决该问题，因此研究人员不得不求助于简单的经验法则，或者忽略该问题并收集任意大小的样本，希望样本足够大以产生可靠的参数估算值，或被迫对不太可能在实践中掌握的数据做出假设。在本文中，我们演示了如何使用最近提出的样本大小计算方法，使用先验参数值来估计面板混合多项式logit模型，从而生成所谓的S效率设计。在SC研究中研究了此类设计的样本量要求。在数值案例研究中表明，与随机正交设计相比，D高效设计甚至S高效设计所需的样本大小要小得多，以便在统计显着性水平上估算所有参数。此外，表明宽的水平范围对设计的效率具有显着的积极影响，因此对参数估计的可靠性也具有显着的积极影响。

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