Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.

离散选择实验已成为衡量偏好的最新技术，但它们大多用于横断面研究中。为了使它们适用于纵向研究，我们的研究解决了两个共同的挑战：与不同的受访者合作以及处理变化的属性。我们提出了一个基于样本的纵向离散选择实验，并结合了协变量扩展的分层贝叶斯Logit估计器，该模型可以测试变化的统计显着性。我们在六年的电动汽车偏好研究中展示了这种方法的使用，并通过经验观察到偏好以不可预测的，非单调的方式发展。我们还发现，仅检查样本之间偏好上的绝对差异可能会导致误导性推断。而且，随时间推移，对一个新样本进行调查所产生的结果与对同一样本的受访者进行询问的结果相似。最后，我们通过实验测试添加或删除属性如何影响其他属性的首选项。