Scale Heterogeneity and Its Implications for Discrete Choice Analysis
指出，若忽视误差方差（尺度）异质性，研究者可能错误解读模型参数。通过两个离散选择实验数据，展示了参数因归一化决策不同而变化，并举例说明论文中常见的陷阱。
This paper shows how researchers can make incorrect interpretations of models that include heterogeneity in error variance (scale) if they do not recognize the implications of parameter estimates being conflated with scale. The research contribution is to define best practice management of scale heterogeneity in discrete choice experiments and to empirically demonstrate issues that arise when best practice is ignored. We illustrate the problem using data from two discrete choice experiments, showing that reported parameters vary due to arbitrary normalization decisions. We report examples of papers where authors fall into this trap, potentially leading to erroneous evaluation of models..