The inclusion of attitudinal indicator variables within discrete choice models is now largely common practice. Typically, this involves the estimation of multiple indicator multiple cause (MIMIC) type models which are used to construct latent attitudinal variables that are then employed as independent variables within standard discrete choice models. Such models, collectively termed hybrid choice models (HCM) assume a particular causal relationship between the indicator variables, latent construct, and choice. In effect, the underlying assumption of such a model system is that latent variables of interest exist independent of the indicator variables used to measure them, and that the survey items used are reflective in nature insofar as responses to such questions reflect the underlying constructs. In this paper, we describe an alternative form of attitude measure, known as formative measures, where the items themselves are used to create the latent variable rather than the other way around. In addition to making a distinction between formative and reflective attitudinal measures, the paper seeks to describe how the HCM can be adapted to model different types of attitude question formats. Further the paper seeks to act as a catalyst for choice modellers to think more about the quality and validity of attitudinal items capture in survey questionnaires, by placing more emphasis on proper scale development techniques.

在离散选择模型中包含态度指标变量现在在很大程度上是常见的做法。通常，这涉及多指标多原因 (MIMIC) 类型模型的估计，这些模型用于构建潜在的态度变量，然后在标准离散选择模型中用作自变量。这些模型统称为混合选择模型 (HCM)，假设指标变量、潜在结构和选择之间存在特定的因果关系。实际上，这种模型系统的基本假设是感兴趣的潜在变量独立于用于衡量它们的指标变量而存在，并且所使用的调查项目本质上是反映性的，因为对此类问题的回答反映了基本结构。在本文中，我们描述了另一种态度测量形式，称为形成性测量，其中项目本身用于创建潜在变量，而不是相反。除了区分形成性和反思性态度措施外，本文还试图描述 HCM 如何适用于模拟不同类型的态度问题格式。此外，本文试图通过更加强调适当的量表开发技术，作为选择建模者的催化剂，更多地思考调查问卷中捕获的态度项目的质量和有效性。该论文旨在描述 HCM 如何适用于模拟不同类型的态度问题格式。此外，本文试图通过更加强调适当的量表开发技术，作为选择建模者的催化剂，更多地思考调查问卷中捕获的态度项目的质量和有效性。该论文旨在描述 HCM 如何适用于模拟不同类型的态度问题格式。此外，本文试图通过更加强调适当的量表开发技术，作为选择建模者的催化剂，更多地思考调查问卷中捕获的态度项目的质量和有效性。