Formative versus reflective attitude measures: Extending the hybrid choice model

Abstract

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.