Posterior inference of attitude-behaviour relationships using latent class choice models

The link between attitudes and behaviour has been a key topic in choice modelling for two decades, with the widespread application of ever more complex hybrid choice models. This paper proposes a pragmatic and computationally tractable alternative framework for empirically examining the relationship between attitudes and behaviours using latent class choice models (LCCMs). Rather than embedding attitudinal constructs within the structural model, as in hybrid choice frameworks, we recover class-specific attitudinal profiles through posterior inference. This approach enables analysts to explore attitude-behaviour associations without the complexity and convergence issues often associated with integrated estimation. Two case studies are used to demonstrate the framework: one on employee preferences for working from home, and another on public acceptance of COVID-19 vaccines. Across both studies, we compare posterior profiling of indicator means, fractional multinomial logit (FMNL) models, factor-based representations, and hybrid specifications. We find that posterior inference methods provide behaviourally rich insights with minimal additional complexity, while factor-based models risk discarding key attitudinal information, and full-information hybrid models offer little gain in explanatory power and incur substantially greater estimation burden. Our findings suggest that when the goal is to explain preference heterogeneity, posterior inference offers a practical alternative to hybrid models, one that retains interpretability and robustness without sacrificing behavioural depth.