A Heckman-type approach to sample selection correction is inappropriate for the conditional logit choice models used to analyze choice experiments. Logit model errors preclude reliance on the conventional assumption of potentially correlated bivariate normal errors. We propose and demonstrate a novel method of sample selection correction for multi-alternative conditional logit models that adapts mixed logit estimation methods.
Research concerning methodological issues in choice modeling
We show in a theoretical model that the benefit from additional attention to the marginal attribute within a choice set depends upon the expected utility loss from making a suboptimal choice if it is ignored. Guided by this analysis, we then develop an empirical method to measure an individual’s propensity to attend to attributes. As a proof of concept, we offer an empirical example of our method using a conjoint analysis of demand for programs to reduce health risks.
Seven independent samples of survey respondents were asked to value the identical good. Elicitation methods include one actual purchase and six widely used hypothetical choice formats. Using a common underlying indirect utility function (and stochastic structure) allows data for different elicitation methods to be used independently, compared pair-wise (as in much of the earlier literature) or pooled across all samples in one unified model.