Observing Unobserved Heterogeneity: Using Process Data to Enhance Choice Models
Abstract
People use different strategies when making choices. Modeling this choice process heterogeneity, however, is difficult using just the data provided by most standard choice experiments. We try to capture process heterogeneity by augmenting choice models with variables derived from information-acquisition data gathered unobtrusively during choice tasks. These variables supplement standard logit specifications which identify how an individual used the attributes and attribute values to screen and rank alternatives in making a choice. The approach improves in-sample fit, prediction in a holdout sample, and residuals indicate that the models are providing better specified estimates of choice probabilities.
Download PDF
Citation
Johnson, Eric, Bruce G. S. Hardie, R. J. Meyer, and John Walsh. "Observing Unobserved Heterogeneity: Using Process Data to Enhance Choice Models." Working paper, Columbia Business School, December 1, 2010.
Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.
Each topic is linked to an index of publications on that topic.