A Latent Class Binomial Logit Methodology for the Analysis of Paired Comparison Choice Data

A latent class model for identifying classes of subjects in paired comparison choice experiments is developed. The model simultaneously estimates a probabilistic classification of subjects and the logit models’ coefficients relating characteristics of objects to choices for each respective group among two alternatives in paired comparison experiments. A modest Monte Car10 analysis of algorithm performance is presented. The proposed model is illustrated with empirical data from a consumer psychology experiment that examines the determinants of perceived consumer risk. The predictive validity of the method is assessed and compared to that of several other procedures. The sensitivity of the method to (randomly) eliminate comparisons, which is important in view of reducing respondent fatigue in the task, is investigated. Subject Areas: Market Segmentation, Risk & Uncertainty, and Statistical Techniques.