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A Latent Class Binomial Logit Methodology for the Analysis of Paired Comparison Choice Data
Author(s) -
Wedel Michel,
DeSarbo Wayne S.
Publication year - 1993
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1993.tb00508.x
Subject(s) - respondent , latent class model , logit , mixed logit , econometrics , computer science , probabilistic logic , logistic regression , class (philosophy) , statistics , task (project management) , sensitivity (control systems) , monte carlo method , mathematics , machine learning , artificial intelligence , economics , engineering , political science , electronic engineering , management , law
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 Carlo 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.