Latent Class Scaling Analysis (Quantitative Applications in the Social Sciences)

Chan Dayton is a Professor Emeritus and past Chair in the Department of Measurement & Statistics. For more than 20 years, he has pursued a research interest in latent class analysis which is a specialized field within the realm of discrete mixture models. In 1999, he published a Sage book dealing with latent class scaling models. Recently, he has focused on model comparison procedures with a special interest in approaches based on information theory and Bayes factors. In particular, he has been working on an innovative alternative to pairwise comparison procedures such as Tukey test. His research has appeared in journals such as The Journal of The American Statistical Association, Psychometrika, American Statistician, Multivariate Behavioral Research, Applied Psychological Measurement, Journal of Educational and Behavioral Statistics, British Journal of Mathematical and Statistical Psychology, Psychological Methods, and Journal of Educational Measurement.