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Informationen zum Autor 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. Klappentext With empirical examples which are both plentiful and well chosen to teach the technique, this book provides a thorough guide to latent class scaling models for binary response variables. Substantive illustrations include a survey on academic cheating, children's mastery of spatial tasks, medical diagnosis of lung disease, attitudes toward the Army and behaviour during role conflict. The computer programs for latent class analysis are carefully reviewed and a website is offered for keeping abreast of the latest developments. This book will be of particular interest to those doing detailed scale analysis in psychology, sociology and education. Zusammenfassung With empirical examples which are both plentiful and well chosen to teach the technique! this book provides a thorough guide to latent class scaling models for binary response variables. Inhaltsverzeichnis Introduction and Overview Latent Class Models Extreme-Types Models Linear Scales Joint Scales Multiple Groups Analysis Concomitant-Variable Models ...
