Filled with well-chosen empirical examples 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 behavior 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.
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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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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