An Introduction to Latent Class Analysis: Methods and Applications: 14 (Behaviormetrics: Quantitative Approaches to Human Behavior, 14) - Tapa dura

Libro 12 de 14: Behaviormetrics: Quantitative Approaches to Human Behavior

Eshima, Nobuoki

 
9789811909719: An Introduction to Latent Class Analysis: Methods and Applications: 14 (Behaviormetrics: Quantitative Approaches to Human Behavior, 14)
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ISBN 10: 9811909717 ISBN 13: 9789811909719
Editorial: Springer, 2022

Sinopsis

This book provides methods and applications of latent class analysis, and the following topics are taken up in the focus of discussion: basic latent structure models in a framework of generalized linear models, exploratory latent class analysis, latent class analysis with ordered latent classes, a latent class model approach for analyzing learning structures, the latent Markov analysis for longitudinal data, and path analysis with latent class models. The maximum likelihood estimation procedures for latent class models are constructed via the expectation-maximization (EM) algorithm, and along with it, latent profile and latent trait models are also treated. Entropy-based discussions for latent class models are given as advanced approaches, for example, comparison of latent classes in a latent class cluster model, assessing latent class models, path analysis, and so on. In observing human behaviors and responses to various stimuli and test items, it is valid to assume they are dominated by certain factors. This book plays a significant role in introducing latent structure analysis to not only young researchers and students studying behavioral sciences, but also to those investigating other fields of scientific research. 

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Acerca del autor

Nobuoki Eshima was born in Fukuoka, Japan, in 1957. He was received B.Sc. and D.Sc. degrees in Mathematics from Kyushu University, Fukuoka, Japan, in 1980 and 1993, respectively. In 1993, he joined Department of Statistics, Faculty of General Education, Nagasaki University, as Associate Professor. In 1996, he joined Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University, as Professor. In 2016, he joined Center for Educational Outreach and Admissions, Kyoto University, as Professor. In 2021, he was granted the title of Emeritus Professor of Oita University, and from 2021, he is Guest Professor of Faculty of Medicine, Kurume University.

De la contraportada

This book provides methods and applications of latent class analysis, and the following topics are taken up in the focus of discussion: basic latent structure models in a framework of generalized linear models, exploratory latent class analysis, latent class analysis with ordered latent classes, a latent class model approach for analyzing learning structures, the latent Markov analysis for longitudinal data, and path analysis with latent class models. The maximum likelihood estimation procedures for latent class models are constructed via the expectation–maximization (EM) algorithm, and along with it, latent profile and latent trait models are also treated. Entropy-based discussions for latent class models are given as advanced approaches, for example, comparison of latent classes in a latent class cluster model, assessing latent class models, path analysis, and so on. In observing human behaviors and responses to various stimuli and test items, it is valid to assume they are dominated by certain factors. This book plays a significant role in introducing latent structure analysis to not only young researchers and students studying behavioral sciences, but also to those investigating other fields of scientific research. 

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Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
9811909717
ISBN 13 
9789811909719
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
204
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Otras ediciones populares con el mismo título

9789811909740: An Introduction to Latent Class Analysis: Methods and Applications: 14 (Behaviormetrics: Quantitative Approaches to Human Behavior)

Edición Destacada

ISBN 10:  9811909741 ISBN 13:  9789811909740
Editorial: Springer, 2023
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