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An Introduction to Latent Class Analysis: Methods and Applications /

An Introduction to Latent Class Analysis: Methods and Applications /
Catalogue Information
Field name Details
Dewey Class 300.727
Title An Introduction to Latent Class Analysis ( EBook) : Methods and Applications / / by Nobuoki Eshima.
Author Eshima, Nobuoki
Other name(s) SpringerLink (Online service)
Edition statement 1st ed. 2022.
Publication Singapore : : Springer Nature Singapore : : Imprint: Springer, , 2022.
Physical Details XI, 190 p. 45 illus., 1 illus. in color. : online resource.
Series Behaviormetrics: Quantitative Approaches to Human Behavior 2524-4035 ; ; 14
ISBN 9789811909726
Summary Note 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. .:
Contents note Overview of Basic Latent Structure Models -- Latent Class Cluster Analysis -- Latent Class Analysis with Ordered Latent Classes -- Latent Class Analysis with Latent Binary Variables: Application for Analyzing Learning Structures -- The Latent Markov Chain Model -- Mixed Latent Markov Chain Models -- Path Analysis in Latent Class Models.
Mode of acces to digital resource Digital reproduction.-
Cham :
Springer International Publishing,
2022. -
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site https://doi.org/10.1007/978-981-19-0972-6
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