An Introduction to Latent Class Analysis - Methods and Applications (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer Nature
Country of origin:	Singapore
Series:	Behaviormetrics: Quantitative Approaches to Human Behavior, 14
Release date:	April 2022
First published:	2022
Authors:	Nobuoki Eshima
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	190
Edition:	1st ed. 2022
ISBN-13:	978-981-19-0971-9
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Psychology > Psychological methodology > Psychological testing & measurement
LSN:	981-19-0971-7
Barcode:	9789811909719

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