An introduction to latent class analysis : methods and applications / Nobuoki Eshima.
2022
QA278.6 .E75 2022
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Details
Title
An introduction to latent class analysis : methods and applications / Nobuoki Eshima.
Author
Eshima, Nobuoki, author.
ISBN
9789811909726 (electronic bk.)
9811909725 (electronic bk.)
9789811909719
9811909717
9811909725 (electronic bk.)
9789811909719
9811909717
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (some color).
Other Standard Identifiers
10.1007/978-981-19-0972-6 doi
Call Number
QA278.6 .E75 2022
Dewey Decimal Classification
519.5/35
Summary
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 expectationmaximization (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. .
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 13, 2022).
Series
Behaviormetrics - quantitative approaches to human behavior ; volume 14.
Available in Other Form
Print version: 9789811909719
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Table of Contents
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.
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.