An Introduction to Latent Class Analysis: Methods and Applications

This book discusses latent class analysis methods and applications, including basic models, exploratory analysis, ordered latent classes, learning structures, longitudinal data, and path analysis. It uses the EM algorithm for estimation and covers entropy-based discussions. It is useful for researchers and students in behavioral sciences and other fields.

Format: Hardback
Length: 190 pages
Publication date: 10 April 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the realm of latent class analysis, presenting a wealth of methods and applications for understanding and analyzing data. It explores a range of topics, including basic latent structure models within the 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, latent Markov analysis for longitudinal data, and path analysis with latent class models.

The text employs the expectation-maximization (EM) algorithm to construct maximum likelihood estimation procedures for latent class models, along with latent profile and latent trait models. Additionally, entropy-based discussions are provided as advanced approaches, encompassing comparisons of latent classes in latent class cluster models, assessing latent class models, path analysis, and other relevant topics.

By applying latent structure analysis, researchers and students in behavioral sciences, as well as those engaged in other scientific research fields, can gain valuable insights into the underlying patterns and structures within their data. This book serves as a valuable resource for those seeking to expand their knowledge and expertise in this field.

Weight: 477g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811909719
Edition number: 1st ed. 2022