An Introduction to Latent Class Analysis: Methods and Applications

This book discusses latent class analysis methods and applications, including basic models, exploratory analysis, longitudinal data, and path analysis. It is useful for researchers and students in behavioral sciences and other fields.

Format: Paperback / softback
Length: 190 pages
Publication date: 11 April 2023
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 complex data. It explores a range of topics, including basic latent structure models within a generalized linear model framework, 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, enabling the analysis of both latent profile and latent trait models. Additionally, it offers entropy-based discussions as advanced approaches, covering topics such as comparing latent classes in a latent class cluster model, assessing latent class models, path analysis, and more.

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: 320g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811909740
Edition number: 1st ed. 2022