Application of Latent Class Analysis to Predict Children’s Internalizing Problems

An application of Measurement error evaluation using latent class analysis

‎Latent class analysis (LCA) is a method of evaluating non sampling errors‎, ‎especially measurement error in categorical data‎. ‎Biemer (2011) introduced four latent class modeling approaches‎: ‎probability model parameterization‎, ‎log linear model‎, ‎modified path model‎, ‎and graphical model using path diagrams‎. ‎These models are interchangeable‎. ‎Latent class probability models express l...

A Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters deter...

Latent Class Cluster Analysis

Dynamic Latent Class Analysis

ISSN: 1070-5511 (Print) 1532-8007 (Online) Journal homepage: http://www.tandfonline.com/loi/hsem20 Dynamic Latent Class Analysis Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén To cite this article: Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén (2017) Dynamic Latent Class Analysis, Structural Equation Modeling: A Multidisciplinary Journal, 24:2, 257-269, DOI: 10.1080/10705511.2016.125...

Latent Class Analysis

The basic idea underlying latent class (LC) analysis is a very simple one: some of the parameters of a postulated statistical model differ across unobserved subgroups. These subgroups form the categories of a categorical latent variable (see entry latent variable). This basic idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimati...