Latent class analysis with multiple latent group variables

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...

Latent Growth Modeling and Latent Class Analysis

Geared towards capturing change, longitudinal research is able to provide insight into a variety of phenomena of interest to IS researchers, especially IT adoption and use. However, its potential is constrained by the data analysis methods typically used. In this paper, I introduce an advanced technique – Latent Curve Modeling – and demonstrate how this technique supports longitudinal data anal...

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...

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...