Latent Class Analysis in health research

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...

Applications of Latent Class Analysis in Social Science Research

An overview is provided of recent developments in the use of latent class (LC) models in social science research. Special attention is paid to the application of LC analysis as a factor-analytic tool and as a tool for random-effects modeling. Furthermore, an extension of the LC model to deal with nested data structures is presented.

Virtual Social Networks Addiction and High-Risk Group among Health Science Students in Iran: A Latent Class Analysis

Background and purpose: Virtual social networks (VSNs) are among the most popular communication paths that have become an integral part of most people's lives, including students. This study aimed to investigate the prevalence of VSNs addiction and their related factors, and identify the patterns of addictive-related factors among the students in Kerman, Iran in 2019. Materials and Methods: Th...

Optimization in latent class analysis

In latent class analysis (LCA) one seeks a clustering of categorical data, such as patterns of symptoms of a patient, in terms of locally independent stochastic models. This leads to practical definitions of criteria, e.g., whether to include patients in further diagnostic examinations. The clustering is often determined by parameters that are estimated by the maximum likelihood method. The lik...

Latent Class Models in Longitudinal Research