Muthén, Bengt O. Beyond SEM: General latent variable modeling. (English) Zbl 1017.62125 Behaviormetrika 29, No. 1, 81-117 (2002). Summary: This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random effects, missing data, sources of variation in hierarchical data, finite mixtures, latent classes, and clusters. These latent variable applications go beyond the traditional latent variable useage in psychometrics with its focus on measurement error and hypothetical constructs measured by multiple indicators.The article argues for the value of integrating statistical and psychometric modeling ideas. Different applications are discussed in a unifying framework that brings together in one general model such different analysis types as factor models, growth curve models, multilevel models, latent class models and discrete-time survival models. Several possible combinations and extensions of these models are made clear due to the unifying framework. Cited in 23 Documents MSC: 62P15 Applications of statistics to psychology Keywords:maximum-likelihood estimation; factor analysis; structural equation modeling; growth curve modeling; multilevel modeling; finite mixture modeling; random effects; missing data; latent classes Software:Mplus PDF BibTeX XML Cite \textit{B. O. Muthén}, Behaviormetrika 29, No. 1, 81--117 (2002; Zbl 1017.62125) Full Text: DOI Link OpenURL