Generalized latent variable models: multilevel, longitudinal, and structural equation models

By A. Skrondal, Sophia Rabe-Hesketh. 2004

766 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cd2c59b3d55defe135495d2d9b224fdae9ad2002

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Citations in the corpus

Multilevel Modeling with Correlated Effects (2007, background): “Fielding (2004) provided a review of the literature from Hausman (1978) to Skrondal and Rabe-Hesketh (2004) on these issues.”
fines, leniency, and rewards in antitrust (2009, unknown): “7This deterrence channel was named ‘protection from fines effect’ in Spagnolo (2004) and ‘deviator amnesty effect’in Harrington (2008).”
Variable Selection in Finite Mixture of Regression Models (2007, methodology): “The books by Wedel and Kamukura (2000) and Skrondal and Rabe-Hesketh (2004), among others, contain comprehensive reviews on the applications of FMR models in market segmentation and the social sciences.”
Structural equation modeling: strengths, limitations, and misconceptions. (2005, methodology): “From this perspective, the development of multilevel modeling and mixture modeling capabilities within the SEM domain represents a further extension of the GLM framework to incorporate latent continuous and categorical variables (Muthén & Muthén 2004, Skrondal & Rabe-Hesketh 2004).”
Longitudinal Research Using Mixture Models (2010, methodology): “This yields what is referred to as a non-parametric maximum likelihood (NPML) approach to random effects modeling, which cannot only be used in the context of longitudinal data analysis but in any type of two-level regression model (Aitkin, 1999; Skrondal and Rabe-Hesketh, 2004; Vermunt 2004; Vermun”
General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations (2011, methodology): “METHODOLOGICAL ISSUES: THE IMPACT OF UNTESTED INVARIANCE ASSUMPTIONS IN GGMA GGMA is part of the generalized latent variable modeling framework proposed by B. O. Muthén (2002; also see Skrondal & Rabe-Hesketh, 2004).”
Latent Class Models (2004, unknown): “In addition, routines for the estimation of specific types of LC models are available as SAS, R, and Stata macros (see, e.g., Lanza et al., 2007; Skrondal and Rabe-Hesketh, 2004).”
Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. (2014, background): “Many multivariate statistics such as factor analyses, path analyses, growth models, multilevel models, item response theory and mixture models have been integrated into the general SEM framework (Muthén & Muthén, 2012; Skrondal & Rabe-Hesketh, 2004).”
Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. (2014, methodology): “…latent and/or manifest variables ICM-CFA: independent clusters model of confirmatory factor analysis ESEM: exploratory structural equation modeling Skrondal & Rabe-Hesketh 2004) integrated these features into an even more generic framework (generalized SEM), allowing for the estimation of…”