7. Multilevel Latent Class Models

By Jeroen K. Vermunt. 2003

388 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b418c9829e31c59ee2b25d1a19293a1ce74820eb

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In Genetics

In Statistics and Probability

Citations in the corpus

Longitudinal Research Using Mixture Models (2010, methodology): “ Palardy and Vermunt (in press) presented a multilevel mixture growth model for such data sets and illustrated the model with an application in which higher-level units (schools) are clustered based on the learning rates of children.”
Introduction to Latent Class Analysis With Applications (2017, background): “The idea underlying MLCA is that the parameters of the regression model may differ across unobserved subgroups (Hagenaars & McCutcheon, 2002; Vermunt, 2003, 2008).”
Resource Efficiency and Circular Economy in European SMEs: Investigating the Role of Green Jobs and Skills (2021, methodology): “Multilevel latent class modelling (Vermunt, 2003) is an approach based on the assumption that some model parameters can vary across groups, clusters or level-2 units.”
Multiple Imputation of Missing Data for Multilevel Models (2018, background): “…data) or the group level (e.g., research in large organizations), a larger variety of patterns of effects and missing data mechanisms (see Newman, 2009), low ICCs, or a large number of continuous and categorical variables (see Vermunt, 2003; Vermunt, van Ginkel, van der Ark, & Sijtsma, 2008).”
Multilevel Latent Class Analysis: Parametric and Nonparametric Models (2014, methodology): “When such data structures are present, models that account for this dependence should be used to avoid parameter estimation inaccuracy, biased standard errors, and inflated Type I error rates for hypothesis tests regarding the parameters (Asparouhov & Muthén, 2008; Vermunt, 2003, 2008).”
Latent Class Models (2004, neutral): “, 1991; Heinen, 1996), models with multiple latent variables (Magidson and Vermunt, 2001), models for longitudinal data (Van de Pol and Langeheine, 1990), and models for multilevel data (Vermunt, 2003).”
Latent Class Modeling with Covariates: Two Improved Three-Step Approaches (2010, methodology): “…methods are in LC analysis for longitudinal or multilevel data; that is, as an alternative to one-step approaches such as latent Markov modeling (Van de Pol and Langeheine 1990; Collins and Wugalter 1992; Vermunt, Langeheine, and Böckenholt 1999) and multilevel LC modeling (Vermunt 2003, 2008).”
Investigation of model fit and score scale comparability in international assessments (2011, methodology): “In addition, we specifically calibrated a hierarchical extension of the 2PL MIRT model (von Davier, 2007), which extends the hierarchical latent class analysis (LCA) model (Vermunt, 2003) to a mixture 2PL IRT model using partially-unique-to-country parameters.”
Model-Based Clustering (2016, neutral): “For example, Vermunt (2003, 2007) develop a multilevel latent class models to account for conditional dependency between the response variables, and Marbac, Biernacki, and Vanderwalle (2014) propose a conditional modes model that assigns response variables into conditionally independent blocks.”