Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

By K. Nylund, T. Asparouhov, B. Muthén. 2007

10,984 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/439672067967d3501b0d201bdb50a60886459b00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Poly-victimization and risk of posttraumatic, depressive, and substance use disorders and involvement in delinquency in a national sample of adolescents. (2010, methodology): “The Lo-Mendell-Rubin adjusted likelihood ratio test [34] was used to compare each model with K classes against a model with K – 1 classes [35].”
Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification (2015, methodology): “One additional test that has shown promise (Nylund et al., 2007) for identifying the correct number of latent classes is the bootstrap likelihood ratio test (BLRT; McLachlan & Peel, 2000).”
A review of recent approaches for capturing heterogeneity in partial least squares path modelling (2008, background): “However, it has to be noted that this approach was primarily designed to handle sensory data (Palumbo et al., 2008). 2.2.3 PLS genetic algorithm segmentation. In a recent contribution, Ringle and Schlittgen (2007) consider the PLS path modelling segmentation task as a NP-complete data assignment pro”
Latent class analysis and latent profile analysis (2016, methodology): “Of all of these fit statistics, the BIC has been identified as performing the most reliably, although the Bootstrapped Likelihood Ratio Test (BLRT) has also been commended (Nylund, Asparouhov, et al., 2007).”
Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, methodology): “For example, in the class enumeration paper by Nylund et al. (2007), the authors considered both crosssectional and longitudinal mixture models with varying complexity in the measurement and structural models.”
A Latent Transition Mixture Model Using the Three-Step Specification (2014, background): “In this application, the BIC never reached a minimum value; however, we looked for an “elbow” (i.e., the last relatively large decrease in the BIC value; Nylund et al., 2007), which occurred with the five-class model.”
Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (2016, methodology): “Because of this weighting, robust standard errors should be used (Bakk, Oberski, Vermunt, 2014). In the BCH adjustment, instead of estimating a LC model, one performs the logistic regression analysis or the computation of the cross-tabulations or ANOVAs in the usual manner, with the difference that ”
Why Tenth Graders Fail to Finish High School: A Dropout Typology Latent Class Analysis (2012, methodology): “In the LCA literature, a typology is defined as latent classes , a model of a certain number of classes is specified and tested, model fit is assessed, and, if the model is significant and fits well, then the responses of each of the latent classes (a typology) to the survey questions used to fit t”
Measurement Invariance Testing with Many Groups: A Comparison of Five Approaches (2017, result): “Several studies supported the BLRT for different models (Kim et al., 2016; Nylund et al., 2007; Tein et al., 2013), but it was reported that the execution time was extremely long with a large sample, nonconvergence frequently occurred, and the performance of BLRT was often comparable to that of…”
Types of boredom: An experience sampling approach (2014, methodology): “However, LPA differs from cluster analysis in that it is model-based and probabilistic in nature (Nylund et al. 2007).”