LATENT GROWTH MIXTURE MODELING: A SIMULATION STUDY

By Ur Mathematik, Und Statistik. 2007

75 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/155d622b16943e65f8724a893943308c53ad8f65

How researchers see this paper

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

Citations in the corpus

Complementary Variable- and Person-Centered Approaches to the Dimensionality of Psychometric Constructs: Application to Psychological Wellbeing at Work (2016, background): “Simulation studies indicate that four of these indicators (CAIC, BIC, ABIC, and BLRT) are particularly effective and that when the indicators fail to retain the optimal model, the ABIC and BLRT tend to overestimate the number of classes, whereas the BIC, CAIC, and aLMR tend to underestimate it (Nylu”
Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness (2015, background): “…true parameters in mixture models (Henson, Reise, & Kim, 2007; McLachlan & Peel, 2000; Nylund, Asparouhov, & Muthén, 2007; Tofighi & Enders, 2008; Tolvanen, 2007; Yang, 2006): (a) the consistent Akaike’s Information Criterion (CAIC), (b) the Bayesian Information Criterion (BIC), (c) the…”
Mixture Modeling for Lifespan Developmental Research (2019, unknown): “…and BLRT) are particularly effective, while the AIC and LMR/ALMR should not be used (Diallo, Morin, & Lu, 2016, 2017; Henson et al., 2007; McLachlan & Peel, 2000; Nylund, Asparouhov, & Muthén, 2007; Peugh & Fan, 2013; Tein, Coxe, & Cham, 2013; Tofighi & Enders, 2008; Tolvanen, 2007; Yang, 2006).”
A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis (2011, background): “…been over parameterized, especially in the context of the relatively low sample size that was available to estimate so many parameters (Chen, Bollen, Paxton, Curran, & Kirby, 2001; Tolvanen, 2007), and the fact that more parsimonious models may be more appropriate (e.g., Bauer & Shanahan, 2007).”
Multiple-Group Analysis of Similarity in Latent Profile Solutions (2016, unknown): “…best recovers the sample’s true parameters, whereas the AIC is not and presents a marked tendency for overextraction (e.g., Henson, Reise, & Kim, 2007; McLachlan & Peel, 2000; Nylund, Asparouhov, & Muthén, 2007; Peugh & Fan, 2013; Tein, Coxe, & Cham, 2013; Tofighi & Enders, 2008; Tolvanen, 2007).”
Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models (2016, background): “This restriction is not inherent to GMMs, which are fully suited to the examination of within-class variability.”