How Well Does Growth Mixture Modeling Identify Heterogeneous Growth Trajectories? A Simulation Study Examining GMM's Performance Characteristics

By J. Peugh, Xitao Fan. 2012

76 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3b6b876cec8a62958f272a3952b27a3dad8e3f3e

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Citations in the corpus

Modeling Unobserved Heterogeneity Using Latent Profile Analysis: A Monte Carlo Simulation (2013, background): “…2 (y D 4 or 8) D 48 design condition combinations, and consistent with other enumeration research studies (Lubke & Neale, 2006; Nylund et al., 2007; Peugh & Fan, 2012; Tofighi & Enders, 2008), 200 data set replications were generated from the respective LPA population models (i.e., either k D 1 or…”
Motivation profiles at work: A self-determination theory approach (2016, background): “However, the entropy should not be used in itself to determine the optimal number of profiles (Lubke & Muthén, 2007; Peugh & Fan, 2012, 2013, 2015; Tein, Coxe, & Cham, 2013).”
Impact of Misspecifications of the Latent Variance–Covariance and Residual Matrices on the Class Enumeration Accuracy of Growth Mixture Models (2016, background): “…had an impact on the ability to identify the true number of latent classes in a GMM (Enders & Tofighi, 2008; Li & Hser, 2011; Lubke & Muthén, 2007; Peugh & Fan, 2012; Tofighi & Enders, 2007), three additional factors were also manipulated for the heterogeneous population models (Studies 2–4): the…”
The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored (2017, background): “…selection indices in nonclustered data in the context of latent class analysis (LCA; Nylund et al., 2007; Yang, 2006), GMM (Nylund et al., 2007; Peugh & Fan, 2012; Tofighi & Enders, 2008), latent profile analysis (Morgan, Hodge, & Baggett, 2016), latent variable mixture model (Henson et al.,…”