General Growth Mixture Analysis with Antecedents and Consequences of Change

By H. Petras, Katherine E. Masyn. 2010

291 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c30e77ece9a4ac262600a9c3e33e997030d4bb80

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Citations in the corpus

Trajectories of change in students’ self-concepts of ability and values in math and college major choice (2015, methodology): “…growth mixture modelling does not suppress between-subject variability. aik = mak + g1kxi + zaik b1ik = mb1k + g2kxi + zb1ik b2ik = mb2k + g2kxi + zb2ik Covariates were entered next, following recommendations by Petras and Masyn (2010), and allowed to influence the intercept and growth parameters.”
Complementary Variable- and Person-Centered Approaches to the Dimensionality of Psychometric Constructs: Application to Psychological Wellbeing at Work (2016, methodology): “In these cases, information criteria should be graphically presented through ‘‘elbow plots’’ illustrating the gains associated with additional profiles (Morin et al. 2011; Petras and Masyn 2010).”
Ten Frequently Asked Questions About Latent Class Analysis (2018, unknown): “Growth mixture models (GMM), as alluded to previously, are used to model the heterogeneity in growth trajectories (e.g., often termed latent trajectory classes) over time (Petras & Masyn, 2010; Ram & Grimm, 2009).”
The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored (2017, background): “Petras and Masyn (2010) recommended the “elbow criterion” to determine the optimal number of classes when using IC indices (i.e., AIC, BIC, SABIC).”
Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes (2016, methodology): “The main disadvantage of this simultaneous modeling procedure is that the inclusion of Z in the model can alter the meaning of the classes (Petras & Masyn, 2010), especially when the normal distribution assumption for f (Zi”
Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness (2015, background): “Thus, consistent with previous recommendations (e.g., Morin, Maïano, et al., 2011; Petras & Masyn, 2010) we relied on elbow plots to help in the selection of the final solution.”
Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “Numerous applications of this approach appear in recent literature, including Fried et al. (2009) and Petras and Masyn (2010).”
Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models (2019, methodology): “In fact, if the distal-as-indicator approach is used, then the entire measurement model building process, including the class enumeration, should be done with the distal outcome included with the other latent class indicators (Petras & Masyn, 2010).”
Mixture Modeling for Lifespan Developmental Research (2019, background): “…into the SEM framework, GSEM affords an arguably more flexible way to incorporate predictors and outcomes of profiles directly into the model (Petras & Masyn, 2010), to account for longitudinal dependencies inherent in longitudinal data (but see Hofmans, Vantilborgh, & Solinger, 2018), and…”
General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations (2011, methodology): “…that although additional misspecifications, such as assuming linear trajectories (Bauer & Curran, 2004; Voelkle, 2008) or class-invariant covariate effects (Petras & Masyn, 2010), were also reported to result in biased estimates, the effects of these misspecifications are not illustrated here.”
Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, methodology): “Others argue that it is best to do class enumeration without covariates, and then include covariates using latent class regression after the number of classes has been decided (Collins & Lanza, 2009; Masyn, 2013; Petras & Masyn, 2010).”
Multiple-Group Analysis of Similarity in Latent Profile Solutions (2016, background): “In these cases, information criteria should be graphically presented through ‘‘elbow plots’’ illustrating the gains associated with additional profiles (Morin, Maı̈ano, et al., 2011; Morin & Marsh, 2015; Petras & Masyn, 2010).”
Profiles of dual commitment to the occupation and organization: Relations to well-being and turnover intentions (2015, background): “These covariates were not incorporated directly into the model, since doing so would involve allowing them to influence the nature of the observed profiles (Morin et al., 2011a; Petras & Masyn, 2010).”
Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches (2013, background): “A related problem with the one-step approach is that the inclusion of covariates or distal outcomes can distort the class solution because additional assumptions are made that may be violated (Bauer and Curran 2003; Tofighi and Enders 2008; Huang et al. 2010; Petras and Masyn 2010).”
Dual commitment to organization and supervisor: A person-centered approach (2015, background): “This means that they can continue to improve with the information criteria be examined to identify where the decreasing values begin to flatten out (Morin, Maïano, et al., 2011; Petras & Masyn, 2010).”
A person‐centered approach to commitment research: Theory, research, and methodology (2016, methodology): “However, when multiple outcomes are considered, this method will almost always result in a change in the nature of the profiles (Morin, Morizot, et al., 2011; Petras & Masyn, 2010).”
Motivation profiles at work: A self-determination theory approach (2016, methodology): “In these cases, information criteria should be graphically presented through “elbow plots” illustrating the gains associated with additional profiles (Morin & Marsh, 2015; Morin, Maïano, Nagengast, Marsh, Morizot & Janosz, 2011; Petras & Masyn, 2010).”