Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003).

By B. Muthén. Psychological methods, 2003

705 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d96fce79a82b94e0da306eaf7c20f14e8ab4170a

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

Aggressive behavior among girls and boys during middle childhood: predictors and sequelae of trajectory group membership (2006, methodology): “We selected the optimal model based on the Bayesian Information Criterion (BIC) [Kass and Raftery, 1995; Raftery, 1995]; theory and findings of prior research [Muthén, 2003] were also considered.”
The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored (2017, background): “A combination of substantive knowledge and statistical criteria has been recommended for selecting the optimal number of classes in GMM (B. Muthén, 2003).”
Modeling Unobserved Heterogeneity Using Latent Profile Analysis: A Monte Carlo Simulation (2013, neutral): “In addition, the sample data generated in this study were multivariate normal, which is unlikely to occur in many research investigations (e.g., Micceri, 1989; Muthén, 2003), and Bauer and Curran (2003, 2004) showed that data nonnormality results in the enumeration of additional latent classes.”
CHAPTER 25 Latent Class Analysis and Finite Mixture Modeling (2013, neutral): “However , by careful and systematic consideration of a set of plausible models, and utilizing a combination of statistical and substantive model checking (Muthen, 2003), researchers can improve their confidence in the tenability of their decision regarding the number of latent classes.”
Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, background): “Current best practices recommend the simultaneous consideration of multiple fit indexes (along with substantive criteria, e.g., interpretability) to make a final model selection regarding the number of latent classes (Masyn, 2013; Muthén, 2003).”
A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis (2011, background): “Clearly, one set of criteria used to guide this decision has to do with the substantive meaning and theoretical conformity of the extracted classes (Marsh et al., 2009; Muthén, 2003).”
General Growth Mixture Analysis with Antecedents and Consequences of Change (2010, neutral): “…Nylund, Asparouhov, & Muthén, 2007); but by careful and systematic consideration of a set of plausible models, and utilizing a combination of statistical and substantive model checking (Muthén, 2003), researchers can improve their confidence in the tenability of their resultant model selection.”
Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, methodology): “To address this issue, Muthén (2003) formulated a test that considers the higher-order moments (e.g., skewness and kurtosis) of the observed data.”
An introduction to latent class growth analysis and growth mixture modeling. (2008, methodology): “Analogous to determining the number of factors using exploratory factor analysis, the number of classes should ultimately be determined by a combination of factors in addition to fit indices, including one’s research question, parsimony, theoretical justification, and interpretability (cf. Bauer & C”
Teacher burnout profiles and proactive strategies (2020, methodology): “Several statistical fit indices as well as the content criterion were applied to determine the most appropriate number of latent profiles (see Bauer and Curran 2003; Muthén 2003; Nylund et al. 2007).”
Overextraction of latent trajectory classes: Much ado about nothing? Reply to Rindskopf (2003), Muthén (2003), and Cudeck and Henly (2003) (2003, background): “Distinguishing Between Mixtures and Nonmixtures Both Rindskopf (2003) and B. Muthén (2003) suggested that whereas in some instances it may be difficult to distinguish between data that are simply nonnormal and data generated by a mixture, in other Daniel J. Bauer, Department of Psychology, North…”
Investigating population heterogeneity with factor mixture models. (2005, methodology): “Determining the number of classes is the subject of a stillgrowing body of research and has led to interesting discussions (Bauer & Curran, 2003a; Bauer & Curran, 2003b; Cudeck & Henly, 2003; B. O. Muthén, 2003; Rindskopf, 2003).”