Overextraction of latent trajectory classes: Much ado about nothing? Reply to Rindskopf (2003), Muthén (2003), and Cudeck and Henly (2003)

By Daniel J. Bauer, P. Curran. 2003

181 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c3b2432256e193f51089e74a8a82dc3e681eca21

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Citations in the corpus

Observations on the Use of Growth Mixture Models in Psychological Research (2007, methodology): “…is this: Our measurement instruments are typically incapable of producing observations that meet the conditional normality assumption, yet even mild violations of this assumption will typically result in the estimation of too many latent classes (Bauer & Curran, 2003a; Tofighi & Enders, 2007).”
Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups (2009, background): “There are real dangers of finding and identifying false groups (see Bauer & Curran, 2003a, 2004, and associated commentaries).”
Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories (2016, methodology): “Because both GBTM and LGMM are data-driven procedures, researchers employing these methods must take particular caution against reifying and overinterpreting results (for thorough discussions of this issue, see Bauer & Curran, 2003a, 2003b; Cudeck & Henly, 2003; B. Muthén, 2003; Rindskopf, 2003).”
An introduction to latent class growth analysis and growth mixture modeling. (2008, methodology): “…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 & Curran, 2003b; Muthén, 2003; Rindskopf, 2003).”
An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. (2014, background): “In summary, deciding on the number of classes can be difficult, and should involve consideration of the research question, fit indices, the substantive meaning of each solution, parsimony, and/or theory (Bauer & Curran, 2003).”
Modeling Longitudinal Data from Research on Adolescence (2009, background): “In other words, nonnormal data can easily lead to the retention of spurious latent classes (Bauer, 2007 ; Bauer & Curran, 2003 a, 2003 b; Tofighi & Enders, 2007 ).”
How Well Does Growth Mixture Modeling Identify Heterogeneous Growth Trajectories? A Simulation Study Examining GMM's Performance Characteristics (2012, methodology): “Second, Rindskopf noted that Bauer and Curran (2003a) did not examine the possibility that their homogeneous nonnormally distributed data were actually intrinsically normally distributed, or that a linear transformation might result in normally distributed data that might have been accurately…”
Asymmetric properties of the Pearson correlation coefficient: Correlation as the negative association between linear regression residuals (2016, background): “…non normality is an inherent characteristic of the variable of interest or whether observed non normality simply results from the mixture of various normal distributions reflecting different subpopulations (see Bauer and Curran, 2003a, 2003b; Muthén, 2003, Rindskopf, 2003, Cudeck and Henly, 2003).”
LATENT GROWTH MIXTURE MODELING: A SIMULATION STUDY (2007, methodology): “As noted in the paper of Bauer and Curran (2003b), it is not surprising that this dynamic way of analyzing longitudinal data takes an increasingly central position in the social sciences, e.g. in psychology (Fuzhong, Barrera, Hops, Fisher, 2002; Parrila, Aunola, Leskinen, Nurmi & Kirby, 2005; van…”