Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data

By B. Muthén. 2004

1,658 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a60c571ffeceab5029e3a762838fb7b5e52fdb7b

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Citations in the corpus

Taking Stock of Developmental Trajectories of Criminal Activity over the Life Course (2008, methodology): “Additionally, other statistical applications, such as LATENT GOLD and M-PLUS, also perform trajectory estimations, and other researchers have developed methods for trajectory methods including Bengt Muthén (see Muthén, 2004; Muthén & Muthén, 2000).”
Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories (2016, methodology): “Prior research or theory about the ways that individuals are believed to vary from one another are helpful guides for this decision-making process (B. Muthén, 2004), although this information is rarely available.”
The number of autonomy-supportive relationships : are more relationships better for motivation, perceived competence, and achievement? (2013, background): “LCA uncovers unobserved heterogeneity in a sample and identifies meaningful groups of individuals who are similar in their pattern of responses on specified variables (Muthén, 2004), in this case perceived autonomy support by mother, father, and teacher.”
Number sense growth in kindergarten: a longitudinal investigation of children at risk for mathematics difficulties. (2006, methodology): “The use of GMM, however, does not obviate the need to describe the sample using conventional growth curve modeling.”
How Well Does Growth Mixture Modeling Identify Heterogeneous Growth Trajectories? A Simulation Study Examining GMM's Performance Characteristics (2012, methodology): “: (13) Entropy is known to be impacted by between-trajectory separation distance and within-trajectory variance (e.g., Lubke & Muthén, 2007; Muthén, 2004).”
Assessing Measurement Invariance in Multiple-Group Latent Profile Analysis (2018, background): “These subgroups are comprised of observations with similar indicator response profiles (Muthén, 2004).”
The Dynamics of Economic Disadvantage and Children's Life Chances (2006, methodology): “Recent research (Muthén 2004) suggests that proper model specification and accurate estimation of class proportions and class membership require that antecedents (predictors) of class membership be included in the set of covariates, along with the distal outcomes.”
Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax 1 (2016, background): “Another interesting application of random effects within latent classes occurs in the context of LC growth modeling (Muthén, 2004; Vermunt, 2007).”
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus (2014, methodology): “Finally the imputed class variables are analyzed together with the auxiliary variable using the multiple imputation technique developed in Rubin (1987). We call this method the pseudoclass (PC) method.”
Observations on the Use of Growth Mixture Models in Psychological Research (2007, background): “B. Muthén (2004) provides an explanation of why this may occur: “[A] GGMM that incorrectly divides up trajectories in, say, low, medium and high classes might find that the covariates have lower and insignificant influence in the low class due to selection on the dependent variable” (p. 253).”
Trajectories of change in students’ self-concepts of ability and values in math and college major choice (2015, methodology): “…gives threshold values that can be converted to probabilities in order to examine whether classification trends differ across latent classes (B. Muthén, 2004) using the following equation, where ui represents the college major category: Pr ui|ci = k, xi( ) = 11+ exp tk − vkxi{ } tk is a…”
Annual Research Review: Positive adjustment to adversity -Trajectories of minimal-impact resilience and emergent resilience (2012, neutral): “…centered around a sample mean, recent advances in research and theory on the impact of significant life events more broadly defined have dramatically underscored the heterogeneity of longterm outcomes (Curran & Hussong, 2003; Duncan et al., 2006; Feldman, Masyn, & Conger, 2009; Muthen, 2004).”
Exposure to Neighborhood Affluence and Poverty in Childhood and Adolescence and Academic Achievement and Behavior (2014, background): “These models assigns respondents to subgroups within a sample based on latent associations among selected variables by identifying meaningful patterns of initial status and change over time (Muthen, 2004).”
General Growth Mixture Analysis with Antecedents and Consequences of Change (2010, neutral): “…group-based modeling, also known as latent class growth analysis (LCGA; Nagin & Land, 1993; Roeder, Lynch, & Nagin, 1999; Nagin, 2005), and general growth mixture analysis (GGMA; Muthén, 2001, 2002, 2004; Muthen & Asparouhov, 2008; Muthén & Muthén, 1998-2008; Muthén & Shedden, 1999).”
Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups (2009, methodology): “Lo, Y., Mendell, N.R., & Rubin, D.B. (2001).”
Joint trajectories of behavioral, affective, and cognitive engagement in elementary school (2017, methodology): “We modeled the joint trajectories of student engagement using multiple-process growth mixture modeling (MPGMM) implemented in Mplus (Muth en, 2001, 2004).”