An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications (2nd ed.)

By K. Stamm, Lisa L. Harlow, Theodore A. Walls. 2007

515 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d2ad3093df5b969359b3b911a8a9f77cb0104838

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Citations in the corpus

Multivariate latent change modeling of developmental decline in academic intrinsic math motivation and achievement: Childhood through adolescence (2007, methodology): “Freeing the loadings of the remaining assessment occasions on this factor implies that they denote the part of overall change that occurs between the first and each of these later measurement occasions (Duncan et al., 1999; McArdle & Anderson, 1990).”
Using Group-Based Trajectory and Growth Mixture Modeling to Identify Classes of Change Trajectories (2016, background): “In other words, the degree of variability from the group trajectory is identified and predictors of that variability can be included in the model (Duncan et al., 2009; Weinfurt, 2000).”
Religious Development: How (and Why) Does Religiosity Change from Adolescence to Young Adulthood? (2010, background): “Using a latent growth curve to study change in religious behaviors and attitudes is attractive for several reasons (Bollen and Curran 2006; Duncan et al. 1999).”
Family Processes and Suicidal Ideation among Chinese Adolescents in Hong Kong (2011, unknown): “Besides capturing developmental changes over time, many researchers advocated the use of IGC when examining the longitudinal pattern of treatment effects over time[1,8,9,10] and a number of advantages of using this method were identified[1,11].”
Applications of structural equation modeling (SEM) in ecological studies: an updated review (2016, background): “Therefore, these latent variables lack a meaningful explanation in regard to the hypothesis of an SEM (Bollen 2002; Duncan et al 2013).”
Community embeddedness and work outcomes: The mediating role of organizational embeddedness (2014, background): “LGM is an extension of structural equation modeling; it assesses changes in levels of variables and examines how these changes are related to other constructs in the nomological network (Bollen and Curran, 2006; Duncan et al., 2006).”