Latent curve analysis

Meredith, William; Tisak, John

doi:10.1007/bf02294746

Cited by 1,351 publications

(1,064 citation statements)

References 15 publications

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“…Here the factor loadings for the random intercept would be set to 1 and those for the random coefficient to the times z b1 , z b2 and z b3 . Alternatively, one of the factor loadings could be estimated to allow for nonlinear growth (e.g., McArdle, 1986;Meredith & Tisak, 1990). …”

Section: Latent Response Variablementioning

confidence: 99%

Generalized multilevel structural equation modeling

2004

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A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

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Section: Latent Response Variablementioning

confidence: 99%

Generalized multilevel structural equation modeling

2004

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“…The data were analyzed with structural equation models, and specifically with a multivariate LGCM (Meredith & Tisak, 1990) and bivariate DCSMs (McArdle & Hamagami, 2001). The LGCM is, generally speaking, nested under the DCSM.…”

Section: Statistical Proceduresmentioning

confidence: 99%

“…The DSCM has been fruitfully applied to structurally analogous developmental hypotheses (Ferrer & McArdle, 2004;Ghisletta & de Ribaupierre, 2005;Ghisletta & Lindenberger, 2003; 2005; Ghisletta, Bickel, & Lövdén, 2006;Lövdén, Ghisletta, & Lindenberger, 2005;McArdle et al, 2004). The DCSM represents a dynamic extension of standard latent growth curve models (LGCM; Meredith & Tisak, 1990) or longitudinal multilevel models (Bryk & Raudenbush, 1987; for a comparison, see . Like the LGCM, the DCSM estimates means and variances for the intercept (i.e., initial level) and slope (i.e., change) of a longitudinal time series at the latent level that are unbiased by measurement error.…”

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confidence: 99%

Within-person trial-to-trial variability precedes and predicts cognitive decline in old and very old age: Longitudinal data from the Berlin Aging Study

et al. 2007

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Neurocomputational modeling and empirical evidence suggest that losses in neuronal signaling fidelity cause senescent changes in behavior. We applied structural equation modeling to five-occasion 13-year longitudinal data from the Berlin Aging Study (n = 447; age range at t1 = 70-102 years) to test whether trial-to-trial reaction time variability in perceptual speed (identical pictures) antecedes and signals longitudinal decline in levels of performance on perceptual speed (digit letter and identical pictures) and ideational fluency (category fluency). Higher trial-to-trial variability preceded and predicted greater cognitive decline in perceptual speed and ideational fluency. We conclude that trial-to-trial variability signals impending decline in cognitive performance, and that theories of neurocognitive aging need to postulate developmental cascades between senescent changes in variability and central tendency.

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“…Meredith and Tisak (1990) give a derivation of the maximum-likelihood function with structured means. The covariance structure model with structured means [see Eqs.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

1994

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The object of this paper is to indicate that the Pearson--Lawley selection rules form a plausible general theory for the simultaneous genetic analysis of means and covariance structure. Models are presented based on phenotypic selection and latent selection. Previously presented quantitative genetic models to decompose means and covariance structure simultaneously are reconsidered as instances of latent selection. The selection rules are very useful in the context of behavior genetic modeling because they lead to testable models and a conceptual framework for explaining variation between and within groups by the same genetic and environmental factors.

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Latent curve analysis

Abstract: longitudinal analysis, individual growth curves, structural equation modeling,

Cited by 1,351 publications

References 15 publications

Generalized multilevel structural equation modeling

Generalized multilevel structural equation modeling

Within-person trial-to-trial variability precedes and predicts cognitive decline in old and very old age: Longitudinal data from the Berlin Aging Study

Simultaneous genetic analysis of means and covariance structure: Pearson-Lawley selection rules

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