The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model

2018 ◽  
Vol 37 (1) ◽  
pp. 153-183
Author(s):  
Song Yi Park ◽  
Seungmin Jahng
Keyword(s):  
Growth Model ◽  
Factorial Invariance ◽  
Model Fit ◽  
Parameter Estimates ◽  
Latent Growth Model ◽  
Latent Growth ◽  
Multiple Indicator

scholarly journals Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data

2020 ◽  
Author(s):  
D. Angus Clark ◽  
Amy K. Nuttall ◽  
Ryan Bowles
Keyword(s):  
Longitudinal Data ◽  
Growth Model ◽  
Change Process ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Parameter Estimates ◽  
Latent Growth Model ◽  
Latent Growth ◽  
Additional Time ◽  
Time Points

Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates. This study thus used Monte Carlo simulation methods to examine associations between change process separability, the number of time points in a model, and the consequences of misspecification, across three prominent hybrid autoregressive-latent growth models: the Latent Change Score model (LCS; McArdle, 2001), the Autoregressive Latent Trajectory Model (ALT; Bollen & Curran, 2006), and the Latent Growth Model with Structured Residuals (LGM-SR; Curran et al., 2014). Results suggest that for the LCS and ALT including more time points increases process separability and can somewhat improve robustness to misspecification. Alternatively, regardless of how many time points were in the model process separability was high in the LGM-SR, as was robustness to misspecification. Overall, results suggest that the LGM-SR may be the most reliable hybrid autoregressive-latent growth model when a smaller number of time points (< 20) are available for analysis.

scholarly journals A latent growth model of adolescent physical activity as a function of depressive symptoms

2012 ◽  
Vol 5 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Susan C. Duncan ◽  
John R. Seeley ◽  
Jeff M. Gau ◽  
Lisa A. Strycker ◽  
Richard F. Farmer
Keyword(s):  
Physical Activity ◽  
Depressive Symptoms ◽  
Growth Model ◽  
Latent Growth Model ◽  
Latent Growth ◽  
Adolescent Physical Activity
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