scholarly journals Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models? Investigating the benefits and drawbacks of the latent curve model with structured residuals and the random intercept cross-lagged panel model.

2021 ◽  
Author(s):  
Henrik Kenneth Andersen
Keyword(s):  
Unobserved Heterogeneity ◽  
Panel Models ◽  
Panel Model ◽  
Random Intercept ◽  
Curve Model ◽  
Latent Curve ◽  
Latent Curve Model

scholarly journals Changes in Size and Interpretation of Parameter Estimates in Within-Person Models in the Presence of Time-Invariant and Time-Varying Covariates

2021 ◽  
Vol 12 ◽  
Author(s):  
Marcus Mund ◽  
Matthew D. Johnson ◽  
Steffen Nestler
Keyword(s):  
Parameter Estimates ◽  
Methodological Research ◽  
Panel Model ◽  
Random Intercept ◽  
Curve Model ◽  
Latent Curve ◽  
Research Questions ◽  
Time Invariant ◽  
Time Varying Covariates ◽  
Reciprocal Associations

For several decades, cross-lagged panel models (CLPM) have been the dominant statistical model in relationship research for investigating reciprocal associations between two (or more) constructs over time. However, recent methodological research has questioned the frequent usage of the CLPM because, amongst other things, the model commingles within-person associations with between-person associations, while most developmental research questions pertain to within-person processes. Furthermore, the model presumes that there are no third variables that confound the relationships between the longitudinally assessed variables. Therefore, the usage of alternative models such as the Random-Intercept Cross-Lagged Panel Model (RI-CLPM) or the Latent Curve Model with Structured Residuals (LCM-SR) has been suggested. These models separate between-person from within-person variation and they also control for time constant covariates. However, there might also be third variables that are not stable but rather change across time and that can confound the relationships between the variables studied in these models. In the present article, we explain the differences between the two types of confounders and investigate how they affect the parameter estimates of within-person models such as the RI-CLPM and the LCM-SR.

scholarly journals Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models?

2020 ◽  
Author(s):  
Henrik Kenneth Andersen
Keyword(s):  
Structural Equation ◽  
Initial Conditions ◽  
Unobserved Heterogeneity ◽  
Real Data ◽  
Equation Modeling ◽  
Panel Models ◽  
Latent Trajectory ◽  
Random Intercept ◽  
Residual Level ◽  
Latent Curve

Panel models in structural equation modeling that combine static and dynamic components for investigating reciprocal relations while controlling for time-invariant unobserved heterogeneity are becoming increasingly popular. Recently, the Latent Curve Model with Structured Residuals and the Random-Intercept Cross-Lagged Panel Model were suggested as ‘residual-level’ versions of the more traditional Autoregressive Latent Trajectory and Dynamic Panel Models, respectively. Their main benefit is that they allow for a more straightforward interpretation of the trajectory factors. It is not widely known, however, that the residual-level models place potentially strong assumptions on the initial conditions, i.e., the process that was occurring before the observation period began. If the process under investigation is nonstationary (e.g., growing exponentially), has not been going on for long enough to reach equilibrium, or has been ‘knocked’ out of equilibrium, potentially due to an intervention, then the residual-level models are not appropriate. This is shown analytically and with demonstrations using real data. A simple amendment is suggested to relax the assumptions on the initial conditions while retaining the models’ beneficial aspects.

A Latent Curve Model Approach To Studying L2 N-Gram Development

2018 ◽  
Vol 102 (3) ◽  
pp. 494-511 ◽  
Author(s):  
JAMES GARNER ◽  
SCOTT CROSSLEY
Keyword(s):  
Curve Model ◽  
Latent Curve ◽  
N Gram ◽  
Model Approach ◽  
Latent Curve Model

scholarly journals Accounting for Time-Varying Inter-Individual Differences of Trajectories when Assessing Cross-Lagged Models

2020 ◽  
Author(s):  
Paul Scott
Keyword(s):  
Bayes Factor ◽  
Time Varying ◽  
Fit Indices ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Latent Trajectory ◽  
Panel Model ◽  
Random Intercept ◽  
True Model ◽  
Curve Model

This paper explores relationships amongst cross-lagged models allowing trajectories to be freely estimated, some accounting for time-varying differences amongst individuals (Autoregressive Latent Trajectory (ALT), General Cross-lagged Model (GCLM), and Latent Growth Curve Model with Structured Residuals and Unspecified Growth Trajectory (LGCM-SR-UGT)) and some not (Cross-lagged Panel Model (CLPM), Random Intercept Cross-lagged Panel Model (RI-CLPM), and Mean Stationary GCLM). An applied example using LSAY data demonstrates these models. Simulations examine (1) fit indices assessing “good” fit and Bayes Factor for model selection; (2) consequences of ignoring variability in trajectories on cross-lagged estimates. Findings were (1) RMSEA discerned “good” fit and Bayes Factor tended to select models closely related to true model over less related models; (2) various patterns of bias in path estimates and standard errors are found, in particular, causal dominance in conjunction with time-variant between-person variance and covariance were notably influential on bias in path estimates.

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