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Missing data can impact dynamic structural equation modeling (DSEM). This study simulated two-level vector autoregressive (VAR) cross-lagged models to assess parameter recovery under various missing data conditions, offering guidance for DSEM applications.

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Longitudinal Data Analysis

Background:

  • Dynamic structural equation modeling (DSEM) is valuable for intensive longitudinal data.
  • Missing data poses a significant challenge in DSEM applications.
  • The impact of missing data on two-level vector autoregressive (VAR) cross-lagged models is not well understood.

Purpose of the Study:

  • To evaluate the recovery of fixed effects and variance parameters in two-level bivariate VAR models under missing data.
  • To investigate the influence of missingness percentage, sample size, time points, and heterogeneity on parameter recovery.
  • To provide guidance on conducting Monte Carlo simulations for DSEM in Mplus.

Main Methods:

  • Two simulation studies were conducted.
  • Evaluated parameter recovery in two-level bivariate VAR models.
  • Varied missingness percentages, sample sizes, number of time points, and missingness distribution heterogeneity.

Main Results:

  • Assessed the accuracy and precision of parameter estimates under different missing data scenarios.
  • Identified conditions under which DSEM parameter recovery is reliable.
  • Demonstrated how to conduct Monte Carlo simulations in Mplus for DSEM.

Conclusions:

  • Understanding the impact of missing data is crucial for valid DSEM results.
  • Simulation studies are essential for determining appropriate data configurations for DSEM.
  • The findings offer practical guidance for researchers using DSEM with longitudinal data.