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Multilevel autoregressive mediation models: Specification, estimation, and applications.

Qian Zhang1, Lijuan Wang2, C S Bergeman2

  • 1Department of Educational Psychology and Learning Systems, College of Education, Florida State University.

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Multilevel autoregressive mediation models (MAMMs) offer nuanced insights into mediation effects compared to cross-lagged panel models (CLPMs). Bayesian estimation accurately captures these complex dynamics, especially with sufficient data points.

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Area of Science:

  • Psychological methods
  • Statistical modeling
  • Longitudinal data analysis

Background:

  • Cross-lagged panel models (CLPMs) are established for analyzing longitudinal data.
  • Existing models may not fully capture interindividual variability in mediation effects.
  • There is a need for advanced statistical frameworks to address complex mediation dynamics.

Purpose of the Study:

  • To introduce Multilevel Autoregressive Mediation Models (MAMMs) that allow for individual differences in mediation.
  • To investigate the utility of Bayesian estimation for MAMMs.
  • To evaluate the performance of MAMMs against CLPMs and assess the impact of model misspecification.

Main Methods:

  • Development and application of Multilevel Autoregressive Mediation Models (MAMMs).
  • Utilizing Bayesian estimation for complex model parameterization.
  • Conducting simulation studies to assess estimation accuracy and model misspecification effects.
  • Comparing MAMM and CLPM on daily diary data.

Main Results:

  • MAMMs provide different conclusions on average mediation effects compared to CLPMs.
  • Bayesian estimation for MAMMs shows accuracy in fixed effect estimates with N ≥ 50 and T ≥ 5.
  • Accurate estimation of Level-2 variances and covariances requires larger sample sizes (N ≥ 500, T ≥ 50).
  • Average mediation effect estimates are reliable with N ≥ 100, T ≥ 10 or N ≥ 50, T ≥ 20.

Conclusions:

  • MAMMs offer a more flexible approach to mediation analysis in longitudinal data.
  • Bayesian estimation is a viable method for complex MAMMs.
  • CLPMs may yield biased results when interindividual differences in mediation are present.
  • Model selection can be guided by criteria like DIC.