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Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

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This study introduces a new multilevel threshold autoregressive model to analyze state-dependent affect regulation. The model effectively captures intra-individual variations in psychological processes, improving data analysis for intensive longitudinal studies.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Intensive longitudinal data offer rich insights into psychological processes.
  • Multilevel autoregressive models are useful but assume fixed autoregressive parameters.
  • Existing models may not capture state-dependent psychological dynamics.

Purpose of the Study:

  • To propose a novel statistical model for analyzing state-dependent psychological regulation.
  • To address the limitation of fixed autoregressive parameters in current models.
  • To introduce a multilevel threshold autoregressive model for intra-individual variation.

Main Methods:

  • Development of a multilevel threshold autoregressive model.
  • Simulation studies to evaluate model performance (power and Type I error).
  • Application of the model to empirical datasets on affect regulation and alcohol use.

Main Results:

  • The proposed model demonstrates adequate power and Type I error control in simulations.
  • The model successfully detects state-dependent regulation.
  • Empirical applications showcase the model's flexibility and utility.

Conclusions:

  • The multilevel threshold autoregressive model is a valuable tool for analyzing state-dependent psychological phenomena.
  • This approach enhances the analysis of intensive longitudinal data.
  • The model extends the understanding of affect regulation and substance use dynamics.