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

Author

Listed:
  • Silvia Haan-Rietdijk
  • John Gottman
  • Cindy Bergeman
  • Ellen Hamaker

Abstract

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions. Copyright The Author(s) 2016

Suggested Citation

  • Silvia Haan-Rietdijk & John Gottman & Cindy Bergeman & Ellen Hamaker, 2016. "Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 217-241, March.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:1:p:217-241
    DOI: 10.1007/s11336-014-9417-x
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    References listed on IDEAS

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