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This study explores how daily life events influence emotions using dynamic models. Robust autoregressive models effectively capture these effects, outperforming classical models by managing contextual influences.

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Area of Science:

  • Psychology
  • Affective Science
  • Longitudinal Data Analysis

Background:

  • Dynamic paradigm in affect research aims to characterize daily affective processes.
  • Contextual conditions and events significantly influence affective processes.
  • Explicitly modeling context faces challenges when events are hard to define or measure.

Purpose of the Study:

  • To investigate the impact of contextual events on affective processes.
  • To examine how short-lasting contextual events affect emotions.
  • To demonstrate the advantages of robust autoregressive models in handling contextual contamination.

Main Methods:

  • Utilizing intense longitudinal data.
  • Employing dynamic, autoregressive-type models.
  • Focusing on events with short-lasting main effects on affective processes.

Main Results:

  • Contextual events can act as hidden confounders obscuring affective dynamics.
  • Specific forms of contamination can beneficially trigger and leverage autoregressive dynamics.
  • Robust autoregressive models outperform classical models in managing contextual contamination.

Conclusions:

  • Robust autoregressive models are effective in characterizing daily affective processes.
  • These models capitalize on positive leverage effects from contextual events.
  • They mitigate negative obscuring effects of contextual events on emotional dynamics.