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Evaluating dynamics in affect structure with latent Markov factor analysis.

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Affect structure is not static; it changes across people and situations. This study reveals dynamic affect structures and transitions, influenced by negative events but not neuroticism.

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Area of Science:

  • Psychology
  • Affective Science
  • Quantitative Psychology

Background:

  • Affect structure is often assumed stable in intensive longitudinal research.
  • Existing methods typically ignore dynamic and individual variations in affect structure.
  • Understanding affect structure dynamics offers insights into affective experiences.

Purpose of the Study:

  • To investigate dynamic affect structures using latent Markov factor analysis (LMFA).
  • To examine transitions between affect structures and individual differences in these patterns.
  • To explore the influence of negative event intensity and neuroticism on affect transitions.

Main Methods:

  • Latent Markov factor analysis (LMFA) applied to experience sampling data (N=153).
  • Identification of distinct affect structures and latent subgroups based on transition patterns.
  • Analysis of relationships between negative event intensity, neuroticism, and affect structure dynamics.

Main Results:

  • Two distinct affect structures were identified: one with three and one with four dimensions.
  • The presence of negative emotionality characterized one structure, with more inversely related dimensions.
  • Three latent subgroups with differing transition patterns were found.
  • Higher negative event intensity predicted adopting affect structures with negative emotionality.
  • Neuroticism was not associated with subgroup membership.

Conclusions:

  • Affect structure is dynamic and varies across individuals and contexts.
  • LMFA provides a method to model these dynamic and individual differences.
  • Negative event intensity influences momentary affect structure adoption.
  • Future research should consider the dynamic nature of affect structure in affective science.