Exploring within-person variability in qualitative negative and positive Emotional Granularity by means of Latent Markov Factor Analysis

M.C. Schmitt*, Leonie V.D.E. Vogelsmeier, Y. Erbaş, S. Stuber, T. Lischetzke

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

30 Downloads (Pure)

Abstract

Emotional granularity (EG) is an individual’s ability to describe their emotional experiences in a nuanced and specific way. In this paper, we propose that researchers adopt latent Markov factor analysis (LMFA) to investigate within-person variability in qualitative EG (i.e., variability in distinct granularity patterns between specific emotions across time). LMFA clusters measurement occasions into latent states according to state-specific measurement models. We argue that state-specific measurement models of repeatedly assessed emotion items can provide information about qualitative EG at a given point in time. Applying LMFA to the area of EG for negative and positive emotions separately by using data from an experience sampling study with 11,662 measurement occasions across 139 participants, we found three latent EG states for the negative emotions and three for the positive emotions. Momentary stress significantly predicted transitions between the EG states for both the negative and positive emotions. We further identified two and three latent classes of individuals who differed in state trajectories for negative and positive emotions, respectively. Neuroticism and dispositional mood regulation predicted latent class membership for negative (but not for positive) emotions. We conclude that LMFA may enrich EG research by enabling more fine-grained insights into variability in qualitative EG patterns.
Original languageEnglish
Pages (from-to)781-800
Number of pages20
JournalMultivariate Behavioral Research
Volume59
Issue number4
Early online date2024
DOIs
Publication statusPublished - 2024

Keywords

  • Emotion
  • emotional granularity
  • emotion differentiation
  • latent Markov factor analysis
  • qualitative differences

Fingerprint

Dive into the research topics of 'Exploring within-person variability in qualitative negative and positive Emotional Granularity by means of Latent Markov Factor Analysis'. Together they form a unique fingerprint.

Cite this