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Estimating Latent State-Trait Models for Experience-Sampling Data in R with the lsttheory Package: A Tutorial.

Julia Norget1, Alexa Weiss1, Axel Mayer1

  • 1Department of Psychology, Bielefeld University.

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|April 25, 2025
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Summary
This summary is machine-generated.

Latent state-trait (LST) models help differentiate situation-specific from enduring influences in experience-sampling data. Analysis revealed well-being depends more on stable personality traits than immediate situations.

Keywords:
Experience samplingLST theorylatent state-trait theorystructural equation modelingtutorialwell-being

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Area of Science:

  • Psychological Methods
  • Quantitative Psychology
  • Personality Psychology

Background:

  • Experience-sampling methodology (ESM) is increasingly used, necessitating advanced analytical techniques.
  • Distinguishing transient situational effects from stable individual differences is crucial in ESM.
  • Latent state-trait (LST) models offer a framework for this differentiation.

Purpose of the Study:

  • To provide a tutorial on multiple-indicator wide-format LST models for ESM data.
  • To introduce user-friendly software (browser app and R-function in 'lsttheory') for specifying LST models.
  • To demonstrate the application of LST models in analyzing well-being dynamics.

Main Methods:

  • Discussion of first-order and second-order LST model specifications and their assumptions.
  • Introduction of a new R-package 'lsttheory' with a browser app for LST model specification.
  • Application of LST models to a five-day experience-sampling study on well-being.

Main Results:

  • An autoregressive model with indicator-specific traits was optimal for the ESM data.
  • Results indicated high consistency in well-being, suggesting a stronger influence of person-level traits than situational factors.
  • Extraversion, emotional stability, and agreeableness predicted trait well-being.

Conclusions:

  • LST models, particularly with the introduced software, offer a flexible yet accessible approach for ESM data analysis.
  • Well-being appears to be predominantly influenced by stable personality characteristics.
  • Recommendations for model fit assessment and comparative analyses are provided.