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Level-specific reliability coefficients from the perspective of latent state-trait theory.

Lennart Nacke1, Axel Mayer1

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The British Journal of Mathematical and Statistical Psychology
|December 27, 2025
PubMed
Summary

Multilevel latent state-trait (ML-LST) models offer new insights into intensive longitudinal data (ILD). This study clarifies the interpretation of within-subject and between-subject reliability coefficients in ML-LST models, enhancing their application in psychological research.

Keywords:
intensive longitudinal datalatent state‐trait theorymultilevel latent state‐trait modelsnarcissismreliability

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Area of Science:

  • Psychological research methods
  • Quantitative psychology
  • Statistical modeling

Background:

  • Ecological momentary assessment (EMA) is increasingly used in psychological research.
  • Intensive longitudinal data (ILD) requires advanced statistical models.
  • Multilevel latent state-trait (ML-LST) models, based on LST-R theory, are a potential approach for analyzing ILD.

Purpose of the Study:

  • To clarify the interpretation and identification of Level 1 (within-subject) and Level 2 (between-subject) reliability coefficients within LST-R theory.
  • To extend the understanding of reliability beyond traditional LST-R coefficients (reliability, consistency, occasion-specificity).
  • To provide a theoretical framework for newly defined level-specific coefficients in ML-LST models.

Main Methods:

  • The study discusses the interpretation of level-specific coefficients using multilevel versions of the Multistate-Singletrait (MSST), Multistate-Indicator-specific trait (MSIT), and Multistate-Singletrait model with M-1 correlated method factors (MSST-M-1).
  • Focuses on the MSST-M-1 model to define between-subject coefficients as measures of indicator-unspecificity or scale unidimensionality.
  • Highlights distinctions between occasion-specificity and within-subject reliability.

Main Results:

  • In the MSST-M-1 model, the between-subject coefficient quantifies indicator-unspecificity (shared between-level variance) or scale unidimensionality.
  • The study clarifies the theoretical meaning of within-subject reliability and occasion-specificity.
  • The performance of the multilevel MSST-M-1 model is demonstrated with empirical data.

Conclusions:

  • The interpretation of within-subject and between-subject reliability in ML-LST models is clarified, enhancing LST-R theory.
  • The MSST-M-1 model provides a framework for understanding item properties and scale structure in ILD.
  • These findings support the robust application of ML-LST models in psychological research using EMA data.