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Area of Science:

  • Psychometrics
  • Structural Equation Modeling
  • Longitudinal Data Analysis

Background:

  • Multi-trait multi-method (MTMM) models assess construct validity.
  • Longitudinal MTMM models examine changes in trait and method factors over time.
  • Distinguishing stable traits from transient states is crucial in psychological research.

Purpose of the Study:

  • Propose a novel longitudinal ordinal MTMM model.
  • Effectively differentiate volatile "state" processes from stable "trait" processes.
  • Evaluate changes in state and method factors over time.

Main Methods:

  • Developed a longitudinal multi-trait-state-method (LM-TSM) model.
  • Incorporated a measurement model for ordinal data.
  • Utilized a latent vector autoregressive moving average model and a second-order factor-analytic model.
  • Applied the model to data from the Affective Dynamics and Individual Differences (ADID) study.

Main Results:

  • The LM-TSM model successfully distinguishes between time-invariant trait factors and time-varying state factors.
  • Demonstrated the model's utility in analyzing longitudinal ordinal data.
  • Provided insights into the dynamics of affective processes.

Conclusions:

  • The proposed LM-TSM model offers a robust framework for analyzing complex longitudinal data in psychometrics.
  • This approach enhances the understanding of trait and state dynamics.
  • Facilitates more nuanced validity assessments in psychological research.