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Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.

Lijuan Wang1, Yuan Fang1, Cindy S Bergeman1

  • 1University of Notre Dame.

Structural Equation Modeling : a Multidisciplinary Journal
|January 9, 2026
PubMed
Summary
This summary is machine-generated.

For intensive longitudinal data, one-step dynamic structural equation modeling (DSEM) and two-step DSEM with auxiliary variables are recommended. Two-step DSEM without auxiliary variables shows significant estimation bias and poor performance.

Keywords:
BayesianDSEMintensive longitudinal dataone-steptwo-step

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Intensive longitudinal data (ILD) analysis requires sophisticated statistical methods.
  • Dynamic structural equation modeling (DSEM) is a powerful technique for ILD.
  • Comparing one-step and two-step DSEM approaches is crucial for accurate analysis.

Purpose of the Study:

  • To evaluate and compare the performance of one-step and two-step DSEM for ILD.
  • To investigate the impact of auxiliary variables on two-step DSEM.
  • To provide recommendations for DSEM application in ILD research.

Main Methods:

  • A simulation study was conducted to compare DSEM approaches.
  • One-step DSEM estimates within- and between-person models simultaneously.
  • Two-step DSEM separates within-person and between-person model estimation, with and without auxiliary variables.

Main Results:

  • Two-step DSEM without auxiliary variables demonstrated estimation bias, low coverage, and deflated Type I error rates.
  • One-step DSEM and two-step DSEM with auxiliary variables performed satisfactorily with sufficient data (≥30 time points, ≥100 individuals).
  • Auxiliary variables improved the performance of two-step DSEM.

Conclusions:

  • One-step DSEM is a reliable approach for analyzing ILD.
  • Two-step DSEM requires careful implementation, preferably with auxiliary variables, for valid results.
  • Researchers should consider these findings when choosing DSEM methods for ILD analysis.