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Estimating Local Structural Equation Models.

Alexander Robitzsch1,2

  • 1IPN-Leibniz Institute for Science and Mathematics Education, Olshausenstraße 62, 24118 Kiel, Germany.

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Summary
This summary is machine-generated.

This study introduces a new joint estimation method for local structural equation models (LSEM) to analyze how moderators affect model parameters. The R package sirt implementation offers improved simultaneous estimation across all moderator values.

Keywords:
confirmatory factor analysisdedifferentiationdifferentiationinvariancelocal structural equation modeling

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Local structural equation models (LSEM) analyze how moderator variables influence model parameters.
  • Previous LSEM estimation involved fitting separate models for each moderator value, which is inefficient.

Purpose of the Study:

  • To review and extend existing LSEM estimation methods.
  • To propose and detail a novel joint estimation approach for LSEM.
  • To provide guidance on implementing LSEM using the R package sirt.

Main Methods:

  • A joint estimation method is proposed for simultaneous estimation across all moderator values.
  • The approach allows for model parameters to be invariant across moderators.
  • Implementation details for the R package sirt are provided, including illustrative datasets and an empirical example.
  • Two simulation studies were conducted to assess statistical properties.

Main Results:

  • The joint estimation approach provides a simultaneous estimation method for LSEM.
  • The R package sirt facilitates practical implementation of LSEM.
  • Simulation studies investigate the statistical properties of parameter estimation and significance testing in LSEM.

Conclusions:

  • The proposed joint estimation method offers an advancement in analyzing moderated structural equation models.
  • The R package sirt is a valuable tool for implementing these advanced LSEM techniques.
  • Further research should explore the statistical properties of the new estimation method.