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A note on using random starting values in small sample SEM.

Julie De Jonckere1, Yves Rosseel2

  • 1Department of Data Analysis, Ghent University, Henri Dunantlaan 1, 9000, Ghent, Belgium. julie.dejonckere@ugent.be.

Behavior Research Methods
|January 14, 2025
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Summary
This summary is machine-generated.

Using bounded random starting values in structural equation modeling (SEM) analyses significantly improves model convergence. This method offers a promising alternative to default strategies, reducing nonconvergence issues in SEM software.

Keywords:
ConvergenceSEMSmall sample sizeStarting values

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Model estimation in structural equation modeling (SEM) often uses iterative optimization.
  • These procedures can frequently result in nonconvergence issues, hindering reliable analysis.
  • Current default starting value strategies in SEM software are susceptible to these problems.

Purpose of the Study:

  • To propose and evaluate the use of bounded random starting values as an alternative to default strategies in SEM.
  • To investigate the impact of this novel approach on model convergence rates.
  • To provide empirical evidence for the efficacy of bounded random starting values in reducing nonconvergence.

Main Methods:

  • Generating random starting values for SEM parameters from uniform distributions within data-driven lower and upper bounds.
  • Implementing and testing this method across three small-scale simulation studies.
  • Comparing convergence rates obtained with bounded random starting values against default strategies.

Main Results:

  • Bounded random starting values significantly reduced the nonconvergence rate in SEM analyses.
  • Convergence rates increased substantially, ranging from 87% to 96% in the first two simulation studies.
  • The proposed method demonstrated a marked improvement over default starting value strategies.

Conclusions:

  • Bounded random starting values present a viable and effective alternative to default starting values in SEM software.
  • This approach offers a promising solution to mitigate common nonconvergence issues in SEM.
  • Widespread adoption of bounded random starting values could enhance the reliability and efficiency of SEM analyses.