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The problem of model selection uncertainty in structural equation modeling.

Kristopher J Preacher1, Edgar C Merkle

  • 1Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA. kris.preacher@vanderbilt.edu

Psychological Methods
|January 25, 2012
PubMed
Summary
This summary is machine-generated.

Model selection in structural equation modeling (SEM) can be unstable due to sampling variability. Researchers should account for this uncertainty to ensure reliable model choices and robust findings.

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Area of Science:

  • Statistics
  • Psychometrics
  • Behavioral Sciences

Background:

  • Structural Equation Modeling (SEM) relies on selection criteria for choosing the best model.
  • Current SEM practices often overlook sampling variability in these criteria.
  • This oversight can lead to unreliable model superiority claims.

Purpose of the Study:

  • To investigate the instability of model selection criteria in SEM.
  • To demonstrate how sampling variability affects model selection decisions.
  • To evaluate methods for addressing model selection uncertainty in SEM.

Main Methods:

  • Simulations were used to assess the stability of information criteria over repeated sampling.
  • The impact of sample size on selection uncertainty was examined.
  • Methods for handling model selection uncertainty were evaluated.

Main Results:

  • Model selection decisions using information criteria were found to be highly unstable across different samples.
  • This instability did not consistently decrease with larger sample sizes.
  • Significant uncertainty exists in SEM model selection.

Conclusions:

  • Assertions of model superiority in SEM may not be replicable due to unaddressed sampling variability.
  • Practitioners should consider methods to account for model selection uncertainty.
  • More robust approaches are needed for reliable SEM model selection.