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Parameter accuracy in meta-analyses of factor structures.

Timo Gnambs1, Thomas Staufenbiel2

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Research Synthesis Methods
|June 12, 2016
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Summary

Two new meta-analysis methods for factor loadings were simulated. The indirect method, synthesizing correlation matrices, offered less biased estimates, especially for smaller samples, making it a viable alternative for analyzing factor structures.

Keywords:
biasfactor analysisfactor congruencefactor loadingsmeta-analysis

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Area of Science:

  • Psychometrics
  • Statistical Analysis

Background:

  • Factor loadings are crucial in structural equation modeling.
  • Meta-analysis of factor loadings requires robust methodologies.
  • Existing methods may have limitations in accuracy and applicability.

Purpose of the Study:

  • Introduce and evaluate two novel methods for meta-analysis of factor loadings: the direct and indirect methods.
  • Compare the accuracy and bias of these methods using Monte Carlo simulations.
  • Determine factors influencing the precision of meta-analytically derived factor loadings.

Main Methods:

  • Direct method: Pools individual factor loadings.
  • Indirect method: Synthesizes correlation matrices reproduced from factor loadings.
  • Monte Carlo simulations to assess method performance under various conditions.

Main Results:

  • Accuracy of meta-analytic factor loadings is mainly influenced by pooled factor structure characteristics (model error, communality).
  • Sample size and number of studies had a lesser impact on accuracy.
  • The indirect method generally yielded less biased estimates, particularly for small-sample studies.

Conclusions:

  • The indirect method is a viable alternative for meta-analysis of factor structures.
  • The indirect method can also be used to test moderator hypotheses.
  • Method choice has a minor impact, but the indirect method shows advantages in specific scenarios.