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Calculating and Interpreting Maximal Reliability in Bifactor Models.

Sijia Li1, Victoria Savalei1

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Summary
This summary is machine-generated.

Researchers often misuse maximal reliability for bifactor models. New equations are provided, but optimal composites (OLCs) and sub-composites (OLSCs) are unreliable for group factors, showing poor reliability and interpretation issues.

Keywords:
Bifactor modelcoefficient Hconfirmatory factor analysismaximal reliabilityregression factor scores

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Area of Science:

  • Psychometrics
  • Psychology

Background:

  • Confirmatory bifactor models are common in psychology for multidimensional constructs.
  • Maximal reliability assesses how well an optimal linear composite (OLC) represents a latent variable.

Purpose of the Study:

  • To correct the inaccurate generalization of coefficient H for bifactor models.
  • To present accurate equations for maximal reliability using OLCs and optimal sub-composites (OLSCs).

Main Methods:

  • Derived new equations for maximal reliability for bifactor models.
  • Applied equations to simulated and real data.
  • Compared OLCs and OLSCs to other reliability coefficients.

Main Results:

  • OLCs and OLSCs are unreliable for group factors with fewer than 100 indicators.
  • OLCs and OLSCs frequently received negative weights in simulations.
  • Maximal reliability indices can still assess bifactor model quality.

Conclusions:

  • Recommends against using OLCs or OLSCs as proxies for group factors due to poor reliability and interpretation challenges.
  • Highlights the importance of accurate maximal reliability calculations for bifactor models.