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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Recovering bifactor models: A comparison of seven methods.

Casey Giordano1, Niels G Waller1

  • 1Department of Psychology.

Psychological Methods
|July 26, 2019
PubMed
Summary

Newer methods for exploratory bifactor analysis were compared. The rank-deficient Schmid-Leiman and Direct Schmid-Leiman methods demonstrated superior recovery of both nonhierarchical and hierarchical bifactor structures.

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Exploratory bifactor analysis models have seen renewed interest.
  • New estimation methods have emerged rapidly, outpacing previous comparisons.

Purpose of the Study:

  • To compare the model recovery capabilities of existing and newer exploratory bifactor analysis methods.
  • To expand upon previous research by including more algorithms and both hierarchical and nonhierarchical models.

Main Methods:

  • Conducted nearly 3 million exploratory bifactor analyses.
  • Compared 5 established methods with 2 newer methods (Waller, 2018a).
  • Included both nonhierarchical and hierarchical bifactor model designs.

Main Results:

  • The rank-deficient Schmid-Leiman and Direct Schmid-Leiman methods showed superior performance.

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  • These methods better recovered both nonhierarchical and hierarchical bifactor structures compared to alternatives.

    • Conclusions:

    • The Schmid-Leiman and Direct Schmid-Leiman methods are recommended for exploratory bifactor analysis.
    • These methods offer improved accuracy in recovering complex bifactor structures.