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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
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.
- 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.
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