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A Note on Comparing the Bifactor and Second-Order Factor Models: Is the Bayesian Information Criterion a Routinely
Tenko Raykov1, Christine DiStefano2, Lisa Calvocoressi3
1Michigan State University, East Lansing, USA.
Educational and Psychological Measurement
|June 20, 2024
Summary
The Bayesian Information Criterion (BIC) may not reliably select between bifactor and second-order factor models. Researchers should exercise caution, as BIC may incorrectly favor the second-order model even when data fits the bifactor model.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- The Bayesian Information Criterion (BIC) is commonly used for model selection in statistical analysis.
- Bifactor and second-order factor models are frequently employed to explain the structure of multidimensional data.
Purpose of the Study:
- To evaluate the dependability of the BIC for distinguishing between bifactor and second-order factor models.
- To investigate potential discrepancies in BIC-based model selection when data is generated from a bifactor structure.
Main Methods:
- Simulated data generation following a bifactor model across various sample sizes.
- Comparison of model fit indices, specifically BIC, for both bifactor and second-order factor models.
Main Results:
- The bifactor model was consistently found to be inferior to the second-order model based on BIC values.
- This occurred despite the data being generated from the bifactor model in numerous replications.
Conclusions:
- Routine reliance on BIC for model selection between bifactor and second-order models can be misleading.
- Researchers should be aware of BIC's limitations in accurately identifying the data-generating model in these specific contexts.
Keywords:
Bayesian information criterionbifactor modelconfirmatory factor analysismodel selectionsecond-order factor modelMore Related Videos
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