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Understanding the Model Size Effect on SEM Fit Indices
Dexin Shi1, Taehun Lee2, Alberto Maydeu-Olivares1
1University of South Carolina, Columbia, SC, USA.
Educational and Psychological Measurement
|March 27, 2019
Summary
The number of variables (p) impacts structural equation modeling fit indices. Higher p generally lowers RMSEA but can affect CFI and TLI depending on model errors, especially in smaller samples.
Area of Science:
- Psychometrics
- Statistical Modeling
Background:
- Structural Equation Modeling (SEM) relies on fit indices to evaluate model adequacy.
- The influence of the number of observed variables (p) on these indices is not fully understood.
Purpose of the Study:
- To investigate how the number of observed variables (p) affects key SEM fit indices: CFI, TLI, and RMSEA.
- To compare population fit indices with their sample estimates under varying conditions.
Main Methods:
- Manipulated the number of observed variables (p), model misspecification types, sample size, and factor loading magnitudes.
- Analyzed the behavior of population fit indices and their sample estimates.
Main Results:
- Population CFI and TLI were influenced by p and the type of model misspecification.
- Population RMSEA consistently decreased with higher p, irrespective of misspecification.
- Sample estimates for all indices indicated poorer fit than population values, particularly with smaller sample sizes, higher p, and lower factor loadings.
Conclusions:
- The number of observed variables is a critical factor influencing SEM fit indices.
- Researchers should be cautious when interpreting fit indices, especially in studies with many variables or small sample sizes.
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