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Dominance Analysis for Latent Variable Models: A Comparison of Methods With Categorical Indicators and Misspecified
1Ball State University, Muncie, IN, USA.
Educational and Psychological Measurement
|June 20, 2024
Summary
Dominance analysis (DA) accurately orders variables in latent variable models, even with categorical data or model misspecification. This statistical method enhances understanding of variable importance in complex regression and structural equation models.
Area of Science:
- Statistics
- Psychometrics
- Social Sciences
Background:
- Dominance analysis (DA) is a statistical method for assessing independent variable importance in regression.
- DA has been extended to structural equation models (SEMs) for latent variables.
- Previous research confirmed DA's accuracy for latent variable models with normally distributed indicators and correct specification.
Purpose of the Study:
- To compare the extended DA approach for latent variable models with observed regression DA.
- To evaluate DA's performance when latent variable models use two-stage least squares with categorical indicators or model misspecification.
Main Methods:
- A simulation study was conducted.
- Compared DA for latent variable models against observed regression DA.
- Investigated scenarios with categorical indicators and model misspecification.
Main Results:
- The DA approach for latent variable models provided accurate variable ordering.
- The method demonstrated correct hypothesis selection even with categorical indicators and model misspecification.
- Results suggest robustness of the DA approach in complex SEMs.
Conclusions:
- Dominance analysis is a reliable tool for variable importance in latent variable models.
- The DA approach is effective even when assumptions of normality or correct model specification are violated.
- This study supports the use of DA for robust analysis of complex statistical models.
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