October 18, 2025

Mixture Multigroup Structural Equation Modeling for Ordinal Data

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Authors

AAAndres Felipe Perez AlonsoTilburg University JVJeroen K. VermuntTilburg University YRYves RosseelGhent University Hospital

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Implication

This approach improves measurement invariance testing and latent variable comparisons in groups, highlighting performance enhancements over maximum likelihood methods.

Key Points

The new method enhances recovery of measurement model parameters, particularly for ordinal data with fewer categories.
Performance analysis shows the extended approach outperforms traditional maximum likelihood estimation with ordinal data.
Utilizing diagonal weighted least squares improves estimation accuracy in multigroup analysis of latent variables.
A simulation study demonstrates the efficacy of the stepwise approach over standard MMG-SEM techniques.

Cite This Study

Alonso et al. (2025) studied this question.

synapsesocial.com/papers/68f396388da44caaba02c88e https://doi.org/10.31234/osf.io/6cr74_v2

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Implication

This approach improves measurement invariance testing and latent variable comparisons in groups, highlighting performance enhancements over maximum likelihood methods.

Mixture Multigroup Structural Equation Modeling for Ordinal Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Mixture Multigroup Structural Equation Modeling for Ordinal Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider