Improving measurement-invariance assessments: correcting entrenched testing deficiencies.
1Department of Sociology, University of Alberta, Edmonton, T6G 2H4, Canada. LHayduk@ualberta.ca.
BMC Medical Research Methodology
|October 8, 2016
Summary
Structural equation modeling assessments of measurement invariance need better model testing. Improving configural model assessment is crucial for reliable results, addressing a historical deficiency.
Area of Science:
- Statistics
- Psychometrics
Background:
- Historically, factor analysis prioritized measurement, while path analysis assumed error-free variables.
- The integration into structural equation modeling (SEM) led to factor analytic dominance in measurement, including measurement invariance assessments.
- This tradition neglected rigorous model testing, embedding deficiencies in invariance evaluations.
Discussion:
- Contemporary model testing must be applied to the configural model for improved invariance assessments.
- This article highlights issues, presents an example, proposes a superior strategy, and documents inadequate testing in publications.
- Utilizing fewer, high-quality indicators for latent variables enhances the probability of correctly specified SEMs capable of demonstrating measurement invariance.
Key Insights:
- Rigorous model testing is essential for valid measurement invariance in SEM.
- Configural model misspecification invalidates subsequent invariance estimates.
- Prioritizing methodologically sound indicators improves causal model specification.
Outlook:
- Implementing contemporary model testing standards for configural models is vital.
- Addressing the backlog of deficient invariance assessments requires a shift in methodology.
- Future research should focus on properly specified SEMs to ensure robust measurement invariance.
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