ArticleEducational and psychological measurement2024
The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability.
Article in Educational and psychological measurement, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- English teachers' social-emotional competence and occupational burnout: the mediating role of teaching efficacy and the moderating role of perceived organizational support.Frontiers in psychology · 2026Article
- Reproducible structure with measurement invariance for the Parent-Report Early Adolescent Temperament Questionnaire: Findings from three independent samples.Psychological assessment · 2025Article
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Abstract
Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.
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