ArticleEducational and psychological measurement2022
The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling.
Article in Educational and psychological measurement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Penalized eigenvalue block averaging: Extension to nested model comparison and Monte Carlo evaluations.Behavior research methods · 2026Article
- Humility Throughout the Lifespan and a Global Pandemic: Evidence From a Large-Scale Cross-Sectional Study.Journal of personality · 2026Article
- Discriminating Between Attribute, Item-Position, and Wording Effects by the Congeneric and Tau-Equivalent Confirmatory Factor Analysis Models.Educational and psychological measurement · 2026Article
- Is (critical) health literacy a key to better psychosomatic functioning in patients with inflammatory bowel disease? Testing a mediation model.Frontiers in psychiatry · 2026Article
- Comparing Bayesian estimation and structural-after-measurement approaches for structural equation models with latent interactions and complex data structures.Behavior research methods · 2025Article
- Person-environment fit, turnover intention and satisfaction with life. The role of seniority.Health psychology report · 2025Article
- semPower: General power analysis for structural equation models.Behavior research methods · 2024Article
- Overfactoring in rating scale data: A comparison between factor analysis and item response theory.Frontiers in psychology · 2022Article
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3 authors.
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Abstract
Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods.
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