ArticleStructural equation modeling : a multidisciplinary journal2017
Likelihood-based confidence intervals for a parameter with an upper or lower bound.
Article in Structural equation modeling : a multidisciplinary journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Manifestation of emotion regulation difficulties in intraindividual emotion dynamics.Cognition & emotion · 2026Article
- Preliminary KNN-LCB-Based Individualized Recommendation of Ultrasound Therapy Intensity for Low-Back Pain.Journal of multidisciplinary healthcare · 2026Article
- Comparing Bayesian estimation and structural-after-measurement approaches for structural equation models with latent interactions and complex data structures.Behavior research methods · 2025Article
- The superiority of likelihood-based confidence interval for variance estimation in a single group.Translational and clinical pharmacology · 2025Article
- Strong Genetic Effects on Bone Mineral Density in Multiple Locations with Two Different Techniques: Results from a Cross-Sectional Twin Study.Medicina (Kaunas, Lithuania) · 2021Article
- Model Fit Estimation for Multilevel Structural Equation Models.Structural equation modeling : a multidisciplinary journal · 2020Article
- A Population-Based Twin Study of Childhood Irritability and Internalizing Syndromes.Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53Article
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Authors and funding
3 authors.
Funding
Abstract
The precision of estimates in many statistical models can be expressed by a confidence interval (CI). CIs based on standard errors (SE) are common in practice, but likelihood-based CIs are worth consideration. In comparison to SEs, likelihood-based CIs are typically more difficult to estimate, but are more robust to model (re)parameterization. In latent variable models, some parameters may take on values outside of their interpretable range. Therefore, it is desirable to place a bound to keep the parameter interpretable. For likelihood-based CI, a correction is needed when a parameter is bounded. The correction is known (Wu & Neale, 2012), but is difficult to implement in practice. A novel automatic implementation that is simple for an applied researcher to use is introduced. A simulation study demonstrates the accuracy of the correction using a latent growth curve model and the method is illustrated with a multilevel confirmatory factor analysis.
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Registered trials
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