ArticleBehavior research methods2024
Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
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- Validation and reliability of the Turkish version of the life transition scale for parents of children with disabilities: a methodological study.BMC pediatrics · 2026Article
- Examining the Correlation Between School Belonging, Academic Support, and Emotional Self-Awareness in High School Students.International journal of school health · 2025Article
- Decoding student cognitive abilities: a comparative study of explainable AI algorithms in educational data mining.Scientific reports · 2025Article
- Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.Behavior research methods · 2024Article
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Authors and funding
5 authors.
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Abstract
Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.
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