Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R

被引:0
作者
Buchberger, Elisa S. [1 ]
Ngo, Chi T. [1 ]
Peikert, Aaron [1 ,2 ,3 ]
Brandmaier, Andreas M. [1 ,2 ,4 ]
Werkle-Bergner, Markus [1 ]
机构
[1] Max Planck Inst Human Dev, Ctr Lifespan Psychol, Lentzeallee 94, D-14195 Berlin, Germany
[2] Max Planck UCL Ctr Computat Psychiat & Ageing Res, Berlin, Germany
[3] UCL, Dept Imaging Neurosci, London, England
[4] MSB Med Sch Berlin, Dept Psychol, Berlin, Germany
关键词
Structural equation modeling; Statistical power; Monte Carlo simulation; Sample size planning; COMPLEMENTARY-LEARNING-SYSTEMS; MONTE-CARLO SIMULATIONS; SAMPLE-SIZE; PSYCHOLOGICAL-RESEARCH; COVARIANCE; MEMORY; INTELLIGENCE; HIPPOCAMPUS; PERSISTENCE; FRAMEWORK;
D O I
10.3758/s13428-024-02396-2
中图分类号
B841 [心理学研究方法];
学科分类号
040201 ;
摘要
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.
引用
收藏
页码:29 / 29
页数:1
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